U.S. patent application number 17/544580 was filed with the patent office on 2022-06-02 for computer-implemented methods for controlling rights related to digital knowledge.
The applicant listed for this patent is Strong Force TX Portfolio 2018, LLC. Invention is credited to Andrew Cardno, Charles Howard Cella, Taylor D. Charon, Teymour S. El-Tahry.
Application Number | 20220172207 17/544580 |
Document ID | / |
Family ID | 1000006138015 |
Filed Date | 2022-06-02 |
United States Patent
Application |
20220172207 |
Kind Code |
A1 |
Cella; Charles Howard ; et
al. |
June 2, 2022 |
COMPUTER-IMPLEMENTED METHODS FOR CONTROLLING RIGHTS RELATED TO
DIGITAL KNOWLEDGE
Abstract
A computer-implemented method for controlling rights related to
digital knowledge is disclosed. The method includes creating and
managing a distributed ledger which includes a plurality of blocks
linked via cryptography distributed over a plurality of nodes of a
network. The method further includes implementing and managing a
smart contract which includes a triggering event and a smart
contract action. The method further includes receiving, tokenizing,
and storing an instance of the digital knowledge in the distributed
ledger. The method includes managing, rights of control of and
access to the tokenized digital knowledge based on the smart
contract, and performing, in response to an occurrence of the
triggering event, the corresponding smart contract action with
respect to the tokenized digital knowledge.
Inventors: |
Cella; Charles Howard;
(Pembroke, MA) ; Cardno; Andrew; (San Diego,
CA) ; Charon; Taylor D.; (Troy, MI) ;
El-Tahry; Teymour S.; (Detroit, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Strong Force TX Portfolio 2018, LLC |
Fort Lauderdale |
FL |
US |
|
|
Family ID: |
1000006138015 |
Appl. No.: |
17/544580 |
Filed: |
December 7, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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17378393 |
Jul 16, 2021 |
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17544580 |
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17243145 |
Apr 28, 2021 |
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17378393 |
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17332700 |
May 27, 2021 |
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17378393 |
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16780519 |
Feb 3, 2020 |
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17332700 |
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16998668 |
Aug 20, 2020 |
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16780519 |
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PCT/US19/58671 |
Oct 29, 2019 |
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16998668 |
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16803387 |
Feb 27, 2020 |
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17378393 |
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16457890 |
Jun 28, 2019 |
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16803387 |
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PCT/US19/30934 |
May 6, 2019 |
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16457890 |
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63016975 |
Apr 28, 2020 |
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63054603 |
Jul 21, 2020 |
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62751713 |
Oct 29, 2018 |
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62843992 |
May 6, 2019 |
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62818100 |
Mar 13, 2019 |
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62843455 |
May 5, 2019 |
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62843456 |
May 5, 2019 |
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62787206 |
Dec 31, 2018 |
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62667550 |
May 6, 2018 |
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62751713 |
Oct 29, 2018 |
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63052475 |
Jul 16, 2020 |
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63054603 |
Jul 21, 2020 |
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63069542 |
Aug 24, 2020 |
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63127980 |
Dec 18, 2020 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 20/401 20130101;
H04L 9/3213 20130101; H04L 9/3236 20130101; G06Q 20/389 20130101;
H04L 9/50 20220501; H04L 9/0819 20130101; G06Q 50/184 20130101 |
International
Class: |
G06Q 20/40 20060101
G06Q020/40; G06Q 20/38 20060101 G06Q020/38; G06Q 50/18 20060101
G06Q050/18; H04L 9/32 20060101 H04L009/32; H04L 9/08 20060101
H04L009/08 |
Claims
1. A computer-implemented method for controlling rights related to
digital knowledge, the computer-implemented method comprising:
creating and managing a distributed ledger, wherein the distributed
ledger comprises a plurality of blocks linked via cryptography
distributed over a plurality of nodes of a network; implementing
and managing a smart contract, wherein the smart contract comprises
a triggering event and corresponding smart contract action and is
stored in the distributed ledger; receiving an instance of the
digital knowledge; tokenizing the digital knowledge; storing the
tokenized digital knowledge via the distributed ledger; processing
commitments of a plurality of parties to the smart contract;
managing, according to the smart contract, rights of control of and
access to the tokenized digital knowledge; and performing, in
response to an occurrence of the triggering event, the
corresponding smart contract action with respect to the tokenized
digital knowledge.
2. The computer-implemented method of claim 1, further comprising
orchestrating, based on the smart contract, an exchange of new
digital knowledge for the tokenized digital knowledge.
3. The computer-implemented method of claim 2, further comprising
integrating the knowledge exchange with a separate exchange,
wherein the knowledge exchange facilitates an exchange of at least
one of valuable and sensitive knowledge related to a subject matter
of the separate exchange.
4. A computer-implemented method for controlling rights related to
digital knowledge comprising: creating and managing a distributed
ledger, wherein the digital ledger comprises a plurality of blocks
linked via cryptography distributed over a plurality of nodes of a
network; implementing and managing a smart contract, wherein the
smart contract comprises a triggering event; performing a smart
contract action with respect to the digital knowledge in response
to an occurrence of the triggering event; receiving, from a
knowledge provider device, an instance of the digital knowledge
that comprises a three-dimensional (3D) printer instruction set for
3D printing an object; tokenizing the digital knowledge such that
the instance of the digital knowledge is manipulable as a token on
the distributed ledger; storing the tokenized digital knowledge on
the distributed ledger; processing commitments of the knowledge
provider and a knowledge recipient of the 3D printer instruction
set to the smart contract; managing rights of control of and access
to the tokenized digital knowledge according to the smart contract;
and managing the smart contract action according to a condition and
the triggering event.
5. The computer-implemented method of claim 4 further comprising
crowdsourcing an element of the instance of the digital knowledge
via the smart contract, wherein the element of the instance of the
digital knowledge is managed by a smart contract system according
to the smart contract.
6. The computer-implemented method of claim 5, further comprising:
crowdsourcing information regarding: an element of the instance of
the digital knowledge, the knowledge provider, or a knowledge
recipient; and updating the smart contract in response to the
crowdsourced information.
7. The computer-implemented method of claim 6, further comprising
updating a condition, or a smart contract action based, at least in
part, on the crowdsourced information.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 17/378,393 (SFTX-0018-U01), filed on Jul. 16,
2021, entitled "SYSTEMS AND METHODS FOR CONTROLLING RIGHTS RELATED
TO DIGITAL KNOWLEDGE."
[0002] U.S. patent application Ser. No. 17/378,393 (SFTX-0018-U01)
is a continuation-in-part of U.S. patent application Ser. No.
17/243,145 (SFTX-0017-U01), filed Apr. 28, 2021, entitled
"ARTIFICIAL INTELLIGENCE SELECTION AND CONFIGURATION."
[0003] U.S. patent application Ser. No. 17/243,145 (SFTX-0017-U01)
claims the benefit of priority to U.S. Provisional Applications No.
63/016,975 (SFTX-0017-P01), filed on Apr. 28, 2020, entitled
"DIGITAL TWIN SYSTEMS FOR FINANCIAL SYSTEMS"; and 63/054,603
(SFTX-0017-P02), filed on Jul. 21, 2020, entitled "DIGITAL TWIN
SYSTEMS AND METHODS FOR FINANCIAL SYSTEMS."
[0004] U.S. patent application Ser. No. 17/378,393 (SFTX-0018-U01)
is a continuation-in-part of U.S. patent application Ser. No.
17/332,700 (SFTX-0013-U01), filed May 27, 2021, entitled "AUTOMATED
ROBOTIC PROCESS SELECTION AND CONFIGURATION."
[0005] U.S. patent application Ser. No. 17/378,393 (SFTX-0018-U01)
is a continuation-in-part of U.S. patent application Ser. No.
16/780,519 (SFTX-0012-U01), filed Feb. 3, 2020, entitled "ADAPTIVE
INTELLIGENCE AND SHARED INFRASTRUCTURE LENDING TRANSACTION
ENABLEMENT PLATFORM RESPONSIVE TO CROWD SOURCED INFORMATION."
[0006] U.S. patent application Ser. No. 17/378,393 (SFTX-0018-U01)
is a continuation-in-part of U.S. patent application Ser. No.
16/998,668 (SFTX-0010-U01), filed Aug. 20, 2020, entitled "ROBOTIC
PROCESS AUTOMATION SYSTEM FOR NEGOTIATION."
[0007] U.S. patent application Ser. No. 16/998,668 (SFTX-0010-U01)
is a bypass continuation of PCT Application PCT/US19/58671
(Attorney Docket No. SFTX-0010-WO), filed Oct. 29, 2019, entitled
"METHODS AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT
AUTOMATE EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN
SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER
RESOURCES."
[0008] PCT Application PCT/US19/58671 (SFTX-0010-WO) claims the
benefit of priority to the following U.S. Provisional Patent
Applications: Ser. No. 62/751,713 (Attorney Docket No.
SFTX-0003-P01), filed Oct. 29, 2018, entitled "METHODS AND SYSTEMS
FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OF
DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD
MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES", Ser. No.
62/843,992 (Attorney Docket No. SFTX-0005-P01), filed May 6, 2019,
entitled "ADAPTIVE INTELLIGENCE AND SHARED INFRASTRUCTURE LENDING
TRANSACTION ENABLEMENT PLATFORM WITH ROBOTIC PROCESS ARCHITECTURE";
Ser. No. 62/818,100 Attorney Docket No. SFTX-0006-P01), filed Mar.
13, 2019, entitled "ROBOTIC PROCESS AUTOMATION ARCHITECTURE,
SYSTEMS AND METHODS IN TRANSACTION ENVIRONMENTS"; Ser. No.
62/843,455 (Attorney Docket No. SFTX-0007-P01), filed May 5, 2019,
entitled "ADAPTIVE INTELLIGENCE AND SHARED INFRASTRUCTURE LENDING
TRANSACTION ENABLEMENT PLATFORM WITH ROBOTIC PROCESS ARCHITECTURE";
and Ser. No. 62/843,456 (Attorney Docket No. SFTX-0008-P01), filed
May 5, 2019, entitled ADAPTIVE INTELLIGENCE AND SHARED
INFRASTRUCTURE LENDING TRANSACTION ENABLEMENT PLATFORM WITH ROBOTIC
PROCESS ARCHITECTURE."
[0009] U.S. patent application Ser. No. 17/378,393 (SFTX-0018-U01)
is a continuation-in-part of U.S. patent application Ser. No.
16/803,387 (SFTX-0009-U01), filed Feb. 27, 2020, entitled "SYSTEM
THAT VARIES THE TERMS AND CONDITIONS OF A SUBSIDIZED LOAN."
[0010] U.S. patent application Ser. No. 17/378,393 (SFTX-0018-U01)
is a continuation-in-part of U.S. patent application Ser. No.
16/457,890 (SFTX-0004-U01), filed Jun. 28, 2019, entitled
"TRANSACTION-ENABLING SYSTEMS AND METHODS FOR USING A SMART
CONTRACT WRAPPER TO ACCESS EMBEDDED CONTRACT TERMS."
[0011] U.S. patent application Ser. No. 16/457,890 (SFTX-0004-U01)
is a bypass continuation of International Application Serial No.
PCT/US2019/030934 (SFTX-0004-WO), filed May 6, 2019, entitled
"METHODS AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT
AUTOMATE EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN
SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER
RESOURCES."
[0012] International Application Serial No. PCT/US2019/030934
(SFTX-0004-WO) claims the benefit of priority to the following U.S.
Provisional Patent Applications: Ser. No. 62/787,206 (Attorney
Docket No. SFTX-0001-P01), filed Dec. 31, 2018, entitled "METHODS
AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE
EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND
FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES";
Ser. No. 62/667,550 (Attorney Docket No. SFTX-0002-P01), filed May
6, 2018, entitled "METHODS AND SYSTEMS FOR IMPROVING MACHINES AND
SYSTEMS THAT AUTOMATE EXECUTION OF DISTRIBUTED LEDGER AND OTHER
TRANSACTIONS IN SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE,
STORAGE AND OTHER RESOURCES"; and Ser. No. 62/751,713 (Attorney
Docket No. SFTX-0003-P01), filed Oct. 29, 2018, entitled "METHODS
AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE
EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND
FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER
RESOURCES."
[0013] U.S. patent application Ser. No. 17/378,393 (SFTX-0018-U01)
claims the benefit of priority to the following U.S. Provisional
Patent Applications: Ser. No. 63/052,475 (Attorney Docket No.
SFTX-0018-P01), filed Jul. 16, 2020, entitled "METHODS AND SYSTEMS
FOR MANAGEMENT OF DIGITAL KNOWLEDGE", Ser. No. 63/054,603 (Attorney
Docket No. SFTX-0017-P02), filed Jul. 21, 2020, entitled "DIGITAL
TWIN SYSTEMS AND METHODS FOR FINANCIAL SYSTEMS"; Ser. No.
63/069,542 (Attorney Docket No. SFTX-0015-P01), filed Aug. 24,
2020, entitled "INFORMATION TECHNOLOGY SYSTEMS AND METHODS FOR
TRANSACTION ARTIFICIAL INTELLIGENCE LEVERAGING DIGITAL TWINS"; and
Ser. No. 63/127,980 (Attorney Docket No. SFTX-0016-P01), filed Dec.
18, 2020, entitled "MARKET ORCHESTRATION SYSTEM FOR FACILITATING
ELECTRONIC MARKETPLACE TRANSACTIONS".
[0014] Each of the foregoing applications is incorporated herein by
reference in its entirety for all purposes.
BACKGROUND
[0015] An incredible amount of information is digitally exchanged
on a regular basis, and the amount is increasing each day. This
information can include valuable and sensitive information, such as
trade secrets, know how, patented material, and works of
authorship. Some of the information is subject to access and
control restrictions, such as restrictions on who can view, edit,
change, use, transmit, sell, buy, rent, review, license, and source
the digital information (e.g., vis-a-vis patent licenses, trademark
licenses, contract agreements, copyright licenses, and the like).
Setting and enforcing access and control restrictions is difficult,
as any computer-based system for doing so has potential flaws, such
as risks of impropriety or unreliability of an owner or maintainer
of the system, or risks of other parties gaining unauthorized
access and illegitimately accessing, copying, editing, or otherwise
tampering with the digital knowledge.
[0016] Lending transactions provide financing for a wide variety of
needs, ranging from housing and education to corporate and
government projects, among many others, while enabling lenders to
earn financial returns. However, lending transactions are plagued
by a number of problems, including opacity and asymmetry of
information, moral hazard induced by shifting of the consequences
of risky or inappropriate behavior, complexity of application and
negotiation processes, burdensome regulatory and policy regimes,
difficulty in determining the value of property that is used as
collateral or backing for obligations, difficulty in determining
the reliability or financial health of entities, and others.
[0017] Machines and automated agents are increasingly involved in
market activities, including for data collection, forecasting,
planning, transaction execution, and other activities. This
includes increasingly high-performance systems, such as used in
high-speed trading. A need exists for methods and systems that
improve the machines that enable markets, including for increased
efficiency, speed, reliability, and the like for participants in
such markets.
[0018] Many markets are increasingly distributed, rather than
centralized, with distributed ledgers like Blockchain, peer-to-peer
interaction models, and micro-transactions replacing or
complementing traditional models that involve centralized
authorities or intermediaries. A need exists for improved machines
that enable distributed transactions to occur at scale among large
numbers of participants, including human participants and automated
agents.
[0019] Operations on blockchains, such as ones using
cryptocurrency, increasingly require energy-intensive computing
operations, such as calculating very large hash functions on
growing chains of blocks. Systems using proof-of-work,
proof-of-stake, and the like have led to "mining" operations by
which computer processing power is applied at a large scale in
order to perform calculations that support collective trust in
transactions that are recorded in blockchains.
[0020] Many applications of artificial intelligence also require
energy-intensive computing operations, such as where very large
neural networks, with very large numbers of interconnections,
perform operations on large numbers of inputs to produce one or
more outputs, such as a prediction, classification, optimization,
control output, or the like.
[0021] The growth of the Internet of Things and cloud computing
platforms have also led to the proliferation of devices,
applications, and connections among them, such that data centers,
housing servers and other IT components, consume a significant
fraction of the energy consumption of the United States and other
developed countries.
[0022] As a result of these and other trends, energy consumption
has become a major factor in utilization of computing resources,
such that energy resources and computing resources (or simply
"energy and compute") have begun to converge from various
standpoints, such as requisitioning, purchasing, provisioning,
configuration, and management of inputs, activities, outputs and
the like. Projects have been undertaken, for example, to place
large scale computing resource facilities, such as Bitcoin.TM. or
other cryptocurrency mining operations, in close proximity to
large-scale hydropower sources, such as Niagara Falls.
[0023] A major challenge for facility owners and operators is the
uncertainty involved in optimizing a facility, such as resulting
from volatility in the cost and availability of inputs (in
particular where less stable renewable resources are involved),
variability in the cost and availability of computing and
networking resources (such as where network performance varies),
and volatility and uncertainty in various end markets to which
energy and compute resources can be applied (such as volatility in
cryptocurrencies, volatility in energy markets, volatility in
pricing in various other markets, and uncertainty in the utility of
artificial intelligence in a wide range of applications), among
other factors.
SUMMARY
[0024] Example embodiments herein disclose systems, procedures, and
aspects that provide cryptographically secure blockchains for
knowledge systems capable of storing digital knowledge for
providing convenient and secure control of the same. Example
methods and systems herein provide for improvements in determining
property valuation, reliability of financial health of entities,
transparency, symmetry of information, and application and
negotiation processes in the lending environment. Example methods
and systems herein provide for improvements to the machines that
enable markets, providing for increased efficiency, speed, and/or
reliability for participants in such markets. Example methods and
systems herein provide for improvements to data collection, storage
and processing, automated configuration of inputs, resource, and
outputs, and means for facility optimization for an energy and
compute facility.
[0025] In one or more example embodiments, a knowledge distribution
system for controlling rights related to digital knowledge is
disclosed. The knowledge distribution system may be a blockchain
for knowledge system that allows for storage of digital knowledge,
buying and selling of digital knowledge, tokenization of digital
knowledge, and/or reviewing/auditing of the digital knowledge via a
cryptographically secure distributed ledger. Smart contracts may be
implemented on the distributed ledger and controlling of rights to
digital knowledge, transferring digital knowledge, and adherence of
parties to agreements related to the digital knowledge. The
blockchain for knowledge system can also facilitate third parties
reviewing, auditing, or verifying information related to digital
knowledge.
[0026] There can be a number of practical obstacles to the sharing
of knowledge such as the absence of trust between parties that
could potentially benefit from sharing of the knowledge. A platform
exists for a digital knowledge distribution system that facilitates
orchestration of the sharing of knowledge by providing a high
degree of control over the extent to which counterparties can
access shared knowledge. Even where knowledge is secure and
well-controlled, some types of knowledge are so sensitive that an
owner may be unwilling to share the entire set of knowledge with a
single counterparty. In embodiments, a platform is disclosed for a
digital knowledge distribution system that facilitates handling and
control of subsets of knowledge, including automated handling of
aggregation of knowledge, or related outputs, that result from
division of knowledge subsets.
[0027] The knowledge distribution system may include a ledger
management system configured to create and manage a distributed
ledger where the distributed ledger may be distributed over nodes
of a network and may include blocks linked via cryptography. A
smart contract system may be communication with the distributed
ledger and may be configured to implement and manage a smart
contract via the distributed ledger. The smart contract may be
stored in the distributed ledger and may include a triggering
event. The smart contract may be configured to perform a smart
contract action with respect to the digital knowledge in response
to an occurrence of the triggering event. The knowledge
distribution system may be configured to receive from a user an
instance of the digital knowledge. The digital knowledge may be
tokenized such that the instance of the digital knowledge can be
manipulated as a token on the distributed ledger. The tokenized
digital knowledge may be stored via the distributed ledger.
Commitments of parties to the smart contract may be processed. The
knowledge distribution system may be configured to manage rights of
control of and access to the tokenized digital knowledge according
to the smart contract and manage the smart contract action in
response to the triggering event.
[0028] One or more of the following example features may be
included. The digital knowledge may include intellectual property
where the smart contract embeds intellectual property licensing
terms for intellectual property embedded in the distributed ledger,
and where executing an operation on the distributed ledger may
provide access to the intellectual property and may process a
commitment of a party to the smart contract to the intellectual
property licensing terms. A smart contract wrapper on the
distributed ledger may allow an operation on the ledger to add
intellectual property to an aggregate stack of intellectual
property, may allow an operation on the ledger to add intellectual
property to agree to an apportionment of royalties among the
parties in the ledger, may allow an operation on the ledger to add
intellectual property to an aggregate stack of intellectual
property, and/or may allow an operation on the ledger to process a
commitment of a party to a contract term. The tokenized digital
knowledge may include an instruction set. The distributed ledger
may be configured to provide provable access to the instruction set
and execute the instruction set on a system resulting in recording
a transaction in the distributed ledger. The tokenized digital
knowledge may include executable algorithmic logic, a
three-dimensional (3D) printer instruction set, an instruction set
for a coating process, an instruction set for a semiconductor
fabrication process, a firmware program, an instruction set for a
field-programmable gate array, serverless code logic, an
instruction set for a crystal fabrication system, an instruction
set for a food preparation process, an instruction set for a
polymer production process, an instruction set for a chemical
synthesis process, an instruction set for a biological production
process, a data set for a digital twin, and/or a trade secret with
an expert wrapper. The system may be configured to aggregate views
of a trade secret into a chain that proves which knowledge
recipients of the parties have viewed the trade secret. The
knowledge distribution system may include a reporting system
configured to report an analytic result based on operations
performed on the distributed ledger or the digital knowledge. The
distributed ledger may be configured to aggregate a set of
instructions where an operation on the distributed ledger may add
at least one instruction to a pre-existing set of instructions to
provide a modified set of instructions. The smart contract may be
configured to manage allocation of instruction sub-sets to the
distributed ledger and access to the instruction sub-sets. The
distributed ledger may be configured to log parties who have
contributed to an instance of the digital knowledge by storing data
related to the parties in at least one of the blocks. The knowledge
distribution system may be configured to log a source of an
instance of the digital knowledge by storing data related to the
source in at least one of the blocks. The distributed ledger may be
configured such that a private network of authorized participants
may establish cryptography-based consensus required for
verification of new blocks to be added to the blocks. The ledger
management system may be configured to facilitate crowdsourcing of
information added to a block of the blocks of the distributed
ledger. The distributed ledger may be configured such to store a
review of an instance of the digital knowledge by a crowdsourcer in
a block of the blocks. The distributed ledger may be configured
such to store a signature of an instance of the digital knowledge
by a crowdsourcer in a block of the blocks. The distributed ledger
may be configured such to store a verification of an instance of
the digital knowledge by a crowdsourcer in a block of the blocks.
The ledger management system may be configured to establish
cryptographic currency tokens that may be tradeable among users of
the distributed ledger. The knowledge distribution system may
include an account management system in communication with the
distributed ledger that may be configured to facilitate creation
and management of user accounts related to users of the knowledge
distribution system. The knowledge distribution system may include
a user interface system in communication with the distributed
ledger and may be configured to present a user interface to a user
of the knowledge distribution system where the user interface
allows the user to view data related to an instance of the digital
knowledge. The knowledge distribution system may include a
marketplace system in communication with the distributed ledger and
may be configured to establish and maintain a digital marketplace
that may be configured to visually present data related to an
instance of the digital knowledge to a user of the knowledge
distribution system. The knowledge distribution system may include
a knowledge datastore in communication with the distributed ledger
and may be configured to store data related to the digital
knowledge. The knowledge distribution system may include a client
datastore in communication with the distributed ledger and may be
configured to store data related to users of the knowledge
distribution system. The knowledge distribution system may include
a smart contract datastore in communication with the distributed
ledger and may be configured to store data related to the smart
contract. The knowledge distribution system may include a reporting
system in communication with the distributed ledger and may be
configured to analyze said tokenized digital knowledge and report
an analytic result based on the analysis of the tokenized digital
knowledge. The smart contract may be generated using a
parameterizable smart contract template. The smart contract may
include parameters based on type of digital knowledge to be
tokenized. The parameters may include financial parameters, royalty
parameters, usage parameters, output produced parameters,
allocation of consideration parameters, identity parameters, and/or
access condition parameters.
[0029] In other example embodiments, a knowledge distribution
system may use a distributed ledger and smart contracts to
facilitate management and exchange of access, licensing, and
ownership rights of digital knowledge.
[0030] In other example embodiments, a computer-implemented method
for controlling rights related to digital knowledge is disclosed.
The method may include creating and managing a distributed ledger
that is distributed over nodes of a network and includes blocks
linked via cryptography. A smart contract may be implemented and
managed via the distributed ledger where the smart contract may be
stored in the distributed ledger and may include a triggering
event. A smart contract action may be performed with respect to the
digital knowledge in response to an occurrence of the triggering
event. An instance of the digital knowledge may be received. The
digital knowledge may be tokenized such that the instance of the
digital knowledge can be manipulated as a token on the distributed
ledger. The tokenized digital knowledge may be stored via the
distributed ledger. Commitments of parties to the smart contract
may be processed. The method may include management of rights over
control of and access to the tokenized digital knowledge according
to the smart contract and management of the smart contract action
in response to the triggering event.
[0031] One or more of the following example features may be
included. A knowledge exchange for the exchange of the tokenized
digital knowledge based on the smart contract may be orchestrated.
The knowledge exchange of the tokenized digital knowledge may be
integrated with another exchange where the knowledge exchange
facilitates exchange of valuable and/or sensitive knowledge related
to a subject matter of the other exchange.
[0032] In other example embodiments, a knowledge distribution
system for controlling rights related to digital knowledge is
disclosed. The knowledge distribution system may include a ledger
management system configured to create and manage a distributed
ledger. The distributed ledger may be distributed over nodes of a
network and may include blocks linked via cryptography. A smart
contract system may be in communication with the distributed ledger
and may be configured to implement and manage a smart contract via
the distributed ledger. The smart contract may be stored in the
distributed ledger and may include a triggering event. The smart
contract may be configured to perform a smart contract action with
respect to the digital knowledge in response to an occurrence of
the triggering event. The knowledge distribution system may be
configured to receive from a knowledge provider device an instance
of the digital knowledge including a three-dimensional (3D) printer
instruction set for 3D printing an object. The digital knowledge
may be tokenized such that the instance of the digital knowledge
may be manipulated as a token on the distributed ledger. The
tokenized digital knowledge may be stored via the distributed
ledger. Commitments of the knowledge provider and a knowledge
recipient of the 3D printer instruction set to the smart contract
may be processed. The knowledge distribution system may be
configured to manage rights of control of and access to the
tokenized digital knowledge according to the smart contract and may
manage the smart contract action according to a condition and the
triggering event.
[0033] One or more of the following example features may be
included. The 3D printer instruction set may include a 3D printing
schematic. The object may be at least one of a custom part, a
custom product, a manufacturing part, a replacement part, a toy, a
medical device, and a tool. The knowledge recipient may use a
knowledge recipient device to download and use the 3D printer
instruction set. The knowledge recipient device may be at least one
of a computing device, a server, a 3D printer, and a manufacturing
device. The knowledge recipient may use a knowledge recipient
device to purchase the tokenized digital knowledge corresponding to
the 3D printer instruction set. The knowledge distribution system
may include an event listener configured to listen to an
application programming interface (API) that may provide a
connection between the knowledge distribution system and a
knowledge recipient device of the knowledge recipient. The smart
contract may be configured to trigger the condition of the
knowledge recipient to make a payment when the 3D printer
instruction set may be transferred or used based on the rights of
control of and access to the tokenized digital knowledge. The
rights of control of and access to the tokenized digital knowledge
may include a permission for a user to 3D print using multiple
instances of the 3D printer instruction set. The rights of control
of and access to the tokenized digital knowledge may include at
least one of 3D printer requirements, a time period during which
the object can be 3D printed, whether the tokenized digital
knowledge is transferred to a downstream knowledge recipient,
warranties, disclaimers, indemnifications, and certifications with
respect to the object. Information related to the 3D printer
instruction set of the tokenized digital knowledge may be modified
on the distributed ledger when the 3D printer instruction set is at
least one of purchased, downloaded, and used. In examples,
information related to the 3D printer instruction set may include
at least one of origin, date of creation, names of one or more
contributing individuals, groups, and/or companies, pricing, market
trends for related schematics, serial numbers, and part
identifiers. The smart contract action may be one of an assignment
of a serial number to the object that is 3D printed, monitoring for
the triggering event, verifying fulfillment of an obligation based
on the condition, verifying payment and/or transfer of the
tokenized digital knowledge, transferring the tokenized digital
knowledge, logging one or more transactions in the distributed
ledger, performing one or more operations with respect to the
distributed ledger, and creating one or more new blocks in the
distributed ledger. The smart contract action may include verifying
that the condition is met as defined in the smart contract where
the condition may be one of printer requirements, payment received
or currency transferred from a knowledge recipient device of the
knowledge recipient, and transfer of the tokenized digital
knowledge to the knowledge recipient device. When the tokenized
digital knowledge may be transferred to a knowledge recipient
device of a knowledge recipient, a 3D printer may be configured to
print the object according to the 3D printer instruction set. The
knowledge distribution system may include a smart contract
generator that may be configured to parametrize a smart contract
template based on at least one of information provided by the
knowledge provider, the condition, and the triggering event.
[0034] In other example embodiments, a computer-implemented method
for controlling rights related to digital knowledge is disclosed.
The method may include creating and managing a distributed ledger
that is distributed over nodes of a network and includes blocks
linked via cryptography. A smart contract may be implemented and
managed via the distributed ledger where the smart contract may be
stored in the distributed ledger and may include a triggering
event. A smart contract action may be performed with respect to the
digital knowledge in response to an occurrence of the triggering
event. The method may include receiving from a knowledge provider
device an instance of the digital knowledge that includes a
three-dimensional (3D) printer instruction set for 3D printing an
object. The digital knowledge may be tokenized such that the
instance of the digital knowledge can be manipulated as a token on
the distributed ledger. The tokenized digital knowledge may be
stored via the distributed ledger. Commitments of the knowledge
provider and a knowledge recipient of the 3D printer instruction
set to the smart contract may be processed. The method may include
management of rights of control of and access to the tokenized
digital knowledge according to the smart contract, and management
of the smart contract action according to a condition and the
triggering event.
[0035] One or more of the following example features may be
included. An element of the instance of the digital knowledge via
the smart contract may be crowdsourced. The element of the instance
of the digital knowledge may be managed by a smart contract system
according to the smart contract.
[0036] Provided herein is a lending transaction enablement platform
having a set of data-integrated microservices including data
collection and monitoring services, blockchain services, and smart
contract services for handling lending entities and transactions.
The platform is capable of enabling a wide range of dedicated
solutions, which may share data collection and storage
infrastructure, and which may share or exchange inputs, events,
activities, and outputs, such as to reinforce learning, enable
automation, and enable adaptive intelligence across the various
solutions.
[0037] Aspects of the present disclosure relate to a method for
electronically facilitating licensing of one or more personality
rights of a licensor. The method may include receiving an access
request from a licensee to obtain approval to license personality
rights from a set of available licensors. The method may include
selectively granting access to the licensee based on the access
request. The method may include receiving confirmation of a deposit
of an amount of funds from the licensee. The method may include
issuing an amount of cryptocurrency corresponding to the amount of
funds deposited by the licensee to an account of the licensee. The
method may include receiving a smart contract request to create a
smart contract governing the licensing of the one or more
personality rights of the licensor by the licensee. The smart
contract request may indicate one or more terms including a
consideration amount of cryptocurrency to be paid to the licensor
in exchange for one or more obligations on the licensor. The method
may include generating the smart contract based on the smart
contract request. The method may include escrowing the
consideration amount of cryptocurrency from the account of the
licensee. The method may include deploying the smart contract to a
distributed ledger. The method may include verifying, by the smart
contract, that the licensor has performed the one or more
obligations. The method may include, in response to receiving
verification that the licensor has performed the one or more
obligations, releasing at least a portion of the consideration
amount of cryptocurrency into a licensor account of the licensor.
The method may include outputting a record indicating a completion
of a licensing transaction defined by the smart contract to the
distributed ledger.
[0038] Other aspects of the present disclosure relate to a system
configured for electronically facilitating licensing of one or more
personality rights of a licensor. The system may include one or
more hardware processors configured by machine-readable
instructions. The processor(s) may be configured to receive an
access request from a licensee to obtain approval to license
personality rights from a set of available licensors. The
processor(s) may be configured to selectively grant access to the
licensee based on the access request. The processor(s) may be
configured to receive confirmation of a deposit of an amount of
funds from the licensee. The processor(s) may be configured to
issue an amount of cryptocurrency corresponding to the amount of
funds deposited by the licensee to an account of the licensee. The
processor(s) may be configured to receive a smart contract request
to create a smart contract governing the licensing of the one or
more personality rights of the licensor by the licensee. The smart
contract request may indicate one or more terms including a
consideration amount of cryptocurrency to be paid to the licensor
in exchange for one or more obligations on the licensor. The
processor(s) may be configured to generate the smart contract based
on the smart contract request. The processor(s) may be configured
to escrow the consideration amount of cryptocurrency from the
account of the licensee. The processor(s) may be configured to
deploy the smart contract to a distributed ledger. The processor(s)
may be configured to verify, by the smart contract, that the
licensor has performed the one or more obligations. The
processor(s) may be configured to, in response to receiving
verification that the licensor has performed the one or more
obligations, release at least a portion of the consideration amount
of cryptocurrency into a licensor account of the licensor. The
processor(s) may be configured to output a record indicating a
completion of a licensing transaction defined by the smart contract
to the distributed ledger.
BRIEF DESCRIPTION OF THE FIGURES
[0039] The disclosure and the following detailed description of
certain embodiments thereof may be understood by reference to the
following figures:
[0040] FIG. 1 is a schematic diagram of components of a platform
for enabling intelligent transactions in accordance with
embodiments of the present disclosure.
[0041] FIGS. 2A and 2B are schematic diagrams of additional
components of a platform for enabling intelligent transactions in
accordance with embodiments of the present disclosure.
[0042] FIG. 3 is a schematic diagram of additional components of a
platform for enabling intelligent transactions in accordance with
embodiments of the present disclosure.
[0043] FIG. 4 to FIG. 31 are schematic diagrams of embodiments of
neural net systems that may connect to, be integrated in, and be
accessible by the platform for enabling intelligent transactions
including ones involving expert systems, self-organization, machine
learning, artificial intelligence and including neural net systems
trained for pattern recognition, for classification of one or more
parameters, characteristics, or phenomena, for support of
autonomous control, and other purposes in accordance with
embodiments of the present disclosure.
[0044] FIG. 32 is a schematic diagram of components of an
environment including an intelligent energy and compute facility, a
host intelligent energy and compute facility resource management
platform, a set of data sources, a set of expert systems,
interfaces to a set of market platforms and external resources, and
a set of user or client systems and devices in accordance with
embodiments of the present disclosure.
[0045] FIG. 33 depicts components and interactions of a
transactional, financial and marketplace enablement system.
[0046] FIG. 34 depicts components and interactions of a set of data
handling layers of a transactional, financial and marketplace
enablement system.
[0047] FIG. 35 depicts adaptive intelligence and robotic process
automation capabilities of a transactional, financial and
marketplace enablement system.
[0048] FIG. 36 depicts opportunity mining capabilities of a
transactional, financial and marketplace enablement system.
[0049] FIG. 37 depicts adaptive edge computation management and
edge intelligence capabilities of a transactional, financial and
marketplace enablement system.
[0050] FIG. 38 depicts protocol adaptation and adaptive data
storage capabilities of a transactional, financial and marketplace
enablement system.
[0051] FIG. 39 depicts robotic operational analytic capabilities of
a transactional, financial and marketplace enablement system.
[0052] FIG. 40 depicts a blockchain and smart contract platform for
a forward market for access rights to events.
[0053] FIG. 41 depicts an algorithm and a dashboard of a blockchain
and smart contract platform for a forward market for access rights
to events.
[0054] FIG. 42 depicts a blockchain and smart contract platform for
forward market demand aggregation.
[0055] FIG. 43 depicts an algorithm and a dashboard of a blockchain
and smart contract platform for forward market demand
aggregation.
[0056] FIG. 44 depicts a blockchain and smart contract platform for
crowdsourcing for innovation.
[0057] FIG. 45 depicts an algorithm and a dashboard of a blockchain
and smart contract platform for crowdsourcing for innovation.
[0058] FIG. 46 depicts a blockchain and smart contract platform for
crowdsourcing for evidence.
[0059] FIG. 47 depicts an algorithm and a dashboard of a blockchain
and smart contract platform for crowdsourcing for evidence.
[0060] FIG. 48 depicts components and interactions of an embodiment
of a lending platform having a set of data-integrated microservices
including data collection and monitoring services for handling
lending entities and transactions.
[0061] FIG. 49 depicts components and interactions of an embodiment
of a lending platform in which a set of lending solutions are
supported by a data-integrated set of data collection and
monitoring services, adaptive intelligent systems, and data storage
systems.
[0062] FIG. 50 depicts components and interactions of an embodiment
of a lending platform having a set of data integrated blockchain
services, smart contract services, social network analytic
services, crowdsourcing services and Internet of Things data
collection and monitoring services for collecting, monitoring and
processing information about entities involved in or related to a
lending transaction.
[0063] FIG. 51 depicts components and interactions of a lending
platform having an Internet of Things and sensor platform for
monitoring at least one of a set of assets, a set of collateral,
and a guarantee for a loan, a bond, or a debt transaction.
[0064] FIG. 52 depicts components and interactions of a lending
platform having a crowdsourcing system for collecting information
related to entities involved in a lending transaction.
[0065] FIG. 53 depicts an embodiment of a crowdsourcing workflow
enabled by a lending platform.
[0066] FIG. 54 depicts components and interactions of an embodiment
of a lending platform having a smart contract system that
automatically adjusts an interest rate for a loan based on
information collected via at least one of an Internet of Things
system, a crowdsourcing system, a set of social network analytic
services and a set of data collection and monitoring services.
[0067] FIG. 55 depicts components and interactions of an embodiment
of a lending platform having a smart contract that automatically
restructures debt based on a monitored condition.
[0068] FIG. 56 depicts components and interactions of a lending
platform having a set of data collection and monitoring systems for
validating the reliability of a guarantee for a loan, including an
Internet of Things system and a social network analytics
system.
[0069] FIG. 57 depicts components and interactions of a lending
platform having a robotic process automation system for negotiation
of a set of terms and conditions for a loan.
[0070] FIG. 58 depicts components and interactions of a lending
platform having a robotic process automation system for loan
collection.
[0071] FIG. 59 depicts components and interactions of a lending
platform having a robotic process automation system for
consolidating a set of loans.
[0072] FIG. 60 depicts components and interactions of a lending
platform having a robotic process automation system for managing a
factoring loan.
[0073] FIG. 61 depicts components and interactions of a lending
platform having a robotic process automation system for brokering a
mortgage loan.
[0074] FIG. 62 depicts components and interactions of a lending
platform having a crowdsourcing and automated classification system
for validating condition of an issuer for a bond, a social network
monitoring system with artificial intelligence for classifying a
condition about a bond, and an Internet of Things data collection
and monitoring system with artificial intelligence for classifying
a condition about a bond.
[0075] FIG. 63 depicts components and interactions of a lending
platform having a system that manages the terms and conditions of a
loan based on a parameter monitored by the IoT, by a parameter
determined by a social network analytic system, or a parameter
determined by a crowdsourcing system.
[0076] FIG. 64 depicts components and interactions of a lending
platform having an automated blockchain custody service for
managing a set of custodial assets.
[0077] FIG. 65 depicts components and interactions of a lending
platform having an underwriting system for a loan with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions.
[0078] FIG. 66 depicts components and interactions of a lending
platform having a loan marketing system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services and smart contract services for marketing a loan to a set
of prospective parties.
[0079] FIG. 67 depicts components and interactions of a lending
platform having a rating system with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for rating a set of loan-related entities.
[0080] FIG. 68 depicts components and interactions of a lending
platform having a regulatory and/or compliance system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for automatically
facilitating compliance with at least one of a law, a regulation
and a policy that applies to a lending transaction.
[0081] FIG. 69, depicts a system for automated loan management.
[0082] FIG. 70 depicts a system.
[0083] FIG. 71 depicts a method for handling a loan.
[0084] FIG. 72 depicts a system for adaptive intelligence and
robotic process automation capabilities of a transactional,
financial and marketplace enablement.
[0085] FIG. 73 depicts a method for automated smart contract
creation and collateral assignment.
[0086] FIG. 74 depicts a system for handling a loan.
[0087] FIG. 75 depicts a method for handling a loan.
[0088] FIG. 76 depicts a system for adaptive intelligence and
robotic process automation.
[0089] FIG. 77 depicts a method for loan creation and
management.
[0090] FIG. 78 depicts a system for adaptive intelligence and
robotic process automation capabilities of a transactional,
financial and marketplace enablement.
[0091] FIG. 79 depicts a method for robotic process automation of
transactional, financial and marketplace activities.
[0092] FIG. 80 depicts a system for adaptive intelligence and
robotic process automation.
[0093] FIG. 81 depicts a method for automated transactional,
financial and marketplace activities.
[0094] FIG. 82 depicts a system for adaptive intelligence and
robotic process.
[0095] FIG. 83 depicts a method for performing loan related
actions.
[0096] FIG. 84 depicts a system for adaptive intelligence and
robotic process.
[0097] FIG. 85 depicts a method for performing loan related
actions.
[0098] FIG. 86 depicts a system for adaptive intelligence and
robotic process.
[0099] FIG. 87 depicts a method for performing loan related
actions.
[0100] FIG. 88 depicts a smart contract system for managing
collateral for a loan.
[0101] FIG. 89 depicts a smart contract method for managing
collateral for a loan.
[0102] FIG. 90 depicts a system for validating conditions of
collateral or a guarantor for a loan.
[0103] FIG. 91 depicts a crowdsourcing method for validating
conditions of collateral or a guarantor for a loan.
[0104] FIG. 92 depicts a smart contract system for modifying a
loan.
[0105] FIG. 93 depicts a smart contract method for modifying a
loan.
[0106] FIG. 94 depicts a smart contract system for modifying a
loan.
[0107] FIG. 95 depicts a smart contract method for modifying a
loan.
[0108] FIG. 96 depicts a smart contract system for modifying a
loan.
[0109] FIG. 97 depicts a smart contract method for modifying a
loan.
[0110] FIG. 98 depicts a monitoring system for validating
conditions of a guarantee for a loan.
[0111] FIG. 99 depicts a monitoring method for validating
conditions of a guarantee for a loan.
[0112] FIG. 100 depicts a robotic process automation system for
negotiating a loan.
[0113] FIG. 101 depicts a robotic process automation method for
negotiating a loan.
[0114] FIG. 102 depicts a system for adaptive intelligence and
robotic process automation.
[0115] FIG. 103 depicts a loan collection method.
[0116] FIG. 104 depicts a system for adaptive intelligence and
robotic process automation.
[0117] FIG. 105 depicts a loan refinancing method.
[0118] FIG. 106 depicts a system for adaptive intelligence and
robotic process automation.
[0119] FIG. 107 depicts a for loan consolidation method.
[0120] FIG. 108 depicts a system for adaptive intelligence and
robotic process automation.
[0121] FIG. 109 depicts a loan factoring method.
[0122] FIG. 110 depicts a system for adaptive intelligence and
robotic process automation.
[0123] FIG. 111 depicts a mortgage brokering method.
[0124] FIG. 112 depicts a system for adaptive intelligence and
robotic process automation.
[0125] FIG. 113 depicts a method for debt management.
[0126] FIG. 114 depicts a system for adaptive intelligence and
robotic process automation.
[0127] FIG. 115 depicts a method for bond management.
[0128] FIG. 116 depicts a system for monitoring a condition of an
issuer for a bond.
[0129] FIG. 117 depicts a method for monitoring a condition of an
issuer for a bond
[0130] FIG. 118 depicts a system for monitoring a condition of an
issuer for a bond.
[0131] FIG. 119 depicts a method for monitoring a condition of an
issuer for a bond.
[0132] FIG. 120 depicts a system for automatic subsidized loan
management.
[0133] FIG. 121 depicts a method for automatically modifying
subsidized loan terms and conditions.
[0134] FIG. 122 depicts a system to automatically modify terms and
conditions of a loan.
[0135] FIG. 123 depicts a method for collecting social network
information about an entity involved in a subsidized loan
transaction.
[0136] FIG. 124 depicts a system for automating handling of a
subsidized loan using crowdsourcing.
[0137] FIG. 125 depicts a method for automating handling of a
subsidized loan.
[0138] FIG. 126 depicts a system for asset access control.
[0139] FIG. 127 depicts a method for asset access control.
[0140] FIG. 128 depicts a system automated handling of loan
foreclosure.
[0141] FIG. 129 depicts a method for facilitating foreclosure on
collateral.
[0142] FIG. 130 depicts an example energy and computing resource
platform.
[0143] FIG. 131 depicts an example facility data record.
[0144] FIG. 132 depicts an example schema of a person data
record.
[0145] FIG. 133 depicts a cognitive processing system.
[0146] FIG. 134 depicts a process for a lead generation system to
generate a lead list.
[0147] FIG. 135 depicts a process for a lead generation system to
determine facility outputs for identified leads.
[0148] FIG. 136 depicts a process to generate and output
personalized content.
[0149] FIG. 137 depicts a schematic illustrating an example of a
portion of an information technology system for transaction
artificial intelligence leveraging digital twins according to some
embodiments of the present disclosure.
[0150] FIG. 138 depicts a schematic illustrating a compliance
system that facilitates the licensing of personality rights
according to some embodiments of the present disclosure.
[0151] FIG. 139 depicts a schematic illustrating an example set of
components of a compliance system according to some embodiments of
the present disclosure.
[0152] FIG. 140 depicts a set of operations of a method for vetting
a potential licensee for purposes of licensing personality rights
of a licensor according to some embodiments of the present
disclosure.
[0153] FIG. 141 depicts a set of operations of a method for
facilitating the licensing of personality rights of a licensor by a
licensee according to some embodiments of the present
disclosure.
[0154] FIG. 142 depicts a set of operations of a method for
detecting potential circumvention of rules or regulations by a
licensor and/or licensee according to some embodiments of the
present disclosure.
[0155] FIG. 143 depicts a method for selecting an AI solution.
[0156] FIG. 144 depicts a method for selecting an AI solution.
[0157] FIG. 145 depicts an example of an assembled AI solution.
[0158] FIG. 146 depicts a method for selecting an AI solution.
[0159] FIG. 147 depicts a method for selecting an AI solution.
[0160] FIG. 148 depicts an AI solution selection and configuration
system.
[0161] FIG. 149 depicts an AI solution selection and configuration
system.
[0162] FIG. 150 depicts an AI solution selection and configuration
system.
[0163] FIG. 151 depicts a component configuration circuit.
[0164] FIG. 152 depicts an AI solution selection and configuration
system.
[0165] FIG. 153 depicts a system for selecting and configuring an
artificial intelligence model.
[0166] FIG. 154 depicts a method of selecting and configuring an
artificial intelligence model.
[0167] FIG. 155 is a schematic illustrating examples of
architecture of a digital twin system according to embodiments of
the present disclosure.
[0168] FIG. 156 is a schematic illustrating exemplary components of
a digital twin management system according to embodiments of the
present disclosure.
[0169] FIG. 157 is a schematic illustrating examples of a digital
twin I/O system that interfaces with an environment, the digital
twin system, and/or components thereof to provide bi-directional
transfer of data between coupled components according to
embodiments of the present disclosure.
[0170] FIG. 158 is a schematic illustrating an example set of
identified states related to industrial environments that the
digital twin system may identify and/or store for access by
intelligent systems (e.g., a cognitive intelligence system) or
users of the digital twin system according to embodiments of the
present disclosure.
[0171] FIG. 159 is a schematic illustrating example embodiments of
methods for updating a set of properties of a digital twin of the
present disclosure on behalf of a client application and/or one or
more embedded digital twins.
[0172] FIG. 160 illustrates example embodiments of a display
interface of the present disclosure that renders a digital twin of
a dryer centrifuge with information relating to the dryer
centrifuge.
[0173] FIG. 161 is a schematic illustrating an example embodiment
of a method for updating a set of vibration fault level states of
machine components such as bearings in the digital twin of an
industrial machine, on behalf of a client application.
[0174] FIG. 162 is a schematic illustrating an example embodiment
of a method for updating a set of vibration severity unit values of
machine components such as bearings in the digital twin of a
machine on behalf of a client application.
[0175] FIG. 163 is a schematic illustrating an example embodiment
of a method for updating a set of probability of failure values in
the digital twins of machine components on behalf of a client
application.
[0176] FIG. 164 is a schematic illustrating an example embodiment
of a method for updating a set of probability of downtime values of
machines in the digital twin of a manufacturing facility on behalf
of a client application.
[0177] FIG. 165 is a schematic illustrating an example embodiment
of a method for updating a set of probability of shutdown values of
manufacturing facilities in the digital twin of an enterprise on
behalf of a client application.
[0178] FIG. 166 is a schematic illustrating an example embodiment
of a method for updating a set of cost of downtime values of
machines in the digital twin of a manufacturing facility.
[0179] FIG. 167 is a schematic illustrating an example embodiment
of a method for updating one or more manufacturing KPI values in a
digital twin of a manufacturing facility, on behalf of a client
application.
[0180] FIG. 168 is a schematic diagram of components of a knowledge
distribution system and a communication network for facilitating
management of digital knowledge in accordance with embodiments of
the present disclosure.
[0181] FIG. 169 is a schematic diagram of a ledger network of the
knowledge distribution system in accordance with embodiments of the
present disclosure.
[0182] FIG. 170 is a schematic diagram of the knowledge
distribution system of FIG. 168 including details of a smart
contract and a smart contract system of the knowledge distribution
system in accordance with embodiments of the present
disclosure.
[0183] FIG. 171 is a schematic diagram of a plurality of datastores
of the knowledge distribution system in accordance with embodiments
of the present disclosure.
[0184] FIG. 172 illustrates a method of deploying a knowledge token
and related smart contract via the knowledge distribution system in
accordance with embodiments of the present disclosure.
[0185] FIG. 173 illustrates a method of performing high level
process flow of a smart contract that distributes digital knowledge
via the knowledge distribution system in accordance with
embodiments of the present disclosure.
[0186] FIG. 174 is a schematic diagram of another embodiment of
components of the knowledge distribution system and a communication
network for facilitating management of digital knowledge in
accordance with embodiments of the present disclosure.
[0187] FIG. 175 depicts a knowledge distribution system for
controlling rights related to digital knowledge.
[0188] FIG. 176 depicts a computer-implemented method for
controlling rights related to digital knowledge.
[0189] FIG. 177 depicts a computer-implemented method for
controlling rights related to digital knowledge.
[0190] FIG. 178 depicts a knowledge distribution system for
controlling rights related to digital knowledge.
[0191] FIG. 179 depicts possible components of a 3D printer
instruction set.
[0192] FIG. 180 depicts possible content of tokenized digital
knowledge.
[0193] FIG. 181 depicts possible smart contract actions.
[0194] FIG. 182 depicts possible conditions relating to triggering
events.
[0195] FIG. 183 depicts possible control and access rights.
[0196] FIG. 184 depicts possible triggering events.
[0197] FIG. 185 depicts a computer-implemented method for
controlling rights related to digital knowledge.
[0198] FIG. 186 depicts a computer-implemented method for
controlling rights related to digital knowledge.
[0199] FIG. 187 depicts possible crowdsourced information.
[0200] FIG. 188 depicts possible contents of a distributed
ledger.
[0201] FIG. 189 depicts possible parameters.
[0202] FIG. 190 depicts an embodiment of a knowledge distribution
system for controlling rights related to digital knowledge.
[0203] FIGS. 191-196 depict embodiments of operations for
controlling rights related to digital knowledge.
DETAILED DESCRIPTION
[0204] The term services/microservices (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, a
service/microservice includes any system (or platform) configured
to functionally perform the operations of the service, where the
system may be data-integrated, including data collection circuits,
blockchain circuits, artificial intelligence circuits, and/or smart
contract circuits for handling lending entities and transactions.
Services/microservices may facilitate data handling and may include
facilities for data extraction, transformation and loading; data
cleansing and deduplication facilities; data normalization
facilities; data synchronization facilities; data security
facilities; computational facilities (e.g., for performing
pre-defined calculation operations on data streams and providing an
output stream); compression and de-compression facilities; analytic
facilities (such as providing automated production of data
visualizations), data processing facilities, and/or data storage
facilities (including storage retention, formatting, compression,
migration, etc.), and others.
[0205] Services/microservices may include controllers, processors,
network infrastructure, input/output devices, servers, client
devices (e.g., laptops, desktops, terminals, mobile devices, and/or
dedicated devices), sensors (e.g., IoT sensors associated with one
or more entities, equipment, and/or collateral), actuators (e.g.,
automated locks, notification devices, lights, camera controls,
etc.), virtualized versions of any one or more of the foregoing
(e.g., outsourced computing resources such as a cloud storage,
computing operations; virtual sensors; subscribed data to be
gathered such as stock or commodity prices, recordal logs, etc.),
and/or include components configured as computer readable
instructions that, when performed by a processor, cause the
processor to perform one or more functions of the service, etc.
Services may be distributed across a number of devices, and/or
functions of a service may be performed by one or more devices
cooperating to perform the given function of the service.
[0206] Services/microservices may include application programming
interfaces that facilitate connection among the components of the
system performing the service (e.g., microservices) and between the
system to entities (e.g., programs, web sites, user devices, etc.)
that are external to the system. Without limitation to any other
aspect of the present disclosure, example microservices that may be
present in certain embodiments include (a) a multi-modal set of
data collection circuits that collect information about and monitor
entities related to a lending transaction; (b) blockchain circuits
for maintaining a secure historical ledger of events related to a
loan, the blockchain circuits having access control features that
govern access by a set of parties involved in a loan; (c) a set of
application programming interfaces, data integration services, data
processing workflows and user interfaces for handling loan-related
events and loan-related activities; and (d) smart contract circuits
for specifying terms and conditions of smart contracts that govern
at least one of loan terms and conditions, loan-related events and
loan-related activities. Any of the services/microservices may be
controlled by or have control over a controller. Certain systems
may not be considered to be a service/microservice. For example, a
point of sale device that simply charges a set cost for a good or
service may not be a service. In another example, a service that
tracks the cost of a good or service and triggers notifications
when the value changes may not be a valuation service itself, but
may rely on valuation services, and/or may form a portion of a
valuation service in certain embodiments. It can be seen that a
given circuit, controller, or device may be a service or a part of
a service in certain embodiments, such as when the functions or
capabilities of the circuit, controller, or device are configured
to support a service or microservice as described herein, but may
not be a service or part of a service for other embodiments (e.g.,
where the functions or capabilities of the circuit, controller, or
device are not relevant to a service or microservice as described
herein). In another example, a mobile device being operated by a
user may form a portion of a service as described herein at a first
point in time (e.g., when the user accesses a feature of the
service through an application or other communication from the
mobile device, and/or when a monitoring function is being performed
via the mobile device), but may not form a portion of the service
at a second point in time (e.g., after a transaction is completed,
after the user un-installs an application, and/or when a monitoring
function is stopped and/or passed to another device). Accordingly,
the benefits of the present disclosure may be applied in a wide
variety of processes or systems, and any such processes or systems
may be considered a service (or a part of a service) herein.
[0207] One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure will benefit a particular system,
how to combine processes and systems from the present disclosure to
construct, provide performance characteristics (e.g., bandwidth,
computing power, time response, etc.), and/or provide operational
capabilities (e.g., time between checks, up-time requirements
including longitudinal (e.g., continuous operating time) and/or
sequential (e.g., time-of-day, calendar time, etc.), resolution
and/or accuracy of sensing, data determinations (e.g., accuracy,
timing, amount of data), and/or actuator confirmation capability)
of components of the service that are sufficient to provide a given
embodiment of a service, platform, and/or microservice as described
herein. Certain considerations for the person of skill in the art,
in determining the configuration of components, circuits,
controllers, and/or devices to implement a service, platform,
and/or microservice ("service" in the listing following) as
described herein include, without limitation: the balance of
capital costs versus operating costs in implementing and operating
the service; the availability, speed, and/or bandwidth of network
services available for system components, service users, and/or
other entities that interact with the service; the response time of
considerations for the service (e.g., how quickly decisions within
the service must be implemented to support the commercial function
of the service, the operating time for various artificial
intelligence or other high computation operations) and/or the
capital or operating cost to support a given response time; the
location of interacting components of the service, and the effects
of such locations on operations of the service (e.g., data storage
locations and relevant regulatory schemes, network communication
limitations and/or costs, power costs as a function of the
location, support availability for time zones relevant to the
service, etc.); the availability of certain sensor types, the
related support for those sensors, and the availability of
sufficient substitutes (e.g., a camera may require supportive
lighting, and/or high network bandwidth or local storage) for the
sensing purpose; an aspect of the underlying value of an aspect of
the service (e.g., a principal amount of a loan, a value of
collateral, a volatility of the collateral value, a net worth or
relative net worth of a lender, guarantor, and/or borrower, etc.)
including the time sensitivity of the underlying value (e.g., if it
changes quickly or slowly relative to the operations of the service
or the term of the loan); a trust indicator between parties of a
transaction (e.g., history of performance between the parties, a
credit rating, social rating, or other external indicator,
conformance of activity related to the transaction to an industry
standard or other normalized transaction type, etc.); and/or the
availability of cost recovery options (e.g., subscriptions, fees,
payment for services, etc.) for given configurations and/or
capabilities of the service, platform, and/or microservice. Without
limitation to any other aspect of the present disclosure, certain
operations performed by services herein include: performing
real-time alterations to a loan based on tracked data; utilizing
data to execute a collateral-backed smart contract; re-evaluating
debt transactions in response to a tracked condition or data, and
the like. While specific examples of services/microservices and
considerations are described herein for purposes of illustration,
any system benefitting from the disclosures herein, and any
considerations understood to one of skill in the art having the
benefit of the disclosures herein, are specifically contemplated
within the scope of the present disclosure.
[0208] Without limitation, services include a financial service
(e.g., a loan transaction service), a data collection service
(e.g., a data collection service for collecting and monitoring
data), a blockchain service (e.g., a blockchain service to maintain
secure data), data integration services (e.g., a data integration
service to aggregate data), smart contract services (e.g., a smart
contract service to determine aspects of smart contracts), software
services (e.g., a software service to extract data related to the
entities from publicly available information sites), crowdsourcing
services (e.g., a crowdsourcing service to solicit and report
information), Internet of Things services (e.g., an Internet of
Things service to monitor an environment), publishing services
(e.g., a publishing services to publish data), microservices (e.g.,
having a set of application programming interfaces that facilitate
connection among the microservices), valuation services (e.g., that
use a valuation model to set a value for collateral based on
information), artificial intelligence services, market value data
collection services (e.g., that monitor and report on marketplace
information), clustering services (e.g., for grouping the
collateral items based on similarity of attributes), social
networking services (e.g., that enables configuration with respect
to parameters of a social network), asset identification services
(e.g., for identifying a set of assets for which a financial
institution is responsible for taking custody), identity management
services (e.g., by which a financial institution verifies
identities and credentials), and the like, and/or similar
functional terminology. Example services to perform one or more
functions herein include computing devices; servers; networked
devices; user interfaces; inter-device interfaces such as
communication protocols, shared information and/or information
storage, and/or application programming interfaces (APIs); sensors
(e.g., IoT sensors operationally coupled to monitored components,
equipment, locations, or the like); distributed ledgers; circuits;
and/or computer readable code configured to cause a processor to
execute one or more functions of the service. One or more aspects
or components of services herein may be distributed across a number
of devices, and/or may consolidated, in whole or part, on a given
device. In embodiments, aspects or components of services herein
may be implemented at least in part through circuits, such as, in
non-limiting examples, a data collection service implemented at
least in part as a data collection circuit structed to collect and
monitor data, a blockchain service implemented at least in part as
a blockchain circuit structured to maintain secure data, data
integration services implemented at least in part as a data
integration circuit structured to aggregate data, smart contract
services implemented at least in part as a smart contract circuit
structed to determine aspects of smart contracts, software services
implemented at least in part as a software service circuit
structured to extract data related to the entities from publicly
available information sites, crowdsourcing services implemented at
least in part as a crowdsourcing circuit structured to solicit and
report information, Internet of Things services implemented at
least in part as an Internet of Things circuit structured to
monitor an environment, publishing services implemented at least in
part as a publishing services circuit structured to publish data,
microservice service implemented at least in part as a microservice
circuit structured to interconnect a plurality of service circuits,
valuation service implemented at least in part as valuation
services circuit structured to access a valuation model to set a
value for collateral based on data, artificial intelligence service
implemented at least in part as an artificial intelligence services
circuit, market value data collection service implemented at least
in part as market value data collection service circuit structured
to monitor and report on marketplace information, clustering
service implemented at least in part as a clustering services
circuit structured to group collateral items based on similarity of
attributes, a social networking service implemented at least in
part as a social networking analytic services circuit structured to
configure parameters with respect to a social network, asset
identification services implemented at least in part as an asset
identification service circuit for identifying a set of assets for
which a financial institution is responsible for taking custody,
identity management services implemented at least in part as an
identity management service circuit enabling a financial
institution to verify identities and credentials, and the like.
Accordingly, the benefits of the present disclosure may be applied
in a wide variety of systems, and any such systems may be
considered with respect to items and services herein, while in
certain embodiments a given system may not be considered with
respect to items and services herein. One of skill in the art,
having the benefit of the disclosure herein and knowledge about a
contemplated system ordinarily available to that person, can
readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Among the considerations that one of skill in
the art may contemplate to determine a configuration for a
particular service include: the distribution and access devices
available to one or more parties to a particular transaction;
jurisdictional limitations on the storage, type, and communication
of certain types of information; requirements or desired aspects of
security and verification of information communication for the
service; the response time of information gathering, inter-party
communications, and determinations to be made by algorithms,
machine learning components, and/or artificial intelligence
components of the service; cost considerations of the service,
including capital expenses and operating costs, as well as which
party or entity will bear the costs and availability to recover
costs such as through subscriptions, service fees, or the like; the
amount of information to be stored and/or communicated to support
the service; and/or the processing or computing power to be
utilized to support the service.
[0209] The terms items and services (and similar terms) as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, items and
service include any items and service, including, without
limitation, items and services used as a reward, used as
collateral, become the subject of a negotiation, and the like, such
as, without limitation, an application for a warranty or guarantee
with respect to an item that is the subject of a loan, collateral
for a loan, or the like, such as a product, a service, an offering,
a solution, a physical product, software, a level of service,
quality of service, a financial instrument, a debt, an item of
collateral, performance of a service, or other items. Without
limitation to any other aspect or description of the present
disclosure, items and service include any items and service,
including, without limitation, items and services as applied to
physical items (e.g., a vehicle, a ship, a plane, a building, a
home, real estate property, undeveloped land, a farm, a crop, a
municipal facility, a warehouse, a set of inventory, an antique, a
fixture, an item of furniture, an item of equipment, a tool, an
item of machinery, and an item of personal property), a financial
item (e.g., a commodity, a security, a currency, a token of value,
a ticket, a cryptocurrency), a consumable item (e.g., an edible
item, a beverage), a highly valued item (e.g., a precious metal, an
item of jewelry, a gemstone), an intellectual item (e.g., an item
of intellectual property, an intellectual property right, a
contractual right), and the like. Accordingly, the benefits of the
present disclosure may be applied in a wide variety of systems, and
any such systems may be considered with respect to items and
services herein, while in certain embodiments a given system may
not be considered with respect to items and services herein. One of
skill in the art, having the benefit of the disclosure herein and
knowledge about a contemplated system ordinarily available to that
person, can readily determine which aspects of the present
disclosure will benefit a particular system, and/or how to combine
processes and systems from the present disclosure to enhance
operations of the contemplated system.
[0210] The terms agent, automated agent, and similar terms as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, an agent
or automated agent may process events relevant to at least one of
the value, the condition, and the ownership of items of collateral
or assets. The agent or automated agent may also undertake an
action related to a loan, debt transaction, bond transaction,
subsidized loan, or the like to which the collateral or asset is
subject, such as in response to the processed events. The agent or
automated agent may interact with a marketplace for purposes of
collecting data, testing spot market transactions, executing
transactions, and the like, where dynamic system behavior involves
complex interactions that a user may desire to understand, predict,
control, and/or optimize. Certain systems may not be considered an
agent or an automated agent. For example, if events are merely
collected but not processed, the system may not be an agent or
automated agent. In some embodiments, if a loan-related action is
undertaken not in response to a processed event, it may not have
been undertaken by an agent or automated agent. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure
include and/or benefit from agents or automated agent. Certain
considerations for the person of skill in the art, or embodiments
of the present disclosure with respect to an agent or automated
agent include, without limitation: rules that determine when there
is a change in a value, condition or ownership of an asset or
collateral, and/or rules to determine if a change warrants a
further action on a loan or other transaction, and other
considerations. While specific examples of market values and
marketplace information are described herein for purposes of
illustration, any embodiment benefitting from the disclosures
herein, and any considerations understood to one of skill in the
art having the benefit of the disclosures herein are specifically
contemplated within the scope of the present disclosure.
[0211] The term marketplace information, market value and similar
terms as utilized herein should be understood broadly. Without
limitation to any other aspect or description of the present
disclosure, marketplace information and market value describe a
status or value of an asset, collateral, food, or service at a
defined point or period in time. Market value may refer to the
expected value placed on an item in a marketplace or auction
setting, or pricing or financial data for items that are similar to
the item, asset, or collateral in at least one public marketplace.
For a company, market value may be the number of its outstanding
shares multiplied by the current share price. Valuation services
may include market value data collection services that monitor and
report on marketplace information relevant to the value (e.g.,
market value) of collateral, the issuer, a set of bonds, and a set
of assets. a set of subsidized loans, a party, and the like. Market
values may be dynamic in nature because they depend on an
assortment of factors, from physical operating conditions to
economic climate to the dynamics of demand and supply. Market value
may be affected by, and marketplace information may include,
proximity to other assets, inventory or supply of assets, demand
for assets, origin of items, history of items, underlying current
value of item components, a bankruptcy condition of an entity, a
foreclosure status of an entity, a contractual default status of an
entity, a regulatory violation status of an entity, a criminal
status of an entity, an export controls status of an entity, an
embargo status of an entity, a tariff status of an entity, a tax
status of an entity, a credit report of an entity, a credit rating
of an entity, a website rating of an entity, a set of customer
reviews for a product of an entity, a social network rating of an
entity, a set of credentials of an entity, a set of referrals of an
entity, a set of testimonials for an entity, a set of behavior of
an entity, a location of an entity, and a geolocation of an entity.
In certain embodiments, a market value may include information such
as a volatility of a value, a sensitivity of a value (e.g.,
relative to other parameters having an uncertainty associated
therewith), and/or a specific value of the valuated object to a
particular party (e.g., an object may have more value as possessed
by a first party than as possessed by a second party).
[0212] Certain information may not be marketplace information or a
market value. For example, where variables related to a value are
not market-derived, they may be a value-in-use or an investment
value. In certain embodiments, an investment value may be
considered a market value (e.g., when the valuating party intends
to utilize the asset as an investment if acquired), and not a
market value in other embodiments (e.g., when the valuating party
intends to immediately liquidate the investment if acquired). One
of skill in the art, having the benefit of the disclosure herein
and knowledge about a contemplated system ordinarily available to
that person, can readily determine which aspects of the present
disclosure will benefit from marketplace information or a market
value. Certain considerations for the person of skill in the art,
in determining whether the term market value is referring to an
asset, item, collateral, good, or service include: the presence of
other similar assets in a marketplace, the change in value
depending on location, an opening bid of an item exceeding a list
price, and other considerations. While specific examples of market
values and marketplace information are described herein for
purposes of illustration, any embodiment benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein are
specifically contemplated within the scope of the present
disclosure.
[0213] The term apportion value or apportioned value and similar
terms as utilized herein should be understood broadly. Without
limitation to any other aspect or description of the present
disclosure, apportion value describes a proportional distribution
or allocation of value proportionally, or a process to divide and
assign value according to a rule of proportional distribution.
Apportionment of the value may be to several parties (e.g., each of
the several parties is a beneficiary of a portion of the value), to
several transactions (e.g., each of the transactions utilizes a
portion of the value), and/or in a many-to-many relationship (e.g.,
a group of objects has an aggregate value that is apportioned
between a number of parties and/or transactions). In some
embodiments, the value may be a net loss and the apportioned value
is the allocation of a liability to each entity. In other
embodiments, apportioned value may refer to the distribution or
allocation of an economic benefit, real estate, collateral or the
like. In certain embodiments, apportionment may include a
consideration of the value relative to the parties--for example, a
$10 million asset apportioned 50/50 between two parties, where the
parties have distinct value considerations for the asset, may
result in one party crediting the apportionment differing resulting
values from the apportionment. In certain embodiments,
apportionment may include a consideration of the value relative to
given transactions--for example a first type of transaction (e.g.,
a long-term loan) may have a different valuation of a given asset
than a second type of transaction (e.g., a short-term line of
credit).
[0214] Certain conditions or processes may not relate to
apportioned value. For example, the total value of an item may
provide its inherent worth, but not how much of the value is held
by each identified entity. One of skill in the art, having the
benefit of the disclosure herein and knowledge about apportioned
value, can readily determine which aspects of the present
disclosure will benefit a particular application for apportioned
value. Certain considerations for the person of skill in the art,
or embodiments of the present disclosure with respect to an
apportioned value include, without limitation: the currency of the
principal sum, the anticipated transaction type (loan, bond or
debt), the specific type of collateral, the ratio of the loan to
value, the ratio of the collateral to the loan, the gross
transaction/loan amount, the amount of the principal sum, the
number of entities owed, the value of the collateral, and the like.
While specific examples of apportioned values are described herein
for purposes of illustration, any embodiment benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein are
specifically contemplated within the scope of the present
disclosure.
[0215] The term financial condition and similar terms as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, financial
condition describes a current status of an entity's assets,
liabilities, and equity positions at a defined point or period in
time. The financial condition may be memorialized in financial
statement. The financial condition may further include an
assessment of the ability of the entity to survive future risk
scenarios or meet future or maturing obligations. Financial
condition may be based on a set of attributes of the entity
selected from among a publicly stated valuation of the entity, a
set of property owned by the entity as indicated by public records,
a valuation of a set of property owned by the entity, a bankruptcy
condition of an entity, a foreclosure status of an entity, a
contractual default status of an entity, a regulatory violation
status of an entity, a criminal status of an entity, an export
controls status of an entity, an embargo status of an entity, a
tariff status of an entity, a tax status of an entity, a credit
report of an entity, a credit rating of an entity, a website rating
of an entity, a set of customer reviews for a product of an entity,
a social network rating of an entity, a set of credentials of an
entity, a set of referrals of an entity, a set of testimonials for
an entity, a set of behavior of an entity, a location of an entity,
and a geolocation of an entity. A financial condition may also
describe a requirement or threshold for an agreement or loan. For
example, conditions for allowing a developer to proceed may be
various certifications and their agreement to a financial payout.
That is, the developer's ability to proceed is conditioned upon a
financial element, among others. Certain conditions may not be a
financial condition. For example, a credit card balance alone may
be a clue as to the financial condition, but may not be the
financial condition on its own. In another example, a payment
schedule may determine how long a debt may be on an entity's
balance sheet, but in a silo may not accurately provide a financial
condition. One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure include and/or will benefit from
a financial condition. Certain considerations for the person of
skill in the art, in determining whether the term financial
condition is referring to a current status of an entity's assets,
liabilities, and equity positions at a defined point or period in
time and/or for a given purpose include: the reporting of more than
one financial data point, the ratio of a loan to value of
collateral, the ratio of the collateral to the loan, the gross
transaction/loan amount, the credit scores of the borrower and the
lender, and other considerations. While specific examples of
financial conditions are described herein for purposes of
illustration, any embodiment benefitting from the disclosures
herein, and any considerations understood to one of skill in the
art having the benefit of the disclosures herein are specifically
contemplated within the scope of the present disclosure.
[0216] The term interest rate and similar terms, as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, interest rate
includes an amount of interest due per period, as a proportion of
an amount lent, deposited or borrowed. The total interest on an
amount lent or borrowed may depend on the principal sum, the
interest rate, the compounding frequency, and the length of time
over which it is lent, deposited or borrowed. Typically, interest
rate is expressed as an annual percentage but can be defined for
any time period. The interest rate relates to the amount a bank or
other lender charges to borrow its money, or the rate a bank or
other entity pays its savers for keeping money in an account.
Interest rate may be variable or fixed. For example, an interest
rate may vary in accordance with a government or other stakeholder
directive, the currency of the principal sum lent or borrowed, the
term to maturity of the investment, the perceived default
probability of the borrower, supply and demand in the market, the
amount of collateral, the status of an economy, or special features
like call provisions. In certain embodiments, an interest rate may
be a relative rate (e.g., relative to a prime rate, an inflation
index, etc.). In certain embodiments, an interest rate may further
consider costs or fees applied (e.g., "points") to adjust the
interest rate. A nominal interest rate may not be adjusted for
inflation while a real interest rate takes inflation into account.
Certain examples may not be an interest rate for purposes of
particular embodiments. For example, a bank account growing by a
fixed dollar amount each year, and/or a fixed fee amount, may not
be an example of an interest rate for certain embodiments. One of
skill in the art, having the benefit of the disclosure herein and
knowledge about interest rates, can readily determine the
characteristics of an interest rate for a particular embodiment.
Certain considerations for the person of skill in the art, or
embodiments of the present disclosure with respect to an interest
rate include, without limitation: the currency of the principal
sum, variables for setting an interest rate, criteria for modifying
an interest rate, the anticipated transaction type (loan, bond or
debt), the specific type of collateral, the ratio of the loan to
value, the ratio of the collateral to the loan, the gross
transaction/loan amount, the amount of the principal sum, the
appropriate lifespans of transactions and/or collateral for a
particular industry, the likelihood that a lender will sell and/or
consolidate a loan before the term, and the like. While specific
examples of interest rates are described herein for purposes of
illustration, any embodiment benefitting from the disclosures
herein, and any considerations understood to one of skill in the
art having the benefit of the disclosures herein are specifically
contemplated within the scope of the present disclosure.
[0217] The term valuation services (and similar terms) as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, a valuation
service includes any service that sets a value for a good or
service. Valuation services may use a valuation model to set a
value for collateral based on information from data collection and
monitoring services. Smart contract services may process output
from the set of valuation services and assign items of collateral
sufficient to provide security for a loan and/or apportion value
for an item of collateral among a set of lenders and/or
transactions. Valuation services may include artificial
intelligence services that may iteratively improve the valuation
model based on outcome data relating to transactions in collateral.
Valuation services may include market value data collection
services that may monitor and report on marketplace information
relevant to the value of collateral. Certain processes may not be
considered to be a valuation service. For example, a point of sale
device that simply charges a set cost for a good or service may not
be a valuation service. In another example, a service that tracks
the cost of a good or service and triggers notifications when the
value changes may not be a valuation service itself, but may rely
on valuation services and/or form a part of a valuation service.
Accordingly, the benefits of the present disclosure may be applied
in a wide variety of processes systems, and any such processes or
systems may be considered a valuation service herein, while in
certain embodiments a given service may not be considered a
valuation service herein. One of skill in the art, having the
benefit of the disclosure herein and knowledge about a contemplated
system ordinarily available to that person, can readily determine
which aspects of the present disclosure will benefit a particular
system and how to combine processes and systems from the present
disclosure to enhance operations of the contemplated system and/or
to provide a valuation service. Certain considerations for the
person of skill in the art, in determining whether a contemplated
system is a valuation service and/or whether aspects of the present
disclosure can benefit or enhance the contemplated system include,
without limitation: perform real-time alterations to a loan based
on a value of a collateral; utilize marketplace data to execute a
collateral-backed smart contract; re-evaluate collateral based on a
storage condition or geolocation; the tendency of the collateral to
have a volatile value, be utilized, and/or be moved; and the like.
While specific examples of valuation services and considerations
are described herein for purposes of illustration, any system
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0218] The term collateral attributes (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure,
collateral attributes include any identification of the durability
(ability of the collateral to withstand wear or the useful life of
the collateral), value, identification (does the collateral have
definite characteristics that make it easy to identify or market),
stability of value (does the collateral maintain value over time),
standardization, grade, quality, marketability, liquidity,
transferability, desirability, trackability, deliverability
(ability of the collateral be delivered or transfer without a
deterioration in value), market transparency (is the collateral
value easily verifiable or widely agreed upon), physical or
virtual. Collateral attributes may be measured in absolute or
relative terms, and/or may include qualitative (e.g., categorical
descriptions) or quantitative descriptions. Collateral attributes
may be different for different industries, products, elements,
uses, and the like. Collateral attributes may be assigned
quantitative or qualitative values. Values associated with
collateral attributes may be based on a scale (such as 1-10) or a
relative designation (high, low, better, etc.). Collateral may
include various components; each component may have collateral
attributes. Collateral may, therefore, have multiple values for the
same collateral attribute. In some embodiments, multiple values of
collateral attributes may be combined to generate one value for
each attribute. Some collateral attributes may apply only to
specific portions of collateral. Some collateral attributes, even
for a given component of the collateral, may have distinct values
depending upon the party of interest (e.g., a party that values an
aspect of the collateral more highly than another party) and/or
depending upon the type of transaction (e.g., the collateral may be
more valuable or appropriate for a first type of loan than for a
second type of loan). Certain attributes associated with collateral
may not be collateral attributes as described herein depending upon
the purpose of the collateral attributes herein. For example, a
product may be rated as durable relative to similar products;
however, if the life of the product is much lower than the term of
a particular loan in consideration, the durability of the product
may be rated differently (e.g., not durable) or irrelevant (e.g.,
where the current inventory of the product is attached as the
collateral, and is expected to change out during the term of the
loan). Accordingly, the benefits of the present disclosure may be
applied to a variety of attributes, and any such attributes may be
considered collateral attributes herein, while in certain
embodiments a given attribute may not be considered a collateral
attribute herein. One of skill in the art, having the benefit of
the disclosure herein and knowledge about contemplated collateral
attributes ordinarily available to that person, can readily
determine which aspects of the present disclosure will benefit a
particular collateral attribute. Certain considerations for the
person of skill in the art, in determining whether a contemplated
attribute is a collateral attribute and/or whether aspects of the
present disclosure can benefit or enhance the contemplated system
include, without limitation: the source of the attribute and the
source of the value of the attribute (e.g. does the attribute and
attribute value comes from a reputable source), the volatility of
the attribute (e.g. does the attribute values for the collateral
fluctuate, is the attribute a new attribute for the collateral),
relative differences in attribute values for similar collateral,
exceptional values for attributes (e.g., some attribute values may
be high, such as, in the 98th percentile or very low, such as in
the 2nd percentile, compared to similar class of collateral), the
fungibility of the collateral, the type of transaction related to
the collateral, and/or the purpose of the utilization of collateral
for a particular party or transaction. While specific examples of
collateral attributes and considerations are described herein for
purposes of illustration, any system benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0219] The term blockchain services (and similar terms) as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, blockchain
services include any service related to the processing,
recordation, and/or updating of a blockchain, and may include
services for processing blocks, computing hash values, generating
new blocks in a blockchain, appending a block to the blockchain,
creating a fork in the blockchain, merging of forks in the
blockchain, verifying previous computations, updating a shared
ledger, updating a distributed ledger, generating cryptographic
keys, verifying transactions, maintaining a blockchain, updating a
blockchain, verifying a blockchain, generating random numbers. The
services may be performed by execution of computer readable
instructions on local computers and/or by remote servers and
computers. Certain services may not be considered blockchain
services individually but may be considered blockchain services
based on the final use of the service and/or in a particular
embodiment--for example, a computing a hash value may be performed
in a context outside of a blockchain such in the context of secure
communication. Some initial services may be invoked without first
being applied to blockchains, but further actions or services in
conjunction with the initial services may associate the initial
service with aspects of blockchains. For example, a random number
may be periodically generated and stored in memory; the random
numbers may initially not be generated for blockchain purposes but
may be utilized for blockchains. Accordingly, the benefits of the
present disclosure may be applied in a wide variety of services,
and any such services may be considered blockchain services herein,
while in certain embodiments a given service may not be considered
a blockchain service herein. One of skill in the art, having the
benefit of the disclosure herein and knowledge about a contemplated
blockchain service ordinarily available to that person, can readily
determine which aspects of the present disclosure can be configured
to implement, and/or will benefit, a particular blockchain service.
Certain considerations for the person of skill in the art, in
determining whether a contemplated service is a blockchain service
and/or whether aspects of the present disclosure can benefit or
enhance the contemplated system include, without limitation: the
application of the service, the source of the service (e.g., if the
service is associated with a known or verifiable blockchain service
provider), responsiveness of the service (e.g., some blockchain
services may have an expected completion time, and/or may be
determined through utilization), cost of the service, the amount of
data requested for the service, and/or the amount of data generated
by the service (blocks of blockchain or keys associated with
blockchains may be a specific size or a specific range of sizes).
While specific examples of blockchain services and considerations
are described herein for purposes of illustration, any system
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0220] The term blockchain (and variations such as cryptocurrency
ledger, and the like) as utilized herein may be understood broadly
to describe a cryptocurrency ledger that records, administrates or
otherwise processes online transactions. A blockchain may be
public, private, or a combination thereof, without limitation. A
blockchain may also be used to represent a set of digital
transactions, agreement, terms or other digital value. Without
limitation to any other aspect or description of the present
disclosure, in the former case, a blockchain may also be used in
conjunction with investment applications, token-trading
applications, and/or digital/cryptocurrency based marketplaces. A
blockchain can also be associated with rendering consideration,
such as providing goods, services, items, fees, access to a
restricted area or event, data or other valuable benefit.
Blockchains in various forms may be included where discussing a
unit of consideration, collateral, currency, cryptocurrency or any
other form of value. One of skill in the art, having the benefit of
the disclosure herein and knowledge ordinarily available about a
contemplated system, can readily determine the value symbolized or
represented by a blockchain. While specific examples of blockchains
are described herein for purposes of illustration, any embodiment
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0221] The terms ledger and distributed ledger (and similar terms)
as utilized herein should be understood broadly. Without limitation
to any other aspect or description of the present disclosure, a
ledger may be a document, file, computer file, database, book, and
the like which maintains a record of transactions. Ledgers may be
physical or digital. Ledgers may include records related to sales,
accounts, purchases, transactions, assets, liabilities, incomes,
expenses, capital, and the like. Ledgers may provide a history of
transactions that may be associated with time. Ledgers may be
centralized or decentralized/distributed. A centralized ledger may
be a document that is controlled, updated, or viewable by one or
more selected entities or a clearinghouse and wherein changes or
updates to the ledger are governed or controlled by the entity or
clearinghouse. A distributed ledger may be a ledger that is
distributed across a plurality of entities, participants or regions
which may independently, concurrently, or consensually, update, or
modify their copies of the ledger. Ledgers and distributed ledgers
may include security measures and cryptographic functions for
signing, concealing, or verifying content. In the case of
distributed ledgers, blockchain technology may be used. In the case
of distributed ledgers implemented using blockchain, the ledger may
be Merkle trees comprising a linked list of nodes in which each
node contains hashed or encrypted transactional data of the
previous nodes. Certain records of transactions may not be
considered ledgers. A file, computer file, database, or book may or
may not be a ledger depending on what data it stores, how the data
is organized, maintained, or secured. For example, a list of
transactions may not be considered a ledger if it cannot be trusted
or verified, and/or if it is based on inconsistent, fraudulent, or
incomplete data. Data in ledgers may be organized in any format
such as tables, lists, binary streams of data, or the like which
may depend on convenience, source of data, type of data,
environment, applications, and the like. A ledger that is shared
among various entities may not be a distributed ledger, but the
distinction of distributed may be based on which entities are
authorized to make changes to the ledger and/or how the changes are
shared and processed among the different entities. Accordingly, the
benefits of the present disclosure may be applied in a wide variety
of data, and any such data may be considered ledgers herein, while
in certain embodiments a given data may not be considered a ledger
herein. One of skill in the art, having the benefit of the
disclosure herein and knowledge about contemplated ledgers and
distributed ledger ordinarily available to that person, can readily
determine which aspects of the present disclosure can be utilized
to implement, and/or will benefit a particular ledger. Certain
considerations for the person of skill in the art, in determining
whether a contemplated data is a ledger and/or whether aspects of
the present disclosure can benefit or enhance the contemplated
ledger include, without limitation: the security of the data in the
ledger (can the data be tampered or modified), the time associated
with making changes to the data in the ledger, cost of making
changes (computationally and monetarily), detail of data,
organization of data (does the data need to be processed for use in
an application), who controls the ledger (can the party be trusted
or relied to manage the ledger), confidentiality of the data (who
can see or track the data in the ledger), size of the
infrastructure, communication requirements (distributed ledgers may
require a communication interface or specific infrastructure),
resiliency. While specific examples of blockchain services and
considerations are described herein for purposes of illustration,
any system benefitting from the disclosures herein, and any
considerations understood to one of skill in the art having the
benefit of the disclosures herein, are specifically contemplated
within the scope of the present disclosure.
[0222] The term loan (and similar terms) as utilized herein should
be understood broadly. Without limitation to any other aspect or
description of the present disclosure, a loan may be an agreement
related to an asset that is borrowed, and that is expected to be
returned in kind (e.g., money borrowed and money returned) or as an
agreed transaction (e.g., a first good or service is borrowed, and
money, a second good or service, or a combination, is returned).
Assets may be money, property, time, physical objects, virtual
objects, services, a right (e.g., a ticket, a license, or other
rights), a depreciation amount, a credit (e.g., a tax credit, an
emissions credit, etc.), an agreed assumption of a risk or
liability, and/or any combination thereof. A loan may be based on a
formal or informal agreement between a borrower and a lender
wherein a lender may provide an asset to the borrower for a
predefined amount of time, a variable period of time, or
indefinitely. Lenders and borrowers may be individuals, entities,
corporations, governments, groups of people, organizations, and the
like. Loan types may include mortgage loans, personal loans,
secured loans, unsecured loans, concessional loans, commercial
loans, microloans, and the like. The agreement between the borrower
and the lender may specify terms of the loan. The borrower may be
required to return an asset or repay with a different asset than
was borrowed. In some cases, a loan may require interest to be
repaid on the borrowed asset. Borrowers and lenders may be
intermediaries between other entities and may never possess or use
the asset. In some embodiments, a loan may not be associated with
direct transfer of goods but may be associated with usage rights or
shared usage rights. In certain embodiments, the agreement between
the borrower and the lender may be executed between the borrower
and the lender, and/or executed between an intermediary (e.g., a
beneficiary of a loan right such as through a sale of the loan). In
certain embodiment, the agreement between the borrower and the
lender may be executed through services herein, such as through a
smart contract service that determines at least a portion of the
terms and conditions of the loans, and in certain embodiments may
commit the borrower and/or the lender to the terms of the
agreement, which may be a smart contract. In certain embodiments,
the smart contract service may populate the terms of the agreement,
and present them to the borrower and/or lender for execution. In
certain embodiments, the smart contract service may automatically
commit one of the borrower or the lender to the terms (at least as
an offer) and may present the offer to the other one of the
borrower or the lender for execution. In certain embodiments, a
loan agreement may include multiple borrowers and/or multiple
lenders, for example where a set of loans includes a number of
beneficiaries of payment on the set of loans, and/or a number of
borrowers on the set of loans. In certain embodiments, the risks
and/or obligations of the set of loans may be individualized (e.g.,
each borrower and/or lender is related to specific loans of the set
of loans), apportioned (e.g., a default on a particular loan has an
associated loss apportioned between the lenders), and/or
combinations of these (e.g., one or more subsets of the set of
loans is treated individually and/or apportioned).
[0223] Certain agreements may not be considered a loan. An
agreement to transfer or borrow assets may not be a loan depending
on what assets are transferred, how the assets were transferred, or
the parties involved. For example, in some cases, the transfer of
assets may be for an indefinite time and may be considered a sale
of the asset or a permanent transfer. Likewise, if an asset is
borrowed or transferred without clear or definite terms or lack of
consensus between the lender and the borrower it may, in some
cases, not be considered a loan. An agreement may be considered a
loan even if a formal agreement is not directly codified in a
written agreement as long as the parties willingly and knowingly
agreed to the arrangement, and/or ordinary practices (e.g., in a
particular industry) may treat the transaction as a loan.
Accordingly, the benefits of the present disclosure may be applied
in a wide variety of agreements, and any such agreement may be
considered a loan herein, while in certain embodiments a given
agreement may not be considered a loan herein. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about contemplated loans ordinarily available to that person, can
readily determine which aspects of the present disclosure implement
a loan, utilize a loan, or benefit a particular loan transaction.
Certain considerations for the person of skill in the art, in
determining whether a contemplated data is a loan and/or whether
aspects of the present disclosure can benefit or enhance the
contemplated loan include, without limitation: the value of the
assets involved, the ability of the borrower to return or repay the
loan, the types of assets involved (e.g., whether the asset is
consumed through utilization), the repayment time frame associated
with the loan, the interest on the loan, how the agreement of the
loan was arranged, formality of the agreement, detail of the
agreement, the detail of the agreements of the loan, the collateral
attributes associated with the loan, and/or the ordinary business
expectations of any of the foregoing in a particular context. While
specific examples of loans and considerations are described herein
for purposes of illustration, any system benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0224] The term loan related event(s) (and similar terms, including
loan-related events) as utilized herein should be understood
broadly. Without limitation to any other aspect or description of
the present disclosure, a loan related events may include any event
related to terms of the loan or events triggered by the agreement
associated with the loan. Loan-related events may include default
on loan, breach of contract, fulfillment, repayment, payment,
change in interest, late fee assessment, refund assessment,
distribution, and the like. Loan-related events may be triggered by
explicit agreement terms; for example--an agreement may specify a
rise in interest rate after a time period has elapsed from the
beginning of the loan; the rise in interest rate triggered by the
agreement may be a loan related event. Loan-related events may be
triggered implicitly by related loan agreement terms. In certain
embodiments, any occurrence that may be considered relevant to
assumptions of the loan agreement, and/or expectations of the
parties to the loan agreement, may be considered an occurrence of
an event. For example, if collateral for a loan is expected to be
replaceable (e.g., an inventory as collateral), then a change in
inventory levels may be considered an occurrence of a loan related
event. In another example, if review and/or confirmation of the
collateral is expected, then a lack of access to the collateral,
the disablement or failure of a monitoring sensor, etc. may be
considered an occurrence of a loan related event. In certain
embodiments, circuits, controllers, or other devices described
herein may automatically trigger the determination of a
loan-related events. In some embodiments, loan-related events may
be triggered by entities that manage loans or loan-related
contracts. Loan-related events may be conditionally triggered based
on one or more conditions in the loan agreement. Loan related
events may be related to tasks or requirements that need to be
completed by the lender, borrower, or a third party. Certain events
may be considered loan-related events in certain embodiments and/or
in certain contexts, but may not be considered a loan-related event
in another embodiment or context. Many events may be associated
with loans but may be caused by external triggers not associated
with a loan. However, in certain embodiments, an externally
triggered event (e.g., a commodity price change related to a
collateral item) may be loan-related events. For example,
renegotiation of loan terms initiated by a lender may not be
considered a loan related event if the terms and/or performance of
the existing loan agreement did not trigger the renegotiation.
Accordingly, the benefits of the present disclosure may be applied
in a wide variety of events, and any such event may be considered a
loan related event herein, while in certain embodiments given
events may not be considered a loan related event herein. One of
skill in the art, having the benefit of the disclosure herein and
knowledge about a contemplated system ordinarily available to that
person, can readily determine which aspects of the present
disclosure may be considered a loan-related event for the
contemplated system and/or for particular transactions supported by
the system. Certain considerations for the person of skill in the
art, in determining whether a contemplated data is a loan related
event and/or whether aspects of the present disclosure can benefit
or enhance the contemplated transaction system include, without
limitation: the impact of the related event on the loan (events
that cause default or termination of the loan may have higher
impact), the cost (capital and/or operating) associated with the
event, the cost (capital and/or operating) associated with
monitoring for an occurrence of the event, the entities responsible
for responding to the event, a time period and/or response time
associated with the event (e.g., time required to complete the
event and time that is allotted from the time the event is
triggered to when processing or detection of the event is desired
to occur), the entity responsible for the event, the data required
for processing the event (e.g., confidential information may have
different safeguards or restrictions), the availability of
mitigating actions if an undetected event occurs, and/or the
remedies available to an at-risk party if the event occurs without
detection. While specific examples of loan-related events and
considerations are described herein for purposes of illustration,
any system benefitting from the disclosures herein, and any
considerations understood to one of skill in the art having the
benefit of the disclosures herein, are specifically contemplated
within the scope of the present disclosure.
[0225] The term loan-related activities (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, a loan
related activity may include activities related to the generation,
maintenance, termination, collection, enforcement, servicing,
billing, marketing, ability to perform, or negotiation of a loan.
Loan-related activity may include activities related to the signing
of a loan agreement or a promissory note, review of loan documents,
processing of payments, evaluation of collateral, evaluation of
compliance of the borrower or lender to the loan terms,
renegotiation of terms, perfection of security or collateral for
the loan, and/or a negation of terms. Loan-related activities may
relate to events associated with a loan before formal agreement on
the terms, such as activities associated with initial negotiations.
Loan-related activities may relate to events during the life of the
loan and after the termination of a loan. Loan-related activities
may be performed by a lender, borrower, or a third party. Certain
activities may not be considered loan related activities services
individually but may be considered loan related activities based on
the specificity of the activity to the loan lifecycle--for example,
billing or invoicing related to outstanding loans may be considered
a loan related activity, however when the invoicing or billing of
loans is combined with billing or invoicing for non loan-related
elements the invoicing may not be considered a loan related
activity. Some activities may be performed in relation to an asset
regardless if a loan is associated with the asset; in these cases,
the activity may not be considered a loan related activity. For
example, regular audits related to an asset may occur regardless if
the asset is associated with a loan and may not be considered a
loan related activity. In another example, a regular audit related
to an asset may be required by a loan agreement and would not
typically occur but for the association with a loan, in this case,
the activity may be considered a loan related activity. In some
embodiments, activities may be considered loan-related activities
if the activity would otherwise not occur if the loan is not active
or present, but may still be considered a loan-related activity in
some instances (e.g., if auditing occurs normally, but the lender
does not have the ability to enforce or review the audit, then the
audit may be considered a loan-related activity even though it
already occurs otherwise). Accordingly, the benefits of the present
disclosure may be applied in a wide variety of events, and any such
event may be considered a loan related event herein, while in
certain embodiments given events may not be considered a loan
related events herein. One of skill in the art, having the benefit
of the disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine a loan
related activity for the purposes of the contemplated system.
Certain considerations for the person of skill in the art, in
determining whether a contemplated data is a loan related activity
and/or whether aspects of the present disclosure can benefit or
enhance the contemplated loan include, without limitation: the
necessity of the activity for the loan (can the loan agreement or
terms be satisfied without the activity), the cost of the activity,
the specificity of the activity to the loan (is the activity
similar or identical to other industries), time involved in the
activity, the impact of the activity on a loan life cycle, entity
performing the activity, amount of data required for the activity
(does the activity require confidential information related to the
loan, or personal information related to the entities), and/or the
ability of parties to enforce and/or review the activity. While
specific examples of loan-related events and considerations are
described herein for purposes of illustration, any system
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0226] The terms loan-terms, loan terms, terms for a loan, terms
and conditions, and the like as utilized herein should be
understood broadly ("loan terms"). Without limitation to any other
aspect or description of the present disclosure, loan terms may
relate to conditions, rules, limitations, contract obligations, and
the like related to the timing, repayment, origination, and other
enforceable conditions agreed to by the borrower and the lender of
the loan. Loan terms may be specified in a formal contract between
a borrower and the lender. Loan terms may specify aspects of an
interest rate, collateral, foreclose conditions, consequence of
debt, payment options, payment schedule, a covenant, and the like.
Loan terms may be negotiable or may change during the life of a
loan. Loan terms may be change or be affected by outside parameters
such as market prices, bond prices, conditions associated with a
lender or borrower, and the like. Certain aspects of a loan may not
be considered loan terms. In certain embodiments, aspects of loan
that have not been formally agreed upon between a lender and a
borrower, and/or that are not ordinarily understood in the course
of business (and/or the particular industry) may not be considered
loan terms. Certain aspects of a loan may be preliminary or
informal until they have been formally agreed or confirmed in a
contract or a formal agreement. Certain aspects of a loan may not
be considered loan terms individually but may not be considered
loan terms based on the specificity of the aspect to a specific
loan. Certain aspects of a loan may not be considered loan terms at
a particular time during the loan, but may be considered loan terms
at another time during the loan (e.g., obligations and/or waivers
that may occur through the performance of the parties, and/or
expiration of a loan term). For example, an interest rate may
generally not be considered a loan term until it is defined in
relation of a loan and defined as to how the interest compounded
(annual, monthly), calculated, and the like. An aspect of a loan
may not be considered a term if it is indefinite or unenforceable.
Some aspects may be manifestations or related to terms of a loan
but may themselves not be the terms. For example, a loan term may
be the repayment period of a loan, such as one year. The term may
not specify how the loan is to be repaid in the year. The loan may
be repaid with 12 monthly payments or one annual payment. A monthly
payment plan in this case may not be considered a loan term as it
can be just one or many options for repayment not directly
specified by a loan. Accordingly, the benefits of the present
disclosure may be applied in a wide variety of loan aspects, and
any such aspect may be considered a loan term herein, while in
certain embodiments given aspects may not be considered loan terms
herein. One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure are loan terms for the
contemplated system.
[0227] Certain considerations for the person of skill in the art,
in determining whether a contemplated data is a loan term and/or
whether aspects of the present disclosure can benefit or enhance
the contemplated loan include, without limitation: the
enforceability of the terms (can the conditions be enforced by the
lender or the lender or the borrower), the cost of enforcing the
terms (amount of time, or effort required ensure the conditions are
being followed), the complexity of the terms (how easily can they
be followed or understood by the parties involved, are the terms
error prone or easily misunderstood), entities responsible for the
terms, fairness of the terms, stability of the terms (how often do
they change), observability of the terms (can the terms be verified
by a another party), favorability of the terms to one party (do the
terms favor the borrower or the lender), risk associated with the
loan (terms may depend on the probability that the loan may not be
repaid), characteristics of the borrower or lender (their ability
to meet the terms), and/or ordinary expectations for the loan
and/or related industry.
[0228] While specific examples of loan terms are described herein
for purposes of illustration, any system benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0229] The term loan conditions, loan-conditions, conditions for a
loan, terms and conditions, and the like as utilized herein should
be understood broadly ("loan conditions"). Without limitation to
any other aspect or description of the present disclosure, loan
conditions may relate to rules, limits, and/or obligations related
to a loan. Loan conditions may relate to rules or necessary
obligations for obtaining a loan, for maintaining a loan, for
applying for a loan, for transferring a loan, and the like. Loan
conditions may include principal amount of debt, a balance of debt,
a fixed interest rate, a variable interest rate, a payment amount,
a payment schedule, a balloon payment schedule, a specification of
collateral, a specification of substitutability of collateral,
treatment of collateral, access to collateral, a party, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition,
conditions related to other debts of the borrower, and a
consequence of default.
[0230] Certain aspects of a loan may not be considered loan
conditions. Aspects of loan that have not been formally agreed upon
between a lender and a borrower, and/or that are not ordinarily
understood in the course of business (and/or the particular
industry), may not be considered loan conditions. Certain aspects
of a loan may be preliminary or informal until they have been
formally agreed or confirmed in a contract or a formal agreement.
Certain aspects of a loan may not be considered loan conditions
individually but may be considered loan conditions based on the
specificity of the aspect to a specific loan. Certain aspects of a
loan may not be considered loan conditions at a particular time
during the loan, but may be considered loan conditions at another
time during the loan (e.g., obligations and/or waivers that may
occur through the performance of the parties, and/or expiration of
a loan condition). Accordingly, the benefits of the present
disclosure may be applied in a wide variety of loan aspects, and
any such aspect may be considered loan conditions herein, while in
certain embodiments given aspects may not be considered loan
conditions herein. One of skill in the art, having the benefit of
the disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure are loan conditions for the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated data is a loan
condition and/or whether aspects of the present disclosure can
benefit or enhance the contemplated loan include, without
limitation: the enforceability of the condition (can the conditions
be enforced by the lender or the lender or the borrower), the cost
of enforcing the condition (amount of time, or effort required
ensure the conditions are being followed), the complexity of the
condition (how easily can they be followed or understood by the
parties involved, are the conditions error prone or easily
misunderstood), entities responsible for the conditions, fairness
of the conditions, observability of the conditions (can the
conditions be verified by a another party), favorability of the
conditions to one party (do the conditions favor the borrower or
the lender), risk associated with the loan (conditions may depend
on the probability that the loan may not be repaid), and/or
ordinary expectations for the loan and/or related industry.
[0231] While specific examples of loan conditions are described
herein for purposes of illustration, any system benefitting from
the disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0232] The term loan collateral, collateral, item of collateral,
collateral item, and the like as utilized herein should be
understood broadly. Without limitation to any other aspect or
description of the present disclosure, a loan collateral may relate
to any asset or property that a borrower promises to a lender as
backup in exchange for a loan, and/or as security for the loan.
Collateral may be any item of value that is accepted as an
alternate form of repayment in case of default on a loan.
Collateral may include any number of physical or virtual items such
as a vehicle, a ship, a plane, a building, a home, real estate
property, undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a beverage, a precious metal, an item of jewelry, a
gemstone, an item of intellectual property, an intellectual
property right, a contractual right, an antique, a fixture, an item
of furniture, an item of equipment, a tool, an item of machinery,
and an item of personal property. Collateral may include more than
one item or types of items.
[0233] A collateral item may describe an asset, a property, a value
or other item defined as a security for a loan or a transaction. A
set of collateral items may be defined, and within that set
substitution, removal or addition of collateral items may be
affected. For example, a collateral item may be, without
limitation: a vehicle, a ship, a plane, a building, a home, real
estate property, undeveloped land, a farm, a crop, a municipal
facility, a warehouse, a set of inventory, a commodity, a security,
a currency, a token of value, a ticket, a cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, an item of equipment, a tool, an
item of machinery, or an item of personal property, or the like. If
a set or plurality of collateral items is defined, substitution,
removal or addition of collateral items may be affected, such as
substituting, removing or adding a collateral item to or from a set
of collateral items. Without limitation to any other aspect or
description of the present disclosure, a collateral item or set of
collateral items may also be used in conjunction with other terms
to an agreement or loan, such as a representation, a warranty, an
indemnity, a covenant, a balance of debt, a fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of collateral, a
specification of substitutability of collateral, a security, a
personal guarantee, a lien, a duration, a foreclose condition, a
default condition, and a consequence of default. In certain
embodiments, a smart contract may calculate whether a borrower has
satisfied conditions or covenants and in cases where the borrower
has not satisfied such conditions or covenants, may enable
automated action or trigger another conditions or terms that may
affect the status, ownership or transfer of a collateral item, or
initiate the substitution, removal or addition of collateral items
to a set of collateral for a loan. One of skill in the art, having
the benefit of the disclosure herein and knowledge about collateral
items, can readily determine the purposes and use of collateral
items in various embodiments and contexts disclosed herein,
including the substitution, removal and addition thereof.
[0234] While specific examples of loan collateral are described
herein for purposes of illustration, any system benefitting from
the disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0235] The term smart contract services (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, a smart
contract service includes any service or application that manages a
smart contract or a smart lending contract. For example, the smart
contract service may specify terms and conditions of a smart
contract, such as in a rules database, or process output from a set
of valuation services and assign items of collateral sufficient to
provide security for a loan. Smart contract services may
automatically execute a set of rules or conditions that embody the
smart contract, wherein the execution may be based on or take
advantage of collected data. Smart contract services may
automatically initiate a demand for payment of a loan,
automatically initiate a foreclosure process, automatically
initiate an action to claim substitute or backup collateral or
transfer ownership of collateral, automatically initiate an
inspection process, automatically change a payment or interest rate
term that is based on the collateral, and may also configure smart
contracts to automatically undertake a loan-related action. Smart
contracts may govern at least one of loan terms and conditions,
loan-related events and loan-related activities. Smart contracts
may be agreements that are encoded as computer protocols and may
facilitate, verify, or enforce the negotiation or performance of a
smart contract. Smart contracts may or may not be one or more of
partially or fully self-executing, or partially or fully
self-enforcing.
[0236] Certain processes may not be considered to be smart-contract
related individually, but may be considered smart-contract related
in an aggregated system--for example automatically undertaking a
loan-related action may not be smart contract-related in one
instance, but in another instance, may be governed by terms of a
smart contract. Accordingly, the benefits of the present disclosure
may be applied in a wide variety of processes systems, and any such
processes or systems may be considered a smart contract or smart
contract service herein, while in certain embodiments a given
service may not be considered a smart contract service herein.
[0237] One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure will benefit a particular system
and how to combine processes and systems from the present
disclosure to implement a smart contract service and/or enhance
operations of the contemplated system. Certain considerations for
the person of skill in the art, in determining whether a
contemplated system includes a smart contract service or smart
contract and/or whether aspects of the present disclosure can
benefit or enhance the contemplated system include, without
limitation: ability to transfer ownership of collateral
automatically in response to an event; automated actions available
upon a finding of covenant compliance (or lack of compliance); the
amenity of the collateral to clustering, re-balancing,
distribution, addition, substitution, and removal of items from
collateral; the modification parameters of an aspect of a loan in
response to an event (e.g., timing, complexity, suitability for the
loan type, etc.); the complexity of terms and conditions of loans
for the system, including benefits from rapid determination and/or
predictions of changes to entities (e.g., in the collateral, a
financial condition of a party, offset collateral, and/or in an
industry related to a party) related to the loan; the suitability
of automated generation of terms and conditions and/or execution of
terms and conditions for the types of loans, parties, and/or
industries contemplated for the system; and the like. While
specific examples of smart contract services and considerations are
described herein for purposes of illustration, any system
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0238] The term IoT system (and similar terms) as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, an IoT system
includes any system of uniquely identified and interrelated
computing devices, mechanical and digital machines, sensors and
objects that are able to transfer data over a network without
intervention. Certain components may not be considered an IoT
system individually, but may be considered an IoT system in an
aggregated system--for example a single networked.
[0239] The sensor, smart speaker, and/or medical device may be not
an IoT system, but may be a part of a larger system and/or be
accumulated with a number of other similar components to be
considered an IoT system and/or a part of an IoT system. In certain
embodiments, a system may be considered an IoT system for some
purposes but not for other purposes--for example a smart speaker
may be considered part of an IoT system for certain operations,
such as for providing surround sound, or the like, but not part of
an IoT system for other operations such as directly streaming
content from a single, locally networked source. Additionally, in
certain embodiments, otherwise similar looking systems may be
differentiated in determining whether such systems are IoT systems,
and/or which type of IoT system. For example, one group of medical
devices may not, at a given time, be sharing to an aggregated HER
database, while another group of medical devices may be sharing
data to an aggregate HER for the purposes of a clinical study, and
accordingly one group of medical devices may be an IoT system,
while the other is not. Accordingly, the benefits of the present
disclosure may be applied in a wide variety of systems, and any
such systems may be considered an IoT system herein, while in
certain embodiments a given system may not be considered an IoT
system herein. One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure will benefit a particular system,
how to combine processes and systems from the present disclosure to
enhance operations of the contemplated system, and which circuits,
controllers, and/or devices include an IoT system for the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is an IoT
system and/or whether aspects of the present disclosure can benefit
or enhance the contemplated system include, without limitation: the
transmission environment of the system (e.g., availability of low
power, inter-device networking); the shared data storage of a group
of devices; establishment of a geofence by a group of devices;
service as blockchain nodes; the performance of asset, collateral,
or entity monitoring; the relay of data between devices; ability to
aggregate data from a plurality of sensors or monitoring devices,
and the like. While specific examples of IoT systems and
considerations are described herein for purposes of illustration,
any system benefitting from the disclosures herein, and any
considerations understood to one of skill in the art having the
benefit of the disclosures herein, are specifically contemplated
within the scope of the present disclosure.
[0240] The term data collection services (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, a data
collection service includes any service that collects data or
information, including any circuit, controller, device, or
application that may store, transmit, transfer, share, process,
organize, compare, report on and/or aggregate data. The data
collection service may include data collection devices (e.g.,
sensors) and/or may be in communication with data collection
devices. The data collection service may monitor entities, such as
to identify data or information for collection. The data collection
service may be event-driven, run on a periodic basis, or retrieve
data from an application at particular points in the application's
execution. Certain processes may not be considered to be a data
collection service individually, but may be considered a data
collection service in an aggregated system--for example a networked
storage device may be a component of a data collection service in
one instance, but in another instance, may have stand-alone
functionality. Accordingly, the benefits of the present disclosure
may be applied in a wide variety of processes systems, and any such
processes or systems may be considered a data collection service
herein, while in certain embodiments a given service may not be
considered a data collection service herein. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure will
benefit a particular system and how to combine processes and
systems from the present disclosure implement a data collection
service and/or to enhance operations of the contemplated system.
Certain considerations for the person of skill in the art, in
determining whether a contemplated system is a data collection
service and/or whether aspects of the present disclosure can
benefit or enhance the contemplated system include, without
limitation: ability to modify a business rule on the fly and alter
a data collection protocol; perform real-time monitoring of events;
connection of a device for data collection to a monitoring
infrastructure, execution of computer readable instructions that
cause a processor to log or track events; use of an automated
inspection system; occurrence of sales at a networked
point-of-sale; need for data from one or more distributed sensors
or cameras; and the like. While specific examples of data
collection services and considerations are described herein for
purposes of illustration, any system benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0241] The term data integration services (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, a data
integration service includes any service that integrates data or
information, including any device or application that may extract,
transform, load, normalize, compress, decompress, encode, decode,
and otherwise process data packets, signals, and other information.
The data integration service may monitor entities, such as to
identify data or information for integration. The data integration
service may integrate data regardless of required frequency,
communication protocol, or business rules needed for intricate
integration patterns. Accordingly, the benefits of the present
disclosure may be applied in a wide variety of processes systems,
and any such processes or systems may be considered a data
integration service herein, while in certain embodiments a given
service may not be considered a data integration service herein.
One of skill in the art, having the benefit of the disclosure
herein and knowledge about a contemplated system ordinarily
available to that person, can readily determine which aspects of
the present disclosure will benefit a particular system and how to
combine processes and systems from the present disclosure to
implement a data integration service and/or enhance operations of
the contemplated system. Certain considerations for the person of
skill in the art, in determining whether a contemplated system is a
data integration service and/or whether aspects of the present
disclosure can benefit or enhance the contemplated system include,
without limitation: ability to modify a business rule on the fly
and alter a data integration protocol; communication with third
party databases to pull in data to integrate with; synchronization
of data across disparate platforms; connection to a central data
warehouse; data storage capacity, processing capacity, and/or
communication capacity distributed throughout the system; the
connection of separate, automated workflows; and the like. While
specific examples of data integration services and considerations
are described herein for purposes of illustration, any system
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0242] The term computational services (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure,
computational services may be included as a part of one or more
services, platforms, or microservices, such as blockchain services,
data collection services, data integration services, valuation
services, smart contract services, data monitoring services, data
mining, and/or any service that facilitates collection, access,
processing, transformation, analysis, storage, visualization, or
sharing of data. Certain processes may not be considered to be a
computational service. For example, a process may not be considered
a computational service depending on the sorts of rules governing
the service, an end product of the service, or the intent of the
service. Accordingly, the benefits of the present disclosure may be
applied in a wide variety of processes systems, and any such
processes or systems may be considered a computational service
herein, while in certain embodiments a given service may not be
considered a computational service herein. One of skill in the art,
having the benefit of the disclosure herein and knowledge about a
contemplated system ordinarily available to that person, can
readily determine which aspects of the present disclosure will
benefit a particular system and how to combine processes and
systems from the present disclosure to implement one or more
computational service, and/or to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is a
computational service and/or whether aspects of the present
disclosure can benefit or enhance the contemplated system include,
without limitation: agreement-based access to the service; mediate
an exchange between different services; provides on demand
computational power to a web service; accomplishes one or more of
monitoring, collection, access, processing, transformation,
analysis, storage, integration, visualization, mining, or sharing
of data. While specific examples of computational services and
considerations are described herein for purposes of illustration,
any system benefitting from the disclosures herein, and any
considerations understood to one of skill in the art having the
benefit of the disclosures herein, are specifically contemplated
within the scope of the present disclosure.
[0243] The term sensor as utilized herein should be understood
broadly. Without limitation to any other aspect or description of
the present disclosure, a sensor may be a device, module, machine,
or subsystem that detects or measures a physical quality, event or
change. In embodiments, may record, indicate, transmit, or
otherwise respond to the detection or measurement. Examples of
sensors may be sensors for sensing movement of entities, for
sensing temperatures, pressures or other attributes about entities
or their environments, cameras that capture still or video images
of entities, sensors that collect data about collateral or assets,
such as, for example, regarding the location, condition (health,
physical, or otherwise), quality, security, possession, or the
like. In embodiments, sensors may be sensitive to, but not
influential on, the property to be measured but insensitive to
other properties. Sensors may be analog or digital. Sensors may
include processors, transmitters, transceivers, memory, power,
sensing circuit, electrochemical fluid reservoirs, light sources,
and the like. Further examples of sensors contemplated for use in
the system include biosensors, chemical sensors, black silicon
sensor, IR sensor, acoustic sensor, induction sensor, motion
sensor, optical sensor, opacity sensor, proximity sensor, inductive
sensor, Eddy-current sensor, passive infrared proximity sensor,
radar, capacitance sensor, capacitive displacement sensor,
hall-effect sensor, magnetic sensor, GPS sensor, thermal imaging
sensor, thermocouple, thermistor, photoelectric sensor, ultrasonic
sensor, infrared laser sensor, inertial motion sensor, MEMS
internal motion sensor, ultrasonic 3D motion sensor, accelerometer,
inclinometer, force sensor, piezoelectric sensor, rotary encoders,
linear encoders, ozone sensor, smoke sensor, heat sensor,
magnetometer, carbon dioxide detector, carbon monoxide detector,
oxygen sensor, glucose sensor, smoke detector, metal detector, rain
sensor, altimeter, GPS, detection of being outside, detection of
context, detection of activity, object detector (e.g. collateral),
marker detector (e.g. geo-location marker), laser rangefinder,
sonar, capacitance, optical response, heart rate sensor, or an
RF/micropower impulse radio (MIR) sensor. In certain embodiments, a
sensor may be a virtual sensor--for example determining a parameter
of interest as a calculation based on other sensed parameters in
the system. In certain embodiments, a sensor may be a smart
sensor--for example reporting a sensed value as an abstracted
communication (e.g., as a network communication) of the sensed
value. In certain embodiments, a sensor may provide a sensed value
directly (e.g., as a voltage level, frequency parameter, etc.) to a
circuit, controller, or other device in the system. One of skill in
the art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure will
benefit from a sensor. Certain considerations for the person of
skill in the art, in determining whether a contemplated device is a
sensor and/or whether aspects of the present disclosure can benefit
from or be enhanced by the contemplated sensor include, without
limitation: the conditioning of an activation/deactivation of a
system to an environmental quality; the conversion of electrical
output into measured quantities; the ability to enforce a geofence;
the automatic modification of a loan in response to change in
collateral; and the like. While specific examples of sensors and
considerations are described herein for purposes of illustration,
any system benefitting from the disclosures herein, and any
considerations understood to one of skill in the art having the
benefit of the disclosures herein, are specifically contemplated
within the scope of the present disclosure.
[0244] The term storage condition and similar terms, as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, storage
condition includes an environment, physical location, environmental
quality, level of exposure, security measures, maintenance
description, accessibility description, and the like related to the
storage of an asset, collateral, or an entity specified and
monitored in a contract, loan, or agreement or backing the
contract, loan or other agreement, and the like. Based on a storage
condition of a collateral, an asset, or entity, actions may be
taken to, maintain, improve, and/or confirm a condition of the
asset or the use of that asset as collateral. Based on a storage
condition, actions may be taken to alter the terms or conditions of
a loan or bond. Storage condition may be classified in accordance
with various rules, thresholds, conditional procedures, workflows,
model parameters, and the like and may be based on self-reporting
or on data from Internet of Things devices, data from a set of
environmental condition sensors, data from a set of social network
analytic services and a set of algorithms for querying network
domains, social media data, crowdsourced data, and the like. The
storage condition may be tied to a geographic location relating to
the collateral, the issuer, the borrower, the distribution of the
funds or other geographic locations. Examples of IoT data may
include images, sensor data, location data, and the like. Examples
of social media data or crowdsourced data may include behavior of
parties to the loan, financial condition of parties, adherence to a
party's a term or condition of the loan, or bond, or the like.
Parties to the loan may include issuers of a bond, related
entities, lender, borrower, 3rd parties with an interest in the
debt. Storage condition may relate to an asset or type of
collateral such as a municipal asset, a vehicle, a ship, a plane, a
building, a home, real estate property, undeveloped land, a farm, a
crop, a municipal facility, a warehouse, a set of inventory, a
commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a consumable item, an edible item, a beverage, a
precious metal, an item of jewelry, a gemstone, an item of
intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, and an item of
personal property. The storage condition may include an environment
where environment may include an environment selected from among a
municipal environment, a corporate environment, a securities
trading environment, a real property environment, a commercial
facility, a warehousing facility, a transportation environment, a
manufacturing environment, a storage environment, a home, and a
vehicle. Actions based on the storage condition of a collateral, an
asset or an entity may include managing, reporting on, altering,
syndicating, consolidating, terminating, maintaining, modifying
terms and/or conditions, foreclosing an asset, or otherwise
handling a loan, contract, or agreement. One of skill in the art,
having the benefit of the disclosure herein and knowledge about a
contemplated storage condition, can readily determine which aspects
of the present disclosure will benefit a particular application for
a storage condition. Certain considerations for the person of skill
in the art, or embodiments of the present disclosure in choosing an
appropriate storage condition to manage and/or monitor, include,
without limitation: the legality of the condition given the
jurisdiction of the transaction, the data available for a given
collateral, the anticipated transaction type (loan, bond or debt),
the specific type of collateral, the ratio of the loan to value,
the ratio of the collateral to the loan, the gross transaction/loan
amount, the credit scores of the borrower and the lender, ordinary
practices in the industry, and other considerations. While specific
examples of storage conditions are described herein for purposes of
illustration, any embodiment benefitting from the disclosures
herein, and any considerations understood to one of skill in the
art having the benefit of the disclosures herein are specifically
contemplated within the scope of the present disclosure.
[0245] The term geolocation and similar terms, as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, geolocation
includes the identification or estimation of the real-world
geographic location of an object, including the generation of a set
of geographic coordinates (e.g. latitude and longitude) and/or
street address. Based on a geolocation of a collateral, an asset,
or entity, actions may be taken to maintain or improve a condition
of the asset or the use of that asset as collateral. Based on a
geolocation, actions may be taken to alter the terms or conditions
of a loan or bond. Based on a geolocation, determinations or
predictions related to a transaction may be performed--for example
based upon the weather, civil unrest in a particular area, and/or
local disasters (e.g., an earthquake, flood, tornado, hurricane,
industrial accident, etc.). Geolocations may be determined in
accordance with various rules, thresholds, conditional procedures,
workflows, model parameters, and the like and may be based on
self-reporting or on data from Internet of Things devices, data
from a set of environmental condition sensors, data from a set of
social network analytic services and a set of algorithms for
querying network domains, social media data, crowdsourced data, and
the like. Examples of geolocation data may include GPS coordinates,
images, sensor data, street address, and the like. Geolocation data
may be quantitative (e.g., longitude/latitude, relative to a plat
map, etc.) and/or qualitative (e.g., categorical such as "coastal",
"rural", etc.; "within New York City", etc.). Geolocation data may
be absolute (e.g., GPS location) or relative (e.g., within 100
yards of an expected location). Examples of social media data or
crowdsourced data may include behavior of parties to the loan as
inferred by their geolocation, financial condition of parties
inferred by geolocation, adherence of parties to a term or
condition of the loan, or bond, or the like. Geolocation may be
determined for an asset or type of collateral such as a municipal
asset, a vehicle, a ship, a plane, a building, a home, real estate
property, undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a consumable item, an edible item, a
beverage, a precious metal, an item of jewelry, a gemstone, an
antique, a fixture, an item of furniture, an item of equipment, a
tool, an item of machinery, and an item of personal property.
Geolocation may be determined for an entity such as one of the
parties, a third-party (e.g., an inspection service, maintenance
service, cleaning service, etc. relevant to a transaction), or any
other entity related to a transaction. The geolocation may include
an environment selected from among a municipal environment, a
corporate environment, a securities trading environment, a real
property environment, a commercial facility, a warehousing
facility, a transportation environment, a manufacturing
environment, a storage environment, a home, and a vehicle. Actions
based on the geolocation of a collateral, an asset or an entity may
include managing, reporting on, altering, syndicating,
consolidating, terminating, maintaining, modifying terms and/or
conditions, foreclosing an asset, or otherwise handling a loan,
contract, or agreement. One of skill in the art, having the benefit
of the disclosure herein and knowledge about a contemplated system,
can readily determine which aspects of the present disclosure will
benefit a particular application for a geolocation, and which
location aspect of an item is a geolocation for the contemplated
system. Certain considerations for the person of skill in the art,
or embodiments of the present disclosure in choosing an appropriate
geolocation to manage, include, without limitation: the legality of
the geolocation given the jurisdiction of the transaction, the data
available for a given collateral, the anticipated transaction type
(loan, bond or debt), the specific type of collateral, the ratio of
the loan to value, the ratio of the collateral to the loan, the
gross transaction/loan amount, the frequency of travel of the
borrower to certain jurisdictions and other considerations, the
mobility of the collateral, and/or a likelihood of
location-specific event occurrence relevant to the transaction
(e.g., weather, location of a relevant industrial facility,
availability of relevant services, etc.). While specific examples
of geolocation are described herein for purposes of illustration,
any embodiment benefitting from the disclosures herein, and any
considerations understood to one of skill in the art having the
benefit of the disclosures herein are specifically contemplated
within the scope of the present disclosure.
[0246] The term jurisdictional location and similar terms, as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure,
jurisdictional location refers to the laws and legal authority
governing a loan entity. The jurisdictional location may be based
on a geolocation of an entity, a registration location of an entity
(e.g. a ship's flag state, a state of incorporation for a business,
and the like), a granting state for certain rights such as
intellectual priority, and the like. In certain embodiments, a
jurisdictional location may be one or more of the geolocations for
an entity in the system. In certain embodiments, a jurisdictional
location may not be the same as the geolocation of any entity in
the system (e.g., where an agreement specifies some other
jurisdiction). In certain embodiments, a jurisdictional location
may vary for entities in the system (e.g., borrower at A, lender at
B, collateral positioned at C, agreement enforced at D, etc.). In
certain embodiments, a jurisdictional location for a given entity
may vary during the operations of the system (e.g., due to movement
of collateral, related data, changes in terms and conditions,
etc.). In certain embodiments, a given entity of the system may
have more than one jurisdictional location (e.g., due to operations
of the relevant law, and/or options available to one or more
parties), and/or may have distinct jurisdictional locations for
different purposes. A jurisdictional location of an item of
collateral, an asset, or entity, actions may dictate certain terms
or conditions of a loan or bond, and/or may indicate different
obligations for notices to parties, foreclosure and/or default
execution, treatment of collateral and/or debt security, and/or
treatment of various data within the system. While specific
examples of jurisdictional location are described herein for
purposes of illustration, any embodiment benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein are
specifically contemplated within the scope of the present
disclosure.
[0247] The terms token of value, token, and variations such as
cryptocurrency token, and the like, as utilized herein, in the
context of increments of value, may be understood broadly to
describe either: (a) a unit of currency or cryptocurrency (e.g. a
cryptocurrency token), and (b) may also be used to represent a
credential that can be exchanged for a good, service, data or other
valuable consideration (e.g. a token of value). Without limitation
to any other aspect or description of the present disclosure, in
the former case, a token may also be used in conjunction with
investment applications, token-trading applications, and
token-based marketplaces. In the latter case, a token can also be
associated with rendering consideration, such as providing goods,
services, fees, access to a restricted area or event, data or other
valuable benefit. Tokens can be contingent (e.g. contingent access
token) or not contingent. For example, a token of value may be
exchanged for accommodations, (e.g. hotel rooms), dining/food goods
and services, space (e.g. shared space, workspace, convention
space, etc.), fitness/wellness goods or services, event tickets or
event admissions, travel, flights or other transportation, digital
content, virtual goods, license keys, or other valuable goods,
services, data or consideration. Tokens in various forms may be
included where discussing a unit of consideration, collateral, or
value, whether currency, cryptocurrency or any other form of value
such as goods, services, data or other benefits. One of skill in
the art, having the benefit of the disclosure herein and knowledge
about a token, can readily determine the value symbolized or
represented by a token, whether currency, cryptocurrency, good,
service, data or other value. While specific examples of tokens are
described herein for purposes of illustration, any embodiment
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0248] The term pricing data as utilized herein may be understood
broadly to describe a quantity of information such as a price or
cost, of one or more items in a marketplace. Without limitation to
any other aspect or description of the present disclosure, pricing
data may also be used in conjunction with spot market pricing,
forward market pricing, pricing discount information, promotional
pricing, and other information relating to the cost or price of
items. Pricing data may satisfy one or more conditions, or may
trigger application of one or more rules of a smart contract.
Pricing data may be used in conjunction with other forms of data
such as market value data, accounting data, access data, asset and
facility data, worker data, event data, underwriting data, claims
data or other forms of data. Pricing data may be adjusted for the
context of the valued item (e.g., condition, liquidity, location,
etc.) and/or for the context of a particular party. One of skill in
the art, having the benefit of the disclosure herein and knowledge
about pricing data, can readily determine the purposes and use of
pricing data in various embodiments and contexts disclosed
herein.
[0249] Without limitation to any other aspect or description of the
present disclosure, a token includes any token including, without
limitation, a token of value, such as collateral, an asset, a
reward, such as in a token serving as representation of value, such
as a value holding voucher that can be exchanged for goods or
services. Certain components may not be considered tokens
individually, but may be considered tokens in an aggregated
system--for example, a value placed on an asset may not be in
itself be a token, but the value of an asset may be placed in a
token of value, such as to be stored, exchanged, traded, and the
like. For instance, in a non-limiting example, a blockchain circuit
may be structured to provide lenders a mechanism to store the value
of assets, where the value attributed to the token is stored in a
distributed ledger of the blockchain circuit, but the token itself,
assigned the value, may be exchanged or traded such as through a
token marketplace. In certain embodiments, a token may be
considered a token for some purposes but not for other
purposes--for example a token may be used as an indication of
ownership of an asset, but this use of a token would not be traded
as a value where a token including the value of the asset might.
Accordingly, the benefits of the present disclosure may be applied
in a wide variety of systems, and any such systems may be
considered a token herein, while in certain embodiments a given
system may not be considered a token herein. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is a token
and/or whether aspects of the present disclosure can benefit or
enhance the contemplated system include, without limitation, access
data such as relating to rights of access, tickets, and tokens; use
in an investment application such as for investment in shares,
interests, and tokens; a token-trading application; a token-based
marketplace; forms of consideration such as monetary rewards and
tokens; translating the value of a resources in tokens; a
cryptocurrency token; indications of ownership such as identity
information, event information, and token information; a
blockchain-based access token traded in a marketplace application;
pricing application such as for setting and monitoring pricing for
contingent access rights, underlying access rights, tokens, and
fees; trading applications such as for trading or exchanging
contingent access rights or underlying access rights or tokens;
tokens created and stored on a blockchain for contingent access
rights resulting in an ownership (e.g., a ticket); and the
like.
[0250] The term financial data as utilized herein may be understood
broadly to describe a collection of financial information about an
asset, collateral or other item or items. Financial data may
include revenues, expenses, assets, liabilities, equity, bond
ratings, default, return on assets (ROA), return on investment
(ROI), past performance, expected future performance, earnings per
share (EPS), internal rate of return (IRR), earnings announcements,
ratios, statistical analysis of any of the foregoing (e.g. moving
averages), and the like. Without limitation to any other aspect or
description of the present disclosure, financial data may also be
used in conjunction with pricing data and market value data.
Financial data may satisfy one or more conditions, or may trigger
application of one or more rules of a smart contract. Financial
data may be used in conjunction with other forms of data such as
market value data, pricing data, accounting data, access data,
asset and facility data, worker data, event data, underwriting
data, claims data or other forms of data. One of skill in the art,
having the benefit of the disclosure herein and knowledge about
financial data, can readily determine the purposes and use of
pricing data in various embodiments and contexts disclosed
herein.
[0251] The term covenant as utilized herein may be understood
broadly to describe a term, agreement or promise, such as
performance of some action or inaction. For example, a covenant may
relate to behavior of a party or legal status of a party. Without
limitation to any other aspect or description of the present
disclosure, a covenant may also be used in conjunction with other
related terms to an agreement or loan, such as a representation, a
warranty, an indemnity, a balance of debt, a fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of collateral, a
specification of substitutability of collateral, a party, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a foreclose condition, a default condition, and a
consequence of default. A covenant or lack of performance of a
covenant may satisfy one or more conditions, or may trigger
collection, breach or other terms and conditions. In certain
embodiments, a smart contract may calculate whether a covenant is
satisfied and in cases where the covenant is not satisfied, may
enable automated action or trigger other conditions or terms. One
of skill in the art, having the benefit of the disclosure herein
and knowledge about covenants, can readily determine the purposes
and use of covenants in various embodiments and contexts disclosed
herein.
[0252] The term entity as utilized herein may be understood broadly
to describe a party, a third-party (e.g., an auditor, regulator,
service provider, etc.), and/or an identifiable related object such
as an item of collateral related to a transaction. Example entities
include an individual, partnership, corporation, limited liability
company or other legal organization. Other example entities include
an identifiable item of collateral, offset collateral, potential
collateral, or the like. For example, an entity may be a given
party, such as an individual, to an agreement or loan. Data or
other terms herein may be characterized as having a context
relating to an entity, such as entity-oriented data. An entity may
be characterized with a specific context or application, such as a
human entity, physical entity, transactional entity or a financial
entity, without limitation. An entity may have representatives that
represent or act on its behalf. Without limitation to any other
aspect or description of the present disclosure, an entity may also
be used in conjunction with other related entities or terms to an
agreement or loan, such as a representation, a warranty, an
indemnity, a covenant, a balance of debt, a fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of collateral, a
specification of substitutability of collateral, a party, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a foreclose condition, a default condition, and a
consequence of default. An entity may have a set of attributes such
as: a publicly stated valuation, a set of property owned by the
entity as indicated by public records, a valuation of a set of
property owned by the entity, a bankruptcy condition, a foreclosure
status, a contractual default status, a regulatory violation
status, a criminal status, an export controls status, an embargo
status, a tariff status, a tax status, a credit report, a credit
rating, a website rating, a set of customer reviews for a product
of an entity, a social network rating, a set of credentials, a set
of referrals, a set of testimonials, a set of behavior, a location,
and a geolocation, without limitation. In certain embodiments, a
smart contract may calculate whether an entity has satisfied
conditions or covenants and in cases where the entity has not
satisfied such conditions or covenants, may enable automated action
or trigger other conditions or terms. One of skill in the art,
having the benefit of the disclosure herein and knowledge about
entities, can readily determine the purposes and use of entities in
various embodiments and contexts disclosed herein.
[0253] The term party as utilized herein may be understood broadly
to describe a member of an agreement, such as an individual,
partnership, corporation, limited liability company or other legal
organization. For example, a party may be a primary lender, a
secondary lender, a lending syndicate, a corporate lender, a
government lender, a bank lender, a secured lender, a bond issuer,
a bond purchaser, an unsecured lender, a guarantor, a provider of
security, a borrower, a debtor, an underwriter, an inspector, an
assessor, an auditor, a valuation professional, a government
official, an accountant or other entities having rights or
obligations to an agreement, transaction or loan. A party may
characterize a different term, such as transaction as in the term
multi-party transaction, where multiple parties are involved in a
transaction, or the like, without limitation. A party may have
representatives that represent or act on its behalf In certain
embodiments, the term party may reference a potential party or a
prospective party--for example an intended lender or borrower
interacting with a system, that may not yet be committed to an
actual agreement during the interactions with the system. Without
limitation to any other aspect or description of the present
disclosure, an party may also be used in conjunction with other
related parties or terms to an agreement or loan, such as a
representation, a warranty, an indemnity, a covenant, a balance of
debt, a fixed interest rate, a variable interest rate, a payment
amount, a payment schedule, a balloon payment schedule, a
specification of collateral, a specification of substitutability of
collateral, an entity, a guarantee, a guarantor, a security, a
personal guarantee, a lien, a duration, a foreclose condition, a
default condition, and a consequence of default. A party may have a
set of attributes such as: an identity, a creditworthiness, an
activity, a behavior, a business practice, a status of performance
of a contract, information about accounts receivable, information
about accounts payable, information about the value of collateral,
and other types of information, without limitation. In certain
embodiments, a smart contract may calculate whether a party has
satisfied conditions or covenants and in cases where the party has
not satisfied such conditions or covenants, may enable automated
action or trigger other conditions or terms. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about parties, can readily determine the purposes and use of
parties in various embodiments and contexts disclosed herein.
[0254] The term party attribute, entity attribute, or party/entity
attribute as utilized herein may be understood broadly to describe
a value, characteristic, or status of a party or entity. For
example, attributes of a party or entity may be, without
limitation: value, quality, location, net worth, price, physical
condition, health condition, security, safety, ownership, identity,
creditworthiness, activity, behavior, business practice, status of
performance of a contract, information about accounts receivable,
information about accounts payable, information about the value of
collateral, and other types of information, and the like. In
certain embodiments, a smart contract may calculate values, status
or conditions associated with attributes of a party or entity, and
in cases where the party or entity has not satisfied such
conditions or covenants, may enable automated action or trigger
other conditions or terms. One of skill in the art, having the
benefit of the disclosure herein and knowledge about attributes of
a party or entity, can readily determine the purposes and use of
these attributes in various embodiments and contexts disclosed
herein.
[0255] The term lender as utilized herein may be understood broadly
to describe a party to an agreement offering an asset for lending,
proceeds of a loan, and may include an individual, partnership,
corporation, limited liability company, or other legal
organization. For example, a lender may be a primary lender, a
secondary lender, a lending syndicate, a corporate lender, a
government lender, a bank lender, a secured lender, an unsecured
lender, or other party having rights or obligations to an
agreement, transaction or loan offering a loan to a borrower,
without limitation. A lender may have representatives that
represent or act on its behalf. Without limitation to any other
aspect or description of the present disclosure, an party may also
be used in conjunction with other related parties or terms to an
agreement or loan, such as a borrower, a guarantor, a
representation, a warranty, an indemnity, a covenant, a balance of
debt, a fixed interest rate, a variable interest rate, a payment
amount, a payment schedule, a balloon payment schedule, a
specification of collateral, a specification of substitutability of
collateral, a security, a personal guarantee, a lien, a duration, a
foreclose condition, a default condition, and a consequence of
default. In certain embodiments, a smart contract may calculate
whether a lender has satisfied conditions or covenants and in cases
where the lender has not satisfied such conditions or covenants,
may enable automated action, a notification or alert, or trigger
other conditions or terms. One of skill in the art, having the
benefit of the disclosure herein and knowledge about a lender, can
readily determine the purposes and use of a lender in various
embodiments and contexts disclosed herein.
[0256] The term crowdsourcing services as utilized herein may be
understood broadly to describe services offered or rendered in
conjunction with a crowdsourcing model or transaction, wherein a
large group of people or entities supply contributions to fulfill a
need, such as a loan, for the transaction. Crowdsourcing services
may be provided by a platform or system, without limitation. A
crowdsourcing request may be communicated to a group of information
suppliers and by which responses to the request may be collected
and processed to provide a reward to at least one successful
information supplier. The request and parameters may be configured
to obtain information related to the condition of a set of
collateral for a loan. The crowdsourcing request may be published.
In certain embodiments, without limitation, crowdsourcing services
may be performed by a smart contract, wherein the reward is managed
by a smart contract that processes responses to the crowdsourcing
request and automatically allocates a reward to information that
satisfies a set of parameter configured for the crowdsourcing
request. One of skill in the art, having the benefit of the
disclosure herein and knowledge about crowdsourcing services, can
readily determine the purposes and use of crowdsourcing services in
various embodiments and contexts disclosed herein.
[0257] The term publishing services as utilized herein may be
understood to describe a set of services to publish a crowdsourcing
request. Publishing services may be provided by a platform or
system, without limitation. In certain embodiments, without
limitation, publishing services may be performed by a smart
contract, wherein the crowdsourcing request is published or
publication is initiated by the smart contract. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about publishing services, can readily determine the purposes and
use of publishing services in various embodiments and contexts
disclosed herein.
[0258] The term interface as utilized herein may be understood
broadly to describe a component by which interaction or
communication is achieved, such as a component of a computer, which
may be embodied in software, hardware or a combination thereof. For
example, an interface may serve a number of different purposes or
be configured for different applications or contexts, such as,
without limitation: an application programming interface, a graphic
user interface, user interface, software interface, marketplace
interface, demand aggregation interface, crowdsourcing interface,
secure access control interface, network interface, data
integration interface or a cloud computing interface, or
combinations thereof. An interface may serve to act as a way to
enter, receive or display data, within the scope of lending,
refinancing, collection, consolidation, factoring, brokering or
foreclosure, without limitation. An interface may serve as an
interface for another interface. Without limitation to any other
aspect or description of the present disclosure, an interface may
be used in conjunction with applications, processes, modules,
services, layers, devices, components, machines, products,
sub-systems, interfaces, connections, or as part of a system. In
certain embodiments, an interface may be embodied in software,
hardware or a combination thereof, as well as stored on a medium or
in memory. One of skill in the art, having the benefit of the
disclosure herein and knowledge about an interface, can readily
determine the purposes and use of an interface in various
embodiments and contexts disclosed herein.
[0259] The term graphical user interface as utilized herein may be
understood as a type of interface to allow a user to interact with
a system, computer or other interfaces, in which interaction or
communication is achieved through graphical devices or
representations. A graphical user interface may be a component of a
computer, which may be embodied in computer readable instructions,
hardware, or a combination thereof. A graphical user interface may
serve a number of different purposes or be configured for different
applications or contexts. Such an interface may serve to act as a
way to receive or display data using visual representation,
stimulus or interactive data, without limitation. A graphical user
interface may serve as an interface for another graphical user
interface or other interfaces. Without limitation to any other
aspect or description of the present disclosure, a graphical user
interface may be used in conjunction with applications, processes,
modules, services, layers, devices, components, machines, products,
sub-systems, interfaces, connections, or as part of a system. In
certain embodiments, a graphical user interface may be embodied in
computer readable instructions, hardware or a combination thereof,
as well as stored on a medium or in memory. Graphical user
interfaces may be configured for any input types, including
keyboards, a mouse, a touch screen, and the like. Graphical user
interfaces may be configured for any desired user interaction
environments, including for example a dedicated application, a web
page interface, or combinations of these. One of skill in the art,
having the benefit of the disclosure herein and knowledge about a
graphical user interface, can readily determine the purposes and
use of a graphical user interface in various embodiments and
contexts disclosed herein.
[0260] The term user interface as utilized herein may be understood
as a type of interface to allow a user to interact with a system,
computer or other apparatus, in which interaction or communication
is achieved through graphical devices or representations. A user
interface may be a component of a computer, which may be embodied
in software, hardware or a combination thereof. The user interface
may be stored on a medium or in memory. User interfaces may include
drop-down menus, tables, forms, or the like with default,
templated, recommended, or pre-configured conditions. In certain
embodiments, a user interface may include voice interaction.
Without limitation to any other aspect or description of the
present disclosure, a user interface may be used in conjunction
with applications, circuits, controllers, processes, modules,
services, layers, devices, components, machines, products,
sub-systems, interfaces, connections, or as part of a system. User
interfaces may serve a number of different purposes or be
configured for different applications or contexts. For example, a
lender-side user interface may include features to view a plurality
of customer profiles, but may be restricted from making certain
changes. A debtor-side user interface may include features to view
details and make changes to a user account. A 3rd party
neutral-side interface (e.g. a 3rd party not having an interest in
an underlying transaction, such as a regulator, auditor, etc.) may
have features that enable a view of company oversight and
anonymized user data without the ability to manipulate any data,
and may have scheduled access depending upon the 3rd party and the
purpose for the access. A 3rd party interested-side interface (e.g.
a 3rd party that may have an interest in an underlying transaction,
such as a collector, debtor advocate, investigator, partial owner,
etc.) may include features enabling a view of particular user data
with restrictions on making changes. Many more features of these
user interfaces may be available to implements embodiments of the
systems and/or procedures described throughout the present
disclosure. Accordingly, the benefits of the present disclosure may
be applied in a wide variety of processes and systems, and any such
processes or systems may be considered a service herein. One of
skill in the art, having the benefit of the disclosure herein and
knowledge about a user interface, can readily determine the
purposes and use of a user interface in various embodiments and
contexts disclosed herein. Certain considerations for the person of
skill in the art, in determining whether a contemplated interface
is a user interface and/or whether aspects of the present
disclosure can benefit or enhance the contemplated system include,
without limitation: configurable views, ability to restrict
manipulation or views, report functions, ability to manipulate user
profile and data, implement regulatory requirements, provide the
desired user features for borrowers, lenders, and 3rd parties, and
the like.
[0261] Interfaces and dashboards as utilized herein may further be
understood broadly to describe a component by which interaction or
communication is achieved, such as a component of a computer, which
may be embodied in software, hardware or a combination thereof.
Interfaces and dashboards may acquire, receive, present or
otherwise administrate an item, service, offering or other aspects
of a transaction or loan. For example, interfaces and dashboards
may serve a number of different purposes or be configured for
different applications or contexts, such as, without limitation: an
application programming interface, a graphic user interface, user
interface, software interface, marketplace interface, demand
aggregation interface, crowdsourcing interface, secure access
control interface, network interface, data integration interface or
a cloud computing interface, or combinations thereof. An interface
or dashboard may serve to act as a way to receive or display data,
within the context of lending, refinancing, collection,
consolidation, factoring, brokering or foreclosure, without
limitation. An interface or dashboard may serve as an interface or
dashboard for another interface or dashboard. Without limitation to
any other aspect or description of the present disclosure, an
interface may be used in conjunction with applications, circuits,
controllers, processes, modules, services, layers, devices,
components, machines, products, sub-systems, interfaces,
connections, or as part of a system. In certain embodiments, an
interface or dashboard may be embodied in computer readable
instructions, hardware or a combination thereof, as well as stored
on a medium or in memory. One of skill in the art, having the
benefit of the disclosure herein and knowledge ordinarily available
about a contemplated system, can readily determine the purposes and
use of interfaces and/or dashboards in various embodiments and
contexts disclosed herein.
[0262] The term domain as utilized herein may be understood broadly
to describe a scope or context of a transaction and/or
communications related to a transaction. For example, a domain may
serve a number of different purposes or be configured for different
applications or contexts, such as, without limitation: a domain for
execution, a domain for a digital asset, domains to which a request
will be published, domains to which social network data collection
and monitoring services will be applied, domains to which Internet
of Things data collection and monitoring services will be applied,
network domains, geolocation domains, jurisdictional location
domains, and time domains. Without limitation to any other aspect
or description of the present disclosure, one or more domains may
be utilized relative to any applications, circuits, controllers,
processes, modules, services, layers, devices, components,
machines, products, sub-systems, interfaces, connections, or as
part of a system. In certain embodiments, a domain may be embodied
in computer readable instructions, hardware, or a combination
thereof, as well as stored on a medium or in memory. One of skill
in the art, having the benefit of the disclosure herein and
knowledge about a domain, can readily determine the purposes and
use of a domain in various embodiments and contexts disclosed
herein.
[0263] The term request (and variations) as utilized herein may be
understood broadly to describe the action or instance of initiating
or asking for a thing (e.g. information, a response, an object, and
the like) to be provided. A specific type of request may also serve
a number of different purposes or be configured for different
applications or contexts, such as, without limitation: a formal
legal request (e.g. a subpoena), a request to refinance (e.g. a
loan), or a crowdsourcing request. Systems may be utilized to
perform requests as well as fulfill requests. Requests in various
forms may be included where discussing a legal action, a
refinancing of a loan, or a crowdsourcing service, without
limitation. One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system, can
readily determine the value of a request implemented in an
embodiment. While specific examples of requests are described
herein for purposes of illustration, any embodiment benefitting
from the disclosures herein, and any considerations understood to
one of skill in the art having the benefit of the disclosures
herein, are specifically contemplated within the scope of the
present disclosure.
[0264] The term reward (and variations) as utilized herein may be
understood broadly to describe a thing or consideration received or
provided in response to an action or stimulus. Rewards can be of a
financial type, or non-financial type, without limitation. A
specific type of reward may also serve a number of different
purposes or be configured for different applications or contexts,
such as, without limitation: a reward event, claims for rewards,
monetary rewards, rewards captured as a data set, rewards points,
and other forms of rewards. Rewards may be triggered, allocated,
generated for innovation, provided for the submission of evidence,
requested, offered, selected, administrated, managed, configured,
allocated, conveyed, identified, without limitation, as well as
other actions. Systems may be utilized to perform the
aforementioned actions. Rewards in various forms may be included
where discussing a particular behavior, or encouragement of a
particular behavior, without limitation. In certain embodiments
herein, a reward may be utilized as a specific incentive (e.g.,
rewarding a particular person that responds to a crowdsourcing
request) or as a general incentive (e.g., providing a reward
responsive to a successful crowdsourcing request, in addition to or
alternatively to a reward to the particular person that responded).
One of skill in the art, having the benefit of the disclosure
herein and knowledge about a reward, can readily determine the
value of a reward implemented in an embodiment. While specific
examples of rewards are described herein for purposes of
illustration, any embodiment benefitting from the disclosures
herein, and any considerations understood to one of skill in the
art having the benefit of the disclosures herein, are specifically
contemplated within the scope of the present disclosure.
[0265] The term robotic process automation system as utilized
herein may be understood broadly to describe a system capable of
performing tasks or providing needs for a system of the present
disclosure. For example, a robotic process automation system,
without limitation, can be configured for: negotiation of a set of
terms and conditions for a loan, negotiation of refinancing of a
loan, loan collection, consolidating a set of loans, managing a
factoring loan, brokering a mortgage loan, training for foreclosure
negotiations, configuring a crowdsourcing request based on a set of
attributes for a loan, setting a reward, determining a set of
domains to which a request will be published, configuring the
content of a request, configuring a data collection and monitoring
action based on a set of attributes of a loan, determining a set of
domains to which the Internet of Things data collection and
monitoring services will be applied, and iteratively training and
improving based on a set of outcomes. A robotic process automation
system may include: a set of data collection and monitoring
services, an artificial intelligence system, and another robotic
process automation system which is a component of the higher level
robotic process automation system. The robotic process automation
system may include: at least one of the set of mortgage loan
activities and the set of mortgage loan interactions includes
activities among marketing activity, identification of a set of
prospective borrowers, identification of property, identification
of collateral, qualification of borrower, title search, title
verification, property assessment, property inspection, property
valuation, income verification, borrower demographic analysis,
identification of capital providers, determination of available
interest rates, determination of available payment terms and
conditions, analysis of existing mortgage, comparative analysis of
existing and new mortgage terms, completion of application
workflow, population of fields of application, preparation of
mortgage agreement, completion of schedule to mortgage agreement,
negotiation of mortgage terms and conditions with capital provider,
negotiation of mortgage terms and conditions with borrower,
transfer of title, placement of lien and closing of mortgage
agreement. Example and non-limiting robotic process automation
systems may include one or more user interfaces, interfaces with
circuits and/or controllers throughout the system to provide,
request, and/or share data, and/or one or more artificial
intelligence circuits configured to iteratively improve one or more
operations of the robotic process automation system. One of skill
in the art, having the benefit of the disclosure herein and
knowledge ordinarily available about a contemplated robotic process
automation system, can readily determine the circuits, controllers,
and/or devices to include to implement a robotic process automation
system performing the selected functions for the contemplated
system. While specific examples of robotic process automation
systems are described herein for purposes of illustration, any
embodiment benefitting from the disclosures herein, and any
considerations understood.
[0266] The term loan-related action (and other related terms such
as loan-related event and loan-related activity) are utilized
herein and may be understood broadly to describe one or multiple
actions, events or activities relating to a transaction that
includes a loan within the transaction. The action, event or
activity may occur in many different contexts of loans, such as
lending, refinancing, consolidation, factoring, brokering,
foreclosure, administration, negotiating, collecting, procuring,
enforcing and data processing (e.g. data collection), or
combinations thereof, without limitation. A loan-related action may
be used in the form of a noun (e.g. a notice of default has been
communicated to the borrower with formal notice, which could be
considered a loan-related action). A loan-related action, event, or
activity may refer to a single instance, or may characterize a
group of actions, events or activities. For example, a single
action such as providing a specific notice to a borrower of an
overdue payment may be considered a loan-related action. Similarly,
a group of actions from start to finish relating to a default may
also be considered a single loan-related action. Appraisal,
inspection, funding and recording, without limitation, may all also
be considered loan-related actions that have occurred, as well as
events relating to the loan, and may also be loan-related events.
Similarly, these activities of completing these actions may also be
considered loan-related activities (e.g. appraising, inspecting,
funding, recording, etc.), without limitation. In certain
embodiments, a smart contract or robotic process automation system
may perform loan-related actions, loan-related events, or
loan-related activities for one or more of the parties, and process
appropriate tasks for completion of the same. In some cases the
smart contract or robotic process automation system may not
complete a loan-related action, and depending upon such outcome
this may enable an automated action or may trigger other conditions
or terms. One of skill in the art, having the benefit of the
disclosure herein and knowledge about loan-related actions, events,
and activities can readily determine the purposes and use of this
term in various forms and embodiments as described throughout the
present disclosure.
[0267] The term loan-related action, events, and activities, as
noted herein, may also more specifically be utilized to describe a
context for calling of a loan. A calling of a loan is an action
wherein the lender can demand the loan be repaid, usually triggered
by some other condition or term, such as delinquent payment(s). For
example, a loan-related action for calling of the loan may occur
when a borrower misses three payments in a row, such that there is
a severe delinquency in the loan payment schedule, and the loan
goes into default. In such a scenario, a lender may be initiating
loan-related actions for calling of the loan to protect its rights.
In such a scenario, perhaps the borrower pays a sum to cure the
delinquency and penalties, which may also be considered as a
loan-related action for calling of the loan. In some circumstances,
a smart contract or robotic process automation system may initiate,
administrate or process loan-related actions for calling of the
loan, which without limitation, may including providing notice,
researching and collecting payment history, or other tasks
performed as a part of the calling of the loan. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about loan-related actions for calling of the loan, or other forms
of the term and its various forms, can readily determine the
purposes and use of this term in the context of an event or other
various embodiments and contexts disclosed herein.
[0268] The term loan-related action, events, and activities, as
noted herein, may also more specifically be utilized to describe a
context for payment of a loan. Typically in transactions involving
loans, without limitation, a loan is repaid on a payment schedule.
Various actions may be taken to provide a borrower with information
to pay back the loan, as well as actions for a lender to receive
payment for the loan. For example, if a borrower makes a payment on
the loan, a loan-related action for payment of the loan may occur.
Without limitation, such a payment may comprise several actions
that may occur with respect to the payment on the loan, such as:
the payment being tendered to the lender, the loan ledger or
accounting reflecting that a payment has been made, a receipt
provided to the borrower of the payment made, and the next payment
being requested of the borrower. In some circumstances, a smart
contract or robotic process automation system may initiate,
administrate or process such loan-related actions for payment of
the loan, which without limitation, may including providing notice
to the lender, researching and collecting payment history,
providing a receipt to the borrower, providing notice of the next
payment due to the borrower, or other actions associated with
payment of the loan. One of skill in the art, having the benefit of
the disclosure herein and knowledge about loan-related actions for
payment of a loan, or other forms of the term and its various
forms, can readily determine the purposes and use of this term in
the context of an event or other various embodiments and contexts
disclosed herein.
[0269] The term loan-related action, events, and activities, as
noted herein, may also more specifically be utilized to describe a
context for a payment schedule or alternative payment schedule.
Typically in transactions involving loans, without limitation, a
loan is repaid on a payment schedule, which may be modified over
time. Or, such a payment schedule may be developed and agreed in
the alternative, with an alternative payment schedule. Various
actions may be taken in the context of a payment schedule or
alternate payment schedule for the lender or the borrower, such as:
the amount of such payments, when such payments are due, what
penalties or fees may attach to late payments, or other terms. For
example, if a borrower makes an early payment on the loan, a
loan-related action for payment schedule and alternative payment
schedule of the loan may occur; in such case, perhaps the payment
is applied as principal, with the regular payment still being due.
Without limitation, loan-related actions for a payment schedule and
alternative payment schedule may comprise several actions that may
occur with respect to the payment on the loan, such as: the payment
being tendered to the lender, the loan ledger or accounting
reflecting that a payment has been made, a receipt provided to the
borrower of the payment made, a calculation if any fees are
attached or due, and the next payment being requested of the
borrower. In certain embodiments, an activity to determine a
payment schedule or alternative payment schedule may be a
loan-related action, event, or activity. In certain embodiments, an
activity to communicate the payment schedule or alternative payment
schedule (e.g., to the borrower, the lender, or a 3rd party) may be
a loan-related action, event, or activity. In some circumstances, a
smart contract circuit or robotic process automation system may
initiate, administrate, or process such loan-related actions for
payment schedule and alternative payment schedule, which without
limitation, may include providing notice to the lender, researching
and collecting payment history, providing a receipt to the
borrower, calculating the next due date, calculating the final
payment amount and date, providing notice of the next payment due
to the borrower, determining the payment schedule or an alternate
payment schedule, communicating the payment scheduler or an
alternate payment schedule, or other actions associated with
payment of the loan. One of skill in the art, having the benefit of
the disclosure herein and knowledge about loan-related actions for
payment schedule and alternative payment schedule, or other forms
of the term and its various forms, can readily determine the
purposes and use of this term in the context of an event or other
various embodiments and contexts disclosed herein.
[0270] The term regulatory notice requirement (and any derivatives)
as utilized herein may be understood broadly to describe an
obligation or condition to communicate a notification or message to
another party or entity. The regulatory notice requirement may be
required under one or more conditions that are triggered, or
generally required. For example, a lender may have a regulatory
notice requirement to provide notice to a borrower of a default of
a loan, or change of an interest rate of a loan, or other
notifications relating to a transaction or loan. The regulatory
aspect of the term may be attributed to jurisdiction-specific laws,
rules, or codes that require certain obligations of communication.
In certain embodiments, a policy directive may be treated as a
regulatory notice requirement--for example where a lender has an
internal notice policy that may exceed the regulatory requirements
of one or more of the jurisdictional locations related to a
transaction. The notice aspect generally relates to formal
communications, which may take many different forms, but may
specifically be specified as a particular form of notice, such as a
certified mail, facsimile, email transmission, or other physical or
electronic form, a content for the notice, and/or a timing
requirement related to the notice. The requirement aspect relates
to the necessity of a party to complete its obligation to be in
compliance with laws, rules, codes, policies, standard practices,
or terms of an agreement or loan. In certain embodiments, a smart
contract may process or trigger regulatory notice requirements and
provide appropriate notice to a borrower. This may be based on
location of at least one of: the lender, the borrower, the funds
provided via the loan, the repayment of the loan, and the
collateral of the loan, or other locations as designated by the
terms of the loan, transaction, or agreement. In cases where a
party or entity has not satisfied such regulatory notice
requirements, certain changes in the rights or obligations between
the parties may be triggered--for example where a lender provides a
non-compliant notice to the borrower, an automated action or
trigger based on the terms and conditions of the loan, and/or based
on external information (e.g., a regulatory prescription, internal
policy of the lender, etc.) may be affected by a smart contract
circuit and/or robotic process automation system may be
implemented. One of skill in the art, having the benefit of the
disclosure herein and knowledge ordinarily available about a
contemplated system, can readily determine the purposes and use of
regulatory notice requirements in various embodiments and contexts
disclosed herein.
[0271] The term regulatory notice requirement may also be utilized
herein to describe an obligation or condition to communicate a
notification or message to another party or entity based upon a
general or specific policy, rather than based on a particular
jurisdiction, or laws, rules, or codes of a particular location (as
in regulatory notice requirement that may be
jurisdiction-specific). The regulatory notice requirement may be
prudent or suggested, rather than obligatory or required, under one
or more conditions that are triggered, or generally required. For
example, a lender may have a regulatory notice requirement that is
policy based to provide notice to a borrower of a new informational
website, or will experience a change of an interest rate of a loan
in the future, or other notifications relating to a transaction or
loan that are advisory or helpful, rather than mandatory (although
mandatory notices may also fall under a policy basis). Thus, in
policy based uses of the regulatory notice requirement term, a
smart contract circuit may process or trigger regulatory notice
requirements and provide appropriate notice to a borrower which may
or may not necessarily be required by a law, rule or code. The
basis of the notice or communication may be out of prudence,
courtesy, custom, or obligation.
[0272] The term regulatory notice may also be utilized herein to
describe an obligation or condition to communicate a notification
or message to another party or entity specifically, such as a
lender or borrower. The regulatory notice may be specifically
directed toward any party or entity, or a group of parties or
entities. For example, a particular notice or communication may be
advisable or required to be provided to a borrower, such as on
circumstances of a borrower's failure to provide scheduled payments
on a loan resulting in a default. As such, such a regulatory notice
directed to a particular user, such as a lender or borrower, may be
as a result of a regulatory notice requirement that is
jurisdiction-specific or policy-based, or otherwise. Thus, in some
circumstances a smart contract may process or trigger a regulatory
notice and provide appropriate notice to a specific party such as a
borrower, which may or may not necessarily be required by a law,
rule or code, but may otherwise be provided out of prudence,
courtesy or custom. In cases where a party or entity has not
satisfied such regulatory notice requirements to a specific party
or parties, it may create circumstances where certain rights may be
forgiven by one or more parties or entities, or may enable
automated action or trigger other conditions or terms. One of skill
in the art, having the benefit of the disclosure herein and
knowledge ordinarily available about a contemplated system, can
readily determine the purposes and use of regulatory notice
requirements based in various embodiments and contexts disclosed
herein.
[0273] The term regulatory foreclosure requirement (and any
derivatives) as utilized herein may be understood broadly to
describe an obligation or condition in order to trigger, process or
complete default of a loan, foreclosure or recapture of collateral,
or other related foreclosure actions. The regulatory foreclosure
requirement may be required under one or more conditions that are
triggered, or generally required. For example, a lender may have a
regulatory foreclosure requirement to provide notice to a borrower
of a default of a loan, or other notifications relating to the
default of a loan prior to foreclosure. The regulatory aspect of
the term may be attributed to jurisdiction-specific laws, rules, or
codes that require certain obligations of communication. The
foreclosure aspect generally relates to the specific remedy of
foreclosure, or a recapture of collateral property and default of a
loan, which may take many different forms, but may be specified in
the terms of the loan. The requirement aspect relates to the
necessity of a party to complete its obligation in order to be in
compliance or performance of laws, rules, codes or terms of an
agreement or loan. In certain embodiments, a smart contract circuit
may process or trigger regulatory foreclosure requirements and
process appropriate tasks relating to such a foreclosure action.
This may be based on a jurisdictional location of at least one of
the lender, the borrower, the fund provided via the loan, the
repayment of the loan, and the collateral of the loan, or other
locations as designated by the terms of the loan, transaction, or
agreement. In cases where a party or entity has not satisfied such
regulatory foreclosure requirements, certain rights may be forgiven
by the party or entity (e.g. a lender), or such a failure to comply
with the regulatory notice requirement may enable automated action
or trigger other conditions or terms. One of skill in the art,
having the benefit of the disclosure herein and knowledge
ordinarily available about a contemplated system, can readily
determine the purposes and use of regulatory foreclosure
requirements in various embodiments and contexts disclosed
herein.
[0274] The term regulatory foreclosure requirement may also be
utilized herein to describe an obligation or in order to trigger,
process or complete default of a loan, foreclosure or recapture of
collateral, or other related foreclosure actions. based upon a
general or specific policy rather than based on a particular
jurisdiction, or laws, rules, or codes of a particular location (as
in regulatory foreclosure requirement that may be
jurisdiction-specific). The regulatory foreclosure requirement may
be prudent or suggested, rather than obligatory or required, under
one or more conditions that are triggered, or generally required.
For example, a lender may have a regulatory foreclosure requirement
that is policy based to provide notice to a borrower of a default
of a loan, or other notifications relating to a transaction or loan
that are advisory or helpful, rather than mandatory (although
mandatory notices may also fall under a policy basis). Thus, in
policy based uses of the regulatory foreclosure requirement term, a
smart contract may process or trigger regulatory foreclosure
requirements and provide appropriate notice to a borrower which may
or may not necessarily be required by a law, rule or code. The
basis of the notice or communication may be out of prudence,
courtesy, custom, industry practice, or obligation.
[0275] The term regulatory foreclosure requirements may also be
utilized herein to describe an obligation or condition that is to
be performed with regard to a specific user, such as a lender or a
borrower. The regulatory notice may be specifically directed toward
any party or entity, or a group of parties or entities. For
example, a particular notice or communication may be advisable or
required to be provided to a borrower, such as on circumstances of
a borrower's failure to provide scheduled payments on a loan
resulting in a default. As such, such a regulatory foreclosure
requirement is directed to a particular user, such as a lender or
borrower, and may be a result of a regulatory foreclosure
requirement that is jurisdiction-specific or policy-based, or
otherwise. For example, the foreclosure requirement may be related
to a specific entity involved with a transaction (e.g., the current
borrower has been a customer for 30 years, so s/he receives unique
treatment), or to a class of entities (e.g., "preferred" borrowers,
or "first time default" borrowers). Thus, in some circumstances a
smart contract circuit may process or trigger an obligation or
action that must be taken pursuant to a foreclosure, where the
action is directed or from a specific party such as a lender or a
borrower, which may or may not necessarily be required by a law,
rule or code, but may otherwise be provided out of prudence,
courtesy, or custom. In certain embodiments, the obligation or
condition that is to be performed with regard to the specific user
may form a part of the terms and conditions or otherwise be known
to the specific user to which it applies (e.g., an insurance
company or bank that advertises a specific practice with regard to
a specific class of customers, such as first-time default
customers, first-time accident customers, etc.), and in certain
embodiments the obligation or condition that is to be performed
with regard to the specific user may be unknown to the specific
user to which it applies (e.g., a bank has a policy relating to a
class of users to which the specific user belongs, but the specific
user is not aware of the classification).
[0276] The terms value, valuation, valuation model (and similar
terms) as utilized herein should be understood broadly to describe
an approach to evaluate and determine the estimated value for
collateral. Without limitation to any other aspect or description
of the present disclosure, a valuation model may be used in
conjunction with: collateral (e.g. a secured property), artificial
intelligence services (e.g. to improve a valuation model), data
collection and monitoring services (e.g. to set a valuation
amount), valuation services (e.g. the process of informing, using,
and/or improving a valuation model), and/or outcomes relating to
transactions in collateral (e.g. as a basis of improving the
valuation model). "Jurisdiction-specific valuation model" is also
used as a valuation model used in a specific
geographic/jurisdictional area or region; wherein, the jurisdiction
can be specific to jurisdiction of the lender, the borrower, the
delivery of funds, the payment of the loan or the collateral of the
loan, or combinations thereof. In certain embodiments, a
jurisdiction-specific valuation model considers jurisdictional
effects on a valuation of collateral, including at least: rights
and obligations for borrowers and lenders in the relevant
jurisdiction(s); jurisdictional effects on the ability to move,
import, export, substitute, and/or liquidate the collateral;
jurisdictional effects on the timing between default and
foreclosure or collection of collateral; and/or jurisdictional
effects on the volatility and/or sensitivity of collateral value
determinations. In certain embodiments, a geolocation-specific
valuation model considers geolocation effects on a valuation of the
collateral, which may include a similar list of considerations
relative jurisdictional effects (although the jurisdictional
location(s) may be distinct from the geolocation(s)), but may also
include additional effects, such as: weather-related effects;
distance of the collateral from monitoring, maintenance, or seizure
services; and/or proximity of risk phenomenon (e.g., fault lines,
industrial locations, a nuclear plant, etc.). A valuation model may
utilize a valuation of offset collateral (e.g., a similar item of
collateral, a generic value such as a market value of similar or
fungible collateral, and/or a value of an item that correlates with
a value of the collateral) as a part of the valuation of the
collateral. In certain embodiments, an artificial intelligence
circuit includes one or more machine learning and/or artificial
intelligence algorithms, to improve a valuation model, including,
for example, utilizing information over time between multiple
transactions involving similar or offset collateral, and/or
utilizing outcome information (e.g., where loan transactions are
completed successfully or unsuccessfully, and/or in response to
collateral seizure or liquidation events that demonstrate
real-world collateral valuation determinations) from the same or
other transactions to iteratively improve the valuation model. In
certain embodiments, an artificial intelligence circuit is trained
on a collateral valuation data set, for example previously
determined valuations and/or through interactions with a trainer
(e.g., a human, accounting valuations, and/or other valuation
data). In certain embodiments, the valuation model and/or
parameters of the valuation model (e.g., assumptions, calibration
values, etc.) may be determined and/or negotiated as a part of the
terms and conditions of the transaction (e.g., a loan, a set of
loans, and/or a subset of the set of loans). One of skill in the
art, having the benefit of the disclosure herein and knowledge
ordinarily available about a contemplated system, can readily
determine which aspects of the present disclosure will benefit a
particular application for a valuation model, and how to choose or
combine valuation models to implement an embodiment of a valuation
model. Certain considerations for the person of skill in the art,
or embodiments of the present disclosure in choosing an appropriate
valuation model, include, without limitation: the legal
considerations of a valuation model given the jurisdiction of the
collateral; the data available for a given collateral; the
anticipated transaction/loan type(s); the specific type of
collateral; the ratio of the loan to value; the ratio of the
collateral to the loan; the gross transaction/loan amount; the
credit scores of the borrower; accounting practices for the loan
type and/or related industry; uncertainties related to any of the
foregoing; and/or sensitivities related to any of the foregoing.
While specific examples of valuation models and considerations are
described herein for purposes of illustration, any embodiment
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure
[0277] The term market value data, or marketplace information, (and
other forms or variations) as utilized herein may be understood
broadly to describe data or information relating to the valuation
of a property, asset, collateral or other valuable items which may
be used as the subject of a loan, collateral or transaction. Market
value data or marketplace information may change from time to time,
and may be estimated, calculated, or objectively or subjectively
determined from various sources of information. Market value data
or marketplace information may be related directly to an item of
collateral or to an off-set item of collateral. Market value data
or marketplace information may include financial data, market
ratings, product ratings, customer data, market research to
understand customer needs or preferences, competitive intelligence
re. competitors, suppliers, and the like, entities sales,
transactions, customer acquisition cost, customer lifetime value,
brand awareness, churn rate, and the like. The term may occur in
many different contexts of contracts or loans, such as lending,
refinancing, consolidation, factoring, brokering, foreclosure, and
data processing (e.g. data collection), or combinations thereof,
without limitation. Market value data or marketplace information
may be used as a noun to identify a single figure or a plurality of
figures or data. For example, market value data or marketplace
information may be utilized by a lender to determine if a property
or asset will serve as collateral for a secured loan, or may
alternatively be utilized in the determination of foreclosure if a
loan is in default, without limitation to these circumstances in
use of the term. Marketplace value data or marketplace information
may also be used to determine loan-to-value figures or
calculations. In certain embodiments, a collection service, smart
contract circuit, and/or robotic process automation system may
estimate or calculate market value data or marketplace information
from one or more sources of data or information. In some cases
market data value or marketplace information, depending upon the
data/information contained therein, may enable automated action or
trigger other conditions or terms. One of skill in the art, having
the benefit of the disclosure herein and knowledge ordinarily
available about a contemplated system and available relevant
marketplace information, can readily determine the purposes and use
of this term in various forms, embodiments and contexts disclosed
herein.
[0278] The terms similar collateral, similar to collateral, off-set
collateral, and other forms or variations as utilized herein may be
understood broadly to describe a property, asset or valuable item
that may be like in nature to a collateral (e.g. an article of
value held in security) regarding a loan or other transaction.
Similar collateral may refer to a property, asset, collateral or
other valuable item which may be aggregated, substituted, or
otherwise referred to in conjunction with other collateral, whether
the similarity comes in the form of a common attribute such as type
of item of collateral, category of the item of collateral, an age
of the item of collateral, a condition of the item of collateral, a
history of the item of collateral, an ownership of the item of
collateral, a caretaker of the item of collateral, a security of
the item of collateral, a condition of an owner of the item of
collateral, a lien on the item of collateral, a storage condition
of the item of collateral, a geolocation of the item of collateral,
and a jurisdictional location of the item of collateral, and the
like. In certain embodiments, an offset collateral references an
item that has a value correlation with an item of collateral--for
example an offset collateral may exhibit similar price movements,
volatility, storage requirements, or the like for an item of
collateral. In certain embodiments, similar collateral may be
aggregated to form a larger security interest or collateral for an
additional loan or distribution, or transaction. In certain
embodiments, offset collateral may be utilized to inform a
valuation of the collateral. In certain embodiments, a smart
contract circuit or robotic process automation system may estimate
or calculate figures, data or information relating to similar
collateral, or may perform a function with respect to aggregating
similar collateral. One of skill in the art, having the benefit of
the disclosure herein and knowledge ordinarily available about a
contemplated system can readily determine the purposes and use of
similar collateral, offset collateral, or related terms as they
relate to collateral in various forms, embodiments, and contexts
disclosed herein.
[0279] The term restructure (and other forms such as restructuring)
as utilized herein may be understood broadly to describe a
modification of terms or conditions, properties, collateral, or
other considerations affecting a loan or transaction. Restructuring
may result in a successful outcome where amended terms or
conditions are adopted between parties, or an unsuccessful outcome
where no modification or restructure occurs, without limitation.
Restructuring can occur in many contexts of contracts or loans,
such as application, lending, refinancing, collection,
consolidation, factoring, brokering, foreclosure, and combinations
thereof, without limitation. Debt may also be restructured, which
may indicate that debts owed to a party are modified as to timing,
amounts, collateral, or other terms. For example, a borrower may
restructure debt of a loan to accommodate a change of financial
conditions, or a lender may offer to a borrower the restructuring
of a debt for its own needs or prudence. In certain embodiments, a
smart contract circuit or robotic process automation system may
automatically or manually restructure debt based on a monitored
condition, or create options for restructuring a debt, administrate
the process of negotiating or effecting the restructuring of a
debt, or other actions in connection with restructuring or
modifying terms of a loan or transaction. One of skill in the art,
having the benefit of the disclosure herein and knowledge
ordinarily available about a contemplated system, can readily
determine the purposes and use of this term, whether in the context
of debt or otherwise, in various embodiments and contexts disclosed
herein.
[0280] The term social network data collection, social network
monitoring services, and social network data collection and
monitoring services (and its various forms or derivatives) as
utilized herein may be understood broadly to describe services
relating to the acquisition, organizing, observing, or otherwise
acting upon data or information derived from one or more social
networks. The social network data collection and monitoring
services may be a part of a related system of services or a
standalone set of services. Social network data collection and
monitoring services may be provided by a platform or system,
without limitation. Social network data collection and monitoring
services may be used in a variety of contexts such as lending,
refinancing, negotiation, collection, consolidation, factoring,
brokering, foreclosure, and combinations thereof, without
limitation. Requests of social network data collection and
monitoring, with configuration parameters, may be requested by
other services, automatically initiated or triggered to occur based
on conditions or circumstances that occur. An interface may be
provided to configure, initiate, display or otherwise interact with
social network data collection and monitoring services. Social
networks, as utilized herein, reference any mass platform where
data and communications occur between individuals and/or entities,
where the data and communications are at least partially accessible
to an embodiment system. In certain embodiments, the social network
data includes publicly available (e.g., accessible without any
authorization) information. In certain embodiments, the social
network data includes information that is properly accessible to an
embodiment system, but may include subscription access or other
access to information that is not freely available to the public,
but may be accessible (e.g., consistent with a privacy policy of
the social network with its users). A social network may be
primarily social in nature, but may additionally or alternatively
include professional networks, alumni networks, industry related
networks, academically oriented networks, or the like. In certain
embodiments, a social network may be a crowdsourcing platform, such
as a platform configured to accept queries or requests directed to
users (and/or a subset of users, potentially meeting specified
criteria), where users may be aware that certain communications
will be shared and accessible to requestors, at least a portion of
users of the platform, and/or publicly available. In certain
embodiments, without limitation, social network data collection and
monitoring services may be performed by a smart contract circuit or
a robotic process automation system. One of skill in the art,
having the benefit of the disclosure herein and knowledge
ordinarily available about a contemplated system, can readily
determine the purposes and use of social network data collection
and monitoring services in various embodiments and contexts
disclosed herein.
[0281] The term crowdsource and social network information as
utilized herein may further be understood broadly to describe
information acquired or provided in conjunction with a
crowdsourcing model or transaction, or information acquired or
provided on or in conjunction with a social network. Crowdsource
and social network information may be provided by a platform or
system, without limitation. Crowdsource and social network
information may be acquired, provided or communicated to or from a
group of information suppliers and by which responses to the
request may be collected and processed. Crowdsource and social
network information may provide information, conditions or factors
relating to a loan or agreement. Crowdsource and social network
information may be private or published, or combinations thereof,
without limitation. In certain embodiments, without limitation,
crowdsource and social network information may be acquired,
provided, organized or processed, without limitation, by a smart
contract circuit, wherein the crowdsource and social network
information may be managed by a smart contract circuit that
processes the information to satisfy a set of configured
parameters. One of skill in the art, having the benefit of the
disclosure herein and knowledge ordinarily available about a
contemplated system can readily determine the purposes and use of
this term in various embodiments and contexts disclosed herein.
[0282] The term negotiate (and other forms such as negotiating or
negotiation) as utilized herein may be understood broadly to
describe discussions or communications to bring about or obtain a
compromise, outcome, or agreement between parties or entities.
Negotiation may result in a successful outcome where terms are
agreed between parties, or an unsuccessful outcome where the
parties do not agree to specific terms, or combinations thereof,
without limitation. A negotiation may be successful in one aspect
or for a particular purpose, and unsuccessful in another aspect or
for another purpose. Negotiation can occur in many contexts of
contracts or loans, such as lending, refinancing, collection,
consolidation, factoring, brokering, foreclosure, and combinations
thereof, without limitation. For example, a borrower may negotiate
an interest rate or loan terms with a lender. In another example, a
borrower in default may negotiate an alternative resolution to
avoid foreclosure with a lender. In certain embodiments, a smart
contract circuit or robotic process automation system may negotiate
for one or more of the parties, and process appropriate tasks for
completing or attempting to complete a negotiation of terms. In
some cases negotiation by the smart contract or robotic process
automation system may not complete or be successful. Successful
negotiation may enable automated action or trigger other conditions
or terms to be implemented by the smart contract circuit or robotic
process automation system. One of skill in the art, having the
benefit of the disclosure herein and knowledge ordinarily available
about a contemplated system, can readily determine the purposes and
use of negotiation in various embodiments and contexts disclosed
herein.
[0283] The term negotiate in various forms may more specifically be
utilized herein in verb form (e.g. to negotiate) or in noun forms
(e.g. a negotiation), or other forms to describe a context of
mutual discussion leading to an outcome. For example, a robotic
process automation system may negotiate terms and conditions on
behalf of a party, which would be a use as a verb clause. In
another example, a robotic process automation system may be
negotiating terms and conditions for modification of a loan, or
negotiating a consolidation offer, or other terms. As a noun
clause, a negotiation (e.g. an event) may be performed by a robotic
process automation system. Thus, in some circumstances a smart
contract circuit or robotic process automation system may negotiate
(e.g. as a verb clause) terms and conditions, or the description of
doing so may be considered a negotiation (e.g. as a noun clause).
One of skill in the art, having the benefit of the disclosure
herein and knowledge about negotiating and negotiation, or other
forms of the word negotiate, can readily determine the purposes and
use of this term in various embodiments and contexts disclosed
herein.
[0284] The term negotiate in various forms may also specifically be
utilized to describe an outcome, such as a mutual compromise or
completion of negotiation leading to an outcome. For example, a
loan may, by robotic process automation system or otherwise, be
considered negotiated as a successful outcome that has resulted in
an agreement between parties, where the negotiation has reached
completion. Thus, in some circumstances a smart contract circuit or
robotic process automation system may have negotiated to completion
a set of terms and conditions, or a negotiated loan. One of skill
in the art, having the benefit of the disclosure herein and
knowledge ordinarily available for a contemplated system, can
readily determine the purposes and use of this term as it relates
to a mutually agreed outcome through completion of negotiation in
various embodiments and contexts disclosed herein.
[0285] The term negotiate in various forms may also specifically be
utilized to characterize an event such as a negotiating event, or
an event negotiation, including reaching a set of agreeable terms
between parties. An event requiring mutual agreement or compromise
between parties may be considered a negotiating event, without
limitation. For example, during the procurement of a loan, the
process of reaching a mutually acceptable set of terms and
conditions between parties could be considered a negotiating event.
Thus, in some circumstances a smart contract circuit or robotic
process automation system may accommodate the communications,
actions, or behaviors of the parties for a negotiated event.
[0286] The term collection (and other forms such as collect or
collecting) as utilized herein may be understood broadly to
describe the acquisition of a tangible (e.g. physical item),
intangible (e.g. data, a license, or a right), or monetary (e.g.
payment) item, or other obligation or asset from a source. The term
generally may relate to the entire prospective acquisition of such
an item from related tasks in early stages to related tasks in late
stages or full completion of the acquisition of the item.
Collection may result in a successful outcome where the item is
tendered to a party, or may or an unsuccessful outcome where the
item is not tendered or acquired to a party, or combinations
thereof (e.g., a late or otherwise deficient tender of the item),
without limitation. Collection may occur in many different contexts
of contracts or loans, such as lending, refinancing, consolidation,
factoring, brokering, foreclosure, and data processing (e.g. data
collection), or combinations thereof, without limitation.
Collection may be used in the form of a noun (e.g. data collection
or the collection of an overdue payment where it refers to an event
or characterizes an event), may refer as a noun to an assortment of
items (e.g. a collection of collateral for a loan where it refers
to a number of items in a transaction), or may be used in the form
of a verb (e.g. collecting a payment from the borrower). For
example, a lender may collect an overdue payment from a borrower
through an online payment, or may have a successful collection of
overdue payments acquired through a customer service telephone
call. In certain embodiments, a smart contract circuit or robotic
process automation system may perform collection for one or more of
the parties, and process appropriate tasks for completing or
attempting collection for one or more items (e.g., an overdue
payment). In some cases negotiation by the smart contract or
robotic process automation system may not complete or be
successful, and depending upon such outcomes this may enable
automated action or trigger other conditions or terms. One of skill
in the art, having the benefit of the disclosure herein and
knowledge ordinarily available about a contemplated system, can
readily determine the purposes and use of collection in various
forms, embodiments, and contexts disclosed herein.
[0287] The term collection in various forms may also more
specifically be utilized herein in noun form to describe a context
for an event or thing, such as a collection event, or a collection
payment. For example, a collection event may refer to a
communication to a party or other activity that relates to
acquisition of an item in such an activity, without limitation. A
collection payment, for example, may relate to a payment made by a
borrower that has been acquired through the process of collection,
or through a collection department with a lender. Although not
limited to an overdue, delinquent or defaulted loan, collection may
characterize an event, payment or department, or other noun
associated with a transaction or loan, as being a remedy for
something that has become overdue. Thus, in some circumstances a
smart contract circuit or robotic process automation system may
collect a payment or installment from a borrower, and the activity
of doing so may be considered a collection event, without
limitation.
[0288] The term collection in various forms may also more
specifically be utilized herein as an adjective or other forms to
describe a context relating to litigation, such as the outcome of a
collection litigation (e.g. litigation regarding overdue or default
payments on a loan). For example, the outcome of a collection
litigation may be related to delinquent payments which are owed by
a borrower or other party, and collection efforts relating to those
delinquent payments may be litigated by parties. Thus, in some
circumstances a smart contract circuit or robotic process
automation system may receive, determine or otherwise administrate
the outcome of collection litigation.
[0289] The term collection in various forms may also more
specifically be utilized herein as an adjective or other forms to
describe a context relating to an action of acquisition, such as a
collection action (e.g. actions to induce tendering or acquisition
of overdue or default payments on a loan or other obligation). The
terms collection yield, financial yield of collection, and/or
collection financial yield may be used. The result of such a
collection action may or may not have a financial yield. For
example, a collection action may result in the payment of one or
more outstanding payments on a loan, which may render a financial
yield to another party such as the lender. Thus, in some
circumstances a smart contract circuit or robotic process
automation system may render a financial yield from a collection
action, or otherwise administrate or in some manner assist in a
financial yield of a collection action. In embodiments, a
collection action may include the need for collection
litigation.
[0290] The term collection in various forms (collection ROI, ROI on
collection, ROI on collection activity, collection activity ROI,
and the like) may also more specifically be utilized herein to
describe a context relating to an action of receiving value, such
as a collection action (e.g. actions to induce tendering or
acquisition of overdue or default payments on a loan or other
obligation), wherein there is a return on investment (ROI). The
result of such a collection action may or may not have an ROI,
either with respect to the collection action itself (as an ROI on
the collection action) or as an ROI on the broader loan or
transaction that is the subject of the collection action. For
example, an ROI on a collection action may be prudent or not with
respect to a default loan, without limitation, depending upon
whether the ROI will be provided to a party such as the lender. A
projected ROI on collection may be estimated, or may also be
calculated given real events that transpire. In some circumstances,
a smart contract circuit or robotic process automation system may
render an estimated ROI for a collection action or collection
event, or may calculate an ROI for actual events transpiring in a
collection action or collection event, without limitation. In
embodiments, such a ROI may be a positive or negative figure,
whether estimated or actual.
[0291] The term reputation, measure of reputation, lender
reputation, borrower reputation, entity reputation, and the like
may include general, widely held beliefs, opinions, and/or
perceptions that are generally held about an individual, entity,
collateral, and the like. A measure for reputation may be
determined based on social data including likes/dislikes, review of
entity or products and services provided by the entity, rankings of
the company or product, current and historic market and financial
data include price, forecast, buy/sell recommendations, financial
news regarding entity, competitors, and partners. Reputations may
be cumulative in that a product reputation and the reputation of a
company leader or lead scientist may influence the overall
reputation of the entity. Reputation of an institute associated
with an entity (e.g. a school being attended by a student) may
influence the reputation of the entity. In some circumstances, a
smart contract circuit or robotic process automation system may
collect or initiate collection of data related to the above and
determine a measure or ranking of reputation. A measure or ranking
of an entity's reputation may be used by a smart contract circuit
or robotic process automation system in determining whether to
enter into an agreement with the entity, determination of terms and
conditions of a loan, interest rates, and the like. In certain
embodiments, indicia of a reputation determination may be related
to outcomes of one or more transactions (e.g., a comparison of
"likes" on a particular social media data set to an outcome index,
such as successful payments, successful negotiation outcomes,
ability to liquidate a particular type of collateral, etc.) to
determine the measure or ranking of an entity's reputation. One of
skill in the art, having the benefit of the disclosure herein and
knowledge ordinarily available about a contemplated system, can
readily determine the purposes and use of the reputation, a measure
or ranking of the reputation, and/or utilization of the reputation
in negotiations, determination of terms and conditions,
determination of whether to proceed with a transaction, and other
various embodiments and contexts disclosed herein.
[0292] The term collection in various forms (e.g. collector) may
also more specifically be utilized herein to describe a party or
entity that induces, administrates, or facilitates a collection
action, collection event, or other collection related context. The
measure of reputation of a party involved, such as a collector, or
during the context of a collection, may be estimated or calculated
using objective, subjective, or historical metrics or data. For
example, a collector may be involved in a collection action, and
the reputation of that collector may be used to determine
decisions, actions or conditions. Similarly, a collection may be
also used to describe objective, subjective or historical metrics
or data to measure the reputation of a party involved, such as a
lender, borrower or debtor. In some circumstances, a smart contract
circuit or robotic process automation system may render a
collection or measures, or implement a collector, within the
context of a transaction or loan.
[0293] The term collection and data collection in various forms,
including data collection systems, may also more specifically be
utilized herein to describe a context relating to the acquisition,
organization, or processing of data, or combinations thereof,
without limitation. The result of such a data collection may be
related or wholly unrelated to a collection of items (e.g.,
grouping of the items, either physically or logically), or actions
taken for delinquent payments (e.g., collection of collateral, a
debt, or the like), without limitation. For example, a data
collection may be performed by a data collection system, wherein
data is acquired, organized or processed for decision-making,
monitoring, or other purposes of prospective or actual transaction
or loan. In some circumstances, a smart contract or robotic process
automation system may incorporate data collection or a data
collection system, to perform portions or entire tasks of data
collection, without limitation. One of skill in the art, having the
benefit of the disclosure herein and knowledge ordinarily available
for a contemplated system, can readily determine and distinguish
the purposes and use of collection in the context of data or
information as used herein.
[0294] The terms refinance, refinancing activity(ies), refinancing
interactions, refinancing outcomes, and similar terms, as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure refinance and
refinancing activities include replacing an existing mortgage,
loan, bond, debt transaction, or the like with a new mortgage,
loan, bond, or debt transaction that pays off or ends the previous
financial arrangement. In certain embodiments, any change to terms
and conditions of a loan, and/or any material change to terms and
conditions of a loan, may be considered a refinancing activity. In
certain embodiments, a refinancing activity is considered only
those changes to a loan agreement that result in a different
financial outcome for the loan agreement. Typically, the new loan
should be advantageous to the borrower or issuer, and/or mutually
agreeable (e.g., improving a raw financial outcome of one, and a
security or other outcome for the other). Refinancing may be done
to reduce interest rates, lower regular payments, change the loan
term, change the collateral associated with the loan, consolidate
debt into a single loan, restructure debt, change a type of loan
(e.g. variable rate to fixed rate), pay off a loan that is due, in
response to an improved credit score, to enlarge the loan, and/or
in response to a change in market conditions (e.g. interest rates,
value of collateral, and the like).
[0295] Refinancing activity may include initiating an offer to
refinance, initiating a request to refinance, configuring a
refinancing interest rate, configuring a refinancing payment
schedule, configuring a refinancing balance in a response to the
amount or terms of the refinanced loan, configuring collateral for
a refinancing including changes in collateral used, changes in
terms and conditions for the collateral, a change in the amount of
collateral and the like, managing use of proceeds of a refinancing,
removing or placing a lien on different items of collateral as
appropriate given changes in terms and conditions as part of a
refinancing, verifying title for a new or existing item of
collateral to be used to secure the refinanced loan, managing an
inspection process title for a new or existing item of collateral
to be used to secure the refinanced loan, populating an application
to refinance a loan, negotiating terms and conditions for a
refinanced loan and closing a refinancing. Refinance and
refinancing activities may be disclosed in the context of data
collection and monitoring services that collect a training set of
interactions between entities for a set of loan refinancing
activities. Refinance and refinancing activities may be disclosed
in the context of an artificial intelligence system that is trained
using the collected training set of interactions that includes both
refinancing activities and outcomes. The trained artificial
intelligence may then be used to recommend a refinance activity,
evaluate a refinance activity, make a prediction around an expected
outcome of refinancing activity, and the like. Refinance and
refinancing activities may be disclosed in the context of smart
contract systems which may automate a subset of the interactions
and activities of refinancing. In an example, a smart contract
system may automatically adjust an interest rate for a loan based
on information collected via at least one of an Internet of Things
system, a crowdsourcing system, a set of social network analytic
services and a set of data collection and monitoring services. The
interest rate may be adjusted based on rules, thresholds, model
parameters that determine, or recommend, an interest rate for
refinancing a loan based on interest rates available to the lender
from secondary lenders, risk factors of the borrower (including
predicted risk based on one or more predictive models using
artificial intelligence), marketing factors (such as competing
interest rates offered by other lenders), and the like. Outcomes
and events of a refinancing activity may be recorded in a
distributed ledger. Based on the outcome of a refinance activity, a
smart contract for the refinance loan may be automatically
reconfigured to define the terms and conditions for the new loan
such as a principal amount of debt, a balance of debt, a fixed
interest rate, a variable interest rate, a payment amount, a
payment schedule, a balloon payment schedule, a specification of
collateral, a specification of substitutability of collateral, a
party, a guarantee, a guarantor, a security, a personal guarantee,
a lien, a duration, a covenant, a foreclose condition, a default
condition, and a consequence of default.
[0296] One of skill in the art, having the benefit of the
disclosure herein and knowledge ordinarily available about a
contemplated system can readily determine which aspects of the
present disclosure will benefit from a particular application of a
refinance activity, how to choose or combine refinance activities,
how to implement systems, services, or circuits to automatically
perform of one or more (or all) aspects of a refinance activity,
and the like. Certain considerations for the person of skill in the
art, or embodiments of the present disclosure in choosing an
appropriate training sets of interactions with which to train an
artificial intelligence to take action, recommend or predict the
outcome of certain refinance activities. While specific examples of
refinance and refinancing activities are described herein for
purposes of illustration, any embodiment benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0297] The terms consolidate, consolidation activity(ies), loan
consolidation, debt consolidation, consolidation plan, and similar
terms, as utilized herein should be understood broadly. Without
limitation to any other aspect or description of the present
disclosure consolidate, consolidation activity(ies), loan
consolidation, debt consolidation, or consolidation plan are
related to the use of a single large loan to pay off several
smaller loans, and/or the use of one or more of a set of loans to
pay off at least a portion of one or more of a second set of loans.
In embodiments, loan consolidation may be secured (i.e., backed by
collateral) or unsecured. Loans may be consolidated to obtain a
lower interest rate than one or more of the current loans, to
reduce total monthly loan payments, and/or to bring a debtor into
compliance on the consolidated loans or other debt obligations of
the debtor. Loans that may be classified as candidates for
consolidation may be determined based on a model that processes
attributes of entities involved in the set of loans including
identity of a party, interest rate, payment balance, payment terms,
payment schedule, type of loan, type of collateral, financial
condition of party, payment status, condition of collateral, and
value of collateral. Consolidation activities may include managing
at least one of identification of loans from a set of candidate
loans, preparation of a consolidation offer, preparation of a
consolidation plan, preparation of content communicating a
consolidation offer, scheduling a consolidation offer,
communicating a consolidation offer, negotiating a modification of
a consolidation offer, preparing a consolidation agreement,
executing a consolidation agreement, modifying collateral for a set
of loans, handling an application workflow for consolidation,
managing an inspection, managing an assessment, setting an interest
rate, deferring a payment requirement, setting a payment schedule,
and closing a consolidation agreement. In embodiments, there may be
systems, circuits, and/or services configured to create, configure
(such as using one or more templates or libraries), modify, set, or
otherwise handle (such as in a user interface) various rules,
thresholds, conditional procedures, workflows, model parameters,
and the like to determine, or recommend, a consolidation action or
plan for a lending transaction or a set of loans based on one or
more events, conditions, states, actions, or the like. In
embodiments, a consolidation plan may be based on various factors,
such as the status of payments, interest rates of the set of loans,
prevailing interest rates in a platform marketplace or external
marketplace, the status of the borrowers of a set of loans, the
status of collateral or assets, risk factors of the borrower, the
lender, one or more guarantors, market risk factors and the like.
Consolidation and consolidation activities may be disclosed in the
context of data collection and monitoring services that collect a
training set of interactions between entities for a set of loan
consolidation activities. consolidation and consolidation
activities may be disclosed in the context of an artificial
intelligence system that is trained using the collected training
set of interactions that includes both consolidation activities and
outcomes associated with those activities. The trained artificial
intelligence may then be used to recommend a consolidation
activity, evaluate a consolidation activity, make a prediction
around an expected outcome of consolidation activity, and the like
based models including status of debt, condition of collateral or
assets used to secure or back a set of loans, the state of a
business or business operation (e.g., receivables, payables, or the
like), conditions of parties (such as net worth, wealth, debt,
location, and other conditions), behaviors of parties (such as
behaviors indicating preferences, behaviors indicating debt
preferences), and others. Debt consolidation, loan consolidation
and associated consolidation activities may be disclosed in the
context of smart contract systems which may automate a subset of
the interactions and activities of consolidation. In embodiments,
consolidation may include consolidation with respect to terms and
conditions of sets of loans, selection of appropriate loans,
configuration of payment terms for consolidated loans,
configuration of payoff plans for pre-existing loans,
communications to encourage consolidation, and the like. In
embodiments, the artificial intelligence of a smart contract may
automatically recommend or set rules, thresholds, actions,
parameters and the like (optionally by learning to do so based on a
training set of outcomes over time), resulting in a recommended
consolidation plan, which may specify a series of actions required
to accomplish a recommended or desired outcome of consolidation
(such as within a range of acceptable outcomes), which may be
automated and may involve conditional execution of steps based on
monitored conditions and/or smart contract terms, which may be
created, configured, and/or accounted for by the consolidation
plan. Consolidation plans may be determined and executed based at
least one part on market factors (such as competing interest rates
offered by other lenders, values of collateral, and the like) as
well as regulatory and/or compliance factors. Consolidation plans
may be generated and/or executed for creation of new consolidated
loans, for secondary loans related to consolidated loans, for
modifications of existing loans related to consolidation, for
refinancing terms of a consolidated loan, for foreclosure
situations (e.g., changing from secured loan rates to unsecured
loan rates), for bankruptcy or insolvency situations, for
situations involving market changes (e.g., changes in prevailing
interest rates) and others. consolidation.
[0298] Certain of the activities related to loans, collateral,
entities and the like may apply to a wide variety of loans and may
not apply explicitly to consolidation activities. The
categorization of the activities as consolidation activities may be
based on the context of the loan for which the activities are
taking place. However, one of skill in the art, having the benefit
of the disclosure herein and knowledge ordinarily available about a
contemplated system can readily determine which aspects of the
present disclosure will benefit from a particular application of a
consolidation activity, how to choose or combine consolidation
activities, how to implement selected services, circuits, and/or
systems described herein to perform certain loan consolidation
operations, and the like. While specific examples of consolidation
and consolidation activities are described herein for purposes of
illustration, any embodiment benefitting from the disclosures
herein, and any considerations understood to one of skill in the
art having the benefit of the disclosures herein, are specifically
contemplated within the scope of the present disclosure.
[0299] The terms factoring a loan, factoring a loan transaction,
factors, factoring a loan interaction, factoring assets or sets of
assets used for factoring and similar terms, as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure factoring may be
applied to factoring assets such as invoices, inventory, accounts
receivable, and the like, where the realized value of the item is
in the future. For example, the accounts receivable is worth more
when it has been paid and there is less risk of default. Inventory
and Work in Progress (WIP) may be worth more as final product
rather than components. References to accounts receivable should be
understood to encompass these terms and not be limiting. Factoring
may include a sale of accounts receivable at a discounted rate for
value in the present (often cash). Factoring may also include the
use of accounts receivable as collateral for a short term loan. In
both cases the value of the accounts receivable or invoices may be
discounted for multiple reasons including the future value of
money, a term of the accounts receivable (e.g., 30 day net payment
vs. 90 day net payment), a degree of default risk on the accounts
receivable, a status of receivables, a status of work-in-progress
(WIP), a status of inventory, a status of delivery and/or shipment,
financial condition(s) of parties owing against the accounts
receivable, a status of shipped and/or billed, a status of
payments, a status of the borrower, a status of inventory, a risk
factor of a borrower, a lender, one or more guarantors, market risk
factors, a status of debt (are there other liens present on the
accounts receivable or payment owed on the inventory, a condition
of collateral assets (e.g. the condition of the inventory--is it
current or out of date, are invoices in arrears), a state of a
business or business operation, a condition of a party to the
transaction (such as net worth, wealth, debt, location, and other
conditions), a behavior of a party to the transaction (such as
behaviors indicating preferences, behaviors indicating negotiation
styles, and the like), current interest rates, any current
regulatory and compliance issues associated with the inventory or
accounts receivable (e.g. if inventory is being factored, has the
intended product received appropriate approvals), and there legal
actions against the borrower, and many others, including predicted
risk based on one or more predictive models using artificial
intelligence). A factor is an individual, business, entity, or
groups thereof which agree to provide value in exchange for either
the outright acquisition of the invoices in a sale or the use of
the invoices as collateral for a loan for the value. Factoring a
loan may include the identification of candidates (both lenders and
borrowers) for factoring, a plan for factoring specifying the
proposed receivables (e.g. all, some, only those meeting certain
criteria), and a proposed discount factor, communication of the
plan to potential parties, proffering an offer and receiving an
offer, verification of quality of receivables, conditions regarding
treatment of the receivables for the term of the loan. While
specific examples of factoring and factoring activities are
described herein for purposes of illustration, any embodiment
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0300] The terms mortgage, brokering a mortgage, mortgage
collateral, mortgage loan activities, and/or mortgage related
activities as utilized herein should be understood broadly. Without
limitation to any other aspect or description of the present
disclosure, a mortgage is an interaction where a borrower provides
the title or a lien on the title of an item of value, typically
property, to a lender as security in exchange for money or another
item of value, to be repaid, typically with interest, to the
lender. The exchange includes the condition that, upon repayment of
the loan, the title reverts to the borrower and/or the lien on the
property is removed. The brokering of a mortgage may include the
identification of potential properties, lenders, and other parties
to the loan, and arranging or negotiating the terms of the
mortgage. Certain components or activities may not be considered
mortgage related individually, but may be considered mortgage
related when used in conjunction with a mortgage, act upon a
mortgage, are related to an entity or party to a mortgage, and the
like. For example, brokering may apply to the offering of a variety
of loans including unsecured loans, outright sale of property and
the like. Mortgage activities and mortgage interactions may include
mortgage marketing activity, identification of a set of prospective
borrowers, identification of property to mortgage, identification
of collateral property to mortgage, qualification of borrower,
title search and/or title verification for prospective mortgage
property, property assessment, property inspection, or property
valuation for prospective mortgage property, income verification,
borrower demographic analysis, identification of capital providers,
determination of available interest rates, determination of
available payment terms and conditions, analysis of existing
mortgage(s), comparative analysis of existing and new mortgage
terms, completion of application workflow (e.g. keep the
application moving forward by initiating next steps in the process
as appropriate), population of fields of application, preparation
of mortgage agreement, completion of schedule for mortgage
agreement, negotiation of mortgage terms and conditions with
capital provider, negotiation of mortgage terms and conditions with
borrower, transfer of title, placement of lien on mortgaged
property and closing of mortgage agreement, and similar terms, as
utilized herein should be understood broadly. While specific
examples of mortgages and mortgage brokering are described herein
for purposes of illustration, any embodiment benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0301] The terms debt management, debt transactions, debt actions,
debt terms and conditions, syndicating debt, consolidating debt,
and/or debt portfolios, as utilized herein should be understood
broadly. Without limitation to any other aspect or description of
the present disclosure a debt includes something of monetary value
that is owed to another. A loan typically results in the borrower
holding the debt (e.g. the money that must be paid back according
to the terms of the loan, which may include interest).
Consolidation of debt includes the use of a new, single loan to pay
back multiple loans (or various other configurations of debt
structuring as described herein, and as understood to one of skill
in the art). Often the new loan may have better terms or lower
interest rates. Debt portfolios include a number of pieces or
groups of debt, often having different characteristics including
term, risk, and the like. Debt portfolio management may involve
decisions regarding the quantity and quality of the debt being held
and how best to balance the various debts to achieve a desired
risk/reward position based on: investment policy, return on risk
determinations for individual pieces of debt, or groups of debt.
Debt may be syndicated where multiple lenders fund a single loan
(or set of loans) to a borrower. Debt portfolios may be sold to a
third party (e.g., at a discounted rate). Debt compliance includes
the various measures taken to ensure that debt is repaid.
Demonstrating compliance may include documentation of the actions
taken to repay the debt.
[0302] Transactions related to a debt (debt transactions) and
actions related to the debt (debt actions) may include offering a
debt transaction, underwriting a debt transaction, setting an
interest rate, deferring a payment requirement, modifying an
interest rate, validating title, managing inspection, recording a
change in title, assessing the value of an asset, calling a loan,
closing a transaction, setting terms and conditions for a
transaction, providing notices required to be provided, foreclosing
on a set of assets, modifying terms and conditions, setting a
rating for an entity, syndicating debt, and/or consolidating debt.
Debt terms and conditions may include a balance of debt, a
principal amount of debt, a fixed interest rate, a variable
interest rate, a payment amount, a payment schedule, a balloon
payment schedule, a specification of assets that back the bond, a
specification of substitutability of assets, a party, an issuer, a
purchaser, a guarantee, a guarantor, a security, a personal
guarantee, a lien, a duration, a covenant, a foreclose condition, a
default condition, and a consequence of default. While specific
examples of debt management and debt management activities are
described herein for purposes of illustration, any embodiment
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0303] The terms condition, condition classification,
classification models, condition management, and similar terms, as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure
condition, condition classification, classification models,
condition management, include classifying or determining a
condition of an asset, issuer, borrower, loan, debt, bond,
regulatory status, term or condition for a bond, loan or debt
transaction that is specified and monitored in the contract, and
the like. Based on a classified condition of an asset, condition
management may include actions to maintain or improve a condition
of the asset or the use of that asset as collateral. Based on a
classified condition of an issuer, borrower, party regulatory
status, and the like, condition management may include actions to
alter the terms or conditions of a loan or bond. Condition
classification may include various rules, thresholds, conditional
procedures, workflows, model parameters, and the like to classify a
condition of an asset, issuer, borrower, loan, debt, bond,
regulatory status, term or condition for a bond, loan or debt
transaction, and the like based on data from Internet of Things
devices, data from a set of environmental condition sensors, data
from a set of social network analytic services and a set of
algorithms for querying network domains, social media data,
crowdsourced data, and the like. Condition classification may
include grouping or labeling entities, or clustering the entities,
as similarly positioned with regard to some aspect of the
classified condition (e.g., a risk, quality, ROI, likelihood for
recovery, likelihood to default, or some other aspect of the
related debt).
[0304] Various classification models are disclosed where the
classification and classification model may be tied to a geographic
location relating to the collateral, the issuer, the borrower, the
distribution of the funds or other geographic locations.
Classification and classification models are disclosed where
artificial intelligence is used to improve a classification model
(e.g. refine a model by making refinements using artificial
intelligence data). Thus artificial intelligence may be considered,
in some instances, as a part of a classification model and vice
versa. Classification and classification models are disclosed where
social media data, crowdsourced data, or IoT data is used as input
for refining a model, or as input to a classification model.
Examples of IoT data may include images, sensor data, location
data, and the like. Examples of social media data or crowdsourced
data may include behavior of parties to the loan, financial
condition of parties, adherence to a parties to a term or condition
of the loan, or bond, or the like. Parties to the loan may include
issuers of a bond, related entities, lender, borrower, 3rd parties
with an interest in the debt. Condition management may be discussed
in connection with smart contract services which may include
condition classification, data collection and monitoring, and bond,
loan and debt transaction management. Data collection and
monitoring services are also discussed in conjunction with
classification and classification models which are related when
classifying an issuer of a bond issuer, an asset or collateral
asset related to the bond, collateral assets backing the bond,
parties to the bond, and sets of the same. In some embodiments a
classification model may be included when discussing bond types.
Specific steps, factors or refinements may be considered a part of
a classification model. In various embodiments, the classification
model may change both in an embodiment, or in the same embodiment
which is tied to a specific jurisdiction. Different classification
models may use different data sets (e.g. based on the issuer, the
borrower, the collateral assets, the bond type, the loan type, and
the like) and multiple classification models may be used in a
single classification. For example, one type of bond, such as a
municipal bond, may allow a classification model that is based on
bond data from municipalities of similar size and economic
prosperity, whereas another classification model may emphasize data
from IoT sensors associated with a collateral asset. Accordingly,
different classification models will offer benefits or risks over
other classification models, depending upon the embodiment and the
specifics of the bond, loan or debt transaction. A classification
model includes an approach or concept for classification.
Conditions classified for a bond, loan, or debt transaction may
include a principal amount of debt, a balance of debt, a fixed
interest rate, a variable interest rate, a payment amount, a
payment schedule, a balloon payment schedule, a specification of
assets that back the bond, loan or debt transaction, a
specification of substitutability of assets, a party, an issuer, a
purchaser, a guarantee, a guarantor, a security, a personal
guarantee, a lien, a duration, a covenant, a foreclose condition, a
default condition, and/or a consequence of default. Conditions
classified may include type of bond issuer such as a municipality,
a corporation, a contractor, a government entity, a
non-governmental entity, and a non-profit entity. Entities may
include a set of issuers, a set of bonds, a set of parties, and/or
a set of assets. Conditions classified may include an entity
condition such as net worth, wealth, debt, location, and other
conditions), behaviors of parties (such as behaviors indicating
preferences, behaviors indicating debt preferences), and the like.
Conditions classified may include an asset or type of collateral
such as a municipal asset, a vehicle, a ship, a plane, a building,
a home, real estate property, undeveloped land, a farm, a crop, a
municipal facility, a warehouse, a set of inventory, a commodity, a
security, a currency, a token of value, a ticket, a cryptocurrency,
a consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, an item of equipment, a tool, an
item of machinery, and an item of personal property. Conditions
classified may include a bond type where bond type may include a
municipal bond, a government bond, a treasury bond, an asset-backed
bond, and a corporate bond. Conditions classified may include a
default condition, a foreclosure condition, a condition indicating
violation of a covenant, a financial risk condition, a behavioral
risk condition, a policy risk condition, a financial health
condition, a physical defect condition, a physical health
condition, an entity risk condition and an entity health condition.
Conditions classified may include an environment where environment
may include an environment selected from among a municipal
environment, a corporate environment, a securities trading
environment, a real property environment, a commercial facility, a
warehousing facility, a transportation environment, a manufacturing
environment, a storage environment, a home, and a vehicle. Actions
based on the condition of an asset, issuer, borrower, loan, debt,
bond, regulatory status and the like, may include managing,
reporting on, syndicating, consolidating, or otherwise handling a
set of bonds (such as municipal bonds, corporate bonds, performance
bonds, and others), a set of loans (subsidized and unsubsidized,
debt transactions and the like, monitoring, classifying,
predicting, or otherwise handling the reliability, quality, status,
health condition, financial condition, physical condition or other
information about a guarantee, a guarantor, a set of collateral
supporting a guarantee, a set of assets backing a guarantee, or the
like. Bond transaction activities in response to a condition of the
bond may include offering a debt transaction, underwriting a debt
transaction, setting an interest rate, deferring a payment
requirement, modifying an interest rate, validating title, managing
inspection, recording a change in title, assessing the value of an
asset, calling a loan, closing a transaction, setting terms and
conditions for a transaction, providing notices required to be
provided, foreclosing on a set of assets, modifying terms and
conditions, setting a rating for an entity, syndicating debt,
and/or consolidating debt.
[0305] One of skill in the art, having the benefit of the
disclosure herein and knowledge ordinarily available about a
contemplated system, can readily determine which aspects of the
present disclosure will benefit a particular application for a
classification model, how to choose or combine classification
models to arrive at a condition, and/or calculate a value of
collateral given the required data. Certain considerations for the
person of skill in the art, or embodiments of the present
disclosure in choosing an appropriate condition to manage, include,
without limitation: the legality of the condition given the
jurisdiction of the transaction, the data available for a given
collateral, the anticipated transaction type (loan, bond or debt),
the specific type of collateral, the ratio of the loan to value,
the ratio of the collateral to the loan, the gross transaction/loan
amount, the credit scores of the borrower and the lender, and other
considerations. While specific examples of conditions, condition
classification, classification models, and condition management are
described herein for purposes of illustration, any embodiment
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0306] The terms classify, classifying, classification,
categorization, categorizing, categorize (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure,
classifying a condition or item may include actions to sort the
condition or item into a group or category based on some aspect,
attribute, or characteristic of the condition or item where the
condition or item is common or similar for all the items placed in
that classification, despite divergent classifications or
categories based on other aspects or conditions at the time.
Classification may include recognition of one or more parameters,
features, characteristics, or phenomena associated with a condition
or parameter of an item, entity, person, process, item, financial
construct, or the like. Conditions classified by a condition
classifying system may include a default condition, a foreclosure
condition, a condition indicating violation of a covenant, a
financial risk condition, a behavioral risk condition, a
contractual performance condition, a policy risk condition, a
financial health condition, a physical defect condition, a physical
health condition, an entity risk condition, and/or an entity health
condition. A classification model may automatically classify or
categorize items, entities, process, items, financial constructs or
the like based on data received from a variety of sources. The
classification model may classify items based on a single attribute
or a combination of attributes, and/or may utilize data regarding
the items to be classified and a model. The classification model
may classify individual items, entities, financial constructs or
groups of the same. A bond may be classified based on the type of
bond ((e.g. municipal bonds, corporate bonds, performance bonds,
and the like), rate of return, bond rating (3rd party indicator of
bond quality with respect to bond issuer's financial strength,
and/or ability to bap bond's principal and interest, and the like.
Lenders or bond issuers may be classified based on the type of
lender or issuer, permitted attributes (e.g. based on income,
wealth, location (domestic or foreign), various risk factors,
status of issuers, and the like. Borrowers may be classified based
on permitted attributes (e.g. income, wealth, total assets,
location, credit history), risk factors, current status (e.g.
employed, a student), behaviors of parties (such as behaviors
indicating preferences, reliability, and the like), and the like. A
condition classifying system may classify a student recipient of a
loan based on progress of the student toward a degree, the
student's grades or standing in their classes, students' status at
the school (matriculated, on probation and the like), the
participation of a student in a non-profit activity, a deferment
status of the student, and the participation of the student in a
public interest activity. Conditions classified by a condition
classifying system may include a state of a set of collateral for a
loan or a state of an entity relevant to a guarantee for a loan.
Conditions classified by a condition classifying system may include
a medical condition of a borrower, guarantor, subsidizer or the
like. Conditions classified by a condition classifying system may
include compliance with at least one of a law, a regulation, or a
policy related to a lending transaction or lending institute.
Conditions classified by a condition classifying system may include
a condition of an issuer for a bond, a condition of a bond, a
rating of a loan-related entity, and the like. Conditions
classified by a condition classifying system may include an
identify of a machine, a component, or an operational mode.
Conditions classified by a condition classifying system may include
a state or context (such as a state of a machine, a process, a
workflow, a marketplace, a storage system, a network, a data
collector, or the like). A condition classifying system may
classify a process involving a state or context (e.g., a data
storage process, a network coding process, a network selection
process, a data marketplace process, a power generation process, a
manufacturing process, a refining process, a digging process, a
boring process, and/or other process described herein. A condition
classifying system may classify a set of loan refinancing actions
based on a predicted outcome of the set of loan refinancing
actions. A condition classifying system may classify a set of loans
as candidates for consolidation based on attributes such as
identity of a party, an interest rate, a payment balance, payment
terms, payment schedule, a type of loan, a type of collateral, a
financial condition of party, a payment status, a condition of
collateral, a value of collateral, and the like. A condition
classifying system may classify the entities involved in a set of
factoring loans, bond issuance activities, mortgage loans, and the
like. A condition classifying system may classify a set of entities
based on projected outcomes from various loan management
activities. A condition classifying system may classify a condition
of a set of issuers based on information from Internet of Things
data collection and monitoring services, a set of parameters
associated with an issuer, a set of social network monitoring and
analytic services, and the like. A condition classifying system may
classify a set of loan collection actions, loan consolidation
actions, loan negotiation actions, loan refinancing actions and the
like based on a set of projected outcomes for those activities and
entities.
[0307] The term subsidized loan, subsidizing a loan, (and similar
terms) as utilized herein should be understood broadly. Without
limitation to any other aspect or description of the present
disclosure, a subsidized loan is the loan of money or an item of
value wherein payment of interest on the value of the loan may be
deferred, postponed or delayed, with or without accrual, such as
while the borrower is in school, is unemployed, is ill, and the
like. In embodiments, a loan may be subsidized when the payment of
interest on a portion or subset of the loan is borne or guaranteed
by someone other than the borrower. Examples of subsidized loans
may include a municipal subsidized loan, a government subsidized
loan, a student loan, an asset-backed subsidized loan, and a
corporate subsidized loan. An example of a subsidized student loan
may include student loans which may be subsidized by the government
and on which interest may be deferred or not accrue based on
progress of the student toward a degree, the participation of a
student in a non-profit activity, a deferment status of the
student, and the participation of the student in a public interest
activity. An example of a government subsidized housing loan may
include governmental subsidies which may exempt the borrower from
paying closing costs, first mortgage payment and the like.
Conditions for such subsidized loans may include location of the
property (rural or urban), income of the borrower, military status
of the borrower, ability of the purchased home to meet health and
safety standards, a limit on the profits you can earn on the sale
of your home, and the like. Certain usages of the word loan may not
apply to a subsidized loan but rather to a regular loan. One of
skill in the art, having the benefit of the disclosure herein and
knowledge about a contemplated system ordinarily available to that
person, can readily determine which aspects of the present
disclosure will benefit from consideration of a subsidized loan
(e.g., in determining the value of the loan, negotiations related
to the loan, terms and conditions related to the loan, etc.)
wherein the borrower may be relieved of some of the loan
obligations common for non-subsidized loans, where the subsidy may
include forgiveness, delay or deferment of interest on a loan, or
the payment of the interest by a third party. The subsidy may
include the payment of closing costs including points, first
payment and the like by a person or entity other than the borrower,
and/or how to combine processes and systems from the present
disclosure to enhance or benefit from title validation.
[0308] The term subsidized loan management (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure,
subsidized loan management may include a plurality of activities
and solutions for managing or responding to one or more events
related to a subsidized loan wherein such events may include
requests for a subsidized loan, offering a subsidized loan,
accepting a subsidized loan, providing underwriting information for
a subsidized loan, providing a credit report on a borrower seeking
a subsidized loan, deferring a required payment as part of the loan
subsidy, setting an interest rate for a subsidized loan where a
lower interest rate may be part of the subsidy, deferring a payment
requirement as part of the loan subsidy, identifying collateral for
a loan, validating title for collateral or security for a loan,
recording a change in title of property, assessing the value of
collateral or security for a loan, inspecting property that is
involved in a loan, identifying a change in condition of an entity
relevant to a loan, a change in value of an entity that is relevant
to a loan, a change in job status of a borrower, a change in
financial rating of a lender, a change in financial value of an
item offered as a security, providing insurance for a loan,
providing evidence of insurance for property related to a loan,
providing evidence of eligibility for a loan, identifying security
for a loan, underwriting a loan, making a payment on a loan,
defaulting on a loan, calling a loan, closing a loan, setting terms
and conditions for a loan, foreclosing on property subject to a
loan, modifying terms and conditions for a loan, for setting terms
and conditions for a loan (such as a principal amount of debt, a
balance of debt, a fixed interest rate, a variable interest rate, a
payment amount, a payment schedule, a balloon payment schedule, a
specification of collateral, a specification of substitutability of
collateral, a party, a guarantee, a guarantor, a security, a
personal guarantee, a lien, a duration, a covenant, a foreclose
condition, a default condition, and a consequence of default), or
managing loan-related activities (such as, without limitation,
finding parties interested in participating in a loan transaction,
handling an application for a loan, underwriting a loan, forming a
legal contract for a loan, monitoring performance of a loan, making
payments on a loan, restructuring or amending a loan, settling a
loan, monitoring collateral for a loan, forming a syndicate for a
loan, foreclosing on a loan, collecting on a loan, consolidating a
set of loans, analyzing performance of a loan, handling a default
of a loan, transferring title of assets or collateral, and closing
a loan transaction), and the like. In embodiments, a system for
handling a subsidized loan may include classifying a set of
parameters of a set of subsidized loans on the basis of data
relating to those parameters obtained from an Internet of Things
data collection and monitoring service. Classifying the set of
parameters of the set of subsidized loans may also be on the bases
of data obtained from one or more configurable data collection and
monitoring services that leverage social network analytic services,
crowd sourcing services, and the like for obtaining parameter data
(e.g., determination that a person or entity is qualified for the
subsidized loan, determining a social value of providing the
subsidized loan or removing a subsidization from a loan,
determining that a subsidizing entity is legitimate, determining
appropriate subsidization terms based on characteristics of the
buyer and/or subsidizer, etc.).
[0309] The term foreclose, foreclosure, foreclose or foreclosure
condition, default foreclosure collateral, default collateral, (and
similar terms) as utilized herein should be understood broadly.
Without limitation to any other aspect or description of the
present disclosure, foreclose condition, default and the like
describe the failure of a borrower to meet the terms of a loan.
Without limitation to any other aspect or description of the
present disclosure foreclose and foreclosure include processes by
which a lender attempts to recover, from a borrower in a foreclose
or default condition, the balance of a loan or take away in lieu,
the right of a borrower to redeem a mortgage held in security for
the loan. Failure to meet the terms of the loan may include failure
to make specified payments, failure to adhere to a payment
schedule, failure to make a balloon payment, failure to
appropriately secure the collateral, failure to sustain collateral
in a specified condition (e.g. in good repair), acquisition of a
second loan, and the like. Foreclosure may include a notification
to the borrower, the public, jurisdictional authorities of the
forced sale of an item collateral such as through a foreclosure
auction. Upon foreclosure, an item of collateral may be placed on a
public auction site (such as eBay, N or an auction site appropriate
for a particular type of property. The minimum opening bid for the
item of collateral may be set by the lender and may cover the
balance of the loan, interest on the loan, fees associated with the
foreclosure and the like. Attempts to recover the balance of the
loan may include the transfer of the deed for an item of collateral
in lieu of foreclosure (e.g. a real-estate mortgage where the
borrower holds the deed for a property which acts as collateral for
the mortgage loan). Foreclosure may include taking possession of or
repossessing the collateral (e.g. a car, a sports vehicle such as a
boat, ATV, ski-mobile, jewelry). Foreclosure may include securing
an item of collateral associated with the loan (such as by locking
a connected device, such as a smart lock, smart container, or the
like that contains or secures collateral). Foreclosure may include
arranging for the shipping of an item of collateral by a carrier,
freight forwarder of the like. Foreclosure may include arranging
for the transport of an item of collateral by a drone, a robot, or
the like for transporting collateral. In embodiments, a loan may
allow for the substitution of collateral or the shifting of the
lien from an item of collateral initially used to secure the loan
to a substitute collateral where the substitute collateral is of
higher value (to the lender) than the initial collateral or is an
item in which the borrower has a greater equity. The result of the
substitution of collateral is that when the loan goes into
foreclosure, it is the substitute collateral that may be the
subject of a forced sale or seizure. Certain usages of the word
default may not apply to such as to foreclose but rather to a
regular or default condition of an item. One of skill in the art,
having the benefit of the disclosure herein and knowledge about a
contemplated system ordinarily available to that person, can
readily determine which aspects of the present disclosure will
benefit from foreclosure, and/or how to combine processes and
systems from the present disclosure to enhance or benefit from
foreclosure. Certain considerations for the person of skill in the
art, in determining whether the term foreclosure, foreclose
condition, default and the like is referring to failure of a
borrower to meet the terms of a loan and the related attempts by
the lender to recover the balance of the loan or obtain ownership
of the collateral.
[0310] The terms validation of tile, title validation, validating
title, and similar terms, as utilized herein should be understood
broadly. Without limitation to any other aspect or description of
the present disclosure validation of title and title validation
include any efforts to verify or confirm the ownership or interest
by an individual or entity in an item of property such as a
vehicle, a ship, a plane, a building, a home, real estate property,
undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a beverage, a precious metal, an item of jewelry, a
gemstone, an item of intellectual property, an intellectual
property right, a contractual right, an antique, a fixture, an item
of furniture, an item of equipment, a tool, an item of machinery,
and an item of personal property. Efforts to verify ownership may
include reference to bills of sale, government documentation of
transfer of ownership, a legal will transferring ownership,
documentation of retirement of liens on the item of property,
verification of assignment of Intellectual Property to the proposed
borrower in the appropriate jurisdiction, and the like. For
real-estate property validation may include a review of deeds and
records at a courthouse of a country, a state, a county or a
district in which a building, a home, real estate property,
undeveloped land, a farm, a crop, a municipal facility, a vehicle,
a ship, a plane, or a warehouse is located or registered. Certain
usages of the word validation may not apply to validation of a
title or title validation but rather to confirmation that a process
is operating correctly, that an individual has been correctly
identified using biometric data, that intellectual property rights
are in effect, that data is correct and meaningful, and the like.
One of skill in the art, having the benefit of the disclosure
herein and knowledge about a contemplated system ordinarily
available to that person, can readily determine which aspects of
the present disclosure will benefit from title validation, and/or
how to combine processes and systems from the present disclosure to
enhance or benefit from title validation. Certain considerations
for the person of skill in the art, in determining whether the term
validation is referring to title validation, are specifically
contemplated within the scope of the present disclosure.
[0311] Without limitation to any other aspect or description of the
present disclosure, validation includes any validating system
including, without limitation, validating title for collateral or
security for a loan, validating conditions of collateral for
security or a loan, validating conditions of a guarantee for a
loan, and the like. For instance, a validation service may provide
lenders a mechanism to deliver loans with more certainty, such as
through validating loan or security information components (e.g.,
income, employment, title, conditions for a loan, conditions of
collateral, and conditions of an asset). In a non-limiting example,
a validation service circuit may be structured to validate a
plurality of loan information components with respect to a
financial entity configured to determine a loan condition for an
asset. Certain components may not be considered a validating system
individually, but may be considered validating in an aggregated
system--for example, an Internet of Things component may not be
considered a validating component on its own, however an Internet
of Things component utilized for asset data collection and
monitoring may be considered a validating component when applied to
validating a reliability parameter of a personal guarantee for a
load when the Internet of Things component is associated with a
collateralized asset. In certain embodiments, otherwise similar
looking systems may be differentiated in determining whether such
systems are for validation. For example, a blockchain-based ledger
may be used to validate identities in one instance and to maintain
confidential information in another instance. Accordingly, the
benefits of the present disclosure may be applied in a wide variety
of systems, and any such systems may be considered a system for
validation herein, while in certain embodiments a given system may
not be considered a validating system herein. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is a
validating system and/or whether aspects of the present disclosure
can benefit or enhance the contemplated system include, without
limitation: a lending platform having a social network monitoring
system for validating the reliability of a guarantee for a loan; a
lending platform having an Internet of Things data collection and
monitoring system for validating reliability of a guarantee for a
loan; a lending platform having a crowdsourcing and automated
classification system for validating conditions of an issuer for a
bond; a crowdsourcing system for validating quality, title, or
other conditions of collateral for a loan; a biometric identify
validation application such as utilizing DNA or fingerprints; IoT
devices utilized to collectively validate location and identity of
a fixed asset that is tagged by a virtual asset tag; validation
systems utilizing voting or consensus protocols; artificial
intelligence systems trained to recognize and validate events;
validating information such as title records, video footage,
photographs, or witnessed statements; validation representations
related to behavior, such as to validate occurrence of conditions
of compliance, to validate occurrence of conditions of default, to
deter improper behavior or misrepresentations, to reduce
uncertainty, or to reduce asymmetries of information; and the
like.
[0312] The term underwriting (and similar terms) as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, underwriting
includes any underwriting, including, without limitation, relating
to underwriters, providing underwriting information for a loan,
underwriting a debt transaction, underwriting a bond transaction,
underwriting a subsidized loan transaction, underwriting a
securities transaction, and the like. Underwriting services may be
provided by financial entities, such as banks, insurance or
investment houses, and the like, whereby the financial entity
guarantees payment in case of a determination of a loss condition
(e.g., damage or financial loss) and accept the financial risk for
liability arising from the guarantee. For instance, a bank may
underwrite a loan through a mechanism to perform a credit analysis
that may lead to a determination of a loan to be granted, such as
through analysis of personal information components related to an
individual borrower requesting a consumer loan (e.g., employment
history, salary and financial statements publicly available
information such as the borrower's credit history), analysis of
business financial information components from a company requesting
a commercial load (e.g., tangible net worth, ratio of debt to worth
(leverage), and available liquidity (current ratio)), and the like.
In a non-limiting example, an underwriting services circuit may be
structured to underwrite a financial transaction including a
plurality of financial information components with respect to a
financial entity configured to determine a financial condition for
an asset. In certain embodiments, underwriting components may be
considered underwriting for some purposes but not for other
purposes--for example, an artificial intelligence system to collect
and analyze transaction data may be utilized in conjunction with a
smart contract platform to monitor loan transactions, but
alternately used to collect and analyze underwriting data, such as
utilizing a model trained by human expert underwriters.
Accordingly, the benefits of the present disclosure may be applied
in a wide variety of systems, and any such systems may be
considered underwriting herein, while in certain embodiments a
given system may not be considered underwriting herein. One of
skill in the art, having the benefit of the disclosure herein and
knowledge about a contemplated system ordinarily available to that
person, can readily determine which aspects of the present
disclosure will benefit a particular system, and/or how to combine
processes and systems from the present disclosure to enhance
operations of the contemplated system. Certain considerations for
the person of skill in the art, in determining whether a
contemplated system is underwriting and/or whether aspects of the
present disclosure can benefit or enhance the contemplated system
include, without limitation: a lending platform having an
underwriting system for a loan with a set of data-integrated
microservices such as including data collection and monitoring
services, blockchain services, artificial intelligence services,
and smart contract services for underwriting lending entities and
transactions; underwriting processes, operations, and services;
underwriting data, such as data relating to identities of
prospective and actual parties involved insurance and other
transactions, actuarial data, data relating to probability of
occurrence and/or extent of risk associated with activities, data
relating to observed activities and other data used to underwrite
or estimate risk; an underwriting application, such as, without
limitation, for underwriting any insurance offering, any loan, or
any other transaction, including any application for detecting,
characterizing or predicting the likelihood and/or scope of a risk,
an underwriting or inspection flow about an entity serving a
lending solution, an analytics solution, or an asset management
solution; underwriting of insurance policies, loans, warranties, or
guarantees; a blockchain and smart contract platform for
aggregating identity and behavior information for insurance
underwriting, such as with an optional distributed ledger to record
a set of events, transactions, activities, identities, facts, and
other information associated with an underwriting process; a
crowdsourcing platform such as for underwriting of various types of
loans, and guarantees; an underwriting system for a loan with a set
of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions; an underwriting solution to create,
configure, modify, set or otherwise handle various rules,
thresholds, conditional procedures, workflows, or model parameters;
an underwriting action or plan for management a set of loans of a
given type or types based on one or more events, conditions,
states, actions, secondary loans or transactions to back loans, for
collection, consolidation, foreclosure, situations of bankruptcy of
insolvency, modifications of existing loans, situations involving
market changes, foreclosure activities; adaptive intelligent
systems including artificial intelligent models trained on a
training set of underwriting activities by experts and/or on
outcomes of underwriting actions to generate a set of predictions,
classifications, control instructions, plans, models; underwriting
system for a loan with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for underwriting lending entities and transactions; and
the like.
[0313] The term insuring (and similar terms) as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, insuring includes
any insuring, including, without limitation, providing insurance
for a loan, providing evidence of insurance for an asset related to
a loan, a first entity accepting a risk or liability for another
entity, and the like. Insuring, or insurance, may be a mechanism
through which a holder of the insurance is provided protection from
a financial loss, such as in a form of risk management against the
risk of a contingent or uncertain loss. The insuring mechanism may
provide for an insurance, determine the need for an insurance,
determine evidence of insurance, and the like, such as related to
an asset, transaction for an asset, loan for an asset, security,
and the like. An entity which provides insurance may be known as an
insurer, insurance company, insurance carrier, underwriter, and the
like. For instance, a mechanism for insuring may provide a
financial entity with a mechanism to determine evidence of
insurance for an asset related to a loan. In a non-limiting
example, an insurance service circuit may be structured to
determine an evidence condition of insurance for an asset based on
a plurality of insurance information components with respect to a
financial entity configured to determine a loan condition for an
asset. In certain embodiments, components may be considered
insuring for some purposes but not for other purposes--for example
a blockchain and smart contract platform may be utilized to manage
aspects of a loan transaction such as for identity and
confidentiality, but may alternately be utilized to aggregate
identity and behavior information for insurance underwriting.
Accordingly, the benefits of the present disclosure may be applied
in a wide variety of systems, and any such systems may be
considered insuring herein, while in certain embodiments a given
system may not be considered insuring herein. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is
insuring and/or whether aspects of the present disclosure can
benefit or enhance the contemplated system include, without
limitation: insurance facilities such as branches, offices, storage
facilities, data centers, underwriting operations and others;
insurance claims, such as for business interruption insurance,
product liability insurance, insurance on goods, facilities, or
equipment, flood insurance, insurance for contract-related risks,
and many others, as well as claims data relating to product
liability, general liability, workers compensation, injury and
other liability claims and claims data relating to contracts, such
as supply contract performance claims, product delivery
requirements, contract claims, claims for damages, claims to redeem
points or rewards, claims of access rights, warranty claims,
indemnification claims, energy production requirements, delivery
requirements, timing requirements, milestones, key performance
indicators and others; insurance-related lending; an insurance
service, an insurance brokerage service, a life insurance service,
a health insurance service, a retirement insurance service, a
property insurance service, a casualty insurance service, a finance
and insurance service, a reinsurance service; a blockchain and
smart contract platform for aggregating identity and behavior
information for insurance underwriting; identities of applicants
for insurance, identities of parties that may be willing to offer
insurance, information regarding risks that may be insured (of any
type, without limitation, such as property, life, travel,
infringement, health, home, commercial liability, product
liability, auto, fire, flood, casualty, retirement, unemployment;
distributed ledger may be utilized to facilitate offering and
underwriting of microinsurance, such as for defined risks related
to defined activities for defined time periods that are narrower
than for typical insurance policies; providing insurance for a
loan, providing evidence of insurance for property related to a
loan; and the like.
[0314] The term aggregation (and similar terms) as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, an aggregation or
to aggregate includes any aggregation including, without
limitation, aggregating items together, such as aggregating or
linking similar items together (e.g., collateral to provide
collateral for a set of loans, collateral items for a set of loans
is aggregated in real time based on a similarity in status of the
set of items, and the like), collecting data together (e.g., for
storage, for communication, for analysis, as training data for a
model, and the like), summarizing aggregated items or data into a
simpler description, or any other method for creating a whole
formed by combining several (e.g., disparate) elements. Further, an
aggregator may be any system or platform for aggregating, such as
described. Certain components may not be considered aggregation
individually but may be considered aggregation in an aggregated
system--for example a collection of loans may not be considered an
aggregation of loans of itself but may be an aggregation if
collected as such. In a non-limiting example, an aggregation
circuit may be structured to provide lenders a mechanism to
aggregate loans together from a plurality of loans, such as based
on a loan attribute, parameter, term or condition, financial
entity, and the like, to become an aggregation of loans. In certain
embodiments, an aggregation may be considered an aggregation for
some purposes but not for other purposes--for example for example,
an aggregation of asset collateral conditions may be collected for
the purpose of aggregating loans together in one instance and for
the purpose of determining a default action in another instance.
Additionally, in certain embodiments, otherwise similar looking
systems may be differentiated in determining whether such systems
are aggregators, and/or which type of aggregating systems. For
example, a first and second aggregator may both aggregate financial
entity data, where the first aggregator aggregates for the sake of
building a training set for an analysis model circuit and where the
second aggregator aggregates financial entity data for storage in a
blockchain-based distributed ledger. Accordingly, the benefits of
the present disclosure may be applied in a wide variety of systems,
and any such systems may be considered as aggregation herein, while
in certain embodiments a given system may not be considered
aggregation herein. One of skill in the art, having the benefit of
the disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure will benefit a particular system,
and/or how to combine processes and systems from the present
disclosure to enhance operations of the contemplated system.
Certain considerations for the person of skill in the art, in
determining whether a contemplated system is aggregation and/or
whether aspects of the present disclosure can benefit or enhance
the contemplated system include, without limitation forward market
demand aggregation (e.g., blockchain and smart contract platform
for forward market demand aggregation, interest expressed or
committed in a demand aggregation interface, blockchain used to
aggregate future demand in a forward market with respect to a
variety of products and services, process a set of potential
configurations having different parameters for a subset of
configurations that are consistent with each other and the subset
of configurations used to aggregate committed future demand for the
offering that satisfies a sufficiently large subset at a profitable
price, and the like); correlated aggregated data (including trend
information) on worker ages, credentials, experience (including by
process type) with data on the processes in which those workers are
involved; demand for accommodations aggregated in advance and
conveniently fulfilled by automatic recognition of conditions that
satisfy pre-configured commitments represented on a blockchain
(e.g., distributed ledger); transportation offerings aggregated and
fulfilled (e.g., with a wide range of pre-defined contingencies);
aggregation of goods and services on the blockchain (e.g., a
distributed ledger used for demand planning); with respect to a
demand aggregation interface (e.g., presented to one or more
consumers); aggregation of multiple submissions; aggregating
identity and behavior information (e.g., insurance underwriting);
accumulation and aggregation of multiple parties; aggregation of
data for a set of collateral; aggregated value of collateral or
assets (e.g., based on real time condition monitoring, rea-time
market data collection and integration, and the like); aggregated
tranches of loans; collateral for smart contract aggregated with
other similar collateral; and the like.
[0315] The term linking (and similar terms) as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, linking includes
any linking, including, without limitation, linking as a
relationship between two things or situations (e.g., where one
thing affects the other). For instance, linking a subset of similar
items such as collateral to provide collateral for a set of loans.
Certain components may not be considered linked individually, but
may be considered in a process of linking in an aggregated
system--for example, a smart contracts circuit may be structured to
operate in conjunction with a blockchain circuit as part of a loan
processing platform but where the smart contracts circuit processes
contracts without storing information through the blockchain
circuit, however the two circuits could be linked through the smart
contracts circuit linking financial entity information through a
distributed ledger on the blockchain circuit. In certain
embodiments, linking may be considered linking for some purposes
but not for other purposes--for example, linking goods and services
for users and radio frequency linking between access points are
different forms of linking, where the linking of goods and services
for users links thinks together while an RF link is a
communications link between transceivers. Additionally, in certain
embodiments, otherwise similar looking systems may be
differentiated in determining whether such system are linking,
and/or which type of linking. For example, linking similar data
together for analysis is different from linking similar data
together for graphing. Accordingly, the benefits of the present
disclosure may be applied in a wide variety of systems, and any
such systems may be considered linking herein, while in certain
embodiments a given system may not be considered a linking herein.
One of skill in the art, having the benefit of the disclosure
herein and knowledge about a contemplated system ordinarily
available to that person, can readily determine which aspects of
the present disclosure will benefit a particular system, and/or how
to combine processes and systems from the present disclosure to
enhance operations of the contemplated system. Certain
considerations for the person of skill in the art, in determining
whether a contemplated system is linking and/or whether aspects of
the present disclosure can benefit or enhance the contemplated
system include, without limitation linking marketplaces or external
marketplaces with a system or platform; linking data (e.g., data
cluster including links and nodes); storage and retrieval of data
linked to local processes; links (e.g. with respect to nodes) in a
common knowledge graph; data linked to proximity or location (e.g.,
of the asset); linking to an environment (e.g., goods, services,
assets, and the like); linking events (e.g., for storage such as in
a blockchain, for communication or analysis); linking ownership or
access rights; linking to access tokens (e.g., travel offerings
linked to access tokens); links to one or more resources (e.g.,
secured by cryptographic or other techniques); linking a message to
a smart contract; and the like.
[0316] The term indicator of interest (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, an
indicator of interest includes any indicator of interest including,
without limitation, an indicator of interest from a user or
plurality of users or parties related to a transaction and the like
(e.g., parties interested in participating in a loan transaction),
the recording or storing of such an interest (e.g., a circuit for
recording an interest input from a user, entity, circuit, system,
and the like), a circuit analyzing interest related data and
setting an indicator of interest (e.g., a circuit setting or
communicating an indicator based on inputs to the circuit, such as
from users, parties, entities, systems, circuits, and the like), a
model trained to determine an indicator of interest from input data
related to an interest by one of a plurality of inputs from users,
parties, or financial entities, and the like. Certain components
may not be considered indicators of interest individually, but may
be considered an indicator of interest in an aggregated system--for
example, a party may seek information relating to a transaction
such as though a translation marketplace where the party is
interested in seeking information, but that may not be considered
an indicator of interest in a transaction. However, when the party
asserts a specific interest (e.g., through a user interface with
control inputs for indicating interest) the party's interest may be
recorded (e.g., in a storage circuit, in a blockchain circuit),
analyzed (e.g., through an analysis circuit, a data collection
circuit), monitored (e.g., through a monitoring circuit), and the
like. In a non-limiting example, indicators of interest may be
recorded (e.g., in a blockchain through a distributed ledger) from
a set of parties with respect to the product, service, or the like,
such as ones that define parameters under which a party is willing
to commit to purchase a product or service. In certain embodiments,
an indicator of interest may be considered an indicator of interest
for some purposes but not for other purposes--for example, a user
may indicate an interest for a loan transaction but that does not
necessarily mean the user is indicating an interest in providing a
type of collateral related to the loan transaction. For instance, a
data collection circuit may record an indicator of interest for the
transaction but may have a separate circuit structure for
determining an indication of interest for collateral. Additionally,
in certain embodiments, otherwise similar looking systems may be
differentiated in determining whether such system are determining
an indication of interest, and/or which type of indicator of
interest exists. For example, one circuit or system may collect
data from a plurality of parties to determine an indicator of
interest in securing a loan and a second circuit or system may
collect data from a plurality of parties to determine an indicator
of interest in determining ownership rights related to a loan.
Accordingly, the benefits of the present disclosure may be applied
in a wide variety of systems, and any such systems may be
considered an indicator of interest herein, while in certain
embodiments a given system may not be considered an indicator of
interest herein. One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure will benefit a particular system,
and/or how to combine processes and systems from the present
disclosure to enhance operations of the contemplated system.
Certain considerations for the person of skill in the art, in
determining whether a contemplated system is an indicator of
interest and/or whether aspects of the present disclosure can
benefit or enhance the contemplated system include, without
limitation parties indicating an interest in participating in a
transaction (e.g., a loan transaction), parties indicating an
interest in securing in a product or service, recording or storing
an indication of interest (e.g., through a storage circuit or
blockchain circuit), analyzing an indication of interest (e.g.,
through a data collection and/or monitoring circuit), and the
like.
[0317] The term accommodations (and similar terms) as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, an
accommodation includes any service, activity, event, and the like
such as including, without limitation, a room, group of rooms,
table, seating, building, event, shared spaces offered by
individuals (e.g., Airbnb spaces), bed-and-breakfasts, workspaces,
conference rooms, convention spaces, fitness accommodations, health
and wellness accommodations, dining accommodations, and the like,
in which someone may live, stay, sit, reside, participate, and the
like. As such, an accommodation may be purchased (e.g., a ticket
through a sports ticketing application), reserved or booked (e.g.,
a reservation through a hotel reservation application), provided as
a reward or gift, traded or exchanged (e.g., through a
marketplace), provided as an access right (e.g., offering by way of
an aggregation demand), provided based on a contingency (e.g., a
reservation for a room being contingent on the availability of a
nearby event), and the like. Certain components may not be
considered an accommodation individually but may be considered an
accommodation in an aggregated system--for example, a resource such
as a room in a hotel may not in itself be considered an
accommodation but a reservation for the room may be. For instance,
a blockchain and smart contract platform for forward market rights
for accommodations may provide a mechanism to provide access rights
with respect to accommodations. In a non-limiting example, a
blockchain circuit may be structured to store access rights in a
forward demand market, where the access rights may be stored in a
distributed ledger with related shared access to a plurality of
actionable entities. In certain embodiments, an accommodation may
be considered an accommodation for some purposes but not for other
purposes--for example, a reservation for a room may be an
accommodation on its own, but may not be accommodation that is
satisfied if a related contingency is not met as agreed upon at the
time of the e.g. reservation. Additionally, in certain embodiments,
otherwise similar looking systems may be differentiated in
determining whether such systems are related to an accommodation,
and/or which type of accommodation. For example, an accommodation
offering may be made based on different systems, such as one where
the accommodation offering is determined by a system collecting
data related to forward demand and a second one where the
accommodation offering is provided as a reward based on a system
processing a performance parameter. Accordingly, the benefits of
the present disclosure may be applied in a wide variety of systems,
and any such systems may be considered as related to an
accommodation herein, while in certain embodiments a given system
may not be considered related to an accommodation herein. One of
skill in the art, having the benefit of the disclosure herein and
knowledge about a contemplated system ordinarily available to that
person, can readily determine which aspects of the present
disclosure will benefit a particular system, and/or how to combine
processes and systems from the present disclosure to enhance
operations of the contemplated system. Certain considerations for
the person of skill in the art, in determining whether a
contemplated system is related to accommodation and/or whether
aspects of the present disclosure can benefit or enhance the
contemplated system include, without limitation accommodations
provided as determined through a service circuit, trading or
exchanging services (e.g., through an application and/or user
interface), as an accommodation offering such as with respect to a
combination of products, services, and access rights, processed
(e.g., aggregation demand for the offering in a forward market),
accommodation through booking in advance, accommodation through
booking in advance upon meeting a certain condition (e.g., relating
to a price within a given time window), and the like.
[0318] The term contingencies (and similar terms) as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, a
contingency includes any contingency including, without limitation,
any action that is dependent upon a second action. For instance, a
service may be provided as contingent on a certain parameter value,
such as collecting data as condition upon an asset tag indication
from an Internet of Things circuit. In another instance, an
accommodation such as a hotel reservation may be contingent upon a
concert (local to the hotel and at the same time as the
reservation) proceeding as scheduled. Certain components may not be
considered as relating to a contingency individually, but may be
considered related to a contingency in an aggregated system--for
example, a data input collected from a data collection service
circuit may be stored, analyzed, processed, and the like, and not
be considered with respect to a contingency, however a smart
contracts service circuit may apply a contract term as being
contingent upon the collected data. For instance, the data may
indicate a collateral status with respect to a loan transaction,
and the smart contracts service circuit may apply that data to a
term of contract that depends upon the collateral. In certain
embodiments, a contingency may be considered contingency for some
purposes but not for other purposes--for example, a delivery of
contingent access rights for a future event may be contingent upon
a loan condition being satisfied, but the loan condition on its own
may not be considered a contingency in the absence of the
contingency linkage between the condition and the access rights.
Additionally, in certain embodiments, otherwise similar looking
systems may be differentiated in determining whether such systems
are related to a contingency, and/or which type of contingency. For
example, two algorithms may both create a forward market event
access right token, but where the first algorithm creates the token
free of contingencies and the second algorithm creates a token with
a contingency for delivery of the token. Accordingly, the benefits
of the present disclosure may be applied in a wide variety of
systems, and any such systems may be considered a contingency
herein, while in certain embodiments a given system may not be
considered a contingency herein. One of skill in the art, having
the benefit of the disclosure herein and knowledge about a
contemplated system ordinarily available to that person, can
readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is a
contingency and/or whether aspects of the present disclosure can
benefit or enhance the contemplated system include, without
limitation a forward market operated within or by the platform may
be a contingent forward market, such as one where a future right is
vested, is triggered, or emerges based on the occurrence of an
event, satisfaction of a condition, or the like; a blockchain used
to make a contingent market in any form of event or access token by
securely storing access rights on a distributed ledger; setting and
monitoring pricing for contingent access rights, underlying access
rights, tokens, fees and the like; optimizing offerings, timing,
pricing, or the like, to recognize and predict patterns, to
establish rules and contingencies; exchanging contingent access
rights or underlying access rights or tokens access tokens and/or
contingent access tokens; creating a contingent forward market
event access right token where a token may be created and stored on
a blockchain for contingent access right that could result in the
ownership of a ticket; discovery and delivery of contingent access
rights to future events; contingencies that influence or represent
future demand for an offering, such as including a set of products,
services, or the like; pre-defined contingencies; optimized
offerings, timing, pricing, or the like, to recognize and predict
patterns, to establish rules and contingencies; creation of a
contingent future offering within the dashboard; contingent access
rights that may result in the ownership of the virtual good or each
smart contract to purchase the virtual good if and when it becomes
available under defined conditions; and the like.
[0319] The term level of service (and similar terms) as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, a level of
service includes any level of service including, without
limitation, any qualitative or quantitative measure of the extent
to which a service is provided, such as, and without limitation, a
first class vs. business class service (e.g., travel reservation or
postal delivery), the degree to which a resource is available
(e.g., service level A indicating that the resource is highly
available vs. service level C indicating that the resource is
constrained, such as in terms of traffic flow restrictions on a
roadway), the degree to which an operational parameter is
performing (e.g., a system is operating at a high state of service
vs a low state of service, and the like. In embodiments, level of
service may be multi-modal such that the level of service is
variable where a system or circuit provides a service rating (e.g.,
where the service rating is used as an input to an analytical
circuit for determining an outcome based on the service rating).
Certain components may not be considered relative to a level of
service individually, but may be considered relative to a level of
service in an aggregated system--for example a system for
monitoring a traffic flow rate may provide data on a current rate
but not indicate a level of service, but when the determined
traffic flow rate is provided to a monitoring circuit the
monitoring circuit may compare the determined traffic flow rate to
past traffic flow rates and determine a level of service based on
the comparison. In certain embodiments, a level of service may be
considered a level of service for some purposes but not for other
purposes--for example, the availability of first class travel
accommodation may be considered a level of service for determining
whether a ticket will be purchased but not to project a future
demand for the flight. Additionally, in certain embodiments,
otherwise similar looking systems may be differentiated in
determining whether such system utilizes a level of service, and/or
which type of level of service. For example, an artificial
intelligence circuit may be trained on past level of service with
respect to traffic flow patterns on a certain freeway and used to
predict future traffic flow patterns based on current flow rates,
but a similar artificial intelligence circuit may predict future
traffic flow patterns based on the time of day. Accordingly, the
benefits of the present disclosure may be applied in a wide variety
of systems, and any such systems may be considered with respect to
levels of service herein, while in certain embodiments a given
system may not be considered with respect to levels of service
herein. One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure will benefit a particular system,
and/or how to combine processes and systems from the present
disclosure to enhance operations of the contemplated system.
Certain considerations for the person of skill in the art, in
determining whether a contemplated system is a level of service
and/or whether aspects of the present disclosure can benefit or
enhance the contemplated system include, without limitation
transportation or accommodation offerings with predefined
contingencies and parameters such as with respect to price, mode of
service, and level of service; warranty or guarantee application,
transportation marketplace, and the like.
[0320] The term payment (and similar terms) as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, a payment includes
any payment including, without limitation, an action or process of
paying (e.g., a payment to a loan) or of being paid (e.g., a
payment from insurance), an amount paid or payable (e.g., a payment
of $1000 being made), a repayment (e.g., to pay back a loan), a
mode of payment (e.g., use of loyalty programs, rewards points, or
particular currencies, including cryptocurrencies) and the like.
Certain components may not be considered payments individually, but
may be considered payments in an aggregated system--for example,
submitting an amount of money may not be considered a payment as
such, but when applied to a payment to satisfy the requirement of a
loan may be considered a payment (or repayment). For instance, a
data collection circuit may provide lenders a mechanism to monitor
repayments of a loan. In a non-limiting example, the data
collection circuit may be structured to monitor the payments of a
plurality of loan components with respect to a financial loan
contract configured to determine a loan condition for an asset. In
certain embodiments, a payment may be considered a payment for some
purposes but not for other purposes--for example a payment to a
financial entity may be for a repayment amount to pay back the
loan, or it may be to satisfy a collateral obligation in a loan
default condition. Additionally, in certain embodiments, otherwise
similar looking systems may be differentiated in determining
whether such system are related to a payment, and/or which type of
payment. For example, funds may be applied to reserve an
accommodation or to satisfy the delivery of services after the
accommodation has been satisfied. Accordingly, the benefits of the
present disclosure may be applied in a wide variety of systems, and
any such systems may be considered a payment herein, while in
certain embodiments a given system may not be considered a payment
herein. One of skill in the art, having the benefit of the
disclosure herein and knowledge about a contemplated system
ordinarily available to that person, can readily determine which
aspects of the present disclosure will benefit a particular system,
and/or how to combine processes and systems from the present
disclosure to enhance operations of the contemplated system.
Certain considerations for the person of skill in the art, in
determining whether a contemplated system is a payment and/or
whether aspects of the present disclosure can benefit or enhance
the contemplated system include, without limitation, deferring a
required payment; deferring a payment requirement; payment of a
loan; a payment amount; a payment schedule; a balloon payment
schedule; payment performance and satisfaction; modes of payment;
and the like.
[0321] The term location (and similar terms) as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, a location
includes any location including, without limitation, a particular
place or position of a person, place, or item, or location
information regarding the position of a person, place, or item,
such as a geolocation (e.g., geolocation of a collateral), a
storage location (e.g., the storage location of an asset), a
location of a person (e.g., lender, borrower, worker), location
information with respect to the same, and the like. Certain
components may not be considered with respect to location
individually, but may be considered with respect to location in an
aggregated system--for example, a smart contract circuit may be
structured to specify a requirement for a collateral to be stored
at a fixed location but not specify the specific location for a
specific collateral. In certain embodiments, a location may be
considered a location for some purposes but not for other
purposes--for example, the address location of a borrower may be
required for processing a loan in one instance, and a specific
location for processing a default condition in another instance.
Additionally, in certain embodiments, otherwise similar looking
systems may be differentiated in determining whether such system
are a location, and/or which type of location. For example, the
location of a music concert may be required to be in a concert hall
seating 10,000 people in one instance but specify the location of
an actual concert hall in another. Accordingly, the benefits of the
present disclosure may be applied in a wide variety of systems, and
any such systems may be considered with respect to a location
herein, while in certain embodiments a given system may not be
considered with respect to a location herein. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is
considered with respect to a location and/or whether aspects of the
present disclosure can benefit or enhance the contemplated system
include, without limitation a geolocation of an item or collateral;
a storage location of item or asset; location information; location
of a lender or a borrower; location-based product or service
targeting application; a location-based fraud detection
application; indoor location monitoring systems (e.g., cameras, IR
systems, motion-detection systems); locations of workers (including
routes taken through a location); location parameters; event
location; specific location of an event; and the like.
[0322] The term route (and similar terms) as utilized herein should
be understood broadly. Without limitation to any other aspect or
description of the present disclosure, a route includes any route
including, without limitation, a way or course taken in getting
from a starting point to a destination, to send or direct along a
specified course, and the like. Certain components may not be
considered with respect to a route individually, but may be
considered a route in an aggregated system--for example a mobile
data collector may specify a requirement for a route for collecting
data based on an input from a monitoring circuit, but only in
receiving that input does the mobile data collector determine what
route to take and begin traveling along the route. In certain
embodiments, a route may be considered a route for some purposes
but not for other purposes--for example possible routes through a
road system may be considered differently than specific routes
taken through from one location to another location. Additionally,
in certain embodiments, otherwise similar looking systems may be
differentiated in determining whether such systems are specified
with respect to a location, and/or which types of locations. For
example, routes depicted on a map may indicate possible routes or
actual routes taken by individuals. Accordingly, the benefits of
the present disclosure may be applied in a wide variety of systems,
and any such systems may be considered with respect to a route
herein, while in certain embodiments a given system may not be
considered with respect to a route herein. One of skill in the art,
having the benefit of the disclosure herein and knowledge about a
contemplated system ordinarily available to that person, can
readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is
utilizing a route and/or whether aspects of the present disclosure
can benefit or enhance the contemplated system include, without
limitation delivery routes; routes taken through a location; heat
map showing routes traveled by customers or workers within an
environment; determining what resources are deployed to what routes
or types of travel; direct route or multi-stop route, such as from
the destination of the consumer to a specific location or to
wherever an event takes place; a route for a mobile data collector;
and the like.
[0323] The term future offering (and similar terms) as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, a future
offing includes any offer of an item or service in the future
including, without limitation, a future offer to provide an item or
service, a future offer with respect to a proposed purchase, a
future offering made through a forward market platform, a future
offering determined by a smart contract circuit, and the like.
Further, a future offering may be a contingent future offer or an
offer based on conditions resulting on the offer being a future
offering, such as where the future offer is contingent upon or with
the conditions imposed by a predetermined condition (e.g., a
security may be purchased for $1000 at a set future date contingent
upon a predetermined state of a market indicator). Certain
components may not be considered a future offering individually,
but may be considered a future offering in an aggregated
system--for example, an offer for a loan may not be considered a
future offering if the offer is not authorized through a collective
agreement amongst a plurality of parties related to the offer, but
may be considered a future offer once a vote has been collected and
stored through a distributed ledger, such as through a blockchain
circuit. In certain embodiments, a future offering may be
considered a future offering for some purposes but not for other
purposes--for example, a future offering may be contingent upon a
condition being met in the future, and so the future offering may
not be considered a future offer until the condition is met.
Additionally, in certain embodiments, otherwise similar looking
systems may be differentiated in determining whether such systems
are future offerings, and/or which type of future offerings. For
example, two security offerings may be determined to be offerings
to be made at a future time, however, one may have immediate
contingences to be met and thus may not be considered to be a
future offering but rather an immediate offering with future
declarations. Accordingly, the benefits of the present disclosure
may be applied in a wide variety of systems, and any such systems
may be considered in association with a future offering herein,
while in certain embodiments a given system may not be considered
in association with a future offering herein. One of skill in the
art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is in
association with a future offering and/or whether aspects of the
present disclosure can benefit or enhance the contemplated system
include, without limitation a forward offering, a contingent
forward offering, a forward offing in a forward market platform
(e.g., for creating a future offering or contingent future offering
associated with identifying offering data from a platform-operated
marketplace or external marketplace); a future offering with
respect to entering into a smart contract (e.g., by executing an
indication of a commitment to purchase, attend, or otherwise
consume a future offering), and the like.
[0324] The term access right (and derivatives or variations) as
utilized herein may be understood broadly to describe an
entitlement to acquire or possess a property, article, or other
thing of value. A contingent access right may be conditioned upon a
trigger or condition being met before such an access right becomes
entitled, vested or otherwise defensible. An access right or
contingent access right may also serve specific purposes or be
configured for different applications or contexts, such as, without
limitation, loan-related actions or any service or offering.
Without limitation, notices may be required to be provided to the
owner of a property, article or item of value before such access
rights or contingent access rights are exercised. Access rights and
contingent access rights in various forms may be included where
discussing a legal action, a delinquent or defaulted loan or
agreement, or other circumstances where a lender may be seeking
remedy, without limitation. One of skill in the art, having the
benefit of the disclosure herein and knowledge ordinarily available
about a contemplated system, can readily determine the value of
such rights implemented in an embodiment. While specific examples
of access rights and contingent access rights are described herein
for purposes of illustration, any embodiment benefitting from the
disclosures herein, and any considerations understood to one of
skill in the art having the benefit of the disclosures herein, are
specifically contemplated within the scope of the present
disclosure.
[0325] The term smart contract (and other forms or variations) as
utilized herein may be understood broadly to describe a method,
system, connected resource or wide area network offering one or
more resources useful to assist or perform actions, tasks or things
by embodiments disclosed herein. A smart contract may be a set of
steps or a process to negotiate, administrate, restructure or
implement an agreement or loan between parties. A smart contract
may also be implemented as an application, website, FTP site,
server, appliance or other connected component or Internet related
system that renders resources to negotiate, administrate,
restructure or implement an agreement or loan between parties. A
smart contract may be a self-contained system, or may be part of a
larger system or component that may also be a smart contract. For
example, a smart contract may refer to a loan or an agreement
itself, conditions or terms, or may refer to a system to implement
such a loan or agreement. In certain embodiments, a smart contract
circuit or robotic process automation system may incorporate or be
incorporated into automatic robotic process automation system to
perform one or more purposes or tasks, whether part of a loan or
transaction process, or otherwise. One of skill in the art, having
the benefit of the disclosure herein and knowledge ordinarily
available about a contemplated system can readily determine the
purposes and use of this term as it relates to a smart contract in
various forms, embodiments and contexts disclosed herein.
[0326] The term allocation of reward (and variations) as utilized
herein may be understood broadly to describe a thing or
consideration allocated or provided as consideration, or provided
for a purpose. The allocation of rewards can be of a financial
type, or non-financial type, without limitation. A specific type of
allocation of reward may also serve a number of different purposes
or be configured for different applications or contexts, such as,
without limitation: a reward event, claims for rewards, monetary
rewards, rewards captured as a data set, rewards points, and other
forms of rewards. Thus an allocation of rewards may be provided as
a consideration within the context of a loan or agreement. Systems
may be utilized to allocate rewards. The allocation of rewards in
various forms may be included where discussing a particular
behavior, or encouragement of a particular behavior, without
limitation. An allocation of a reward may include an actual
dispensation of the award, and/or a recordation of the reward. The
allocation of rewards may be performed by a smart contract circuit
or a robotic processing automation system. One of skill in the art,
having the benefit of the disclosure herein and knowledge
ordinarily available about a contemplated system, can readily
determine the value of the allocation of rewards in an embodiment.
While specific examples of the allocation of rewards are described
herein for purposes of illustration, any embodiment benefitting
from the disclosures herein, and any considerations understood to
one of skill in the art having the benefit of the disclosures
herein, are specifically contemplated within the scope of the
present disclosure.
[0327] The term satisfaction of parameters or conditions (and other
derivatives, forms or variations) as utilized herein may be
understood broadly to describe completion, presence or proof of
parameters or conditions that have been met. The term generally may
relate to a process of determining such satisfaction of parameters
or conditions, or may relate to the completion of such a process
with a result, without limitation. Satisfaction may result in a
successful outcome of other triggers or conditions or terms that
may come into execution, without limitation. Satisfaction of
parameters or conditions may occur in many different contexts of
contracts or loans, such as lending, refinancing, consolidation,
factoring, brokering, foreclosure, and data processing (e.g. data
collection), or combinations thereof, without limitation.
Satisfaction of parameters or conditions may be used in the form of
a noun (e.g. the satisfaction of the debt repayment), or may be
used in a verb form to describe the process of determining a result
to parameters or conditions. For example, a borrower may have
satisfaction of parameters by making a certain number of payments
on time, or may cause satisfaction of a condition allowing access
rights to an owner if a loan defaults, without limitation. In
certain embodiments, a smart contract or robotic process automation
system may perform or determine satisfaction of parameters or
conditions for one or more of the parties and process appropriate
tasks for satisfaction of parameters or conditions. In some cases
satisfaction of parameters or conditions by the smart contract or
robotic process automation system may not complete or be
successful, and depending upon such outcomes, this may enable
automated action or trigger other conditions or terms. One of skill
in the art, having the benefit of the disclosure herein and
knowledge ordinarily available about a contemplated system can
readily determine the purposes and use of this term in various
forms, embodiments and contexts disclosed herein.
[0328] The term information (and other forms such as info or
informational, without limitation) as utilized herein may be
understood broadly in a variety of contexts with respect to an
agreement or a loan. The term generally may relate to a large
context, such as information regarding an agreement or loan, or may
specifically relate to a finite piece of information (e.g. a
specific detail of an event that happened on a specific date).
Thus, information may occur in many different contexts of contracts
or loans, and may be used in the contexts, without limitation of
evidence, transactions, access, and the like. Or, without
limitation, information may be used in conjunction with stages of
an agreement or transaction, such as lending, refinancing,
consolidation, factoring, brokering, foreclosure, and information
processing (e.g. data or information collection), or combinations
thereof. For example, information as evidence, transaction, access,
etc. may be used in the form of a noun (e.g. the information was
acquired from the borrower), or may refer as a noun to an
assortment of informational items (e.g. the information about the
loan may be found in the smart contract), or may be used in the
form of characterizing as an adjective (e.g. the borrower was
providing an information submission). For example, a lender may
collect an overdue payment from a borrower through an online
payment, or may have a successful collection of overdue payments
acquired through a customer service telephone call. In certain
embodiments, a smart contract circuit or robotic process automation
system may perform collection, administration, calculating,
providing, or other tasks for one or more of the parties and
process appropriate tasks relating to information (e.g. providing
notice of an overdue payment). In some cases information by the
smart contract circuit or robotic process automation system may be
incomplete, and depending upon such outcomes this may enable
automated action or trigger other conditions or terms. One of skill
in the art, having the benefit of the disclosure herein and
knowledge ordinarily available about a contemplated system can
readily determine the purposes and use of information as evidence,
transaction, access, etc. in various forms, embodiments and
contexts disclosed herein.
[0329] Information may be linked to external information (e.g.
external sources). The term more specifically may relate to the
acquisition, parsing, receiving, or other relation to an external
origin or source, without limitation. Thus, information linked to
external information or sources may be used in conjunction with
stages of an agreement or transaction, such as lending,
refinancing, consolidation, factoring, brokering, foreclosure, and
information processing (e.g. data or information collection), or
combinations thereof. For example, information linked to external
information may change as the external information changes, such as
a borrower's credit score, which is based on an external source. In
certain embodiments, a smart contract circuit or robotic process
automation system may perform acquisition, administration,
calculating, receiving, updating, providing or other tasks for one
or more of the parties and process appropriate tasks relating to
information that is linked to external information. In some cases
information that is linked to external information by the smart
contract or robotic process automation system may be incomplete,
and depending upon such outcomes this may enable automated action
or trigger other conditions or terms. One of skill in the art,
having the benefit of the disclosure herein and knowledge
ordinarily available about a contemplated system can readily
determine the purposes and use of this term in various forms,
embodiments and contexts disclosed herein.
[0330] Information that is a part of a loan or agreement may be
separated from information presented in an access location. The
term more specifically may relate to the characterization that
information can be apportioned, split, restricted, or otherwise
separated from other information within the context of a loan or
agreement. Thus, information presented or received on an access
location may not necessarily be the whole information available for
a given context. For example, information provided to a borrower
may be different information received by a lender from an external
source, and may be different than information received or presented
from an access location. In certain embodiments, a smart contract
circuit or robotic process automation system may perform separation
of information or other tasks for one or more of the parties and
process appropriate tasks. One of skill in the art, having the
benefit of the disclosure herein and knowledge ordinarily available
about a contemplated system, can readily determine the purposes and
use of this term in various forms, embodiments and contexts
disclosed herein.
[0331] The term encryption of information and control of access
(and other related terms) as utilized herein may be understood
broadly to describe generally whether a party or parties may
observe or possess certain information, actions, events or
activities relating to a transaction or loan. Encryption of
information may be utilized to prevent a party from accessing,
observing or receiving information, or may alternatively be used to
prevent parties outside the transaction or loan from being able to
access, observe or receive confidential (or other) information.
Control of access to information relates to the determination of
whether a party is entitled to such access of information.
Encryption of information or control of access may occur in many
different contexts of loans, such as lending, refinancing,
consolidation, factoring, brokering, foreclosure, administration,
negotiating, collecting, procuring, enforcing, and data processing
(e.g., data collection), or combinations thereof, without
limitation. An encryption of information or control of access to
information may refer to a single instance, or may characterize a
larger amount of information, actions, events or activities,
without limitation. For example, a borrower or lender may have
access to information about a loan, but other parties outside the
loan or agreement may not be able to access the loan information
due to encryption of the information, or a control of access to the
loan details. In certain embodiments, a smart contract circuit or
robotic process automation system may perform encryption of
information or control of access to information for one or more of
the parties and process appropriate tasks for encryption or control
of access of information. One of skill in the art, having the
benefit of the disclosure herein and knowledge ordinarily available
about a contemplated system can readily determine the purposes and
use of this term in various forms, embodiments and contexts
disclosed herein.
[0332] The term potential access party list (and other related
terms) as utilized herein may be understood broadly to describe
generally whether a party or parties may observe or possess certain
information, actions, events or activities relating to a
transaction or loan. A potential access party list may be utilized
to authorize one or more parties to access, observe or receive
information, or may alternatively be used to prevent parties from
being able to do so. A potential access party list information
relates to the determination of whether a party (either on the
potential access party list or not on the list) is entitled to such
access of information. A potential access party list may occur in
many different contexts of loans, such as lending, refinancing,
consolidation, factoring, brokering, foreclosure, administration,
negotiating, collecting, procuring, enforcing and data processing
(e.g. data collection), or combinations thereof, without
limitation. A potential access party list may refer to a single
instance, or may characterize a larger amount of parties or
information, actions, events or activities, without limitation. For
example, a potential access party list may grant (or deny) access
to information about a loan, but other parties outside potential
access party list may not be able to (or may be granted) access the
loan information. In certain embodiments, a smart contract circuit
or robotic process automation system may perform administration or
enforcement of a potential access party list for one or more of the
parties and process appropriate tasks for encryption or control of
access of information. One of skill in the art, having the benefit
of the disclosure herein and knowledge ordinarily available about a
contemplated system can readily determine the purposes and use of
this term in various forms, embodiments and contexts disclosed
herein.
[0333] The term offering, making an offer, and similar terms as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, an
offering includes any offer of an item or service including,
without limitation, an insurance offering, a security offering, an
offer to provide an item or service, an offer with respect to a
proposed purchase, an offering made through a forward market
platform, a future offering, a contingent offering, offers related
to lending (e.g. lending, refinancing, collection, consolidation,
factoring, brokering, foreclosure), an offering determined by a
smart contract circuit, an offer directed to a customer/debtor, an
offering directed to a provider/lender, a 3rd party offer (e.g.
regulator, auditor, partial owner, tiered provider) and the like.
Offerings may include physical goods, virtual goods, software,
physical services, access rights, entertainment content,
accommodations, or many other items, services, solutions, or
considerations. In an example, a third party offer may be to
schedule a band instead of just an offer of tickets for sale.
Further, an offer may be based on pre-determined conditions or
contingencies. Certain components may not be considered an offering
individually, but may be considered an offering in an aggregated
system--for example, an offer for insurance may not be considered
an offering if the offer is not approved by one or more parties
related to the offer, however once approval has been granted, it
may be considered an offer. Accordingly, the benefits of the
present disclosure may be applied in a wide variety of systems, and
any such systems may be considered in association with an offering
herein, while in certain embodiments a given system may not be
considered in association with an offering herein. One of skill in
the art, having the benefit of the disclosure herein and knowledge
about a contemplated system ordinarily available to that person,
can readily determine which aspects of the present disclosure will
benefit a particular system, and/or how to combine processes and
systems from the present disclosure to enhance operations of the
contemplated system. Certain considerations for the person of skill
in the art, in determining whether a contemplated system is in
association with an offering and/or whether aspects of the present
disclosure can benefit or enhance the contemplated system include,
without limitation the item or service being offered, a contingency
related to the offer, a way of tracking if a contingency or
condition has been met, an approval of the offering, an execution
of an exchange of consideration for the offering, and the like.
[0334] The term artificial intelligence (AI) solution should be
understood broadly. Without limitation to any other aspect of the
present disclosure, an AI solution includes a coordinated group of
AI related aspects to perform one or more tasks or operations as
set forth throughout the present disclosure. An example AI solution
includes one or more AI components, including any AI components set
forth herein, including at least a neural network, an expert
system, and/or a machine learning component. The example AI
solution may include as an aspect the types of components of the
solution, such as a heuristic AI component, a model based AI
component, a neural network of a selected type (e.g., recursive,
convolutional, perceptron, etc.), and/or an AI component of any
type having a selected processing capability (e.g., signal
processing, frequency component analysis, auditory processing,
visual processing, speech processing, text recognition, etc.).
Without limitation to any other aspect of the present disclosure, a
given AI solution may be formed from the number and type of AI
components of the AI solution, the connectivity of the AI
components (e.g., to each other, to inputs from a system including
or interacting with the AI solution, and/or to outputs to the
system including or interacting with the AI solution). The given AI
solution may additionally be formed from the connection of the AI
components to each other within the AI solution, and to boundary
elements (e.g., inputs, outputs, stored intermediary data, etc.) in
communication with the AI solution. The given AI solution may
additionally be formed from a configuration of each of the AI
components of the AI solution, where the configuration may include
aspects such as: model calibrations for an AI component;
connectivity and/or flow between AI components (e.g., serial and/or
parallel coupling, feedback loops, logic junctions, etc.); the
number, selected input data, and/or input data processing of inputs
to an AI component; a depth and/or complexity of a neural network
or other components; a training data description of an AI component
(e.g., training data parameters such as content, amount of training
data, statistical description of valid training data, etc.); and/or
a selection and/or hybrid description of a type for an AI
component. An AI solution includes a selection of AI elements, flow
connectivity of those AI elements, and/or configuration of those AI
elements.
[0335] One of skill in the art, having the benefit of the present
disclosure, can readily determine an AI solution for a given
system, and/or configure operations to perform a selection and/or
configuration operation for an AI solution for a given system.
Certain considerations to determining an AI solution, and/or
configuring operations to perform a selection and/or configuration
operation for an AI solution include, without limitation: an
availability of AI components and/or component types for a given
implementation; an availability of supporting infrastructure to
implement given AI components (e.g., data input values available,
including data quality, level of service, resolution, sampling
rate, etc.; availability of suitable training data for a given AI
solution; availability of expert inputs, such as for an expert
system and/or to develop a model training data set; regulatory
and/or policy based considerations including permitted action by
the AI solution, requirements for acquisition and/or retention of
sensitive data, difficult to obtain data, and/or expensive data);
operational considerations for a system including or interacting
with the AI solution, including response time specifications,
safety considerations, liability considerations, etc.; available
computing resources such as processing capability, network
communication capability, and/or memory storage capability (e.g.,
to support initial data, training data, input data such as cached,
buffered, or stored input data, iterative improvement state data,
output data such as cached, buffered, or stored output data, and/or
intermediate data storage, such as data to support ongoing
calculations, historical data, and/or accumulation data); the types
of tasks to be performed by the AI solution, and the suitability of
AI components for those tasks, sensitivity of AI components
performing the tasks (e.g., variability of the output space
relative to a disturbance size of the input space); the
interactions of AI components within the entire AI solution (e.g.,
a low capability rationality AI component may be coupled with a
higher capability AI component that may provide high sensitivity
and/or unbounded response to inputs); and/or model implementation
considerations (e.g., requirements to re-calibrate, aging
constraints of a model, etc.).
[0336] A selected and/or configured AI solution may be utilized
with any of the systems, procedures, and/or aspects of embodiments
as set forth throughout the present disclosure. For example, a
system utilizing an expert system may include the expert system as
all or a part of a selected, configured AI solution. In another
example, a system utilizing a neural network, and/or a combination
of neural networks, may include the neural network(s) as all or a
part of a selected, configured AI solution. The described aspects
of an AI solution, including the selection and configuration of the
AI solution, are non-limiting illustrations.
[0337] Referring to FIGS. 1-2B, a set of systems, methods,
components, modules, machines, articles, blocks, circuits,
services, programs, applications, hardware, software and other
elements are provided, collectively referred to herein
interchangeably as the system 100 or the platform 100, The platform
100 enables a wide range of improvements of and for various
machines, systems, and other components that enable transactions
involving the exchange of value (such as using currency,
cryptocurrency, tokens, rewards or the like, as well as a wide
range of in-kind and other resources) in various markets, including
current or spot markets 170, forward markets 130 and the like, for
various goods, services, and resources. As used herein, "currency"
should be understood to encompass fiat currency issued or regulated
by governments, cryptocurrencies, tokens of value, tickets, loyalty
points, rewards points, coupons, and other elements that represent
or may be exchanged for value. Resources, such as ones that may be
exchanged for value in a marketplace, should be understood to
encompass goods, services, natural resources, energy resources,
computing resources, energy storage resources, data storage
resources, network bandwidth resources, processing resources and
the like, including resources for which value is exchanged and
resources that enable a transaction to occur (such as necessary
computing and processing resources, storage resources, network
resources, and energy resources that enable a transaction). The
platform 100 may include a set of forward purchase and sale
machines 110, each of which may be configured as an expert system
or automated intelligent agent for interaction with one or more of
the set of spot markets 170 and forward markets 130. Enabling the
set of forward purchase and sale machines 110 are an intelligent
resource purchasing system 164 having a set of intelligent agents
for purchasing resources in spot and forward markets; an
intelligent resource allocation and coordination system 168 for the
intelligent sale of allocated or coordinated resources, such as
compute resources, energy resources, and other resources involved
in or enabling a transaction; an intelligent sale engine 172 for
intelligent coordination of a sale of allocated resources in spot
and futures markets; and an automated spot market testing and
arbitrage transaction execution engine 194 for performing spot
testing of spot and forward markets, such as with
micro-transactions and, where conditions indicate favorable
arbitrage conditions, automatically executing transactions in
resources that take advantage of the favorable conditions. Each of
the engines may use model-based or rule-based expert systems, such
as based on rules or heuristics, as well as deep learning systems
by which rules or heuristics may be learned over trials involving a
large set of inputs. The engines may use any of the expert systems
and artificial intelligence capabilities described throughout this
disclosure. Interactions within the platform 100, including of all
platform components, and of interactions among them and with
various markets, may be tracked and collected, such as by a data
aggregation system 144, such as for aggregating data on purchases
and sales in various marketplaces by the set of machines described
herein. Aggregated data may include tracking and outcome data that
may be fed to artificial intelligence and machine learning systems,
such as to train or supervise the same. The various engines may
operate on a range of data sources, including aggregated data from
marketplace transactions, tracking data regarding the behavior of
each of the engines, and a set of external data sources 182, which
may include social media data sources 180 (such as social
networking sites like Facebook.TM. and Twitter.TM.), Internet of
Things (IoT) data sources (including from sensors, cameras, data
collectors, and instrumented machines and systems), such as IoT
sources that provide information about machines and systems that
enable transactions and machines and systems that are involved in
production and consumption of resources. External data sources 182
may include behavioral data sources, such as automated agent
behavioral data sources 188 (such as tracking and reporting on
behavior of automated agents that are used for conversation and
dialog management, agents used for control functions for machines
and systems, agents used for purchasing and sales, agents used for
data collection, agents used for advertising, and others), human
behavioral data sources (such as data sources tracking online
behavior, mobility behavior, energy consumption behavior, energy
production behavior, network utilization behavior, compute and
processing behavior, resource consumption behavior, resource
production behavior, purchasing behavior, attention behavior,
social behavior, and others), and entity behavioral data sources
190 (such as behavior of business organizations and other entities,
such as purchasing behavior, consumption behavior, production
behavior, market activity, merger and acquisition behavior,
transaction behavior, location behavior, and others). The IoT,
social and behavioral data from and about sensors, machines,
humans, entities, and automated agents may collectively be used to
populate expert systems, machine learning systems, and other
intelligent systems and engines described throughout this
disclosure, such as being provided as inputs to deep learning
systems and being provided as feedback or outcomes for purposes of
training, supervision, and iterative improvement of systems for
prediction, forecasting, classification, automation and control.
The data may be organized as a stream of events. The data may be
stored in a distributed ledger or other distributed system. The
data may be stored in a knowledge graph where nodes represent
entities and links represent relationships. The external data
sources may be queried via various database query functions. The
data sources 182 may be accessed via APIs, brokers, connectors,
protocols like REST and SOAP, and other data ingestion and
extraction techniques. Data may be enriched with metadata and may
be subject to transformation and loading into suitable forms for
consumption by the engines, such as by cleansing, normalization,
de-duplication and the like.
[0338] The platform 100 may include a set of intelligent
forecasting engines 192 for forecasting events, activities,
variables, and parameters of spot markets 170, forward markets 130,
resources that are traded in such markets, resources that enable
such markets, behaviors (such as any of those tracked in the
external data sources 182), transactions, and the like. The
forecasting engines 192 may operate on data from the data
aggregation system 144 about elements of the platform 100 and on
data from the external data sources 182. The platform may include a
set of intelligent transaction engines 136 for automatically
executing transactions in spot markets 170 and forward markets 130.
This may include executing intelligent cryptocurrency transactions
with an intelligent cryptocurrency execution engine 183 as
described in more detail below. The platform 100 may make use of
asset of improved distributed ledgers 113 and improved smart
contracts 103, including ones that embed and operate on proprietary
information, instruction sets and the like that enable complex
transactions to occur among individuals with reduced (or without)
reliance on intermediaries. These and other components are
described in more detail throughout this disclosure.
[0339] Referring to the block diagrams of FIGS. 2A-2B, further
details and additional components of the platform 100 and
interactions among them are depicted. The set of forward purchase
and sale machines 110 may include a regeneration capacity
allocation engine 102 (such as for allocating energy generation or
regeneration capacity, such as within a hybrid vehicle or system
that includes energy generation or regeneration capacity, a
renewable energy system that has energy storage, or other energy
storage system, where energy is allocated for one or more of sale
on a forward market 130, sale in a spot market 170, use in
completing a transaction (e.g., mining for cryptocurrency), or
other purposes. For example, the regeneration capacity allocation
engine 102 may explore available options for use of stored energy,
such as sale in current and forward energy markets that accept
energy from producers, keeping the energy in storage for future
use, or using the energy for work (which may include processing
work, such as processing activities of the platform like data
collection or processing, or processing work for executing
transactions, including mining activities for
cryptocurrencies).
[0340] The set of forward purchase and sale machines 110 may
include an energy purchase and sale machine 104 for purchasing or
selling energy, such as in an energy spot market 148 or an energy
forward market 122. The energy purchase and sale machine 104 may
use an expert system, neural network or other intelligence to
determine timing of purchases, such as based on current and
anticipated state information with respect to pricing and
availability of energy and based on current and anticipated state
information with respect to needs for energy, including needs for
energy to perform computing tasks, cryptocurrency mining, data
collection actions, and other work, such as work done by automated
agents and systems and work required for humans or entities based
on their behavior. For example, the energy purchase machine may
recognize, by machine learning, that a business is likely to
require a block of energy in order to perform an increased level of
manufacturing based on an increase in orders or market demand and
may purchase the energy at a favorable price on a futures market,
based on a combination of energy market data and entity behavioral
data. Continuing the example, market demand may be understood by
machine learning, such as by processing human behavioral data
sources 184, such as social media posts, e-commerce data and the
like that indicate increasing demand. The energy purchase and sale
machine 104 may sell energy in the energy spot market 148 or the
energy forward market 122. Sale may also be conducted by an expert
system operating on the various data sources described herein,
including with training on outcomes and human supervision.
[0341] The set of forward purchase and sale machines 110 may
include a renewable energy credit (REC) purchase and sale machine
108, which may purchase renewable energy credits, pollution
credits, and other environmental or regulatory credits in a spot
market 150 or forward market 124 for such credits. Purchasing may
be configured and managed by an expert system operating on any of
the external data sources 182 or on data aggregated by the set of
data aggregations systems 144 for the platform. Renewable energy
credits and other credits may be purchased by an automated system
using an expert system, including machine learning or other
artificial intelligence, such as where credits are purchased with
favorable timing based on an understanding of supply and demand
that is determined by processing inputs from the data sources. The
expert system may be trained on a data set of outcomes from
purchases under historical input conditions. The expert system may
be trained on a data set of human purchase decisions and/or may be
supervised by one or more human operators. The renewable energy
credit (REC) purchase and sale machine 108 may also sell renewable
energy credits, pollution credits, and other environmental or
regulatory credits in a spot market 150 or forward market 124 for
such credits. Sale may also be conducted by an expert system
operating on the various data sources described herein, including
with training on outcomes and human supervision.
[0342] The set of forward purchase and sale machines 110 may
include an attention purchase and sale machine 112, which may
purchase one or more attention-related resources, such as
advertising space, search listing, keyword listing, banner
advertisements, participation in a panel or survey activity,
participation in a trial or pilot, or the like in a spot market for
attention 152 or a forward market for attention 128. Attention
resources may include the attention of automated agents, such as
bots, crawlers, dialog managers, and the like that are used for
searching, shopping and purchasing. Purchasing of attention
resources may be configured and managed by an expert system
operating on any of the external data sources 182 or on data
aggregated by the set of data aggregations systems 144 for the
platform. Attention resources may be purchased by an automated
system using an expert system, including machine learning or other
artificial intelligence, such as where resources are purchased with
favorable timing, such as based on an understanding of supply and
demand, that is determined by processing inputs from the various
data sources. For example, the attention purchase machine 112 may
purchase advertising space in a forward market for advertising
based on learning from a wide range of inputs about market
conditions, behavior data, and data regarding activities of agent
and systems within the platform 100. The expert system may be
trained on a data set of outcomes from purchases under historical
input conditions. The expert system may be trained on a data set of
human purchase decisions and/or may be supervised by one or more
human operators. The attention purchase and sale machine 112 may
also sell one or more attention-related resources, such as
advertising space, search listing, keyword listing, banner
advertisements, participation in a panel or survey activity,
participation in a trial or pilot, or the like in a spot market for
attention 152 or a forward market for attention 128, which may
include offering or selling access to, or attention or, one or more
automated agents of the platform 100. Sale may also be conducted by
an expert system operating on the various data sources described
herein, including with training on outcomes and human
supervision.
[0343] The set of forward purchase and sale machines 110 may
include a compute purchase and sale machine 114, which may purchase
one or more computation-related resources, such as processing
resources, database resources, computation resources, server
resources, disk resources, input/output resources, temporary
storage resources, memory resources, virtual machine resources,
container resources, and others in a spot market for compute 154 or
a forward market for compute 132. Purchasing of compute resources
may be configured and managed by an expert system operating on any
of the external data sources 182 or on data aggregated by the set
of data aggregations systems 144 for the platform. Compute
resources may be purchased by an automated system using an expert
system, including machine learning or other artificial
intelligence, such as where resources are purchased with favorable
timing, such as based on an understanding of supply and demand,
that is determined by processing inputs from the various data
sources. For example, the compute purchase machine 114 may purchase
or reserve compute resources on a cloud platform in a forward
market for compute resources based on learning from a wide range of
inputs about market conditions, behavior data, and data regarding
activities of agent and systems within the platform 100, such as to
obtain such resources at favorable prices during surge periods of
demand for computing. The expert system may be trained on a data
set of outcomes from purchases under historical input conditions.
The expert system may be trained on a data set of human purchase
decisions and/or may be supervised by one or more human operators.
The compute purchase and sale machine 114 may also sell one or more
computation-related resources that are connected to, part of, or
managed by the platform 100, such as processing resources, database
resources, computation resources, server resources, disk resources,
input/output resources, temporary storage resources, memory
resources, virtual machine resources, container resources, and
others in a spot market for compute 154 or a forward market for
compute 132. Sale may also be conducted by an expert system
operating on the various data sources described herein, including
with training on outcomes and human supervision.
[0344] The set of forward purchase and sale machines 110 may
include a data storage purchase and sale machine 118, which may
purchase one or more data-related resources, such as database
resources, disk resources, server resources, memory resources, RAM
resources, network attached storage resources, storage attached
network (SAN) resources, tape resources, time-based data access
resources, virtual machine resources, container resources, and
others in a spot market for storage 158 or a forward market for
data storage 134. Purchasing of data storage resources may be
configured and managed by an expert system operating on any of the
external data sources 182 or on data aggregated by the set of data
aggregations systems 144 for the platform. Data storage resources
may be purchased by an automated system using an expert system,
including machine learning or other artificial intelligence, such
as where resources are purchased with favorable timing, such as
based on an understanding of supply and demand, that is determined
by processing inputs from the various data sources. For example,
the compute purchase machine 114 may purchase or reserve compute
resources on a cloud platform in a forward market for compute
resources based on learning from a wide range of inputs about
market conditions, behavior data, and data regarding activities of
agent and systems within the platform 100, such as to obtain such
resources at favorable prices during surge periods of demand for
storage. The expert system may be trained on a data set of outcomes
from purchases under historical input conditions. The expert system
may be trained on a data set of human purchase decisions and/or may
be supervised by one or more human operators. The data storage
purchase and sale machine 118 may also sell one or more data
storage-related resources that are connected to, part of, or
managed by the platform 100 in a spot market for storage resources
158 or a forward market for storage 134. Sale may also be conducted
by an expert system operating on the various data sources described
herein, including with training on outcomes and human
supervision.
[0345] The set of forward purchase and sale machines 110 may
include a bandwidth purchase and sale machine 120, which may
purchase one or more bandwidth-related resources, such as cellular
bandwidth, Wi-Fi bandwidth, radio bandwidth, access point
bandwidth, beacon bandwidth, local area network bandwidth, wide
area network bandwidth, enterprise network bandwidth, server
bandwidth, storage input/output bandwidth, advertising network
bandwidth, market bandwidth, or other bandwidth, in a spot market
for bandwidth 160 or a forward market for bandwidth 138. Purchasing
of bandwidth resources may be configured and managed by an expert
system operating on any of the external data sources 182 or on data
aggregated by the set of data aggregations systems 144 for the
platform. Bandwidth resources may be purchased by an automated
system using an expert system, including machine learning or other
artificial intelligence, such as where resources are purchased with
favorable timing, such as based on an understanding of supply and
demand, that is determined by processing inputs from the various
data sources. For example, the bandwidth purchase and sale machine
120 may purchase or reserve bandwidth on a network resource for a
future networking activity managed by the platform based on
learning from a wide range of inputs about market conditions,
behavior data, and data regarding activities of agent and systems
within the platform 100, such as to obtain such resources at
favorable prices during surge periods of demand for bandwidth. The
expert system may be trained on a data set of outcomes from
purchases under historical input conditions. The expert system may
be trained on a data set of human purchase decisions and/or may be
supervised by one or more human operators. The bandwidth purchase
and sale machine 120 may also sell one or more bandwidth-related
resources that are connected to, part of, or managed by the
platform 100 in a spot market for bandwidth resources 160 or a
forward market for bandwidth 138. Sale may also be conducted by an
expert system operating on the various data sources described
herein, including with training on outcomes and human
supervision.
[0346] The set of forward purchase and sale machines 110 may
include a spectrum purchase and sale machine 142, which may
purchase one or more spectrum-related resources, such as cellular
spectrum, 3G spectrum, 4G spectrum, LTE spectrum, 5G spectrum,
cognitive radio spectrum, peer-to-peer network spectrum, emergency
responder spectrum and the like in a spot market for spectrum 162
or a forward market for spectrum 140. Purchasing of spectrum
resources may be configured and managed by an expert system
operating on any of the external data sources 182 or on data
aggregated by the set of data aggregations systems 144 for the
platform. Spectrum resources may be purchased by an automated
system using an expert system, including machine learning or other
artificial intelligence, such as where resources are purchased with
favorable timing, such as based on an understanding of supply and
demand, that is determined by processing inputs from the various
data sources. For example, the spectrum purchase and sale machine
142 may purchase or reserve spectrum on a network resource for a
future networking activity managed by the platform based on
learning from a wide range of inputs about market conditions,
behavior data, and data regarding activities of agent and systems
within the platform 100, such as to obtain such resources at
favorable prices during surge periods of demand for spectrum. The
expert system may be trained on a data set of outcomes from
purchases under historical input conditions. The expert system may
be trained on a data set of human purchase decisions and/or may be
supervised by one or more human operators. The spectrum purchase
and sale machine 142 may also sell one or more spectrum-related
resources that are connected to, part of, or managed by the
platform 100 in a spot market for spectrum resources 162 or a
forward market for bandwidth 140. Sale may also be conducted by an
expert system operating on the various data sources described
herein, including with training on outcomes and human
supervision.
[0347] In embodiments, the intelligent resource coordination and
allocation engine 168, including the resource purchasing engine
164, the sale engine 172 and the testing and arbitrate engine 194,
may provide coordinated and automated allocation of resources and
coordinated execution of transactions across the various forward
markets 130 and spot markets 170 by coordinating the various
purchase and sale machines, such as by an expert system, such as a
machine learning system (which may model-based or a deep learning
system, and which may be trained on outcomes and/or supervised by
humans). For example, the coordination and allocation engine 168
may coordinate purchasing of resources for a set of assets and
coordinated sale of resources available from a set of assets, such
as a fleet of vehicles, a data center of processing and data
storage resources, an information technology network (on premises,
cloud, or hybrids), a fleet of energy production systems (renewable
or non-renewable), a smart home or building (including appliances,
machines, infrastructure components and systems, and the like
thereof that consume or produce resources), and the like. The
platform 100 may optimize allocation of resource purchasing, sale
and utilization based on data aggregated in the platform, such as
by tracking activities of various engines and agents, as well as by
taking inputs from external data sources 182. In embodiments,
outcomes may be provided as feedback for training the intelligent
resource coordination and allocation engine 168, such as outcomes
based on yield, profitability, optimization of resources,
optimization of business objectives, satisfaction of goals,
satisfaction of users or operators, or the like. For example, as
the energy for computational tasks becomes a significant fraction
of an enterprise's energy usage, the platform 100 may learn to
optimize how a set of machines that have energy storage capacity
allocate that capacity among computing tasks (such as for
cryptocurrency mining, application of neural networks, computation
on data and the like), other useful tasks (that may yield profits
or other benefits), storage for future use, or sale to the provider
of an energy grid. The platform 100 may be used by fleet operators,
enterprises, governments, municipalities, military units, first
responder units, manufacturers, energy producers, cloud platform
providers, and other enterprises and operators that own or operate
resources that consume or provide energy, computation, data
storage, bandwidth, or spectrum. The platform 100 may also be used
in connection with markets for attention, such as to use available
capacity of resources to support attention-based exchanges of
value, such as in advertising markets, micro-transaction markets,
and others.
[0348] Referring still to FIGS. 2A-2B, the platform 100 may include
a set of intelligent forecasting engines 192 that forecast one or
more attributes, parameters, variables, or other factors, such as
for use as inputs by the set of forward purchase and sale machines,
the intelligent transaction engines 126 (such as for intelligent
cryptocurrency execution) or for other purposes. Each of the set of
intelligent forecasting engines 192 may use data that is tracked,
aggregated, processed, or handled within the platform 100, such as
by the data aggregation system 144, as well as input data from
external data sources 182, such as social media data sources 180,
automated agent behavioral data sources 188, human behavioral data
sources 184, entity behavioral data sources 190 and IoT data
sources 198. These collective inputs may be used to forecast
attributes, such as using a model (e.g., Bayesian, regression, or
other statistical model), a rule, or an expert system, such as a
machine learning system that has one or more classifiers, pattern
recognizers, and predictors, such as any of the expert systems
described throughout this disclosure. In embodiments, the set of
intelligent forecasting engines 192 may include one or more
specialized engines that forecast market attributes, such as
capacity, demand, supply, and prices, using particular data sources
for particular markets. These may include an energy price
forecasting engine 215 that bases its forecast on behavior of an
automated agent, a network spectrum price forecasting engine 217
that bases its forecast on behavior of an automated agent, a REC
price forecasting engine 219 that bases its forecast on behavior of
an automated agent, a compute price forecasting engine 221 that
bases its forecast on behavior of an automated agent, a network
spectrum price forecasting engine 223 that bases its forecast on
behavior of an automated agent. In each case, observations
regarding the behavior of automated agents, such as ones used for
conversation, for dialog management, for managing electronic
commerce, for managing advertising and others may be provided as
inputs for forecasting to the engines. The intelligent forecasting
engines 192 may also include a range of engines that provide
forecasts at least in part based on entity behavior, such as
behavior of business and other organizations, such as marketing
behavior, sales behavior, product offering behavior, advertising
behavior, purchasing behavior, transactional behavior, merger and
acquisition behavior, and other entity behavior. These may include
an energy price forecasting engine 225 using entity behavior, a
network spectrum price forecasting engine 227 using entity
behavior, a REC price forecasting engine 229 using entity behavior,
a compute price forecasting engine 231 using entity behavior, and a
network spectrum price forecasting engine 233 using entity
behavior.
[0349] The intelligent forecasting engines 192 may also include a
range of engines that provide forecasts at least in part based on
human behavior, such as behavior of consumers and users, such as
purchasing behavior, shopping behavior, sales behavior, product
interaction behavior, energy utilization behavior, mobility
behavior, activity level behavior, activity type behavior,
transactional behavior, and other human behavior. These may include
an energy price forecasting engine 235 using human behavior, a
network spectrum price forecasting engine 237 using human behavior,
a REC price forecasting engine 239 using human behavior, a compute
price forecasting engine 241 using human behavior, and a network
spectrum price forecasting engine 243 using human behavior.
[0350] Referring still to FIGS. 2A-2B, the platform 100 may include
a set of intelligent transaction engines 136 that automate
execution of transactions in forward markets 130 and/or spot
markets 170 based on determination that favorable conditions exist,
such as by the intelligent resource allocation and coordination
engine 168 and/or with use of forecasts form the intelligent
forecasting engines 192. The intelligent transaction engines 136
may be configured to automatically execute transactions, using
available market interfaces, such as APIs, connectors, ports,
network interfaces, and the like, in each of the markets noted
above. In embodiments, the intelligent transaction engines may
execute transactions based on event streams that come from external
data sources, such as IoT data sources 198 and social media data
sources 180. The engines may include, for example, an IoT forward
energy transaction engine 195 and/or an IoT compute market
transaction engine 106, either or both of which may use data from
the Internet of Things to determine timing and other attributes for
market transaction in a market for one or more of the resources
described herein, such as an energy market transaction, a compute
resource transaction or other resource transaction. IoT data may
include instrumentation and controls data for one or more machines
(optionally coordinated as a fleet) that use or produce energy or
that use or have compute resources, weather data that influences
energy prices or consumption (such as wind data influencing
production of wind energy), sensor data from energy production
environments, sensor data from points of use for energy or compute
resources (such as vehicle traffic data, network traffic data, IT
network utilization data, Internet utilization and traffic data,
camera data from work sites, smart building data, smart home data,
and the like), and other data collected by or transferred within
the Internet of Things, including data stored in IoT platforms and
of cloud services providers like Amazon, IBM, and others. The
engines 136 may include engines that use social data to determine
timing of other attributes for a market transaction in one or more
of the resources described herein, such as a social data forward
energy transaction engine 199 and/or a social data compute market
transaction engine 116. Social data may include data from social
networking sites (e.g., Facebook.TM., YouTube.TM. Twitter.TM.,
Snapchat.TM., Instagram.TM., and others, data from websites, data
from e-commerce sites, and data from other sites that contain
information that may be relevant to determining or forecasting
behavior of users or entities, such as data indicating interest or
attention to particular topics, goods or services, data indicating
activity types and levels (such as may be observed by machine
processing of image data showing individuals engaged in activities,
including travel, work activities, leisure activities, and the
like. Social data may be supplied to machine learning, such as for
learning user behavior or entity behavior, and/or as an input to an
expert system, a model, or the like, such as one for determining,
based on the social data, the parameters for a transaction. For
example, an event or set of events in a social data stream may
indicate the likelihood of a surge of interest in an online
resource, a product, or a service, and compute resources,
bandwidth, storage, or like may be purchased in advance (avoiding
surge pricing) to accommodate the increased interest reflected by
the social data stream.
[0351] Referring to FIG. 3, the platform 100 may include
capabilities for transaction execution that involve one or more
distributed ledgers 113 and one or more smart contracts 103, where
the distributed ledgers 113 and smart contracts 103 are configured
to enable specialized transaction features for specific transaction
domains. One such domain is intellectual property, which
transactions are highly complex, involving licensing terms and
conditions that are somewhat difficult to manage, as compared to
more straightforward sales of goods or services. In embodiments, a
smart contract wrapper 105, such as wrapper aggregating
intellectual property, is provided, using a distributed ledger,
wherein the smart contract embeds IP licensing terms for
intellectual property that is embedded in the distributed ledger
and wherein executing an operation on the distributed ledger
provides access to the intellectual property and commits the
executing party to the IP licensing terms. Licensing terms for a
wide range of goods and services, including digital goods like
video, audio, video game, video game element, music, electronic
book and other digital goods may be managed by tracking
transactions involving them on a distributed ledger, whereby
publishers may verify a chain of licensing and sublicensing. The
distributed ledger may be configured to add each licensee to the
ledger, and the ledger may be retrieved at the point of use of a
digital item, such as in a streaming platform, to validate that
licensing has occurred.
[0352] In embodiments, an improved distributed ledger is provided
with the smart contract wrapper 105, such as an IP wrapper,
container, smart contract or similar mechanism for aggregating
intellectual property licensing terms, wherein a smart contract
wrapper on the distributed ledger allows an operation on the ledger
to add intellectual property to an aggregate stack of intellectual
property. In many cases, intellectual property builds on other
intellectual property, such as where software code is derived from
other code, where trade secrets or know-how for elements of a
process are combined to enable a larger process, where patents
covering sub-components of a system or steps in a process are
pooled, where elements of a video game include sub-component assets
from different creators, where a book contains contributions from
multiple authors, and the like. In embodiments, a smart IP wrapper
aggregates licensing terms for different intellectual property
items (including digital goods, including ones embodying different
types of intellectual property rights, and transaction data
involving the item, as well as optionally one or more portions of
the item corresponding to the transaction data, are stored in a
distributed ledger that is configured to enable validation of
agreement to the licensing terms (such as at appoint of use) and/or
access control to the item. In embodiments, a royalty apportionment
wrapper 115 may be provided in a system having a distributed ledger
for aggregating intellectual property licensing terms, wherein a
smart contract wrapper on the distributed ledger allows an
operation on the ledger to add intellectual property and to agree
to an apportionment of royalties among the parties in the ledger.
Thus, a ledger may accumulate contributions to the ledger along
with evidence of agreement to the apportionment of any royalties
among the contributors of the IP that is embedded in and/or
controlled by the ledger. The ledger may record licensing terms and
automatically vary them as new contributions are made, such as by
one or more rules. For example, contributors may be given a share
of a royalty stack according to a rule, such as based on a
fractional contribution, such as based on lines of code
contributed, lines of authorship, contribution to components of a
system, and the like. In embodiments, a distributed ledger may be
forked into versions that represent varying combinations of
sub-components of IP, such as to allow users to select combinations
that are of most use, thereby allowing contributors who have
contributed the most value to be rewarded. Variation and outcome
tracking may be iteratively improved, such as by machine
learning.
[0353] In embodiments, a distributed ledger is provided for
aggregating intellectual property licensing terms, wherein a smart
contract wrapper on the distributed ledger allows an operation on
the ledger to add intellectual property to an aggregate stack of
intellectual property.
[0354] In embodiments, the platform 100 may have an improved
distributed ledger for aggregating intellectual property licensing
terms, wherein a smart contract wrapper on the distributed ledger
allows an operation on the ledger to commit a party to a contract
term via an IP transaction wrapper 119 of the ledger. This may
include operations involving cryptocurrencies, tokens, or other
operations, as well as conventional payments and in-kind transfers,
such as of various resources described herein. The ledger may
accumulate evidence of commitments to IP transactions by parties,
such as entering into royalty terms, revenue sharing terms, IP
ownership terms, warranty and liability terms, license permissions
and restrictions, field of use terms, and many others.
[0355] In embodiments, improved distributed ledgers may include
ones having a tokenized instruction set, such that operation on the
distributed ledger provides provable access to the instruction set.
A party wishing to share permission to know how, a trade secret or
other valuable instructions may thus share the instruction set via
a distributed ledger that captures and stores evidence of an action
on the ledger by a third party, thereby evidencing access and
agreement to terms and conditions of access. In embodiments, the
platform 100 may have a distributed ledger that tokenizes
executable algorithmic logic 121, such that operation on the
distributed ledger provides provable access to the executable
algorithmic logic. A variety of instruction sets may be stored by a
distributed ledger, such as to verify access and verify agreement
to terms (such as smart contract terms). In embodiments,
instruction sets that embody trade secrets may be separated into
sub-components, so that operations must occur on multiple ledgers
to get (provable) access to a trade secret. This may permit parties
wishing to share secrets, such as with multiple sub-contractors or
vendors, to main provable access control, while separating
components among different vendors to avoid sharing an entire set
with a single party. Various kinds of executable instruction sets
may be stored on specialized distributed ledgers that may include
smart wrappers for specific types of instruction sets, such that
provable access control, validation of terms, and tracking of
utilization may be performed by operations on the distributed
ledger (which may include triggering access controls within a
content management system or other systems upon validation of
actions taken in a smart contract on the ledger. In embodiments,
the platform 100 may have a distributed ledger that tokenizes a 3D
printer instruction set 123, such that operation on the distributed
ledger provides provable access to the instruction set.
[0356] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set for a coating process 125,
such that operation on the distributed ledger provides provable
access to the instruction set.
[0357] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set for a semiconductor
fabrication process 129, such that operation on the distributed
ledger provides provable access to the fabrication process.
[0358] In embodiments, the platform 100 may have a distributed
ledger that tokenizes a firmware program 131, such that operation
on the distributed ledger provides provable access to the firmware
program.
[0359] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set for an FPGA 133, such that
operation on the distributed ledger provides provable access to the
FPGA.
[0360] In embodiments, the platform 100 may have a distributed
ledger that tokenizes serverless code logic 135, such that
operation on the distributed ledger provides provable access to the
serverless code logic.
[0361] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set for a crystal fabrication
system 139, such that operation on the distributed ledger provides
provable access to the instruction set.
[0362] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set for a food preparation
process 141, such that operation on the distributed ledger provides
provable access to the instruction set.
[0363] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set for a polymer production
process 143, such that operation on the distributed ledger provides
provable access to the instruction set.
[0364] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set for chemical synthesis
process 145, such that operation on the distributed ledger provides
provable access to the instruction set.
[0365] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set for a biological
production process 149, such that operation on the distributed
ledger provides provable access to the instruction set.
[0366] In embodiments, the platform 100 may have a distributed
ledger that tokenizes a trade secret with an expert wrapper 151,
such that operation on the distributed ledger provides provable
access to the trade secret and the wrapper provides validation of
the trade secret by the expert. An interface may be provided by
which an expert accesses the trade secret on the ledger and
verifies that the information is accurate and sufficient to allow a
third party to use the secret.
[0367] In embodiments, the platform 100 may have a distributed
ledger that aggregates views of a trade secret into a chain that
proves which and how many parties have viewed the trade secret.
Views may be used to allocate value to creators of the trade
secret, to operators of the platform 100, or the like.
[0368] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an instruction set 111, such that operation
on the distributed ledger provides provable access 155 to the
instruction set and execution of the instruction set on a system
results in recording a transaction in the distributed ledger.
[0369] In embodiments, the platform 100 may have a distributed
ledger that tokenizes an item of intellectual property and a
reporting system that reports an analytic result based on the
operations performed on the distributed ledger or the intellectual
property.
[0370] In embodiments, the platform 100 may have a distributed
ledger that aggregates a set of instructions, where an operation on
the distributed ledger adds at least one instruction to a
pre-existing set of instructions 161 to provide a modified set of
instructions.
[0371] Referring still to FIG. 3, an intelligent cryptocurrency
execution engine 183 may provide intelligence for the timing,
location and other attributes of a cryptocurrency transaction, such
as a mining transaction, an exchange transaction, a storage
transaction, a retrieval transaction, or the like. Cryptocurrencies
like Bitcoin.TM. are increasingly widespread, with specialized
coins having emerged for a wide variety of purposes, such as
exchanging value in various specialized market domains. Initial
offerings of such coins, or ICOs, are increasingly subject to
regulations, such as securities regulations, and in some cases to
taxation. Thus, while cryptocurrency transactions typically occur
within computer networks, jurisdictional factors may be important
in determining where, when and how to execute a transaction, store
a cryptocurrency, exchange it for value. In embodiments,
intelligent cryptocurrency execution engine 183 may use features
embedded in or wrapped around the digital object representing a
coin, such as features that cause the execution of transactions in
the coin to be undertaken with awareness of various conditions,
including geographic conditions, regulatory conditions, tax
conditions, market conditions, and the like.
[0372] In embodiments, the platform 100 may include a tax aware
coin 165 or smart wrapper for a cryptocurrency coin that directs
execution of a transaction involving the coin to a geographic
location based on tax treatment of at least one of the coin and the
transaction in the geographic location.
[0373] In embodiments, the platform 100 may include a
location-aware coin 169 or smart wrapper that enables a
self-executing cryptocurrency coin that commits a transaction upon
recognizing a location-based parameter that provides favorable tax
treatment.
[0374] In embodiments, the platform 100 may include an expert
system or AI agent 171 that uses machine learning to optimize the
execution of cryptocurrency transactions based on tax status.
Machine learning may use one or more models or heuristics, such as
populated with relevant jurisdictional tax data, may be trained on
a training set of human trading operations, may be supervised by
human supervisors, and/or may use a deep learning technique based
on outcomes over time, such as when operating on a wide range of
internal system data and external data sources 182 as described
throughout this disclosure.
[0375] In embodiments, the platform 100 may include regulation
aware coin 173 having a coin, a smart wrapper, and/or an expert
system that aggregates regulatory information covering
cryptocurrency transactions and automatically selects a
jurisdiction for an operation based on the regulatory information.
Machine learning may use one or more models or heuristics, such as
populated with relevant jurisdictional regulatory data, may be
trained on a training set of human trading operations, may be
supervised by human supervisors, and/or may use a deep learning
technique based on outcomes over time, such as when operating on a
wide range of internal system data and external data sources 182 as
described throughout this disclosure.
[0376] In embodiments, the platform 100 may include an energy
price-aware coin 175, wrapper, or expert system that uses machine
learning to optimize the execution of a cryptocurrency transaction
based on real time energy price information for an available energy
source. Cryptocurrency transactions, such as coin mining and
blockchain operations, may be highly energy intensive. An energy
price-aware coin may be configured to time such operations based on
energy price forecasts, such as with one or more of the forecasting
engines 192 described throughout this disclosure.
[0377] In embodiments, the platform 100 may include an energy
source aware coin 179, wrapper, or expert system that uses machine
learning to optimize the execution of a cryptocurrency transaction
based on an understanding of available energy sources to power
computing resources to execute the transaction. For example, coin
mining may be performed only when renewable energy sources are
available. Machine learning for optimization of a transaction may
use one or more models or heuristics, such as populated with
relevant energy source data (such as may be captured in a knowledge
graph, which may contain energy source information by type,
location and operating parameters), may be trained on a training
set of input-output data for human-initiated transactions, may be
supervised by human supervisors, and/or may use a deep learning
technique based on outcomes over time, such as when operating on a
wide range of internal system data and external data sources 182 as
described throughout this disclosure.
[0378] In embodiments, the platform 100 may include a charging
cycle aware coin 181, wrapper, or an expert system that uses
machine learning to optimize charging and recharging cycle of a
rechargeable battery system to provide energy for execution of a
cryptocurrency transaction. For example, a battery may be
discharged for a cryptocurrency transaction only if a minimum
threshold of battery charge is maintained for other operational
use, if re-charging resources are known to be readily available, or
the like. Machine learning for optimization of charging and
recharging may use one or more models or heuristics, such as
populated with relevant battery data (such as may be captured in a
knowledge graph, which may contain energy source information by
type, location and operating parameters), may be trained on a
training set of human operations, may be supervised by human
supervisors, and/or may use a deep learning technique based on
outcomes over time, such as when operating on a wide range of
internal system data and external data sources 182 as described
throughout this disclosure.
[0379] Optimization of various intelligent coin operations may
occur with machine learning that is trained on outcomes, such as
financial profitability. Any of the machine learning systems
described throughout this disclosure may be used for optimization
of intelligent cryptocurrency transaction management.
[0380] In embodiments, compute resources, such as those mentioned
throughout this disclosure, may be allocated to perform a range of
computing tasks, both for operations that occur within the platform
100, ones that are managed by the platform, and ones that involve
the activities, workflows and processes of various assets that may
be owned, operated or managed in conjunction with the platform,
such as sets or fleets of assets that have or use computing
resources. Examples of compute tasks include, without limitation,
cryptocurrency mining, distributed ledger calculations and storage,
forecasting tasks, transaction execution tasks, spot market testing
tasks, internal data collection tasks, external data collection,
machine learning tasks, and others. As noted above, energy, compute
resources, bandwidth, spectrum, and other resources may be
coordinated, such as by machine learning, for these tasks. Outcome
and feedback information may be provided for the machine learning,
such as outcomes for any of the individual tasks and overall
outcomes, such as yield and profitability for business or other
operations involving the tasks.
[0381] In embodiments, networking resources, such as those
mentioned throughout this disclosure, may be allocated to perform a
range of networking tasks, both for operations that occur within
the platform 100, ones that are managed by the platform, and ones
that involve the activities, workflows and processes of various
assets that may be owned, operated or managed in conjunction with
the platform, such as sets or fleets of assets that have or use
networking resources. Examples of networking tasks include
cognitive network coordination, network coding, peer bandwidth
sharing (including, for example cost-based routing, value-based
routing, outcome-based routing and the like), distributed
transaction execution, spot market testing, randomization (e.g.,
using genetic programming with outcome feedback to vary network
configurations and transmission paths), internal data collection
and external data collection. As noted above, energy, compute
resources, bandwidth, spectrum, and other resources may be
coordinated, such as by machine learning, for these networking
tasks. Outcome and feedback information may be provided for the
machine learning, such as outcomes for any of the individual tasks
and overall outcomes, such as yield and profitability for business
or other operations involving the tasks.
[0382] In embodiments, data storage resources, such as those
mentioned throughout this disclosure, may be allocated to perform a
range of data storage tasks, both for operations that occur within
the platform 100, ones that are managed by the platform, and ones
that involve the activities, workflows and processes of various
assets that may be owned, operated or managed in conjunction with
the platform, such as sets or fleets of assets that have or use
networking resources. Examples of data storage tasks include
distributed ledger storage, storage of internal data (such as
operational data with the platform), cryptocurrency storage, smart
wrapper storage, storage of external data, storage of feedback and
outcome data, and others. As noted above, data storage, energy,
compute resources, bandwidth, spectrum, and other resources may be
coordinated, such as by machine learning, for these data storage
tasks. Outcome and feedback information may be provided for the
machine learning, such as outcomes for any of the individual tasks
and overall outcomes, such as yield and profitability for business
or other operations involving the tasks.
[0383] In embodiments, smart contracts, such as ones embodying
terms relating to intellectual property, trade secrets, know how,
instruction sets, algorithmic logic, and the like may embody or
include contract terms, which may include terms and conditions for
options, royalty stacking terms, field exclusivity, partial
exclusivity, pooling of intellectual property, standards terms
(such as relating to essential and non-essential patent usage),
technology transfer terms, consulting service terms, update terms,
support terms, maintenance terms, derivative works terms, copying
terms, and performance-related rights or metrics, among many
others.
[0384] In embodiments where an instruction set is embodied in
digital form, such as contained in or managed by a distributed
ledger transactions system, various systems may be configured with
interfaces that allow them to access and use the instruction sets.
In embodiments, such systems may include access control features
that validate proper licensing by inspection of a distributed
ledger, a key, a token, or the like that indicates the presence of
access rights to an instruction set. Such systems that execute
distributed instruction sets may include systems for 3D printing,
crystal fabrication, semiconductor fabrication, coating items,
producing polymers, chemical synthesis and biological production,
among others.
[0385] Networking capabilities and network resources should be
understood to include a wide range of networking systems,
components and capabilities, including infrastructure elements for
3G, 4G, LTE, 5G and other cellular network types, access points,
routers, and other Wi-Fi elements, cognitive networking systems and
components, mobile networking systems and components, physical
layer, MAC layer and application layer systems and components,
cognitive networking components and capabilities, peer-to-peer
networking components and capabilities, optical networking
components and capabilities, and others.
[0386] Building blocks on expert systems and AI
[0387] Neural Net Systems
[0388] Referring to FIG. 4 through FIG. 31, embodiments of the
present disclosure, including ones involving expert systems,
self-organization, machine learning, artificial intelligence, and
the like, may benefit from the use of a neural net, such as a
neural net trained for pattern recognition, for classification of
one or more parameters, characteristics, or phenomena, for support
of autonomous control, and other purposes. References to a neural
net throughout this disclosure should be understood to encompass a
wide range of different types of neural networks, machine learning
systems, artificial intelligence systems, and the like, such as
feed forward neural networks, radial basis function neural
networks, self-organizing neural networks (e.g., Kohonen
self-organizing neural networks), recurrent neural networks,
modular neural networks, artificial neural networks, physical
neural networks, multi-layered neural networks, convolutional
neural networks, hybrids of neural networks with other expert
systems (e.g., hybrid fuzzy logic--neural network systems),
Autoencoder neural networks, probabilistic neural networks, time
delay neural networks, convolutional neural networks, regulatory
feedback neural networks, radial basis function neural networks,
recurrent neural networks, Hopfield neural networks, Boltzmann
machine neural networks, self-organizing map (SOM) neural networks,
learning vector quantization (LVQ) neural networks, fully recurrent
neural networks, simple recurrent neural networks, echo state
neural networks, long short-term memory neural networks,
bi-directional neural networks, hierarchical neural networks,
stochastic neural networks, genetic scale RNN neural networks,
committee of machines neural networks, associative neural networks,
physical neural networks, instantaneously trained neural networks,
spiking neural networks, neocognitron neural networks, dynamic
neural networks, cascading neural networks, neuro-fuzzy neural
networks, compositional pattern-producing neural networks, memory
neural networks, hierarchical temporal memory neural networks, deep
feed forward neural networks, gated recurrent unit (GCU) neural
networks, auto encoder neural networks, variational auto encoder
neural networks, de-noising auto encoder neural networks, sparse
auto-encoder neural networks, Markov chain neural networks,
restricted Boltzmann machine neural networks, deep belief neural
networks, deep convolutional neural networks, de-convolutional
neural networks, deep convolutional inverse graphics neural
networks, generative adversarial neural networks, liquid state
machine neural networks, extreme learning machine neural networks,
echo state neural networks, deep residual neural networks, support
vector machine neural networks, neural Turing machine neural
networks, and/or holographic associative memory neural networks, or
hybrids or combinations of the foregoing, or combinations with
other expert systems, such as rule-based systems, model-based
systems (including ones based on physical models, statistical
models, flow-based models, biological models, biomimetic models,
and the like).
[0389] In embodiments, FIGS. 5 through 31 depict exemplary neural
networks and FIG. 4 depicts a legend showing the various components
of the neural networks depicted throughout FIGS. 5 to 31. FIG. 4
depicts various neural net components depicted in cells that are
assigned functions and requirements. In embodiments, the various
neural net examples may include back fed data/sensor cells,
data/sensor cells, noisy input cells, and hidden cells. The neural
net components also include probabilistic hidden cells, spiking
hidden cells, output cells, match input/output cells, recurrent
cells, memory cells, different memory cells, kernels, and
convolution or pool cells.
[0390] In embodiments, FIG. 5 depicts an exemplary perceptron
neural network that may connect to, integrate with, or interface
with the platform 100. The platform may also be associated with
further neural net systems such as a feed forward neural network
(FIG. 6), a radial basis neural network (FIG. 7), a deep feed
forward neural network (FIG. 8), a recurrent neural network (FIG.
9), a long/short term neural network (FIG. 10), and a gated
recurrent neural network (FIG. 11). The platform may also be
associated with further neural net systems such as an auto encoder
neural network (FIG. 12), a variational neural network (FIG. 13), a
denoising neural network (FIG. 14), a sparse neural network (FIG.
15), a Markov chain neural network (FIG. 16), and a Hopfield
network neural network (FIG. 17). The platform may further be
associated with additional neural net systems such as a Boltzmann
machine neural network (FIG. 18), a restricted BM neural network
(FIG. 19), a deep belief neural network (FIG. 20), a deep
convolutional neural network (FIG. 21), a deconvolutional neural
network (FIG. 22), and a deep convolutional inverse graphics neural
network (FIG. 23). The platform may also be associated with further
neural net systems such as a generative adversarial neural network
(FIG. 24), a liquid state machine neural network (FIG. 25), an
extreme learning machine neural network (FIG. 26), an echo state
neural network (FIG. 27), a deep residual neural network (FIG. 28),
a Kohonen neural network (FIG. 29), a support vector machine neural
network (FIG. 30), and a neural Turing machine neural network (FIG.
31).
[0391] The foregoing neural networks may have a variety of nodes or
neurons, which may perform a variety of functions on inputs, such
as inputs received from sensors or other data sources, including
other nodes. Functions may involve weights, features, feature
vectors, and the like. Neurons may include perceptrons, neurons
that mimic biological functions (such as of the human senses of
touch, vision, taste, hearing, and smell), and the like. Continuous
neurons, such as with sigmoidal activation, may be used in the
context of various forms of neural net, such as where back
propagation is involved.
[0392] In many embodiments, an expert system or neural network may
be trained, such as by a human operator or supervisor, or based on
a data set, model, or the like. Training may include presenting the
neural network with one or more training data sets that represent
values, such as sensor data, event data, parameter data, and other
types of data (including the many types described throughout this
disclosure), as well as one or more indicators of an outcome, such
as an outcome of a process, an outcome of a calculation, an outcome
of an event, an outcome of an activity, or the like. Training may
include training in optimization, such as training a neural network
to optimize one or more systems based on one or more optimization
approaches, such as Bayesian approaches, parametric Bayes
classifier approaches, k-nearest-neighbor classifier approaches,
iterative approaches, interpolation approaches, Pareto optimization
approaches, algorithmic approaches, and the like. Feedback may be
provided in a process of variation and selection, such as with a
genetic algorithm that evolves one or more solutions based on
feedback through a series of rounds.
[0393] In embodiments, a plurality of neural networks may be
deployed in a cloud platform that receives data streams and other
inputs collected (such as by mobile data collectors) in one or more
transactional environments and transmitted to the cloud platform
over one or more networks, including using network coding to
provide efficient transmission. In the cloud platform, optionally
using massively parallel computational capability, a plurality of
different neural networks of various types (including modular
forms, structure-adaptive forms, hybrids, and the like) may be used
to undertake prediction, classification, control functions, and
provide other outputs as described in connection with expert
systems disclosed throughout this disclosure. The different neural
networks may be structured to compete with each other (optionally
including use evolutionary algorithms, genetic algorithms, or the
like), such that an appropriate type of neural network, with
appropriate input sets, weights, node types and functions, and the
like, may be selected, such as by an expert system, for a specific
task involved in a given context, workflow, environment process,
system, or the like.
[0394] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
feed forward neural network, which moves information in one
direction, such as from a data input, like a data source related to
at least one resource or parameter related to a transactional
environment, such as any of the data sources mentioned throughout
this disclosure, through a series of neurons or nodes, to an
output. Data may move from the input nodes to the output nodes,
optionally passing through one or more hidden nodes, without loops.
In embodiments, feed forward neural networks may be constructed
with various types of units, such as binary McCulloch-Pitts
neurons, the simplest of which is a perceptron.
[0395] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
capsule neural network, such as for prediction, classification, or
control functions with respect to a transactional environment, such
as relating to one or more of the machines and automated systems
described throughout this disclosure.
[0396] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
radial basis function (RBF) neural network, which may be preferred
in some situations involving interpolation in a multi-dimensional
space (such as where interpolation is helpful in optimizing a
multi-dimensional function, such as for optimizing a data
marketplace as described here, optimizing the efficiency or output
of a power generation system, a factory system, or the like, or
other situation involving multiple dimensions. In embodiments, each
neuron in the RBF neural network stores an example from a training
set as a "prototype." Linearity involved in the functioning of this
neural network offers RBF the advantage of not typically suffering
from problems with local minima or maxima.
[0397] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
radial basis function (RBF) neural network, such as one that
employs a distance criterion with respect to a center (e.g., a
Gaussian function). A radial basis function may be applied as a
replacement for a hidden layer, such as a sigmoidal hidden layer
transfer, in a multi-layer perceptron. An RBF network may have two
layers, such as where an input is mapped onto each RBF in a hidden
layer. In embodiments, an output layer may comprise a linear
combination of hidden layer values representing, for example, a
mean predicted output. The output layer value may provide an output
that is the same as or similar to that of a regression model in
statistics. In classification problems, the output layer may be a
sigmoid function of a linear combination of hidden layer values,
representing a posterior probability. Performance in both cases is
often improved by shrinkage techniques, such as ridge regression in
classical statistics. This corresponds to a prior belief in small
parameter values (and therefore smooth output functions) in a
Bayesian framework. RBF networks may avoid local minima, because
the only parameters that are adjusted in the learning process are
the linear mapping from hidden layer to output layer. Linearity
ensures that the error surface is quadratic and therefore has a
single minimum. In regression problems, this may be found in one
matrix operation. In classification problems, the fixed
non-linearity introduced by the sigmoid output function may be
handled using an iteratively re-weighted least squares function or
the like. RBF networks may use kernel methods such as support
vector machines (SVM) and Gaussian processes (where the RBF is the
kernel function). A non-linear kernel function may be used to
project the input data into a space where the learning problem may
be solved using a linear model.
[0398] In embodiments, an RBF neural network may include an input
layer, a hidden layer, and a summation layer. In the input layer,
one neuron appears in the input layer for each predictor variable.
In the case of categorical variables, N-1 neurons are used, where N
is the number of categories. The input neurons may, in embodiments,
standardize the value ranges by subtracting the median and dividing
by the interquartile range. The input neurons may then feed the
values to each of the neurons in the hidden layer. In the hidden
layer, a variable number of neurons may be used (determined by the
training process). Each neuron may consist of a radial basis
function that is centered on a point with as many dimensions as a
number of predictor variables. The spread (e.g., radius) of the RBF
function may be different for each dimension. The centers and
spreads may be determined by training. When presented with the
vector of input values from the input layer, a hidden neuron may
compute a Euclidean distance of the test case from the neuron's
center point and then apply the RBF kernel function to this
distance, such as using the spread values. The resulting value may
then be passed to the summation layer. In the summation layer, the
value coming out of a neuron in the hidden layer may be multiplied
by a weight associated with the neuron and may add to the weighted
values of other neurons. This sum becomes the output. For
classification problems, one output is produced (with a separate
set of weights and summation units) for each target category. The
value output for a category is the probability that the case being
evaluated has that category. In training of an RBF, various
parameters may be determined, such as the number of neurons in a
hidden layer, the coordinates of the center of each hidden-layer
function, the spread of each function in each dimension, and the
weights applied to outputs as they pass to the summation layer.
Training may be used by clustering algorithms (such as k-means
clustering), by evolutionary approaches, and the like.
[0399] In embodiments, a recurrent neural network may have a
time-varying, real-valued (more than just zero or one) activation
(output). Each connection may have a modifiable real-valued weight.
Some of the nodes are called labeled nodes, some output nodes, and
others hidden nodes. For supervised learning in discrete time
settings, training sequences of real-valued input vectors may
become sequences of activations of the input nodes, one input
vector at a time. At each time step, each non-input unit may
compute its current activation as a nonlinear function of the
weighted sum of the activations of all units from which it receives
connections. The system may explicitly activate (independent of
incoming signals) some output units at certain time steps.
[0400] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
self-organizing neural network, such as a Kohonen self-organizing
neural network, such as for visualization of views of data, such as
low-dimensional views of high-dimensional data. The self-organizing
neural network may apply competitive learning to a set of input
data, such as from one or more sensors or other data inputs from or
associated with a transactional environment, including any machine
or component that relates to the transactional environment. In
embodiments, the self-organizing neural network may be used to
identify structures in data, such as unlabeled data, such as in
data sensed from a range of data sources about or sensors in or
about in a transactional environment, where sources of the data are
unknown (such as where events may be coming from any of a range of
unknown sources). The self-organizing neural network may organize
structures or patterns in the data, such that they may be
recognized, analyzed, and labeled, such as identifying market
behavior structures as corresponding to other events and
signals.
[0401] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
recurrent neural network, which may allow for a bi-directional flow
of data, such as where connected units (e.g., neurons or nodes)
form a directed cycle. Such a network may be used to model or
exhibit dynamic temporal behavior, such as involved in dynamic
systems, such as a wide variety of the automation systems, machines
and devices described throughout this disclosure, such as an
automated agent interacting with a marketplace for purposes of
collecting data, testing spot market transactions, execution
transactions, and the like, where dynamic system behavior involves
complex interactions that a user may desire to understand, predict,
control and/or optimize. For example, the recurrent neural network
may be used to anticipate the state of a market, such as one
involving a dynamic process or action, such as a change in state of
a resource that is traded in or that enables a marketplace of
transactional environment. In embodiments, the recurrent neural
network may use internal memory to process a sequence of inputs,
such as from other nodes and/or from sensors and other data inputs
from or about the transactional environment, of the various types
described herein. In embodiments, the recurrent neural network may
also be used for pattern recognition, such as for recognizing a
machine, component, agent, or other item based on a behavioral
signature, a profile, a set of feature vectors (such as in an audio
file or image), or the like. In a non-limiting example, a recurrent
neural network may recognize a shift in an operational mode of a
marketplace or machine by learning to classify the shift from a
training data set consisting of a stream of data from one or more
data sources of sensors applied to or about one or more
resources.
[0402] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
modular neural network, which may comprise a series of independent
neural networks (such as ones of various types described herein)
that are moderated by an intermediary. Each of the independent
neural networks in the modular neural network may work with
separate inputs, accomplishing subtasks that make up the task the
modular network as whole is intended to perform. For example, a
modular neural network may comprise a recurrent neural network for
pattern recognition, such as to recognize what type of machine or
system is being sensed by one or more sensors that are provided as
input channels to the modular network and an RBF neural network for
optimizing the behavior of the machine or system once understood.
The intermediary may accept inputs of each of the individual neural
networks, process them, and create output for the modular neural
network, such an appropriate control parameter, a prediction of
state, or the like.
[0403] Combinations among any of the pairs, triplets, or larger
combinations, of the various neural network types described herein,
are encompassed by the present disclosure. This may include
combinations where an expert system uses one neural network for
recognizing a pattern (e.g., a pattern indicating a problem or
fault condition) and a different neural network for self-organizing
an activity or work flow based on the recognized pattern (such as
providing an output governing autonomous control of a system in
response to the recognized condition or pattern). This may also
include combinations where an expert system uses one neural network
for classifying an item (e.g., identifying a machine, a component,
or an operational mode) and a different neural network for
predicting a state of the item (e.g., a fault state, an operational
state, an anticipated state, a maintenance state, or the like).
Modular neural networks may also include situations where an expert
system uses one neural network for determining a state or context
(such as a state of a machine, a process, a work flow, a
marketplace, a storage system, a network, a data collector, or the
like) and a different neural network for self-organizing a process
involving the state or context (e.g., a data storage process, a
network coding process, a network selection process, a data
marketplace process, a power generation process, a manufacturing
process, a refining process, a digging process, a boring process,
or other process described herein).
[0404] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
physical neural network where one or more hardware elements is used
to perform or simulate neural behavior. In embodiments, one or more
hardware neurons may be configured to stream voltage values,
current values, or the like that represent sensor data, such as to
calculate information from analog sensor inputs representing energy
consumption, energy production, or the like, such as by one or more
machines providing energy or consuming energy for one or more
transactions. One or more hardware nodes may be configured to
stream output data resulting from the activity of the neural net.
Hardware nodes, which may comprise one or more chips,
microprocessors, integrated circuits, programmable logic
controllers, application-specific integrated circuits,
field-programmable gate arrays, or the like, may be provided to
optimize the machine that is producing or consuming energy, or to
optimize another parameter of some part of a neural net of any of
the types described herein. Hardware nodes may include hardware for
acceleration of calculations (such as dedicated processors for
performing basic or more sophisticated calculations on input data
to provide outputs, dedicated processors for filtering or
compressing data, dedicated processors for de-compressing data,
dedicated processors for compression of specific file or data types
(e.g., for handling image data, video streams, acoustic signals,
thermal images, heat maps, or the like), and the like. A physical
neural network may be embodied in a data collector, including one
that may be reconfigured by switching or routing inputs in varying
configurations, such as to provide different neural net
configurations within the data collector for handling different
types of inputs (with the switching and configuration optionally
under control of an expert system, which may include a
software-based neural net located on the data collector or
remotely). A physical, or at least partially physical, neural
network may include physical hardware nodes located in a storage
system, such as for storing data within a machine, a data storage
system, a distributed ledger, a mobile device, a server, a cloud
resource, or in a transactional environment, such as for
accelerating input/output functions to one or more storage elements
that supply data to or take data from the neural net. A physical,
or at least partially physical, neural network may include physical
hardware nodes located in a network, such as for transmitting data
within, to or from an industrial environment, such as for
accelerating input/output functions to one or more network nodes in
the net, accelerating relay functions, or the like. In embodiments,
of a physical neural network, an electrically adjustable resistance
material may be used for emulating the function of a neural
synapse. In embodiments, the physical hardware emulates the
neurons, and software emulates the neural network between the
neurons. In embodiments, neural networks complement conventional
algorithmic computers. They are versatile and may be trained to
perform appropriate functions without the need for any
instructions, such as classification functions, optimization
functions, pattern recognition functions, control functions,
selection functions, evolution functions, and others.
[0405] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
multilayered feed forward neural network, such as for complex
pattern classification of one or more items, phenomena, modes,
states, or the like. In embodiments, a multilayered feed forward
neural network may be trained by an optimization technique, such as
a genetic algorithm, such as to explore a large and complex space
of options to find an optimum, or near-optimum, global solution.
For example, one or more genetic algorithms may be used to train a
multilayered feed forward neural network to classify complex
phenomena, such as to recognize complex operational modes of
machines, such as modes involving complex interactions among
machines (including interference effects, resonance effects, and
the like), modes involving non-linear phenomena, modes involving
critical faults, such as where multiple, simultaneous faults occur,
making root cause analysis difficult, and others. In embodiments, a
multilayered feed forward neural network may be used to classify
results from monitoring of a marketplace, such as monitoring
systems, such as automated agents, that operate within the
marketplace, as well as monitoring resources that enable the
marketplace, such as computing, networking, energy, data storage,
energy storage, and other resources.
[0406] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
feed-forward, back-propagation multi-layer perceptron (MLLP) neural
network, such as for handling one or more remote sensing
applications, such as for taking inputs from sensors distributed
throughout various transactional environments. In embodiments, the
MLP neural network may be used for classification of transactional
environments and resource environments, such as spot markets,
forward markets, energy markets, renewable energy credit (REC)
markets, networking markets, advertising markets, spectrum markets,
ticketing markets, rewards markets, compute markets, and others
mentioned throughout this disclosure, as well as physical resources
and environments that produce them, such as energy resources
(including renewable energy environments, mining environments,
exploration environments, drilling environments, and the like,
including classification of geological structures (including
underground features and above ground features), classification of
materials (including fluids, minerals, metals, and the like), and
other problems. This may include fuzzy classification.
[0407] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
structure-adaptive neural network, where the structure of a neural
network is adapted, such as based on a rule, a sensed condition, a
contextual parameter, or the like. For example, if a neural network
does not converge on a solution, such as classifying an item or
arriving at a prediction, when acting on a set of inputs after some
amount of training, the neural network may be modified, such as
from a feed forward neural network to a recurrent neural network,
such as by switching data paths between some subset of nodes from
unidirectional to bi-directional data paths. The structure
adaptation may occur under control of an expert system, such as to
trigger adaptation upon occurrence of a trigger, rule or event,
such as recognizing occurrence of a threshold (such as an absence
of a convergence to a solution within a given amount of time) or
recognizing a phenomenon as requiring different or additional
structure (such as recognizing that a system is varying dynamically
or in a non-linear fashion). In one non-limiting example, an expert
system may switch from a simple neural network structure like a
feed forward neural network to a more complex neural network
structure like a recurrent neural network, a convolutional neural
network, or the like upon receiving an indication that a
continuously variable transmission is being used to drive a
generator, turbine, or the like in a system being analyzed.
[0408] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
autoencoder, autoassociator or Diabolo neural network, which may be
similar to a multilayer perceptron (MLP) neural network, such as
where there may be an input layer, an output layer and one or more
hidden layers connecting them. However, the output layer in the
auto-encoder may have the same number of units as the input layer,
where the purpose of the MLP neural network is to reconstruct its
own inputs (rather than just emitting a target value). Therefore,
the auto encoders may operate as an unsupervised learning model. An
auto encoder may be used, for example, for unsupervised learning of
efficient codings, such as for dimensionality reduction, for
learning generative models of data, and the like. In embodiments,
an auto-encoding neural network may be used to self-learn an
efficient network coding for transmission of analog sensor data
from a machine over one or more networks or of digital data from
one or more data sources. In embodiments, an auto-encoding neural
network may be used to self-learn an efficient storage approach for
storage of streams of data.
[0409] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
probabilistic neural network (PNN), which, in embodiments, may
comprise a multi-layer (e.g., four-layer) feed forward neural
network, where layers may include input layers, hidden layers,
pattern/summation layers and an output layer. In an embodiment of a
PNN algorithm, a parent probability distribution function (PDF) of
each class may be approximated, such as by a Parzen window and/or a
non-parametric function. Then, using the PDF of each class, the
class probability of a new input is estimated, and Bayes' rule may
be employed, such as to allocate it to the class with the highest
posterior probability. A PNN may embody a Bayesian network and may
use a statistical algorithm or analytic technique, such as Kernel
Fisher discriminant analysis technique. The PNN may be used for
classification and pattern recognition in any of a wide range of
embodiments disclosed herein. In one non-limiting example, a
probabilistic neural network may be used to predict a fault
condition of an engine based on collection of data inputs from
sensors and instruments for the engine.
[0410] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
time delay neural network (TDNN), which may comprise a feed forward
architecture for sequential data that recognizes features
independent of sequence position. In embodiments, to account for
time shifts in data, delays are added to one or more inputs, or
between one or more nodes, so that multiple data points (from
distinct points in time) are analyzed together. A time delay neural
network may form part of a larger pattern recognition system, such
as using a perceptron network. In embodiments, a TDNN may be
trained with supervised learning, such as where connection weights
are trained with back propagation or under feedback. In
embodiments, a TDNN may be used to process sensor data from
distinct streams, such as a stream of velocity data, a stream of
acceleration data, a stream of temperature data, a stream of
pressure data, and the like, where time delays are used to align
the data streams in time, such as to help understand patterns that
involve understanding of the various streams (e.g., changes in
price patterns in spot or forward markets).
[0411] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
convolutional neural network (referred to in some cases as a CNN, a
ConvNet, a shift invariant neural network, or a space invariant
neural network), wherein the units are connected in a pattern
similar to the visual cortex of the human brain. Neurons may
respond to stimuli in a restricted region of space, referred to as
a receptive field. Receptive fields may partially overlap, such
that they collectively cover the entire (e.g., visual) field. Node
responses may be calculated mathematically, such as by a
convolution operation, such as using multilayer perceptrons that
use minimal preprocessing. A convolutional neural network may be
used for recognition within images and video streams, such as for
recognizing a type of machine in a large environment using a camera
system disposed on a mobile data collector, such as on a drone or
mobile robot. In embodiments, a convolutional neural network may be
used to provide a recommendation based on data inputs, including
sensor inputs and other contextual information, such as
recommending a route for a mobile data collector. In embodiments, a
convolutional neural network may be used for processing inputs,
such as for natural language processing of instructions provided by
one or more parties involved in a workflow in an environment. In
embodiments, a convolutional neural network may be deployed with a
large number of neurons (e.g., 100,000, 500,000 or more), with
multiple (e.g., 4, 5, 6 or more) layers, and with many (e.g.,
millions) of parameters. A convolutional neural net may use one or
more convolutional nets.
[0412] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
regulatory feedback network, such as for recognizing emergent
phenomena (such as new types of behavior not previously understood
in a transactional environment).
[0413] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
self-organizing map (SOM), involving unsupervised learning. A set
of neurons may learn to map points in an input space to coordinates
in an output space. The input space may have different dimensions
and topology from the output space, and the SOM may preserve these
while mapping phenomena into groups.
[0414] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
learning vector quantization neural net (LVQ). Prototypical
representatives of the classes may parameterize, together with an
appropriate distance measure, in a distance-based classification
scheme.
[0415] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
echo state network (ESN), which may comprise a recurrent neural
network with a sparsely connected, random hidden layer. The weights
of output neurons may be changed (e.g., the weights may be trained
based on feedback). In embodiments, an ESN may be used to handle
time series patterns, such as, in an example, recognizing a pattern
of events associated with a market, such as the pattern of price
changes in response to stimuli.
[0416] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
Bi-directional, recurrent neural network (BRNN), such as using a
finite sequence of values (e.g., voltage values from a sensor) to
predict or label each element of the sequence based on both the
past and the future context of the element. This may be done by
adding the outputs of two RNNs, such as one processing the sequence
from left to right, the other one from right to left. The combined
outputs are the predictions of target signals, such as ones
provided by a teacher or supervisor. A bi-directional RNN may be
combined with a long short-term memory RNN.
[0417] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
hierarchical RNN that connects elements in various ways to
decompose hierarchical behavior, such as into useful subprograms.
In embodiments, a hierarchical RNN may be used to manage one or
more hierarchical templates for data collection in a transactional
environment.
[0418] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
stochastic neural network, which may introduce random variations
into the network. Such random variations may be viewed as a form of
statistical sampling, such as Monte Carlo sampling.
[0419] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
genetic scale recurrent neural network. In such embodiments, an RNN
(often an LSTM) is used where a series is decomposed into a number
of scales where every scale informs the primary length between two
consecutive points. A first order scale consists of a normal RNN, a
second order consists of all points separated by two indices and so
on. The Nth order RNN connects the first and last node. The outputs
from all the various scales may be treated as a committee of
members, and the associated scores may be used genetically for the
next iteration.
[0420] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
committee of machines (CoM), comprising a collection of different
neural networks that together "vote" on a given example. Because
neural networks may suffer from local minima, starting with the
same architecture and training, but using randomly different
initial weights often gives different results. A CoM tends to
stabilize the result.
[0421] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
associative neural network (ASNN), such as involving an extension
of a committee of machines that combines multiple feed forward
neural networks and a k-nearest neighbor technique. It may use the
correlation between ensemble responses as a measure of distance
amid the analyzed cases for the kNN. This corrects the bias of the
neural network ensemble. An associative neural network may have a
memory that may coincide with a training set. If new data become
available, the network instantly improves its predictive ability
and provides data approximation (self-learns) without retraining.
Another important feature of ASNN is the possibility to interpret
neural network results by analysis of correlations between data
cases in the space of models.
[0422] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
instantaneously trained neural network (ITNN), where the weights of
the hidden and the output layers are mapped directly from training
vector data.
[0423] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
spiking neural network, which may explicitly consider the timing of
inputs. The network input and output may be represented as a series
of spikes (such as a delta function or more complex shapes). SNNs
may process information in the time domain (e.g., signals that vary
over time, such as signals involving dynamic behavior of markets or
transactional environments). They are often implemented as
recurrent networks.
[0424] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
dynamic neural network that addresses nonlinear multivariate
behavior and includes learning of time-dependent behavior, such as
transient phenomena and delay effects. Transients may include
behavior of shifting market variables, such as prices, available
quantities, available counterparties, and the like.
[0425] In embodiments, cascade correlation may be used as an
architecture and supervised learning algorithm, supplementing
adjustment of the weights in a network of fixed topology.
Cascade-correlation may begin with a minimal network, then
automatically trains and add new hidden units one by one, creating
a multi-layer structure. Once a new hidden unit has been added to
the network, its input-side weights may be frozen. This unit then
becomes a permanent feature-detector in the network, available for
producing outputs or for creating other, more complex feature
detectors. The cascade-correlation architecture may learn quickly,
determine its own size and topology, and retain the structures it
has built even if the training set changes and requires no
back-propagation.
[0426] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
neuro-fuzzy network, such as involving a fuzzy inference system in
the body of an artificial neural network. Depending on the type,
several layers may simulate the processes involved in a fuzzy
inference, such as fuzzification, inference, aggregation and
defuzzification. Embedding a fuzzy system in a general structure of
a neural net as the benefit of using available training methods to
find the parameters of a fuzzy system.
[0427] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
compositional pattern-producing network (CPPN), such as a variation
of an associative neural network (ANN) that differs the set of
activation functions and how they are applied. While typical ANNs
often contain only sigmoid functions (and sometimes Gaussian
functions), CPPNs may include both types of functions and many
others. Furthermore, CPPNs may be applied across the entire space
of possible inputs, so that they may represent a complete image.
Since they are compositions of functions, CPPNs in effect encode
images at infinite resolution and may be sampled for a particular
display at whatever resolution is optimal.
[0428] This type of network may add new patterns without
re-training. In embodiments, methods and systems described herein
that involve an expert system or self-organization capability may
use a one-shot associative memory network, such as by creating a
specific memory structure, which assigns each new pattern to an
orthogonal plane using adjacently connected hierarchical
arrays.
[0429] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
hierarchical temporal memory (HTM) neural network, such as
involving the structural and algorithmic properties of the
neocortex. HTM may use a biomimetic model based on
memory-prediction theory. HTM may be used to discover and infer the
high-level causes of observed input patterns and sequences.
[0430] Holographic Associative Memory
[0431] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
holographic associative memory (HAM) neural network, which may
comprise an analog, correlation-based, associative,
stimulus-response system. Information may be mapped onto the phase
orientation of complex numbers. The memory is effective for
associative memory tasks, generalization and pattern recognition
with changeable attention.
[0432] In embodiments, various embodiments involving network coding
may be used to code transmission data among network nodes in a
neural net, such as where nodes are located in one or more data
collectors or machines in a transactional environment.
[0433] Integrated Circuit Building Blocks
[0434] In embodiments, one or more of the controllers, circuits,
systems, data collectors, storage systems, network elements, or the
like as described throughout this disclosure may be embodied in or
on an integrated circuit, such as an analog, digital, or mixed
signal circuit, such as a microprocessor, a programmable logic
controller, an application-specific integrated circuit, a field
programmable gate array, or other circuits, such as embodied on one
or more chips disposed on one or more circuit boards, such as to
provide in hardware (with potentially accelerated speed, energy
performance, input-output performance, or the like) one or more of
the functions described herein. This may include setting up
circuits with up to billions of logic gates, flip-flops,
multiplexers, and other circuits in a small space, facilitating
high speed processing, low power dissipation, and reduced
manufacturing cost compared with board-level integration. In
embodiments, a digital IC, typically a microprocessor, digital
signal processor, microcontroller, or the like may use Boolean
algebra to process digital signals to embody complex logic, such as
involved in the circuits, controllers, and other systems described
herein. In embodiments, a data collector, an expert system, a
storage system, or the like may be embodied as a digital integrated
circuit, such as a logic IC, memory chip, interface IC (e.g., a
level shifter, a serializer, a deserializer, and the like), a power
management IC and/or a programmable device; an analog integrated
circuit, such as a linear IC, RF IC, or the like, or a mixed signal
IC, such as a data acquisition IC (including A/D converters, D/A
converter, digital potentiometers) and/or a clock/timing IC.
[0435] With reference to FIG. 32, the environment includes an
intelligent energy and compute facility (such as a large scale
facility hosting many compute resources and having access to a
large energy source, such as a hydropower source), as well as a
host intelligent energy and compute facility resource management
platform, referred to in some cases for convenience as the energy
and information technology platform (with networking, data storage,
data processing and other resources as described herein), a set of
data sources, a set of expert systems, interfaces to a set of
market platforms and external resources, and a set of user (or
client) systems and devices.
[0436] Intelligent Energy and Compute Facility
[0437] A facility may be configured to access an inexpensive (at
least during some time periods) power source (such as a hydropower
dam, a wind farm, a solar array, a nuclear power plant, or a grid),
to contain a large set of networked information technology
resources, including processing units, servers, and the like that
are capable of flexible utilization (such as by switching inputs,
switching configurations, switching programming and the like), and
to provide a range of outputs that can also be flexibly configured
(such as passing through power to a smart grid, providing
computational results (such as for cryptocurrency mining,
artificial intelligence, or analytics). A facility may include a
power storage system, such as for large scale storage of available
power.
[0438] Intelligent Energy and Compute Facility Resource Management
Platform
[0439] In operation, a user can access the energy and information
technology platform to initiate and manage a set of activities that
involve optimizing energy and computing resources among a diverse
set of available tasks. Energy resources may include hydropower,
nuclear power, wind power, solar power, grid power and the like, as
well as energy storage resources, such as batteries, gravity power,
and storage using thermal materials, such as molten salts.
Computing resources may include GPUs, FPGAs, servers, chips, Asics,
processors, data storage media, networking resources, and many
others. Available tasks may include cryptocurrency hash processing,
expert system processing, computer vision processing, NLP, path
optimization, applications of models such as for analytics,
etc.
[0440] In embodiments, the platform may include various subsystems
that may be implemented as micro services, such that other
subsystems of the system access the functionality of a subsystem
providing a micro service via application programming interface
API. In some embodiments, the various services that are provided by
the subsystems may be deployed in bundles that are integrated, such
as by a set of APIs. Each of the subsystems is described in greater
detail with respect to FIG. 130.
[0441] The External Data Sources can include any system or device
that can provide data to the platform. Examples of data sources can
include market data sources (e.g., for financial markets,
commercial markets (including e-commerce), advertising markets,
energy markets, telecommunication markets, and many others). The
energy and computing resource platform accesses external data
sources via a network (e.g., the Internet) in any suitable manner
(e.g., crawlers, extract-transform-load (ETL) systems, gateways,
brokers, application programming interfaces (APIs), spiders,
distributed database queries, and the like).
[0442] A facility is a facility that has an energy resource (e.g.,
a hydro power resource) and a set of compute resource (e.g., a set
of flexible computing resources that can be provisioned and managed
to perform computing tasks, such as GPUs, FPGAs and many others, a
set of flexible networking resources that can similarly be
provisioned and managed, such as by adjusting network coding
protocols and parameters), and the like.
[0443] User and client systems and devices can include any system
or device that may consume one or more computing or energy resource
made available by the energy and computing resource platform.
Examples include cryptocurrency systems (e.g., for Bitcoin and
other cryptocurrency mining operations), expert and artificial
intelligence systems (such as neural networks and other systems,
such as for computer vision, natural language processing, path
determination and optimization, pattern recognition, deep learning,
supervised learning, decision support, and many others), energy
management systems (such as smart grid systems), and many others.
User and client systems may include user devices, such as
smartphones, tablet computer devices, laptop computing devices,
personal computing devices, smart televisions, gaming consoles, and
the like.
[0444] Energy and computing resource platform Components in FIG.
130.
[0445] FIG. 130 illustrates an example energy and computing
resource platform according to some embodiments of the present
disclosure. In embodiments, the energy and computing resource
platform may include a processing system 13002, a storage system
13004, and a communication system 13006.
[0446] The processing system 13002 may include one or more
processors and memory. The processors may operate in an individual
or distributed manner. The processors may be in the same physical
device or in separate devices, which may or may not be located in
the same facility. The memory may store computer-executable
instructions that are executed by the one or more processors. In
embodiments, the processing system 13002 may execute the facility
management system 13008, the data acquisition system 13010, the
cognitive processes system 13012, the lead generation system 13014,
the content generation system 13016, and the workflow system
13018.
[0447] The storage system 13004 may include one or more
computer-readable storage mediums. The computer-readable storage
mediums may be located in the same physical device or in separate
devices, which may or may not be located in the same facility,
which may or may not be located in the same facility. The
computer-readable storage mediums may include flash devices,
solid-state memory devices, hard disk drives, and the like. In
embodiments, the storage system 13004 stores one or more of a
facility data store 13020, a person data store 13022, and an
external data store 13024.
[0448] The communication system 13006 may include one or more
transceivers that are configured to effectuate wireless or wired
communication with one or more external devices, including user
devices and/or servers, via a network (e.g., the Internet and/or a
cellular network). The communication system 13006 may implement any
suitable communication protocol. For example, the communication
system xxx may implement an IEEE 801.11 wireless communication
protocol and/or any suitable cellular communication protocol to
effectuate wireless communication with external devices and
external data stores 13024 via a wireless network.
[0449] Energy and Computing Resource Management Platform
[0450] Discovers, provisions, manages and optimizes energy and
compute resources using artificial intelligence and expert systems
with sensitivity to market and other conditions by learning on a
set of outcomes. Discovers and facilitates cataloging of resources,
optionally by user entry and/or automated detection (including peer
detection). May implement a graphical user interface to receive
relevant information regarding the energy and compute resources
that are available. This may include a "digital twin" of an energy
and compute facility that allows modeling, prediction and the like.
May generate a set of data record that define the facility or a set
of facilities under common ownership or operation by a host. The
data record may have any suitable schema. In some embodiments
(e.g., FIG. 131), the facility data records may include a facility
identifier (e.g., a unique identifier that corresponds to the
facility), a facility type (e.g., energy system and capabilities,
compute systems and capabilities, networking systems and
capabilities), facility attributes (e.g., name of the facility,
name of the facility initiator, description of the facility,
keywords of the facility, goals of the facility, timing elements,
schedules, and the like), participants/potential participants in
the facility (e.g., identifiers of owners, operators, hosts,
service providers, consumers, clients, users, workers, and others),
and any suitable metadata (e.g., creation date, launch date,
scheduled requirements and the like). May generate content, such as
a document, message, alert, report, webpage and/or application page
based on the contents of the data record. For example, may obtain
the data record of the facility and may populate a webpage template
with the data contained therein. In addition, there can be
management of existing facilities, updates the data record of a
facility, determinations of outcomes (e.g., energy produced,
compute tasks completed, processing outcomes achieved, financial
outcomes achieved, service levels met and many others), and sending
of information (e.g., updates, alerts, requests, instructions, and
the like) to individuals and systems.
[0451] Data Acquisition Systems can acquire various types of data
from different data sources and organizes that data into one or
more data structures. In embodiments, the data acquisition system
receives data from users via a user interface (e.g., user types in
profile information). In embodiments, the data acquisition system
can retrieve data from passive electronic sources. In embodiments,
the data acquisition system can implement crawlers to crawl
different websites or applications. In embodiments, the data
acquisition system can implement an API to retrieve data from
external data sources or user devices (e.g., various contact lists
from user's phone or email account). In embodiments, the data
acquisition system can structure the obtained data into appropriate
data structures. In embodiments, the data acquisition system
generates and maintains person records based on data collected
regarding individuals. In embodiments, a person datastore stores
person records. In some of these embodiments, the person datastore
may include one or more databases, indexes, tables, and the like.
Each person record may correspond to a respective individual and
may be organized according to any suitable schema.
[0452] FIG. 132 illustrates an example schema of a person record.
In the example, each person record may include a unique person
identifier (e.g., username or value), and may define all data
relating to a person, including a person's name, facilities they
are a part of or associated with (e.g., a list of facility
identifiers), attributes of the person (age, location, job,
company, role, skills, competencies, capabilities, education
history, job history, and the like), a list of contacts or
relationships of the person (e.g., in a role hierarchy or graph),
and any suitable metadata (e.g., date joined, dates actions were
taken, dates input was received, and the like).
[0453] In embodiments, the data acquisition system generates and
maintains one or more graphs based on the retrieved data. In some
embodiments, a graph datastore may store the one or more graphs.
The graph may be specific to a facility or may be a global graph.
The graph may be used in many different applications (e.g.,
identifying a set of roles, such as for authentication, for
approvals, and the like for persons, or identifying system
configurations, capabilities, or the like, such as hierarchies of
energy producing, computing, networking, or other systems,
subsystems and/or resources).
[0454] In embodiments, a graph may be stored in a graph database,
where data is stored in a collection of nodes and edges. In some
embodiments, a graph has nodes representing entities and edges
representing relationships, each node may have a node type (also
referred to as an entity type) and an entity value, each edge may
have a relationship type and may define a relationship between two
entities. For example, a person node may include a person ID that
identifies the individual represented by the node and a company
node may include a company identifier that identifies a company. A
"works for" edge that is directed from a person node to a company
node may denote that the person represented by the edge node works
for the company represented by the company node. In another
example, a person node may include a person ID that identifies the
individual represented by the node and a facility node may include
a facility identifier that identifies a facility. A "manages" edge
that is directed from a person node to a facility node may denote
that the person represented by the person node is a manager of the
facility represented by the facility node. Furthermore in
embodiments, an edge or node may contain or reference additional
data. For example, a "manages" edge may include a function that
indicates a specific function within a facility that is managed by
a person. The graph(s) can be used in a number of different
applications, which are discussed with respect to the cognitive
processing system.
[0455] In embodiments, validated Identity information may be
imported from one or more identity information providers, as well
as data from LinkedIn.TM. and other social network sources
regarding data acquisition and structuring data. In embodiments,
the data acquisition system may include an identity management
system (not shown in Figs) of the platform may manage identity
stitching, identity resolution, identity normalization, and the
like, such as determining where an individual represented across
different social networking sites and email contacts is in fact the
same person. In embodiments, the data acquisition system may
include a profile aggregation system (not shown in Figs) that finds
and aggregates disparate pieces of information to generate a
comprehensive profile for a person. The profile aggregation system
may also deduplicate individuals.
[0456] Cognitive Processing Systems
[0457] The cognitive processing system 13312 may implement one or
more of machine learning processes, artificial intelligence
processes, analytics processes, natural language processing
processes, and natural language generation processes. FIG. 133
illustrates an example cognitive processing system according to
some embodiments of the present disclosure. In this example, the
cognitive processing system may include a machine learning system
13302, an artificial intelligence (AI) system 13304, an analytics
system 13306, a natural language processing system 13308, and a
natural language generation system 13310.
[0458] Machine Learning System
[0459] In embodiments, the machine learning system may train
models, such as predictive models (e.g., various types of neural
networks, regression based models, and other machine-learned
models). In embodiments, training can be supervised,
semi-supervised, or unsupervised. In embodiments, training can be
done using training data, which may be collected or generated for
training purposes.
[0460] A facility output model (or prediction model) may be a model
that receive facility attributes and outputs one or more
predictions regarding the production or other output of a facility.
Examples of predictions may be the amount of energy a facility will
produce, the amount of processing the facility will undertake, the
amount of data a network will be able to transfer, the amount of
data that can be stored, the price of a component, service or the
like (such as supplied to or provided by a facility), a profit
generated by accomplishing a given tasks, the cost entailed in
performing an action, and the like. In each case, the machine
learning system optionally trains a model based on training data.
In embodiments, the machine learning system may receive vectors
containing facility attributes (e.g., facility type, facility
capability, objectives sought, constraints or rules that apply to
utilization of resources or the facility, or the like), person
attributes (e.g., role, components managed, and the like), and
outcomes (e.g., energy produced, computing tasks completed, and
financial results, among many others). Each vector corresponds to a
respective outcome and the attributes of the respective facility
and respective actions that led to the outcome. The machine
learning system takes in the vectors and generates predictive model
based thereon. In embodiments, the machine learning system may
store the predictive models in the model datastore.
[0461] In embodiments, training can also be done based on feedback
received by the system, which is also referred to as "reinforcement
learning." In embodiments, the machine learning system may receive
a set of circumstances that led to a prediction (e.g., attributes
of facility, attributes of a model, and the like) and an outcome
related to the facility and may update the model according to the
feedback.
[0462] In embodiments, training may be provided from a training
data set that is created by observing actions of a set of humans,
such as facility managers managing facilities that have various
capabilities and that are involved in various contexts and
situations. This may include use of robotic process automation to
learn on a training data set of interactions of humans with
interfaces, such as graphical user interfaces, of one or more
computer programs, such as dashboards, control systems, and other
systems that are used to manage an energy and compute management
facility.
[0463] Artificial Intelligence (AI) Systems
[0464] In embodiments, the AI system leverages the predictive
models to make predictions regarding facilities. Examples of
predictions include ones related to inputs to a facility (e.g.,
available energy, cost of energy, cost of compute resources,
networking capacity and the like, as well as various market
information, such as pricing information for end use markets), ones
related to components or systems of a facility (including
performance predictions, maintenance predictions, uptime/downtime
predictions, capacity predictions and the like), ones related to
functions or workflows of the facility (such as ones that involved
conditions or states that may result in following one or more
distinct possible paths within a workflow, a process, or the like),
ones related to outputs of the facility, and others. In
embodiments, the AI system receives a facility identifier. In
response to the facility identifier, the AI system may retrieve
attributes corresponding to the facility. In some embodiments, the
AI system may obtain the facility attributes from a graph.
Additionally or alternatively, the AI system may obtain the
facility attributes from a facility record corresponding to the
facility identifier, and the person attributes from a person record
corresponding to the person identifier.
[0465] Examples of additional attributes that can be used to make
predictions about a facility or a related process of system
include: related facility information; owner goals (including
financial goals); client goals; and many more additional or
alternative attributes. In embodiments, the AI system may output
scores for each possible prediction, where each prediction
corresponds to a possible outcome. For example, in using a
prediction model used to determine a likelihood that a
hydroelectric source for a facility will produce 5 MW of power, the
prediction model can output a score for a "will produce" outcome
and a score for a "will not produce" outcome. The AI system may
then select the outcome with the highest score as the prediction.
Alternatively, the AI system may output the respective scores to a
requesting system.
[0466] Clustering Systems
[0467] In embodiments, a clustering system clusters records or
entities based on attributes contained herein. For example, similar
facilities, resources, people, clients, or the like may be
clustered. The clustering system may implement any suitable
clustering algorithm. For example, when clustering people records
to identify a list of customer leads corresponding to resources
that can be sold by a facility, the clustering system may implement
k-nearest neighbors clustering, whereby the clustering system
identifies k people records that most closely relate to the
attributes defined for the facility. In another example, the
clustering system may implement k-means clustering, such that the
clustering system identifies k different clusters of people
records, whereby the clustering system or another system selects
items from the cluster.
[0468] Analytics System
[0469] In embodiments, an analytics system may perform analytics
relating to various aspects of the energy and computing resource
platform. The analytics system may analyze certain communications
to determine which configurations of a facility produce the
greatest yield, what conditions tend to indicate potential faults
or problems, and the like.
[0470] Lead Generation System
[0471] FIG. 134 shows the manner by which the lead generation
system generates a lead list. Lead generation system receives a
list of potential leads 13402 (such as for consumers of available
products or resources). The lead generation system may provide the
list of leads to the clustering system 13404. The clustering system
clusters the profile of the lead with the clusters of facility
attributes 13406 to identify one or more clusters. In embodiments,
the clustering system returns a list of leads 13410. In other
embodiments, the clustering system returns the clusters 13408, and
the lead generation system selects the list of leads 13410 from the
cluster to which a prospect belongs.
[0472] FIG. 135 illustrates the manner by which the lead generation
system determines facility outputs for leads identified in the list
of leads. In embodiments, the lead generation system provides a
lead identifier of a respective lead to the AI system (step 13502).
The AI system may then obtain the lead attributes of the lead and
facility attributes of the facility and may feed the respective
attributes into a prediction model (step 13504). The prediction
model outputs a prediction, which may be scores associated with
each possible outcome, or a single predicted outcome that was
selected based on its respective score (e.g., the outcome having
the highest score) (step 13506). The lead generation system may
iterate in this manner for each lead in the lead list. For example,
the lead generation system may generate leads that are consumers of
compute capabilities, energy capabilities, predictions and
forecasts, optimization results, and others.
[0473] In embodiments, the lead generation system categorizes the
lead (step 13508) and generates a lead list (step 13512) which it
provides to the facility operator or host of the systems, including
an indicator of the reason why a lead may be willing to engage the
facility, such as, for example, that the lead is an intensive user
of computing resources, such as to forecast behavior of a complex,
multi-variable market, or to mine for cryptocurrency. In
embodiments, where more leads are stored and/or categorized, the
lead generation system continues checking the lead list (step
13510).
[0474] Content Generation Systems
[0475] In embodiments, a content generation system of the platform
generates content for a contact event, such as an email, text
message, or a post to a network, or a machine-to-machine message,
such as communicating via an API or a peer-to-peer system. In
embodiments, the content is customized using artificial
intelligence based on the attributes of the facility, attributes of
a recipient (e.g., based on the profile of a person, the role of a
person, or the like), and/or relating to the project or activity to
which the facility relates. The content generation system may be
seeded with a set of templates, which may be customized, such as by
training the content generation system on a training set of data
created by human writers, and which may be further trained by
feedback based on outcomes tracked by the platform, such as
outcomes indicating success of particular forms of communication in
generating donations to a facility, as well as other indicators as
noted throughout this disclosure. The content generation system may
customize content based on attributes of the facility, a project,
and/or one or more people, and the like. For example, a facility
manager may receive short messages about events related to facility
operations, including codes, acronyms and jargon, while an outside
consumer of outputs from the facility may receive a more formal
report relating to the same event.
[0476] FIG. 136 illustrates a manner by which the content
generation system may generate personalized content. The content
generation system receives a recipient id, a sender id (which may
be a person or a system, among others), and a facility id (step
13602). The content generation system may determine the appropriate
template (step 13604) to use based on the relationships among the
recipient, sender and facility and/or based on other considerations
(e.g., a recipient who is a busy manager is more likely to respond
to less formal messages or more formal messages). The content
generation system may provide the template (or an identifier
thereof) to the natural language generation system, along with the
recipient id, the sender id, and the facility id. The natural
language generation system may obtain facility attributes based on
the facility id, and person attributes corresponding to the
recipient or sender based on their identities (step 13606). The
natural language generation system may then generate the
personalized or customized content (step 13608) based on the
selected template, the facility parameters, and/or other attributes
of the various types described herein. The natural language
generation system may output the generated content (step 13610) to
the content generation system.
[0477] In embodiments, a person, such as a facility manager, may
approve the generated content provided by the content generation
system and/or make edits to the generated content, then send the
content, such as via email and/or other channels. In embodiments,
the platform tracks the contact event.
[0478] Referring to FIG. 137, an adaptive intelligence system 13704
may include an artificial intelligence system 13748, a digital twin
system 13720, and an adaptive device (or edge) intelligence system
13730. The artificial intelligence system 13748 may define a
machine learning model 13702 for performing analytics, simulation,
decision making, and prediction making related to data processing,
data analysis, simulation creation, and simulation analysis of one
or more of the transaction entities. The machine learning model
13702 is an algorithm and/or statistical model that performs
specific tasks without using explicit instructions, relying instead
on patterns and inference. The machine learning model 13702 builds
one or more mathematical models based on training data to make
predictions and/or decisions without being explicitly programmed to
perform the specific tasks. The machine learning model 13702 may
receive inputs of sensor data as training data, including event
data 13724 and state data 13772 related to one or more of the
transaction entities through data collection systems 13718 and
monitoring systems 13706 and connectivity facilities 13716. The
event data 13724 and state data 13772 may be stored in a data
storage system 13710 The sensor data input to the machine learning
model 13702 may be used to train the machine learning model 13702
to perform the analytics, simulation, decision making, and
prediction making relating to the data processing, data analysis,
simulation creation, and simulation analysis of the one or more of
the transaction entities. The machine learning model 13702 may also
use input data from a user or users of the information technology
system. The machine learning model 13702 may include an artificial
neural network, a decision tree, a support vector machine, a
Bayesian network, a genetic algorithm, any other suitable form of
machine learning model, or a combination thereof. The machine
learning model 13702 may be configured to learn through supervised
learning, unsupervised learning, reinforcement learning,
self-learning, feature learning, sparse dictionary learning,
anomaly detection, association rules, a combination thereof, or any
other suitable algorithm for learning.
[0479] The artificial intelligence system 13748 may also define the
digital twin system 13720 to create a digital replica of one or
more of the transaction entities. The digital replica of the one or
more of the transaction entities may use substantially real-time
sensor data to provide for substantially real-time virtual
representation of the transaction entity and provides for
simulation of one or more possible future states of the one or more
transaction entities. The digital replica exists simultaneously
with the one or more transaction entities being replicated. The
digital replica provides one or more simulations of both physical
elements and properties of the one or more transaction entities
being replicated and the dynamics thereof, in embodiments,
throughout the lifestyle of the one or more transaction entities
being replicated. The digital replica may provide a hypothetical
simulation of the one or more transaction entities, for example
during a design phase before the one or more transaction entities
are constructed or fabricated, or during or after construction or
fabrication of the one or more transaction entities by allowing for
hypothetical extrapolation of sensor data to simulate a state of
the one or more transaction entities, such as during high stress,
after a period of time has passed during which component wear may
be an issue, during maximum throughput operation, after one or more
hypothetical or planned improvements have been made to the one or
more transaction entities, or any other suitable hypothetical
situation. In some embodiments, the machine learning model 13702
may automatically predict hypothetical situations for simulation
with the digital replica, such as by predicting possible
improvements to the one or more transaction entities, predicting
when one or more components of the one or more transaction entities
may fail, and/or suggesting possible improvements to the one or
more transaction entities, such as changes to timing settings,
arrangement, components, or any other suitable change to the
transaction entities. The digital replica allows for simulation of
the one or more transaction entities during both design and
operation phases of the one or more transaction entities, as well
as simulation of hypothetical operation conditions and
configurations of the one or more transaction entities. The digital
replica allows for invaluable analysis and simulation of the one or
more transaction entities, by facilitating observation and
measurement of nearly any type of metric, including temperature,
wear, light, vibration, etc. not only in, on, and around each
component of the one or more transaction entities, but in some
embodiments within the one or more transaction entities. In some
embodiments, the machine learning model 13702 may process the
sensor data including the event data 13724 and the state data 13772
to define simulation data for use by the digital twin system 13720.
The machine learning model 13702 may, for example, receive state
data 13772 and event data 13724 related to a particular transaction
entity of the plurality of transaction entities and perform a
series of operations on the state data 13772 and the event data
13724 to format the state data 13772 and the event data 13724 into
a format suitable for use by the digital twin system 13720 in
creation of a digital replica of the transaction entity. For
example, one or more transaction entities may include a robot
configured to augment products on an adjacent assembly line. The
machine learning model 13702 may collect data from one or more
sensors positioned on, near, in, and/or around the robot. The
machine learning model 13702 may perform operations on the sensor
data to process the sensor data into simulation data and output the
simulation data to the digital twin system 13720. The digital twin
system 13720 simulation may use the simulation data to create one
or more digital replicas of the robot, the simulation including for
example metrics including temperature, wear, speed, rotation, and
vibration of the robot and components thereof. The simulation may
be a substantially real-time simulation, allowing for a human user
of the information technology to view the simulation of the robot,
metrics related thereto, and metrics related to components thereof,
in substantially real time. The simulation may be a predictive or
hypothetical situation, allowing for a human user of the
information technology to view a predictive or hypothetical
simulation of the robot, metrics related thereto, and metrics
related to components thereof.
[0480] In some embodiments, the machine learning model 13702 and
the digital twin system 13720 may process sensor data and create a
digital replica of a set of transaction entities of the plurality
of transaction entities to facilitate design, real-time simulation,
predictive simulation, and/or hypothetical simulation of a related
group of transaction entities. The digital replica of the set of
transaction entities may use substantially real-time sensor data to
provide for substantially real-time virtual representation of the
set of transaction entities and provide for simulation of one or
more possible future states of the set of transaction entities. The
digital replica exists simultaneously with the set of transaction
entities being replicated. The digital replica provides one or more
simulations of both physical elements and properties of the set of
transaction entities being replicated and the dynamics thereof, in
embodiments throughout the lifestyle of the set of transaction
entities being replicated. The one or more simulations may include
a visual simulation, such as a wire-frame virtual representation of
the one or more transaction entities that may be viewable on a
monitor, using an augmented reality (AR) apparatus, or using a
virtual reality (VR) apparatus. The visual simulation may be able
to be manipulated by a human user of the information technology
system, such as zooming or highlighting components of the
simulation and/or providing an exploded view of the one or more
transaction entities. The digital replica may provide a
hypothetical simulation of the set of transaction entities, for
example during a design phase before the one or more transaction
entities are constructed or fabricated, or during or after
construction or fabrication of the one or more transaction entities
by allowing for hypothetical extrapolation of sensor data to
simulate a state of the set of transaction entities, such as during
high stress, after a period of time has passed during which
component wear may be an issue, during maximum throughput
operation, after one or more hypothetical or planned improvements
have been made to the set of transaction entities, or any other
suitable hypothetical situation. In some embodiments, the machine
learning model 13702 may automatically predict hypothetical
situations for simulation with the digital replica, such as by
predicting possible improvements to the set of transaction
entities, predicting when one or more components of the set of
transaction entities may fail, and/or suggesting possible
improvements to the set of transaction entities, such as changes to
timing settings, arrangement, components, or any other suitable
change to the transaction entities. The digital replica allows for
simulation of the set of transaction entities during both design
and operation phases of the set of transaction entities, as well as
simulation of hypothetical operation conditions and configurations
of the set of transaction entities. The digital replica allows for
invaluable analysis and simulation of the one or more transaction
entities, by facilitating observation and measurement of nearly any
type of metric, including temperature, wear, light, vibration, etc.
not only in, on, and around each component of the set of
transaction entities, but in some embodiments within the set of
transaction entities. In some embodiments, the machine learning
model 13702 may process the sensor data including the event data
13724 and the state data 13772 to define simulation data for use by
the digital twin system 13720. The machine learning model 13702
may, for example, receive state data 13772 and event data 13724
related to a particular transaction entity of the plurality of
transaction entities and perform a series of operations on the
state data 13772 and the event data 13724 to format the state data
13772 and the event data 13724 into a format suitable for use by
the digital twin system 13720 in the creation of a digital replica
of the set of transaction entities. For example, a set of
transaction entities may include a die machine configured to place
products on a conveyor belt, the conveyor belt on which the die
machine is configured to place the products, and a plurality of
robots configured to add parts to the products as they move along
the assembly line. The machine learning model 13702 may collect
data from one or more sensors positioned on, near, in, and/or
around each of the die machines, the conveyor belt, and the
plurality of robots. The machine learning model 13702 may perform
operations on the sensor data to process the sensor data into
simulation data and output the simulation data to the digital twin
system 13720. The digital twin system 13720 simulation may use the
simulation data to create one or more digital replicas of the die
machine, the conveyor belt, and the plurality of robots, the
simulation including for example metrics including temperature,
wear, speed, rotation, and vibration of the die machine, the
conveyor belt, and the plurality of robots and components thereof.
The simulation may be a substantially real-time simulation,
allowing for a human user of the information technology to view the
simulation of the die machine, the conveyor belt, and the plurality
of robots, metrics related thereto, and metrics related to
components thereof, in substantially real time. The simulation may
be a predictive or hypothetical situation, allowing for a human
user of the information technology to view a predictive or
hypothetical simulation of the die machine, the conveyor belt, and
the plurality of robots, metrics related thereto, and metrics
related to components thereof.
[0481] In some embodiments, the machine learning model 13702 may
prioritize collection of sensor data for use in digital replica
simulations of one or more of the transaction entities. The machine
learning model 13702 may use sensor data and user inputs to train,
thereby learning which types of sensor data are most effective for
creation of digital replicate simulations of one or more of the
transaction entities. For example, the machine learning model 13702
may find that a particular transaction entity has dynamic
properties such as component wear and throughput affected by
temperature, humidity, and load. The machine learning model 13702
may, through machine learning, prioritize collection of sensor data
related to temperature, humidity, and load, and may prioritize
processing sensor data of the prioritized type into simulation data
for output to the digital twin system 13720. In some embodiments,
the machine learning model 13702 may suggest to a user of the
information technology system that more and/or different sensors of
the prioritized type be implemented in the information technology
near and around the transaction entity being simulation such that
more and/or better data of the prioritized type may be used in
simulation of the transaction entity via the digital replica
thereof.
[0482] In some embodiments, the machine learning model 13702 may be
configured to learn to determine which types of sensor data are to
be processed into simulation data for transmission to the digital
twin system 13720 based on one or both of a modeling goal and a
quality or type of sensor data. A modeling goal may be an objective
set by a user of the information technology system or may be
predicted or learned by the machine learning model 13702. Examples
of modeling goals include creating a digital replica capable of
showing dynamics of throughput on an assembly line, which may
include collection, simulation, and modeling of, e.g., thermal,
electrical power, component wear, and other metrics of a conveyor
belt, an assembly machine, one or more products, and other
components of the transaction ecosystem. The machine learning model
137102 may be configured to learn to determine which types of
sensor data are necessary to be processed into simulation data for
transmission to the digital twin system 13720 to achieve such a
model. In some embodiments, the machine learning model 13702 may
analyze which types of sensor data are being collected, the quality
and quantity of the sensor data being collected, and what the
sensor data being collected represents, and may make decisions,
predictions, analyses, and/or determinations related to which types
of sensor data are and/or are not relevant to achieving the
modeling goal and may make decisions, predictions, analyses, and/or
determinations to prioritize, improve, and/or achieve the quality
and quantity of sensor data being processed into simulation data
for use by the digital twin system 13720 in achieving the modeling
goal.
[0483] In some embodiments, a user of the information technology
system may input a modeling goal into the machine learning model
13702. The machine learning model 13702 may learn to analyze
training data to output suggestions to the user of the information
technology system regarding which types of sensor data are most
relevant to achieving the modeling goal, such as one or more types
of sensors positioned in, on, or near a transaction entity or a
plurality of transaction entities that is relevant to the
achievement of the modeling goal is and/or are not sufficient for
achieving the modeling goal, and how a different configuration of
the types of sensors, such as by adding, removing, or repositioning
sensors, may better facilitate achievement of the modeling goal by
the machine learning model 13702 and the digital twin system 13720.
In some embodiments, the machine learning model 13702 may
automatically increase or decrease collection rates, processing,
storage, sampling rates, bandwidth allocation, bitrates, and other
attributes of sensor data collection to achieve or better achieve
the modeling goal. In some embodiments, the machine learning model
13702 may make suggestions or predictions to a user of the
information technology system related to increasing or decreasing
collection rates, processing, storage, sampling rates, bandwidth
allocation, bitrates, and other attributes of sensor data
collection to achieve or better achieve the modeling goal. In some
embodiments, the machine learning model 13702 may use sensor data,
simulation data, previous, current, and/or future digital replica
simulations of one or more transaction entities of the plurality of
transaction entities to automatically create and/or propose
modeling goals. In some embodiments, modeling goals automatically
created by the machine learning model 13702 may be automatically
implemented by the machine learning model 13702. In some
embodiments, modeling goals automatically created by the machine
learning model 13702 may be proposed to a user of the information
technology system, and implemented only after acceptance and/or
partial acceptance by the user, such as after modifications are
made to the proposed modeling goal by the user.
[0484] In some embodiments, the user may input the one or more
modeling goals, for example, by inputting one or more modeling
commands to the information technology system. The one or more
modeling commands may include, for example, a command for the
machine learning model 13702 and the digital twin system 13720 to
create a digital replica simulation of one transaction entity or a
set of transaction entities, may include a command for the digital
replica simulation to be one or more of a real-time simulation, and
a hypothetical simulation. The modeling command may also include,
for example, parameters for what types of sensor data should be
used, sampling rates for the sensor data, and other parameters for
the sensor data used in the one or more digital replica
simulations. In some embodiments, the machine learning model 13702
may be configured to predict modeling commands, such as by using
previous modeling commands as training data. The machine learning
model 13702 may propose predicted modeling commands to a user of
the information technology system, for example, to facilitate
simulation of one or more of the transaction entities that may be
useful for the management of the transaction entities and/or to
allow the user to easily identify potential issues with or possible
improvements to the transaction entities. The system of FIG. 137
may include a transactions management platform and
applications.
[0485] In some embodiments, the machine learning model 13702 may be
configured to evaluate a set of hypothetical simulations of one or
more of the transaction entities. The set of hypothetical
simulations may be created by the machine learning model 13702 and
the digital twin system 13720 as a result of one or more modeling
commands, as a result of one or more modeling goals, one or more
modeling commands, by prediction by the machine learning model
13702, or a combination thereof. The machine learning model 13702
may evaluate the set of hypothetical simulations based on one or
more metrics defined by the user, one or more metrics defined by
the machine learning model 13702, or a combination thereof. In some
embodiments, the machine learning model 13702 may evaluate each of
the hypothetical simulations of the set of hypothetical simulations
independently of one another. In some embodiments, the machine
learning model 13702 may evaluate one or more of the hypothetical
simulations of the set of hypothetical simulations in relation to
one another, for example by ranking the hypothetical simulations or
creating tiers of the hypothetical simulations based on one or more
metrics.
[0486] In some embodiments, the machine learning model 13702 may
include one or more model interpretability systems to facilitate
human understanding of outputs of the machine learning model 13702,
as well as information and insight related to cognition and
processes of the machine learning model 13702, i.e., the one or
more model interpretability systems allow for human understanding
of not only "what" the machine learning model 13702 is outputting,
but also "why" the machine learning model 13702 is outputting the
outputs thereof, and what process led to the machine learning
models 13702 formulating the outputs. The one or more model
interpretability systems may also be used by a human user to
improve and guide training of the machine learning model 13702, to
help debug the machine learning model 13702, to help recognize bias
in the machine learning model 13702. The one or more model
interpretability systems may include one or more of linear
regression, logistic regression, a generalized linear model (GLM),
a generalized additive model (GAM), a decision tree, a decision
rule, RuleFit, Naive Bayes Classifier, a K-nearest neighbors
algorithm, a partial dependence plot, individual conditional
expectation (ICE), an accumulated local effects (ALE) plot, feature
interaction, permutation feature importance, a global surrogate
model, a local surrogate (LIME) model, scoped rules, i.e., anchors,
Shapley values, Shapley additive explanations (SHAP), feature
visualization, network dissection, or any other suitable machine
learning interpretability implementation. In some embodiments, the
one or more model interpretability systems may include a model
dataset visualization system. The model dataset visualization
system is configured to automatically provide to a human user of
the information technology system visual analysis related to
distribution of values of the sensor data, the simulation data, and
data nodes of the machine learning model 13702.
[0487] In some embodiments, the machine learning model 13702 may
include and/or implement an embedded model interpretability system,
such as a Bayesian case model (BCM) or glass box. The Bayesian case
model uses Bayesian case-based reasoning, prototype classification,
and clustering to facilitate human understanding of data such as
the sensor data, the simulation data, and data nodes of the machine
learning model 13702. In some embodiments, the model
interpretability system may include and/or implement a glass box
interpretability method, such as a Gaussian process, to facilitate
human understanding of data such as the sensor data, the simulation
data, and data nodes of the machine learning model 13702.
[0488] In some embodiments, the machine learning model 13702 may
include and/or implement testing with concept activation vectors
(TCAV). The TCAV allows the machine learning model 13702 to learn
human-interpretable concepts, such as "running," "not running,"
"powered," "not powered," "robot," "human," "truck," or "ship" from
examples by a process including defining the concept, determining
concept activation vectors, and calculating directional
derivatives. By learning human-interpretable concepts, objects,
states, etc., TCAV may allow the machine learning model 13702 to
output useful information related to the transaction entities and
data collected therefrom in a format that is readily understood by
a human user of the information technology system.
[0489] In some embodiments, the machine learning model 13702 may be
and/or include an artificial neural network, e.g. a connectionist
system configured to "learn" to perform tasks by considering
examples and without being explicitly programmed with task-specific
rules. The machine learning model 13702 may be based on a
collection of connected units and/or nodes that may act like
artificial neurons that may in some ways emulate neurons in a
biological brain. The units and/or nodes may each have one or more
connections to other units and/or nodes. The units and/or nodes may
be configured to transmit information, e.g. one or more signals, to
other units and/or nodes, process signals received from other units
and/or nodes, and forward processed signals to other units and/or
nodes. One or more of the units and/or nodes and connections
therebetween may have one or more numerical "weights" assigned. The
assigned weights may be configured to facilitate learning, i.e.,
training, of the machine learning model 13702. The weights assigned
weights may increase and/or decrease one or more signals between
one or more units and/or nodes, and in some embodiments may have
one or more thresholds associated with one or more of the weights.
The one or more thresholds may be configured such that a signal is
only sent between one or more units and/or nodes if a signal and/or
aggregate signal crosses the threshold. In some embodiments, the
units and/or nodes may be assigned to a plurality of layers, each
of the layers having one or both of inputs and outputs. A first
layer may be configured to receive training data, transform at
least a portion of the training data, and transmit signals related
to the training data and transformation thereof to a second layer.
A final layer may be configured to output an estimate, conclusion,
product, or other consequence of processing of one or more inputs
by the machine learning model 13702. Each of the layers may perform
one or more types of transformations, and one or more signals may
pass through one or more of the layers one or more times. In some
embodiments, the machine learning model 13702 may employ deep
learning and being at least partially modeled and/or configured as
a deep neural network, a deep belief network, a recurrent neural
network, and/or a convolutional neural network, such as by being
configured to include one or more hidden layers.
[0490] In some embodiments, the machine learning model 13702 may be
and/or include a decision tree, e.g. a tree-based predictive model
configured to identify one or more observations and determine one
or more conclusions based on an input. The observations may be
modeled as one or more "branches" of the decision tree, and the
conclusions may be modeled as one or more "leaves" of the decision
tree. In some embodiments, the decision tree may be a
classification tree. the classification tree may include one or
more leaves representing one or more class labels, and one or more
branches representing one or more conjunctions of features
configured to lead to the class labels. In some embodiments, the
decision tree may be a regression tree. The regression tree may be
configured such that one or more target variables may take
continuous values.
[0491] In some embodiments, the machine learning model 13702 may be
and/or include a support vector machine, e.g. a set of related
supervised learning methods configured for use in one or both of
classification and regression-based modeling of data. The support
vector machine may be configured to predict whether a new example
falls into one or more categories, the one or more categories being
configured during training of the support vector machine.
[0492] In some embodiments, the machine learning model 13702 may be
configured to perform regression analysis to determine and/or
estimate a relationship between one or more inputs and one or more
features of the one or more inputs. Regression analysis may include
linear regression, wherein the machine learning model 13702 may
calculate a single line to best fit input data according to one or
more mathematical criteria.
[0493] In embodiments, inputs to the machine learning model 13702
(such as a regression model, Bayesian network, supervised model, or
other type of model) may be tested, such as by using a set of
testing data that is independent from the data set used for the
creation and/or training of the machine learning model, such as to
test the impact of various inputs to the accuracy of the model
13702. For example, inputs to the regression model may be removed,
including single inputs, pairs of inputs, triplets, and the like,
to determine whether the absence of inputs creates a material
degradation of the success of the model 13702. This may assist with
recognition of inputs that are in fact correlated (e.g., are linear
combinations of the same underlying data), that are overlapping, or
the like. Comparison of model success may help select among
alternative input data sets that provide similar information, such
as to identify the inputs (among several similar ones) that
generate the least "noise" in the model, that provide the most
impact on model effectiveness for the lowest cost, or the like.
Thus, input variation and testing of the impact of input variation
on model effectiveness may be used to prune or enhance model
performance for any of the machine learning systems described
throughout this disclosure.
[0494] In some embodiments, the machine learning model 13702 may be
and/or include a Bayesian network. The Bayesian network may be a
probabilistic graphical model configured to represent a set of
random variables and conditional independence of the set of random
variables. The Bayesian network may be configured to represent the
random variables and conditional independence via a directed
acyclic graph. The Bayesian network may include one or both of a
dynamic Bayesian network and an influence diagram.
[0495] In some embodiments, the machine learning model 13702 may be
defined via supervised learning, i.e., one or more algorithms
configured to build a mathematical model of a set of training data
containing one or more inputs and desired outputs. The training
data may consist of a set of training examples, each of the
training examples having one or more inputs and desired outputs,
i.e., a supervisory signal. Each of the training examples may be
represented in the machine learning model 13702 by an array and/or
a vector, i.e., a feature vector. The training data may be
represented in the machine learning model 13702 by a matrix. The
machine learning model 13702 may learn one or more functions via
iterative optimization of an objective function, thereby learning
to predict an output associated with new inputs. Once optimized,
the objective function may provide the machine learning model 13702
with the ability to accurately determine an output for inputs other
than inputs included in the training data. In some embodiments, the
machine learning model 13702 may be defined via one or more
supervised learning algorithms such as active learning, statistical
classification, regression analysis, and similarity learning.
Active learning may include interactively querying, by the machine
learning model 13702, a user and/or an information source to label
new data points with desired outputs. Statistical classification
may include identifying, by the machine learning model 13702, to
which a set of subcategories, i.e., subpopulations, a new
observation belongs based on a training set of data containing
observations having known categories. Regression analysis may
include estimating, by the machine learning model 13702
relationships between a dependent variable, i.e., an outcome
variable, and one or more independent variables, i.e., predictors,
covariates, and/or features. Similarity learning may include
learning, by the machine learning model 13702, from examples using
a similarity function, the similarity function being designed to
measure how similar or related two objects are.
[0496] In some embodiments, the machine learning model 13702 may be
defined via unsupervised learning, i.e., one or more algorithms
configured to build a mathematical model of a set of data
containing only inputs by finding structure in the data such as
grouping or clustering of data points. In some embodiments, the
machine learning model 13702 may learn from test data, i.e.,
training data, that has not been labeled, classified, or
categorized. The unsupervised learning algorithm may include
identifying, by the machine learning model 13702, commonalities in
the training data and learning by reacting based on the presence or
absence of the identified commonalities in new pieces of data. In
some embodiments, the machine learning model 13702 may generate one
or more probability density functions. In some embodiments, the
machine learning model 13702 may learn by performing cluster
analysis, such as by assigning a set of observations into subsets,
i.e., clusters, according to one or more predesignated criteria,
such as according to a similarity metric of which internal
compactness, separation, estimated density, and/or graph
connectivity are factors.
[0497] In some embodiments, the machine learning model 13702 may be
defined via semi-supervised learning, i.e., one or more algorithms
using training data wherein some training examples may be missing
training labels. The semi-supervised learning may be weakly
supervised learning, wherein the training labels may be noisy,
limited, and/or imprecise. The noisy, limited, and/or imprecise
training labels may be cheaper and/or less labor intensive to
produce, thus allowing the machine learning model 13702 to train on
a larger set of training data for less cost and/or labor.
[0498] In some embodiments, the machine learning model 13702 may be
defined via reinforcement learning, such as one or more algorithms
using dynamic programming techniques such that the machine learning
model 13702 may train by taking actions in an environment in order
to maximize a cumulative reward. In some embodiments, the training
data is represented as a Markov Decision Process.
[0499] In some embodiments, the machine learning model 13702 may be
defined via self-learning, wherein the machine learning model 13702
is configured to train using training data with no external rewards
and no external teaching, such as by employing a Crossbar Adaptive
Array (CAA). The CAA may compute decisions about actions and/or
emotions about consequence situations in a crossbar fashion,
thereby driving teaching of the machine learning model 13702 by
interactions between cognition and emotion.
[0500] In some embodiments, the machine learning model 13702 may be
defined via feature learning, i.e., one or more algorithms designed
to discover increasingly accurate and/or apt representations of one
or more inputs provided during training, e.g. training data.
Feature learning may include training via principal component
analysis and/or cluster analysis. Feature learning algorithms may
include attempting, by the machine learning model 13702, to
preserve input training data while also transforming the input
training data such that the transformed input training data is
useful. In some embodiments, the machine learning model 13702 may
be configured to transform the input training data prior to
performing one or more classifications and/or predictions of the
input training data. Thus, the machine learning model 13702 may be
configured to reconstruct input training data from one or more
unknown data-generating distributions without necessarily
conforming to implausible configurations of the input training data
according to the distributions. In some embodiments, the feature
learning algorithm may be performed by the machine learning model
13702 in a supervised, unsupervised, or semi-supervised manner.
[0501] In some embodiments, the machine learning model 13702 may be
defined via anomaly detection, i.e., by identifying rare and/or
outlier instances of one or more items, events and/or observations.
The rare and/or outlier instances may be identified by the
instances differing significantly from patterns and/or properties
of a majority of the training data. Unsupervised anomaly detection
may include detecting of anomalies, by the machine learning model
13702, in an unlabeled training data set under an assumption that a
majority of the training data is "normal." Supervised anomaly
detection may include training on a data set wherein at least a
portion of the training data has been labeled as "normal" and/or
"abnormal."
[0502] In some embodiments, the machine learning model 13702 may be
defined via robot learning. Robot learning may include generation,
by the machine learning model 13702, of one or more curricula, the
curricula being sequences of learning experiences, and cumulatively
acquiring new skills via exploration guided by the machine learning
model 13702 and social interaction with humans by the machine
learning model 13702. Acquisition of new skills may be facilitated
by one or more guidance mechanisms such as active learning,
maturation, motor synergies, and/or imitation.
[0503] In some embodiments, the machine learning model 13702 can be
defined via association rule learning. Association rule learning
may include discovering relationships, by the machine learning
model 13702, between variables in databases, in order to identify
strong rules using some measure of "interestingness." Association
rule learning may include identifying, learning, and/or evolving
rules to store, manipulate and/or apply knowledge. The machine
learning model 13702 may be configured to learn by identifying
and/or utilizing a set of relational rules, the relational rules
collectively representing knowledge captured by the machine
learning model 13702. Association rule learning may include one or
more of learning classifier systems, inductive logic programming,
and artificial immune systems. Learning classifier systems are
algorithms that may combine a discovery component, such as one or
more genetic algorithms, with a learning component, such as one or
more algorithms for supervised learning, reinforcement learning, or
unsupervised learning. Inductive logic programming may include
rule-learning, by the machine learning model 13702, using logic
programming to represent one or more of input examples, background
knowledge, and hypothesis determined by the machine learning model
13702 during training. The machine learning model 13702 may be
configured to derive a hypothesized logic program entailing all
positive examples given an encoding of known background knowledge
and a set of examples represented as a logical database of
facts.
[0504] Referring to FIG. 138, a compliance system 13800 that
facilitates the licensing of personality rights using a distributed
ledger and cryptocurrency is depicted. As used herein, personality
rights may refer to an entity's ability to control the use of his,
her, or its identity for commercial purposes. The term entity, as
used herein, may refer to an individual or an organization (e.g., a
university, a school, a team, a corporation, or the like) that
agrees to license its personality rights, unless context suggests
otherwise. This may include an entity's ability to control the use
of its name, image, likeness, voice, or the like. For example, an
individual exercising their personality rights for commercial
purposes may include appearing in a commercial, television show, or
movie, making a sponsored social media post (e.g., Instagram post,
Facebook post, Twitter tweet, or the like), having their name
appear on clothing (e.g., a jersey, t-shirts, sweatshirts, or the
like) or other goods, appearing in a video game, or the like. In
embodiments, individuals may refer to student athletes or
professional athletes, but may include other classes of individuals
as well. While the current description makes reference to the NCAA,
the system may be used to monitor and facilitate transactions
relating to other individuals and organizations. For example, the
system may be used in the context of professional sports, where
organizations may use sponsorships and other licensing deals to
circumvent salary caps or other league rules (e.g., FIFA fair play
rules).
[0505] In embodiments, the compliance system 13800 maintains one or
more digital ledgers that record transactions relating to the
licensing of personality rights of entities. In embodiments, a
digital ledger may be a distributed ledger that is distributed
amongst a set of computing devices 13870, 13880, 13890 (also
referred to as nodes) and/or may be encrypted. Put another way,
each participating node may store a copy of the distributed ledger.
An example of the digital ledger is a Blockchain ledger. In some
embodiments, a distributed ledger is stored across a set of public
nodes. In other embodiments, a distributed ledger is stored across
a set of whitelisted participant nodes (e.g., on the servers of
participating universities or teams). In some embodiments, the
digital ledger is privately maintained by the compliance system
13800. The latter configuration provides a more energy efficient
means of maintaining a digital ledger; while the former
configurations (e.g., distributed ledgers) provide a more
secure/verifiable means of maintaining a digital ledger.
[0506] In embodiments, a distributed ledger may store tokens. The
tokens may be cryptocurrency tokens that are transferrable to
licensors and licensees. In some embodiments, a distributed ledger
may store the ownership data of each token. A token (or a portion
thereof) may be owned by the compliance system, the governing
organization (e.g., the NCAA), a licensor, a licensee, a team, an
institution, an individual or the like. In embodiments, the
distributed ledger may store event records. Event records may store
information relating to events associated with the entities
involved with the compliance system. For example, an event record
may record an agreement entered into by two parties, the completion
of an obligation by a licensor, the distribution of funds to a
licensor from a license, the non-completion of an obligation by a
licensor, the distribution of funds to entities associated with the
licensee (e.g., teammates, institution, team, etc.), and the
like.
[0507] In embodiments, the digital ledger may store smart contracts
that govern agreements between licensors and licensees. As used
herein, a licensee may be an organization or person that wishes to
enter an agreement to license a licensor's personality rights.
Examples of licensees may include, but are not limited to, a car
dealership that wants a star student athlete to appear in a print
ad, a company that wants the likeness of a licensor (e.g., an
athlete and/or a team) to appear in a commercial, a video game
maker that wants to use team names, team apparel, player names
and/or numbers in a video game, a shoe maker that wants an athlete
to endorse a sneaker, a television show producer that wants an
athlete to appear in the television show, or the like. In
embodiments, the compliance system 13800 generates a smart contract
that memorializes an agreement between the individual and a
licensee and facilitates the transfer of consideration (e.g.,
money) when the parties agree that the individual has performed his
or her requirements as put forth in the agreement. For example, an
athlete may agree to appear in a commercial on behalf of a local
car dealership. The smart contract in this example may include an
identifier of the athlete (e.g., an individual ID and/or an
individual account ID), an identifier of the organization (e.g., an
organization ID and/or an organization account ID), the
requirements of the individual (e.g., to appear in a commercial, to
make a sponsored social media post, to appear at an autograph
signing, or the like), and the consideration (e.g., a monetary
amount). In embodiments, the smart contract may include additional
terms. In embodiments, the additional terms may include an
allocation rule that defines a manner by which the consideration is
allocated to the athlete and one or more other parties (e.g.,
agent, manager, university, team, teammates, or the like). For
example, in the context of a student athlete, a smart contract may
define a split between the licensing athlete, the athletic
department of the student athlete's university, and the student
athlete's teammates. In a specific example, a university may have a
policy that requires a player appearing in any advertisement to
split the funds according to a 60/20/20 split, whereby 60% of the
funds are allocated to the student athlete appearing in the
commercial, 20% of the funds are allocated to the athletic
department, and 20% of the funds are allocated to the student
athlete's teammates. When a smart contract verifies that the
athlete has performed his or her duties with respect to the smart
contract (e.g., appeared for the commercial), the smart contract
can transfer the agreed upon amount from an account of the licensee
to an account of the athlete and accounts of any other entities
that may be allocated a percentage of the funds in the smart
contract (e.g., athletic department and teammates).
[0508] In embodiments, the compliance system 13800 utilizes
cryptocurrency to facilitate the transfer of funds. In embodiments,
the cryptocurrency is mined by participant nodes and/or generated
by the compliance system. The cryptocurrency can be an established
type of cryptocurrency (e.g., Bitcoin, Ethereum, Litecoin, or the
like) or may be a proprietary cryptocurrency. In some embodiments,
the cryptocurrency is a pegged cryptocurrency that is pegged to a
particular fiat currency (e.g., pegged to the US dollar. British
Pound, Euro, or the like). For example, a single unit of
cryptocurrency (also referred to as a "coin") may be pegged to a
single unit of fiat currency (e.g., a US dollar). In embodiments, a
licensee may exchange fiat currency for a corresponding amount of
cryptocurrency. For example, if the cryptocurrency is pegged to the
dollar, the licensee may exchange an amount of US dollars for a
corresponding amount of cryptocurrency. In embodiments, the
compliance system 13800 may keep a percentage of the real-world
currency as a transaction fee (e.g., 5%). For example, in
exchanging $10,000, the compliance system 13800 may distribute
$9,500 dollars' worth of cryptocurrency to an account of the
licensee and may keep the $5,000 dollars as a transaction fee. Once
the cryptocurrency is deposited in an account of a licensee, the
licensee may enter into transactions with individuals.
[0509] In embodiments, the compliance system 13800 may allow
organizations to create smart contract templates that define one or
more conditions/restrictions on the contract. For example, an
organization may predefine the allocation between the licensee, the
organization, and any other individuals (e.g., coaches, teammates,
representatives). Additionally or alternatively, the organization
may place minimum and/or maximum amounts of agreements.
Additionally or alternatively, the organization may place
restrictions on when an agreement can be entered into and/or
performed. For example, players may be restricted from appearing in
commercials or advertisements during the season and/or during exam
periods. These details may be stored in an organization datastore
13856A Organizations may place other conditions/restrictions in a
smart contract. In these embodiments, an individual and licensee
wishing to enter to an agreement must use a smart contract template
provided by the organization to which the individual belongs. In
other words, the compliance system 13800 may only allow an
individual that has an active relationship with an organization
(e.g., plays on a team of a university) to participate in a smart
contract if the smart contract is defined by or otherwise approved
by the organization.
[0510] In embodiments, the compliance system 13800 manages a
clearinghouse process that approves potential licensees. Before a
licensee can participate in agreements facilitated by the
compliance system 13800, the licensee can provide information
relating to the licensee. This may include a tax ID number, an
entity name, incorporation information (e.g., state and type), a
list of key personnel (e.g., directors, executives, board members,
approved decision makers, and/or the like), and any other suitable
information. In embodiments, the potential licensee may be required
to sign (e.g., eSign or wet ink signature) a document indicating
that the organization will not willingly use the compliance system
13800 to circumvent any rules, laws, or regulations (e.g., they
will not circumvent NCAA regulations). In embodiments, the
compliance system 13800 or another entity (e.g., the NCAA) may
verify the licensee. Once verified, the information is stored in a
licensee datastore 13856B and the licensee may participate in
transactions.
[0511] In embodiments, the compliance system 13800 may create
accounts for licensors once they have joined an organization (e.g.,
signed an athletic scholarship with a university). Once a licensor
is verified as being affiliated with the organization, the
compliance system 13800 may create an account for the licensor and
may create a relationship between the individual and the
organization, whereby the licensor may be required to use smart
contracts that are approved or provided by the organization. Should
the licensor join another organization (e.g., transfers to another
school), the compliance system 13800 may sever the relationship
with the previous organization and may create a new relationship
with the other organization. Similarly, once a licensor is no
longer affiliated with any organization (e.g., the player
graduates, enters a professional league, retires, or the like), the
compliance system 13800 may prevent the licensor from participating
in transactions on the compliance system 13800.
[0512] In embodiments, the compliance system 13800 may provide a
graphical user interface that allows users to create smart
contracts governing personality rights licenses. In these
embodiments, the compliance system allows a user (e.g., a licensor)
to select a smart contract template. In some embodiments, the
compliance system 13800 may restrict the user to only select a
smart contract template that is associated with an institution of
the licensor. In embodiments, the graphical user interface allows a
user to define certain terms (e.g., the type or types of
obligations placed on the licensor, an amount of funds to paid, a
date by which the obligations of the licensor must be completed by,
a location at which the obligation is completed, and/or other
suitable terms). Upon a user providing input for parameterizing a
smart contract template, the compliance system 13800 may generate a
smart contract by parameterizing one or more variables in the smart
contract with the provided input. Upon parameterizing an instance
of a smart contract, the compliance system 13800 may deploy the
smart contract. In some embodiments, the compliance system 13800
may deploy the smart contract by broadcasting the parameterized
smart contract to the participant nodes, which in turn may update
each respective instance of the distributed ledger with the new
smart contract. In some embodiments, an institution of the licensor
must approve the parameterized smart contract before the
parameterized smart contract may be deployed to the distributed
ledger.
[0513] In embodiments, the compliance system 13800 may provide a
graphical user interface to verify performance of an obligation by
a licensor. In some of these embodiments, the compliance system
13800 may include an application that is accessed by licensors,
that allows a licensor to prove that he or she performed an
obligation. In some of these embodiments, the application may allow
a user to record locations that the licensor went to (e.g.,
locations of film or photo shoots), to upload records (e.g., screen
shots of social media posts) or to provide other corroborating
evidence that the licensor has performed his or her obligations
with respect to a licensing transaction. In this way, the licensor
can prove that he or she performed the tasks required by the
licensing deal. In some embodiments, the application may interact
with a wearable device or may capture other digital exhaust, such
as social media posts of the user (e.g., licensor) to collect
evidence that supports or disproves a licensor's claim that he or
she performed the obligations under the transaction agreement. In
embodiments, the corroborating evidence collected by the
application may be recorded by the application and stored on the
distributed ledger as a licensor datastore 13856C.
[0514] In embodiments, the compliance system 13800 (or a smart
contract issued in connection with the compliance system 13800) may
complete transactions pertaining to a smart contract governing the
licensing of the personality rights of a licensor upon verification
that licensor has performed his or her obligations defined in the
agreement. As mentioned, the licensor may use an application to
provide evidence of satisfaction of the obligations of the
agreement. Additionally or alternatively, the licensee may provide
verification that the licensor has performed his or her obligations
(e.g., using an application). In embodiments, the smart contract
governing the agreement may receive verification that the licensor
has performed his or her obligations defined by the agreement. In
response the smart contract may release (or initiate the release
of) the cryptocurrency amount defined in the smart contract. The
cryptocurrency amount may be distributed to the accounts of the
licensor and any other parties defined in the agreement (e.g.,
teammates of the licensor, the program of the licensor, the
regulating body, or the like).
[0515] In embodiments, the compliance system 13800 is configured to
perform analytics and provide reports to a regulatory body and/or
other entities (e.g., the other organizations). In these
embodiments, the analytics may be used to identify individuals that
are potentially circumventing the rules and regulations of the
regulatory body. Furthermore, in some embodiments, transaction
records may be maintained on a distributed ledger, whereby
different organizations may be able to view agreements entered into
by individuals affiliated with other organizations such that added
levels of transparency and oversight may disincentivize
individuals, organizations, and/or licensees from circumventing
rules and regulations.
[0516] In embodiments, the compliance system 13800 may train and/or
leverage machine-learned models to identify potential instances of
circumvention of rules or regulations. In these embodiments, the
compliance system 13800 may train machine-learned models using
outcome data. Examples of outcome data may include data relating to
a set of transactions where an organization (e.g., a team or
university), licensee (e.g., a company), and/or licensor (e.g., an
athlete) were determined to be circumventing rules or regulations
and data relating to a set of transactions where an organization,
licensee, and/or licensor were found to be in compliance with the
rules and regulations. Examples of machine-learned models include
neural networks, regression-based models, decisions trees, random
forests, Hidden Markov Models, Bayesian Models, and the like. In
embodiments, the compliance system 13800 may leverage a
machine-learned model by obtaining a set of records relating to
transactions a licensee, a licensor, and/or an organization (e.g.,
a team or university) from the distributed ledger. The compliance
system may extract relevant features, such as the amount paid to a
particular licensor by a licensee, amounts paid to other licensors
on other teams, affiliations of the licensor, amounts paid to a
licensor by other licensees, and the like, and may feed the
features to the machine-learned model. The machine-learned model
may issue a score that indicates a likelihood that the transaction
was legitimate (or illegitimate) based on the extracted features.
In embodiments, the compliance system 13800 may provide
notifications to relevant parties (e.g., regulators) when the
output of a machine-learned model indicates that a transaction was
likely illegitimate.
[0517] FIG. 139 illustrates an example system 13900 configured for
electronically facilitating licensing of one or more personality
rights of a licensor, in accordance with some embodiments of the
present disclosure. In some embodiments, the system 13900 may
include one or more computing platforms 13902. Computing
platform(s) 13902 may be configured to communicate with one or more
remote platforms 13904 according to a client/server architecture, a
peer-to-peer architecture, and/or other architectures. Remote
platform(s) 13904 may be configured to communicate with other
remote platforms via computing platform(s) 13902 and/or according
to a client/server architecture, a peer-to-peer architecture,
and/or other architectures. Users may access system 13900 via
remote platform(s) 13904.
[0518] In embodiments, computing platform(s) 13902 may be
configured by machine-readable instructions 13906. Machine-readable
instructions 13906 may include one or more instruction modules. The
instruction modules may include computer program modules. The
instruction modules may include one or more of an access module
13108, a fund management module 13112, a ledger management module
13116, a verification module 13118, an analytics module 13120,
and/or other instruction modules.
[0519] In embodiments, the access module 13108 may be configured to
receive an access request from a licensee to obtain approval to
license personality rights from a set of available licensors. In
embodiments, the access module 13108 may be configured to
selectively grant access to the licensee based on the access
request. For example, the access module 13108 may receive a name of
a potential licensee (e.g., corporate name), a list of principals
(e.g., executives and/or owners) of the potential licensee, a
location of the licensee, affiliations of the licensee and the
principals thereof, and the like. In embodiments, the access module
13108 may provide this information to a human that grants access
and/or may feed this information into an artificial intelligence
system that vets potential licensees. In embodiments, the access
module 13108 is configured to selectively grant access to a
licensor by verifying that the licensee is permitted to engage with
a set of licensors including the licensor based on the set of
affiliations. Selectively granting access to the licensor may
include, in response to verifying that the licensee is permitted to
engage with the set of licensors, granting the licensee approval to
engage with the set of licensees. The set of affiliations of the
licensee may include organizations to which the licensee or a
principal associated with the licensee donates to or owns.
[0520] In embodiments, the fund management module 13112 may be
configured to receive confirmation of a deposit of an amount of
funds from the licensee. In some embodiments, the fund management
module 13112 may be configured to issue an amount of cryptocurrency
corresponding to the amount of funds deposited by the licensee to
an account of the licensee. In embodiments, the fund management
module 13112 may be configured to escrow the consideration amount
of cryptocurrency from the account of the licensee until the funds
are released by a smart contract.
[0521] In embodiments, the ledger management module 13116 may be
configured to receive a smart contract request to create a smart
contract governing the licensing of the one or more personality
rights of the licensor by the licensee. In embodiments, the ledger
management module 13116 may be configured to generate the smart
contract based on the smart contract request. The smart contract
may be generated using a smart contract template provided by an
interested third party (e.g., a university, a governing body, or
the like) and by one or more parameters provided by a user (e.g.,
the licensor, the team of the licensor, an institution, and/or
licensee) By way of non-limiting example, the interested third
party may be one of a university, a sports team, or a collegiate
athletics governance organization. The smart contract request may
indicate one or more terms including a consideration amount of
cryptocurrency to be paid to the licensor in exchange for one or
more obligations on the licensor. In embodiments, the ledger
management module 13116 may be configured to deploy the smart
contract to a distributed ledger. The distributed ledger may be
auditable by a set of third parties, including the interested third
party. The distributed ledger may be a public ledger. The
distributed ledger may be a private ledger that is only hosted on
computing devices associated with interested third parties. In
embodiments, the distributed ledger may be a blockchain.
[0522] In embodiments, the verification module 13118 may be
configured to verify that the licensor has performed the one or
more obligation. In some embodiments, verifying that a licensor has
performed the one or more obligations may include receiving
location data from a wearable device associated with the licensor
and verifying that the licensor has performed the one or more
obligations based on the location data, whereby the location may be
used to show that the licensor was at a particular location at a
particular time (e.g., a photoshoot or a filming). In embodiments,
verifying that the licensor may have performed the one or more
obligations includes receiving social media data from a social
media website and verifying that the licensor has performed the one
or more obligations based on the social media data, whereby the
social media data may be used to show that the licensor has made a
required social media posting. In embodiments, verifying that the
licensor may have performed the one or more obligations includes
receiving media content from an external data source and verifying
that the licensor has performed the one or more obligations based
on the media content, whereby a licensor and/or licensee may upload
the media content to prove that the licensor has appeared in the
media content. By way of non-limiting example, the media content
may be one of a video, a photograph, or an audio recording. In
embodiments, the verification module 13118 may generate and output
an event record to the participating nodes upon verifying that a
licensor has performed its obligations. In embodiments, the
verification module 13118 may generate and output an event record
to the participating nodes that indicates that the compliance
system 13100 has received corroborating evidence (e.g., social
media data, location data, and/or media contents) that show that
the licensor has performed his or her obligations. In embodiments,
the verification module 13118 may be configured to output an event
record indicating completion of a licensing transaction defined by
the smart contract to the distributed ledger.
[0523] In embodiments, the verification module 13118 may be
configured to verify, by the smart contract, that the licensor has
performed the one or more obligations. In embodiments, the
verification module 13118 and/or a smart contract may be configured
to, in response to receiving verification that the licensor has
performed the one or more obligations, release at least a portion
of the consideration amount of cryptocurrency into a licensor
account of the licensor. Releasing the at least a portion of the
consideration amount of cryptocurrency into a licensee account of
the licensee may include identifying an allocation smart contract
associated with the licensee and distributing the consideration
amount of the cryptocurrency in accordance with the allocation
rules. By way of non-limiting example, the additional entities may
include one or more of teammates of the licensor, coaches of the
licensor, a team of the licensor, a university of the licensee, and
a governing body (e.g., the NCAA).
[0524] In embodiments, an analytics module 13120 may be configured
to obtain a set of records indicating completion of a set of
respective transactions from the distributed ledger. The set of
records may include the record indicating the completion of the
transaction defined by the smart contract. In embodiments, the
analytics module 13120 may be configured to determine whether an
organization associated with the licensor is likely in violation of
one or more regulations based on the set of records and a fraud
detection model. The fraud detection model may be trained using
training data that indicates permissible transactions and
fraudulent transactions.
[0525] In some implementations, the allocation smart contract may
define allocation rules governing a manner by which funds resulting
from licensing the one or more personality rights are to be
distributed amongst the licensor and one or more additional
entities.
[0526] In some implementations, by way of non-limiting example, the
regulations may be provided by one of NCAA, FIFA, NBA, MLB, NFL,
MLS, NHL, and the like.
[0527] In some implementations, computing platform(s) 13902, remote
platform(s) 13904, and/or external resources 13934 may be
operatively linked via one or more electronic communication links.
For example, such electronic communication links may be
established, at least in part, via a network such as the Internet
and/or other networks. It will be appreciated that this is not
intended to be limiting, and that the scope of this disclosure
includes implementations in which computing platform(s) 13902,
remote platform(s) 13904, and/or external resources 13934 may be
operatively linked via some other communication media.
[0528] A given remote platform 13904 may include one or more
processors configured to execute computer program modules. The
computer program modules may be configured to enable an expert or
user associated with the given remote platform 13904 to interface
with compliance system 13100 and/or external resources 13934,
and/or provide other functionality attributed herein to remote
platform(s). 13904. By way of non-limiting example, a given remote
platform 13904 and/or a given computing platform 13902 may include
one or more of a server, a desktop computer, a laptop computer, a
handheld computer, a tablet computing platform, a Netbook, a
Smartphone, a gaming console, and/or other computing platforms.
[0529] External resources 13934 may include sources of information
outside of compliance system 13100, external entities participating
with compliance system 13100, and/or other resources. In some
implementations, some or all of the functionality attributed herein
to external resources 13934 may be provided by resources included
in compliance system 13100.
[0530] Computing platform(s) 202 may include electronic storage
13936, one or more processors 13938, and/or other components.
Computing platform(s) 1202 may include communication lines, or
ports to enable the exchange of information with a network and/or
other computing platforms. Illustration of computing platform(s)
13902 in FIG. 139 is not intended to be limiting. Computing
platform(s) 13902 may include a plurality of hardware, software,
and/or firmware components operating together to provide the
functionality attributed herein to computing platform(s) 13902. For
example, computing platform(s) 13902 may be implemented by a cloud
of computing platforms operating together as computing platform(s)
13902.
[0531] Electronic storage 13936 may comprise non-transitory storage
media that electronically stores information. The electronic
storage media of electronic storage 13936 may include one or both
of system storage that is provided integrally (i.e., substantially
non-removable) with computing platform(s) 13902 and/or removable
storage that is removably connectable to computing platform(s)
13902 via, for example, a port (e.g., a USB port, a firewire port,
etc.) or a drive (e.g., a disk drive, etc.). Electronic storage
13936 may include one or more of optically readable storage media
(e.g., optical disks, etc.), magnetically readable storage media
(e.g., magnetic tape, magnetic hard drive, floppy drive, etc.),
electrical charge-based storage media (e.g., EEPROM, RAM, etc.),
solid-state storage media (e.g., flash drive, etc.), and/or other
electronically readable storage media. Electronic storage 13936 may
include one or more virtual storage resources (e.g., cloud storage,
a virtual private network, and/or other virtual storage resources).
Electronic storage 13936 may store software algorithms, information
determined by processor(s) 13938, information received from
computing platform(s) 13902, information received from remote
platform(s) 13904, and/or other information that enables computing
platform(s) 13902 to function as described herein.
[0532] Processor(s) 13938 may be configured to provide information
processing capabilities in computing platform(s) 13902. As such,
processor(s) 13938 may include one or more of a digital processor,
an analog processor, a digital circuit designed to process
information, an analog circuit designed to process information, a
state machine, and/or other mechanisms for electronically
processing information. Although processor(s) 13938 is shown in
FIG. 139 as a single entity, this is for illustrative purposes
only. In some implementations, processor(s) 13938 may include a
plurality of processing units. These processing units may be
physically located within the same device, or processor(s) 13938
may represent processing functionality of a plurality of devices
operating in coordination. Processor(s) 13938 may be configured to
execute modules 13108, 13112, 13116, 13118, 13120, and/or other
modules. Processor(s) 13938 may be configured to execute modules
13108, 13112, 13116, 13118, 13120, and/or other modules by
software; hardware; firmware; some combination of software,
hardware, and/or firmware; and/or other mechanisms for configuring
processing capabilities on processor(s) 13938. As used herein, the
term "module" may refer to any component or set of components that
perform the functionality attributed to the module. This may
include one or more physical processors during execution of
processor readable instructions, the processor readable
instructions, circuitry, hardware, storage media, or any other
components.
[0533] It should be appreciated that although modules 13108, 13112,
13116, 13118, and 13120 are illustrated in FIG. 139 as being
implemented within a single processing unit, in implementations in
which processor(s) 13938 includes multiple processing units, one or
more of modules 13108, 13112, 13116, 13118, and 13120 may be
implemented remotely from the other modules. The description of the
functionality provided by the different modules 13108, 13112,
13116, 13118, and 13120 described below is for illustrative
purposes, and is not intended to be limiting, as any of modules
13108, 13112, 13116, 13118, and/or 13120 may provide more or less
functionality than is described. For example, one or more of
modules 13108, 13112, 13116, 13118, and/or 13120 may be eliminated,
and some or all of its functionality may be provided by other ones
of modules 13108, 13112, 13116, 13118, and/or 13120. As another
example, processor(s) 13938 may be configured to execute one or
more additional modules that may perform some or all of the
functionality attributed below to one of modules 13108, 13112,
13116, 13118, and/or 13120.
[0534] FIGS. 140 and/or 141 illustrates an example method 14000 for
electronically facilitating licensing of one or more personality
rights of a licensor, in accordance with some embodiments of the
present disclosure. The operations of method 14000 presented below
are intended to be illustrative. In some embodiments, method 14000
may be accomplished with one or more additional operations not
described, and/or without one or more of the operations discussed.
Additionally, the order in which the operations of method 14000 are
illustrated in FIGS. 140 and/or 141 and described below is not
intended to be limiting.
[0535] In some implementations, method 14000 may be implemented in
one or more processing devices (e.g., a digital processor, an
analog processor, a digital circuit designed to process
information, an analog circuit designed to process information, a
state machine, and/or other mechanisms for electronically
processing information). The one or more processing devices may
include one or more devices executing some or all of the operations
of method 14000 in response to instructions stored electronically
on an electronic storage medium. The one or more processing devices
may include one or more devices configured through hardware,
firmware, and/or software to be specifically designed for execution
of one or more of the operations of method 14000.
[0536] FIG. 140 illustrates method 14000, in accordance with one or
more implementations of the present disclosure.
[0537] At 14002, the method includes receiving an access request
from a licensee to obtain approval to license personality rights
from a set of available licensors. Operation 14002 may be performed
by one or more hardware processors configured by machine-readable
instructions including a module that is the same as or similar to
access module 13108, in accordance with one or more
implementations.
[0538] At 14004, the method includes selectively granting access to
the licensee based on the access request. Operation 14004 may be
performed by one or more hardware processors configured by
machine-readable instructions including a module that is the same
as or similar to access module 13108, in accordance with one or
more implementations.
[0539] At 14006, the method includes receiving confirmation of a
deposit of an amount of funds from the licensee. Operation 14006
may be performed by one or more hardware processors configured by
machine-readable instructions including a module that is the same
as or similar to fund management module 13112, in accordance with
one or more implementations.
[0540] At 14008, the method includes issuing an amount of
cryptocurrency corresponding to the amount of funds deposited by
the licensee to an account of the licensee. Operation 14008 may be
performed by one or more hardware processors configured by
machine-readable instructions including a module that is the same
as or similar to fund management module 13112, in accordance with
one or more implementations.
[0541] FIG. 141 illustrates method 14100, in accordance with one or
more implementations of the present disclosure.
[0542] At 14122, the method includes receiving a smart contract
request to create a smart contract governing the licensing of the
one or more personality rights of the licensor by the licensee. The
smart contract request may indicate one or more terms including a
consideration amount of cryptocurrency to be paid to the licensor
in exchange for one or more obligations on the licensor. Operation
14122 may be performed by one or more hardware processors
configured by machine-readable instructions including a module that
is the same as or similar to the ledger management module 13116, in
accordance with one or more implementations.
[0543] At 14124, the method includes generating the smart contract
based on the smart contract request. Operation 14124 may be
performed by one or more hardware processors configured by
machine-readable instructions including a module that is the same
as or similar to ledger management module 13116, in accordance with
one or more implementations.
[0544] At 14126, the method includes escrowing the consideration
amount of cryptocurrency from the account of the licensee.
Operation 14126 may be performed by one or more hardware processors
configured by machine-readable instructions including a module that
is the same as or similar to fund management module 13112, in
accordance with one or more implementations.
[0545] At 14128, the method includes deploying the smart contract
to a distributed ledger. Operation 14128 may be performed by one or
more hardware processors configured by machine-readable
instructions including a module that is the same as or similar to
ledger management module 13116, in accordance with one or more
implementations.
[0546] At 14130, the method includes verifying, by the smart
contract, that the licensor has performed the one or more
obligations. Operation 14130 may be performed by one or more
hardware processors configured by machine-readable instructions
including a module that is the same as or similar to verification
module 13118, in accordance with one or more implementations.
[0547] At 14132, the method includes in response to receiving
verification that the licensor has performed the one or more
obligations, releasing at least a portion of the consideration
amount of cryptocurrency into a licensor account of the licensor.
Operation 14132 may be performed by one or more hardware processors
configured by machine-readable instructions including a module that
is the same as or similar to the verification module 13118, in
accordance with one or more implementations.
[0548] At 14134, the method includes outputting a record indicating
a completion of a licensing transaction defined by the smart
contract to the distributed ledger. Operation 14134 may be
performed by one or more hardware processors configured by
machine-readable instructions including a module that is the same
as or similar to the verification module 13118 and/or the ledger
management module 13116, in accordance with one or more
implementations.
[0549] FIG. 142 illustrates method 14200, in accordance with one or
more implementations.
[0550] At 14202, the method includes obtaining a set of records
indicating completion of a set of respective transactions from the
distributed ledger. The set of records may include the record
indicating the completion of the transaction defined by the smart
contract. Operation 14202 may be performed by one or more hardware
processors configured by machine-readable instructions including a
module that is the same as or similar to the analytics module
13120, in accordance with one or more implementations.
[0551] At 14204, the method includes determining whether an
organization associated with the licensor is likely in violation of
one or more regulations based on the set of records and a fraud
detection model. Operation 14204 may be performed by one or more
hardware processors configured by machine-readable instructions
including a module that is the same as or similar to the analytics
module 13120, in accordance with one or more implementations.
[0552] Referring to FIG. 143, a computer-implemented method 14300
for selecting an AI solution for use in a robotic or automated
process is depicted. The computer-implemented method may include
receiving one or more functional media 14302. The functional media
may include information indicative of brain activity of a worker
engaged in a task to be automated. The functional media may be
functional imaging, such an MRI, an FMRI, and the like from which
an area of neocortex activity may be identified. The functional
media may be an image, a video stream, an audio stream, and the
like, from which a type of brain activity may be inferred. The
functional media may be acquired while the worker is performing the
work or while performing a simulation of the work, for example in
an augmented reality, a virtual reality environment, or on a model
of the equipment and/or environment. After being received, the
functional media(s) are analyzed 14304 to identify an activity
level in at least one brain region 14306. Based on the activity
level, a brain region parameter and/or an activity parameter are
identified 14308. The brain region parameter may represent a
specific region of the neocortex such as frontal, parietal,
occipital, and temporal lobes of the neocortex, including primary
visual cortex and the primary auditory cortex, or subdivisions of
the neocortex, including ventrolateral prefrontal cortex (Broca's
area), and orbitofrontal cortex. The activity parameter may
represent functional areas of the brain, such as visual processing,
inductive reasoning, audio processing, olfactory processing, muscle
control, and the like. An activity parameter may be representative
of a type of activity in which the worker is engaged such as visual
processing (looking) audio processing (listening), olfactory
processing (smelling), motion activity, listening to the sound of
the equipment, watching another negotiator, and the like. An
activity level may be representative of a strength or level of
activity, such as an extent of the brain region involved, a signal
strength, whether a brain region is engaged or unengaged, and the
like.
[0553] Based on one or more of the brain region parameter, the
activity parameter, or the activity level, an action parameter may
be identified 14310. An action parameter may provide additional
information regarding the activity parameter. For example, activity
parameter is indicative of motion, an action parameter may describe
a range of motion, a speed of motion, a repetition of motion, a use
of muscle memory, a smoothness of motion, a flow of motion, a
timing of motion, and the like. Based on one or more of the brain
region parameter, the activity parameter, or the activity level, a
component to be incorporated in the final AI solution may be
selected 14312. The component may include one or more of a model,
an expert system, a neural network, and the like. After the
component for the AI solution has been selected, configuration
parameters may be determined 14314. The configuration parameters
may be based, in part, on the type of component selected, the brain
region parameter, the activity parameter, the activity level, or
the action parameter. Configuring and configuration parameters may
include selecting an input for a machine learning process,
identifying an output to be provided by the machine learning
process, identifying an input for an operational solution process
14316, identifying an output an operational solution process,
tuning a learning parameter, identifying a change rates,
identifying a weighting factor, identifying a parameter for
inclusion, identifying a parameter for exclusion of a parameter,
setting a threshold for input data, setting an output threshold for
the operational robotic process, or setting a parameter threshold.
Additionally, analysis of the functional media 14304 may include
identifying a second brain region parameter or a second activity
parameter 14318. The component of the AI solution may be revised
14320 based on the second brain region parameter or the second
activity parameter. A second component of the AI solution may be
selected 14322 based on the second brain region parameter or the
second activity parameter. The final AI solution may be assembled
from the component 14324 or the second component 14326. In
embodiments, the final AI solution may be assembled from the
component and the second components, optionally along with any
standard or mandatory components that enable operation.
[0554] Referring to FIG. 144, a computer-implemented method 14400
for selecting an AI solution for use in a robotic or automated
process is depicted. The method may include receiving a
user-related input 14402 comprising a timestamp and analyzing the
user-related input 14404. The user-related input may include an
audio feed, a motion sensor, a video feed, a heartbeat monitor, an
eye tracker, a biosensor (e.g., galvanic skin response), and the
like. The analysis may enable the identification of a series of
user actions and associated activity parameters 14406. A component
for an AI solution may be selected based on a user action of the
series of user actions 14408. The analysis may enable the
identification of a second user action of the series of user
actions 14410. Based on the second user action, the selected
component for the AI solution may be revised 14412. A second
component for the AI solution may be selected 14414 based on the
second user action. An action parameter may be identified 14416
based on the user action and/or the associated activity parameters.
For example, if the user action is motion, an action parameter may
include a range of motion, a speed of motion, a repetition of
motion, a use of muscle memory, a smoothness of motion, a flow of
motion, a timing of motion, and the like. The selected component of
the AI solution may be configured 14418 based on the action
parameter. In embodiments, at least one device input performed by
the user may be received (14420). The device input may be
synchronized with the user actions based on the timestamp and a
correlation between the device input and the user action determined
14419. The component may be revised 14423 based on the correlation.
The selection of the component of the AI solution may be partially
based on the correlation between the device input and the
user-related input 14421. The AI solution may be assembled 14422
from the component. The AI solution may be assembled from the
second component 14424. In embodiments, the AI may be assembled
from both the component and the second component, optionally along
with any standard or mandatory components that enable
operation.
[0555] Referring to FIG. 145, an illustrative and non-limiting
example of an assembled AI solution 14502 is shown. The assembled
AI solution 14502 may include the selected component 14504 and a
second selected component 14506, as well as other components 14508.
Configuration data 14514 for the first selected component and
configuration data 14512 for the second selected component may be
provided. Runtime input data 14510 may be specified as part of the
component configuration process. Components may be structured to
run serially (such as the selected component 14504 and the second
selected component 14506 which received input from the selected
component 14504) or in parallel (such as the second component 14506
and the other component(s) 14508). Some of the components may
provide input for other components (such as the selected component
14504 providing input to the second selected component 14506).
Multiple components may provide various portions of the overall AI
solution output 14518 (such as the second selected component 14506
and the other components 14508). This depiction is not meant to be
limiting and the final solution may include a varying number of
components, configuration data and input, as well as other
components (e.g., sensors, voice modulators, and the like) and may
be interconnected in a variety of configurations.
[0556] Referring to FIGS. 146-147, a computer-implemented method
for selecting an AI solution for use in a robotic or automated
process is depicted. The method may include receiving temporal
biometric measurement data 14602 of a worker performing a task and
receiving spatial-temporal environmental data 14604 experienced by
the worker performing the task. Using the received data, a
spatial-temporal activity pattern may be identified 14606. Based on
the spatial-temporal activity pattern, an active area of the
worker's neocortex may be identified 14608. A type of reasoning
used when performing the task may be identified 14610 based on the
active area of the neocortex and/or the biometric measurement data,
or the spatial-temporal environmental data. A component may be
selected 14612 for use in the AI solution to replicate the type of
reasoning. The component of the AI solution may be configured 14614
based on the spatial-temporal environmental input. A determination
may be made as to whether a serial or parallel AI solution is
optimal 14616. A set of configuration inputs to the component may
be identified 14618 and an ordered set of inputs to the component
of the AI solution may be identified 14620. Training the machine
may include providing various subsets of the spatial-temporal
environmental input to determine appropriate input weightings and
identify efficiencies from combinations of spatial-temporal
environmental input 14622. Desirable or undesirable combinations of
the spatial-temporal environmental data may also be identified
14624. Based on the identified required input, input environmental
data may be processed to reduce input noise 14626 (e.g. improve
signal to noise for a signal of interest), filtered to provide the
appropriate input signals to the component, and the like.
[0557] Continuing with reference to FIG. 147, a second temporal
biometric measurement data of the same worker performing the task
may be received 14702 and a plurality of performed tasks identified
from the biometric measurements 14704. A performance parameter may
be extracted from the biometric measurements 14706 (e.g. worker
heartrate, galvanic skin response, and the like). In some
embodiments, the component may be configured based on the
performance parameter 14707. In some embodiments, the second
temporal biometric measurements may be provided to the
configuration module as a training set 14709. Results data related
to the task may be received 14708 and the second temporal biometric
measurement data may be correlated with the received results data
14710. In some embodiments, the component may be selected based, at
least in part, on the correlation 14711. A series of time intervals
between each of the plurality of performed tasks may be identified
14712 and the component of the AI solution configured based on at
least one of the time intervals 14714. For example, if the worker
inspects an object for a long period of time before moving on to
the next action, this may indicate complex visual processing as
well as mental processing and may indicate that the corresponding
component for the task be configured for in-depth, fine detail
processing and the like.
[0558] Referring to FIG. 148, an AI solution selection and
configuration system 14802 is depicted. An example selection and
configuration system 14802 may include a media input module 14804
structured to receive user-related functional media 14814. The
user-related functional media 14814 may include images of a person
engaged in a task to be automated, audio recordings, video feeds,
biometric data (e.g., heartbeat data, galvanic skin response data,
and the like), motion data, and the like. A media analysis module
14806 may analyze the received media and identify an action
parameter. The action parameter may be representative of a type of
activity in which the person appears to be engaged such as
watching, listening, moving, thinking, and the like. In some
embodiments, the functional media is indicative of a type of brain
activity of a human engaged in the task to be automated and the
media analysis module 148206 identifies an activity level in at
least one brain region and provide a brain region parameter
corresponding with the activity level in the identified brain
region. The media analysis module may also identify an activity
parameter indicative of a level of engagement such as engaged,
unengaged, level of activity, type of activity, and the like. A
solution selection module 14808 may be structured to select at
least one component of the AI solution for use in the automated
process based, at least in part, on the action parameter, the brain
region parameter, or the activity parameter. The brain region
parameter or the action parameter may suggest a type of component
to select and the activity parameter may suggest a level of
processing required for that component. For example, an action
parameter of watching would suggest selecting a component suited to
visual processing. If the activity parameter was representative of
olfactory procession, the input specification module may identify
at least one chemical sensor as an input. If the activity parameter
is representative of visual processing the input specification
module 13116 may identify at least one visual sensor as a robotic
input. In some embodiments, the visual sensor may be selected to be
sensitive to a portion of the visible spectrum with wavelengths
between about 380 to 700 nanometers. If the activity parameter is
representative of auditory processing, the input specification
module 13116 may identify at least one microphone as a robotic
input. If the activity parameter was representative of a very high
level of concentration, the solution selection module 14808 may
suggest a level of processing that will be required, where the
processing might occur, and the like. A component configuration
module 14810 may configure the component 14812. Configuring the
component may include: selecting an input for a machine learning
process for the selected component, identifying an output to be
provided by the machine learning process, identifying an input for
an operational solution process, identifying an output an
operational solution process, tuning a learning parameter,
identifying a change rates, identifying a weighting factor,
identifying a parameter for inclusion, identifying a parameter for
exclusion of a parameter, setting a threshold for input data,
setting an output threshold for the operational robotic process,
setting a parameter threshold, and the like. A solution assembly
module 14818 may assemble the final AI solution based on one or
more selected components, configuration components, and required
runtime. An input specification module 14816 may suggest input
sources based on the selected component, the action parameter,
brain region parameter, activity parameter, or the like.
[0559] Referring to FIG. 149, an AI solution selection and
configuration system 14902 is depicted. An example selection system
14902 may include an image input module 14904 structured to receive
functional images 14914 of the brain such as, such as functional
MRI or other magnetic imaging, electroencephalogram (EEG), or other
imaging, such as by identifying broad brain activity (e.g., wave
bands of activity, such as delta, theta, alpha and gamma waves), by
identifying a set of brain regions that are activated and/or
inactive while the worker is performing one of the tasks to be
automated. The image input module 14904 may provide a subset of the
functional images 14914 to the image analysis module 14906. In some
embodiments, the image input module 14904 may perform some
preprocessing for the subset of functional images 14914, such as
noise reduction, histogram adjustment, filtering, and the like,
prior to providing the subset of functional images 14914 to the
image analysis module 14906. The image analysis module 14906, may
identify an activity level in at least one brain region and provide
a brain region parameter based on the subset of functional images.
The brain region parameter may represent a specific region of the
neocortex such as frontal, parietal, occipital, and temporal lobes
of the neocortex, including primary visual cortex and the primary
auditory cortex, or subdivisions of the neocortex, including
ventrolateral prefrontal cortex (Broca's area), and orbitofrontal
cortex. The brain region parameter may represent functional areas
of the brain, such as visual processing, inductive reasoning, audio
processing, olfactory processing, muscle control, and the like. A
solution selection module 14908 may select a component for use in
an AI solution based on the brain region parameter, and provide
input into a component configuration module (such as selecting an
input for a machine learning process, identifying an output to be
provided by the machine learning process, identifying an input for
an operational solution process, identifying an output an
operational solution process, tuning a learning parameter,
identifying a change rates, identifying a weighting factor,
identifying a parameter for inclusion, identifying a parameter for
exclusion of a parameter, setting a threshold for input data,
setting an output threshold for the operational robotic process,
and setting a parameter threshold, and the like. The component
configuration module 14910, may use the input to configure the
component 14912. The solution selection module 14908 may also
supply data to the input specification module 14916. A solution
assembly module 14918 may combine the component, and other
components, to create the AI solution. The AI solution may be set
up to receive inputs as specified by the input specification module
14916. Although one iteration of selecting a component is shown in
this figure, it is envisioned, that multiple components may be
selected, configured and assembled as part of the AI solution
[0560] Referring to FIGS. 150-151, an AI solution selection and
configuration system 15002 is depicted. An example AI solution
selection and configuration system 15002 may include an input
module 15004 structured to receive a variety of user-related input
such as videos, audio recording, heartbeat monitors, galvanic skin
response data, motion data, and the like. There may be temporal
data associated with the user-related input. The input module 15004
may provide a subset of the user-related input data 15014 to the
input analysis module 15006. The analysis module 15006 may include
a temporal analysis module 15018 to identify timing of user-related
actions. The temporal analysis module 15018 may enable
identification of timing of user actions. In some embodiments the
input module 15004 may perform some preprocessing for the subset of
the user-related input data 15014, such as noise reduction,
correlation between types of input data, and the like, prior to
providing the subset of user-related input data 15014 to the input
analysis module 15006. The input analysis module 15006, may
identify a type of brain activity being engaged in (e.g. visual
processing, auditory processing, olfactory processing, motion
control, and the like) and a level of intensity of activity based
on data such as heartbeat data, galvanic skin response data and the
like. A component selection module 15008 may select a component for
use in an AI solution based on the type of brain activity and
provide input into a component configuration module 15010 which may
include an ML input selection module 15102 for selecting an input
for a machine learning process, an MP output identification module
15104 for identifying an output to be provided by the machine
learning process, a runtime input selection module 15106 for
identifying an input for an operational solution process, a runtime
output identification module 15108 for identifying an output of the
component, a settings module 15110 for identifying a change rate,
identifying a weighting factor, setting a threshold for input data,
setting an output threshold for the operational robotic process,
and the like, a parameter settings module 15112 for tuning a
learning parameter, identifying a parameter for inclusion,
identifying a parameter for exclusion, setting a parameter
threshold, and the like. The component configuration module 15010
may configure the selected component 15012. The component selection
module 15008 may also supply data to the input specification module
15016. An AI solution assembly module 15020 may combine the
configured component with other components, along with any standard
or mandatory components, as necessary, to create the AI solution.
The AI solution may be set up to receive inputs as specified by the
input specification module 15016. Although one iteration of
selecting a component is shown in this figure, it is envisioned,
that multiple components may be selected, configured and assembled
as part of the AI solution.
[0561] In embodiments, referring to FIG. 152, an AI solution
selection and configuration system 15202 is depicted. An example AI
solution selection and configuration system 15202 may include a
data input module 15204 to receive an input stream including
temporal user-related data 15214 which may include video streams,
audio streams, equipment interactions (e.g. mouse clicks, mouse
motion, physical input to a machine) user biometrics such as
heartbeat, galvanic skin response, eye tracking, and the like. The
data input module 15204 may also receive temporal environmental
input data 15220 representative of environmental input the user is
receiving such as a visual environment, an auditory environment,
olfactory environment, equipment displays, a device user interface,
and the like. The data input module 15204 may also receive temporal
results input data 15203. The data input module 15204 may provide a
subset of the received data 15214, 15220, 15203 to an input
analysis module 15216. The data input module 15204 may process the
received data 15214, 15220 15203 to reduce noise, compress the
data, correlate some of the data, and the like. The analysis module
15216 may identify a plurality of user actions to provide to the
component selection module 15208. The image analysis module 15216
may include a temporal analysis module 15218 to identify timing of
user actions. The temporal analysis module 15218 may allow for the
correlation between temporal user-related data 15214, environmental
data 15220, and results data 15203. Based on the user actions, the
component selection module 15208 may select a component that would
simulate one or more mental processes of the user needed to perform
at least one of the plurality of user actions. Factors in
identifying the selected component may include the level of
computational intensity needed, time sensitivity, and the like.
This may dictate a type of component, a location of component
(on-board, in the cloud, edge-computing, and the like. The input
analysis module 15216 may also provide information regarding the
user's actions and environmental data to the component
configuration module 15210. This data may be used by the component
configuration module as input to a machine learning algorithm, in
conjunction with the results data to identify which inputs are
beneficial and which are detrimental to enabling the component to
reach desired results, and identify appropriate weighting of
inputs, parameter settings, and the like. The component
configuration module 15210 configures the component 15212 which is
provided to the overall AI solution 15224 together with
configuration information.
[0562] As described elsewhere herein, this disclosure concerns
systems and methods for the discovery of opportunities for
increased automation and intelligence, including solutions to
domain-specific problems. Further, this disclosure also concerns
selection and configuration of an artificial intelligence solution
(e.g. neural networks, machine learning systems, expert systems,
etc.) once opportunities are discovered.
[0563] Referring now to FIG. 153, a controller 15308 includes an
opportunity mining module 153, an artificial intelligence
configuration module 15304, and an artificial intelligence search
engine 15310, optionally having a collaborative filter 15328 and a
clustering engine 15330. The opportunity mining module 153 receives
input 15302, such as attribute input regarding an attribute of a
task, a domain, or a domain-related problem.
[0564] The input 15302 may be processed by the opportunity mining
module 153 to determine whether an artificial intelligence system
can be applied to the task or the domain. For example, the
attribute input 15302 may include an attribute of a task, domain or
problem, such as a negotiating task, a drafting task, a data entry
task, an email response task, a data analysis task, a document
review task, an equipment operation task, a forecasting task, an
NLP task, an image recognition task, a pattern recognition task, a
motion detection task, a route optimization task, and the like. The
opportunity mining module 153 may determine if one or more
attributes of the task are similar to other tasks that have been
automated or to which an intelligence has been applied, or based on
the attribute of the task, if the task is potentially automatable
or suitable to have an intelligence applied to it regardless of
whether it has been done previously. For example, attributes of a
drafting task may include articulating a first idea, articulating a
second idea, articulating a plurality of ideas, combining the
plurality of ideas in a pairwise fashion, and combining the ideas
in a triplicate fashion. Articulating ideas may not be suitable for
automation, but the task of combining ideas pairwise or in
triplicate form may be suitable for automation or to have an
intelligence applied to the task.
[0565] If a determination is made that an artificial intelligence
system can be applied to the task or the domain, the output 15312
regarding that determination may be used to trigger an artificial
intelligence search engine 15310 to perform a search of an
artificial intelligence store 157. The artificial intelligence
store 157 may include a plurality of domain-specific and general
artificial intelligence models 15318, and components of
domain-specific and general artificial intelligence models 15318.
The artificial intelligence store 157 may be organized by a
category. The category may be at least one of an artificial
intelligence model component type, a domain, an input type, a
processing type, an output type, a computational requirement, a
computational capability, a cost, a training status, or an energy
usage. The artificial intelligence store may include at least one
e-commerce feature. The at least one e-commerce feature may include
at least one of a rating, a review, a link to relevant content, a
mechanism for provisioning, a mechanism for licensing, a mechanism
for delivery, or a mechanism for payment. Models 15318 may be
pre-trained, or may be available for training. Components of
domain-specific and general artificial intelligence models 15318
may include artificial intelligence building blocks, such as a
component that detects and translates between languages, or a
component that delivers highly personalized customer
recommendations. One or more models 15318 and/or components of a
model 15318 may be identified in a search of the artificial
intelligence store 157. Components of a model 15318 may be
identified either as a stand-alone element to be used in the
assembly of a custom AI model 15318 or as a component of a
complete, optionally pre-trained, model 15318.
[0566] The artificial intelligence store 157 may include metadata
15324 or other descriptive material indicating a suitability of an
artificial intelligence system for at least one of solving a
particular type of problem or operating on domain-specific inputs,
data, or other entities. The metadata 15324, or other descriptive
material, category, or e-commerce feature may be searched using the
attribute input 15302 and/or other selection criteria 15314. For
example, attributes of a task involving 2D object classification
may be searched in the artificial intelligence store 157 and its
metadata 15324 to reveal that an artificial intelligence model
15318 suitable for a task involving 2D object classification may be
a convolutional neural network. Continuing with the example, there
may be model diversity even within the class of convolutional
neural networks (CNN) in the artificial intelligence store 157,
such as a CNN calibrated to a certain type of 2D object recognition
(e.g., straight edges) and another CNN calibrated to another kind
of 2D object recognition (e.g., combo of curved and straight
edges). In this example, if the further edge vs. curved attribute
of the type of 2D object is searched, the artificial intelligence
store 157 would present the CNN best suited to the 2D object to be
classified.
[0567] In embodiments, in addition to the input 15302, at least one
selection criteria 15314 may be used by the artificial intelligence
search engine 15310 to search the artificial intelligence store 157
for artificial intelligence models 15318 and/or components thereof.
Selection criteria used in the recommendation of an artificial
intelligence model 15318 or model component may include at least
one of if the model is pre-trained or not, an availability of the
at least one artificial intelligence model 15318 or component
thereof to execute in a user environment, an availability of the at
least one artificial intelligence model 15318 or component thereof
to a user, a governance principle, a governance policy, a
computational factor, a network factor, a data availability, a
task-specific factor, a performance factor, a quality of service
factor, a model deployment consideration, a security consideration,
or a human interface, which may be elsewhere described herein. For
example, a governance principle, such as a requirement for an
anti-bias review of pedestrian accident-avoidance systems, may be
used to search an artificial intelligence store 157 for artificial
intelligence models to apply to an autonomous driving task. In
another example, a selection criteria for an artificial
intelligence solution to be used with air traffic control system
may be a requirement for having been trained on adversarial attacks
and deceptive input. In yet another example, a selection criteria
for an artificial intelligence solution to be used with an equities
trading task may be the requirement for human oversight, and
particularly, human-based final decisions.
[0568] The artificial intelligence search engine 15310 may rank one
or more results of the search according to a strength or a weakness
of the at least one artificial intelligence model 15318 or model
component relative to the at least one selection criteria 15314.
The ranked search results may be presented to a user for evaluation
and consideration, and ultimately, selection. In embodiments, the
artificial intelligence search engine 15310 may further include a
collaborative filter 15328 that receives an indication of an
element of the at least one artificial intelligence model 15318 or
model component from a user that is used to filter the search
results. In embodiments, the artificial intelligence search engine
15310 may further include a clustering engine 15330 structured to
cluster search results comprising the at least one artificial
intelligence model 15318 or model component. The clustering engine
15330 may be at least one of a similarity matrix or a k-means
clustering. The clustering engine 15330 may associate at least one
of similar developers, similar domain-specific problems, or similar
artificial intelligence solutions in the search results.
[0569] Once an artificial intelligence model 15318 or components
thereof are identified by the artificial intelligence search engine
15310, either by searching with the input 15302 alone or with both
the input 15302 and a selection criteria 15314, an artificial
intelligence configuration module 15304 may configure one or more
data inputs 15320 to use with the at least one artificial
intelligence model 15318 or model component. The artificial
intelligence configuration module 15304 may, in certain
embodiments, be operative in discovering and selecting what inputs
15320 may enable effective and efficient use of artificial
intelligence for a given problem. In embodiments, the artificial
intelligence configuration module 15304 may further configure the
at least one artificial intelligence model 15318 or model
component(s) in accordance with at least one configuration criteria
15322. In embodiments, individual data inputs and model components
may be configured via one or more configuration criteria, while in
other embodiments, a single configuration criteria governs
configuration of data input, AI component assembly, and the
like.
[0570] In embodiments, the at least one configuration criteria
15322 may include at least one of an availability of the at least
one artificial intelligence model 15318 or model component to
execute in a user environment, an availability of the at least one
artificial intelligence model 15318 or model component to a user, a
governance principle, a governance policy, a computational factor,
a network factor, a data availability, a task-specific factor, a
performance factor, a quality of service factor, a model deployment
consideration, a security consideration, or a human interface. In
embodiments, the at least one configuration criteria may include at
least one of identifying a desired output, identifying training
data, identifying parameters for exclusion or inclusion in training
or operation of the model, an input data threshold, an output data
threshold, a selection of a neural network type, a selection of an
input model type, a setting of initial model weights, a setting of
model size, a selection of computational deployment environment, a
selection of input data sources for training, a selection of input
data sources for operation, a selection of feedback
function/outcome measures, a selection of data integration
language(s) for inputs and outputs, a configuration of APIs for
model training, a configuration of APIs 13114 for model inputs, a
configuration of APIs 13114 for outputs, a configuration of access
controls, a configuration of security parameters, a configuration
of network protocols, a configuration of storage parameters, a
configuration of economic factors, a configuration of data flows, a
configuration of high availability, one or more fault tolerance
environments, a price-based data acquisition strategy, a heuristic
method, a decision to make a decision model, or a coordination of
massively parallel decision making environments. In embodiments,
the at least one configuration criteria may include parameters for
assembly of an AI solution from a plurality of identified model
components, optionally along with other standard or mandatory model
components. For example, the model components may be configured to
run in parallel, to run serially, or in a combination of serial and
parallel.
[0571] For example, the artificial intelligence configuration
module 15304 may configure an artificial intelligence model 15318
to weight one data input 15320 more heavily than another. For
example, in the rain, an autonomous driving solution may weight
input from a traction control system and a forward radar system
more heavily than sensors targeted to increasing fuel efficiency,
such as sensors measuring road slope and vehicle speed. After the
rain, the weighting may be reversed.
[0572] In another example, the artificial intelligence
configuration module 15304 may configure an artificial intelligence
model 15318 to operate within certain thresholds of data input
15320. For example, an artificial intelligence model 15318 may be
used in a combinatorial drafting task. When only two articulated
ideas are provided to the model 15318, the model 15318 may not be
triggered to operate. However, once the model 15318 receives a
third articulated idea, its combinatorial processing of articulated
ideas may commence.
[0573] The artificial intelligence configuration module 15304 may
configure which sensors to use as data input 15320, how frequently
to sample data, how frequently to transmit output, the weighting of
various data inputs 15320, thresholds to apply to data from data
inputs 15320, whether an output of one component of the model 15318
is used as input to another component of the model 15318, an order
of operation of the components of the model 15318, a positioning of
a model component within a workflow of a model, and the like.
[0574] The artificial intelligence configuration module 15304 may
configure an artificial intelligence model 15318 from one or more
model components identified by the artificial intelligence search
engine 15310. For example, if the search result consisted solely of
model components, the AI configuration module 15304 may configure
where to place the identified 127 components in relation to one
another, such as in a workflow or data flow, as well as in relation
to other components that may be required for the model 15318 to
function.
[0575] In embodiments, an artificial intelligence store 157 may
include a set of interfaces to artificial intelligence systems,
such as enabling the download of relevant artificial intelligence
applications, establishment of links or other connections to
artificial intelligence systems (such as links to cloud-deployed
artificial intelligence systems via APIs, ports, connectors, or
other interfaces) and the like.
[0576] Referring now to FIG. 154, a method of artificial
intelligence model identification and selection may include
receiving input regarding an attribute of a task or a domain 15402,
and processing the input to determine whether an artificial
intelligence system can be applied to the task or the domain 15404,
performing a search of an artificial intelligence store of a
plurality of domain-specific and general artificial intelligence
models and model components using the input and/or at least one
selection criteria to identify at least one artificial intelligence
model or model component to apply to the task or the domain 15408,
and configuring one or more data inputs to use with the at least
one artificial intelligence model 15410 or model component. The
artificial intelligence store may include metadata or other
descriptive material indicating a suitability of an artificial
intelligence system for at least one of solving a particular type
of problem or operating on domain-specific inputs, data, or other
entities.
[0577] The method may further include ranking one or more results
of the search according to a strength or a weakness of the at least
one artificial intelligence model relative to the at least one
selection criteria 15412. The method may further include
configuring the at least one artificial intelligence model or model
component in accordance with at least one configuration criteria
15414. The method may further include collaborative filtering
search results comprising the at least one artificial intelligence
model using an element of the at least one artificial intelligence
model selected or model component by a user 15416. The method may
further include clustering search results comprising the at least
one artificial intelligence model or model component with a
clustering engine 15418.
[0578] FIG. 155 illustrates an example environment of a digital
twin system 15500. In embodiments, the digital twin system 15500
generates a set of digital twins of a set of industrial
environments 15520 and/or industrial entities within the set of
industrial environments. In embodiments, the digital twin system
15500 maintains a set of states of the respective industrial
environments 15520, such as using sensor data obtained from
respective sensor systems 15530 that monitor the industrial
environments 15520. In embodiments, the digital twin system 15500
may include a digital twin management system 15502, a digital twin
I/O system 15504, a digital twin simulation system 15506, a digital
twin dynamic model system 15508, a cognitive intelligence system
15510, and/or an environment control module 15512. In embodiments,
the digital twin system 15500 may provide a real time sensor API
that provides a set of capabilities for enabling a set of
interfaces for the sensors of the respective sensor systems 15530.
In embodiments, the digital twin system 15500 may include and/or
employ other suitable APIs, brokers, connectors, bridges, gateways,
hubs, ports, routers, switches, data integration systems,
peer-to-peer systems, and the like to facilitate the transferring
of data to and from the digital twin system 15500. In these
embodiments, these connective components may allow an IoT sensor or
an intermediary device (e.g., a relay, an edge device, a switch, or
the like) within a sensor system 15530 to communicate data to the
digital twin system 15500 and/or to receive data (e.g.,
configuration data, control data, or the like) from the digital
twin system 15500 or another external system. In embodiments, the
digital twin system 15500 may further include a digital twin
datastore 15516 that stores digital twins 15518 of various
industrial environments 15520 and the objects 15522, devices 15524,
sensors 15526, and/or humans 15528 in the environment 15520.
[0579] A digital twin may refer to a digital representation of one
or more industrial entities, such as an industrial environment
15520, a physical object 15522, a device 15524, a sensor 15526, a
human 15528, or any combination thereof. Examples of industrial
environments 15520 include, but are not limited to, a factory, a
power plant, a food production facility (which may include an
inspection facility), a commercial kitchen, an indoor growing
facility, a natural resources excavation site (e.g., a mine, an oil
field, etc.), and the like. Depending on the type of environment,
the types of objects, devices, and sensors that are found in the
environments will differ. Non-limiting examples of physical objects
15522 include raw materials, manufactured products, excavated
materials, containers (e.g., boxes, dumpsters, cooling towers,
vats, pallets, barrels, palates, bins, and the like), furniture
(e.g., tables, counters, workstations, shelving, etc.), and the
like. Non-limiting examples of devices 15524 include robots,
computers, vehicles (e.g., cars, trucks, tankers, trains,
forklifts, cranes, etc.), machinery/equipment (e.g., tractors,
tillers, drills, presses, assembly lines, conveyor belts, etc.),
and the like. The sensors 15526 may be any sensor devices and/or
sensor aggregation devices that are found in a sensor system 15530
within an environment. Non-limiting examples of sensors 15526 that
may be implemented in a sensor system 15530 may include temperature
sensors 15532, humidity sensors 15534, vibration sensors 15536,
LIDAR sensors 15538, motion sensors 15540, chemical sensors 15542,
audio sensors 15544, pressure sensors 15546, weight sensors 15548,
radiation sensors 15550, video sensors 15552, wearable devices
15554, relays 15556, edge devices 15558, crosspoint switches 15560,
and/or any other suitable sensors. Examples of different types of
physical objects 15522, devices 15524, sensors 15526, and
environments 15520 are referenced throughout the disclosure.
[0580] In some embodiments, on-device sensor fusion and data
storage for industrial IoT devices is supported, including
on-device sensor fusion and data storage for an industrial IoT
device, where data from multiple sensors is multiplexed at the
device for storage of a fused data stream. For example, pressure
and temperature data may be multiplexed into a data stream that
combines pressure and temperature in a time series, such as in a
byte-like structure (where time, pressure, and temperature are
bytes in a data structure, so that pressure and temperature remain
linked in time, without requiring separate processing of the
streams by outside systems), or by adding, dividing, multiplying,
subtracting, or the like, such that the fused data can be stored on
the device. Any of the sensor data types described throughout this
disclosure, including vibration data, can be fused in this manner
and stored in a local data pool, in storage, or on an IoT device,
such as a data collector, a component of a machine, or the
like.
[0581] In some embodiments, a set of digital twins may represent an
entire organization, such as energy production organizations, oil
and gas organizations, renewable energy production organizations,
aerospace manufacturers, vehicle manufacturers, heavy equipment
manufacturers, mining organizations, drilling organizations,
offshore platform organizations, and the like. In these examples,
the digital twins may include digital twins of one or more
industrial facilities of the organization.
[0582] In embodiments, the digital twin management system 15502
generates digital twins. A digital twin may be comprised of (e.g.,
via reference) other digital twins. In this way, a discrete digital
twin may be comprised of a set of other discrete digital twins. For
example, a digital twin of a machine may include digital twins of
sensors on the machine, digital twins of components that make up
the machine, digital twins of other devices that are incorporated
in or integrated with the machine (such as systems that provide
inputs to the machine or take outputs from it), and/or digital
twins of products or other items that are made by the machine.
Taking this example one step further, a digital twin of an
industrial facility (e.g., a factory) may include a digital twin
representing the layout of the industrial facility, including the
arrangement of physical assets and systems in or around the
facility, as well as digital assets of the assets within the
facility (e.g., the digital twin of the machine), as well as
digital twins of storage areas in the facility, digital twins of
humans collecting vibration measurements from machines throughout
the facility, and the like. In this second example, the digital
twin of the industrial facility may reference the embedded digital
twins, which may then reference other digital twins embedded within
those digital twins.
[0583] In some embodiments, a digital twin may represent abstract
entities, such as workflows and/or processes, including inputs,
outputs, sequences of steps, decision points, processing loops, and
the like that make up such workflows and processes. For example, a
digital twin may be a digital representation of a manufacturing
process, a logistics workflow, an agricultural process, a mineral
extraction process, or the like. In these embodiments, the digital
twin may include references to the industrial entities that are
included in the workflow or process. The digital twin of the
manufacturing process may reflect the various stages of the
process. In some of these embodiments, the digital twin system
15500 receives real-time data from the industrial facility (e.g.,
from a sensor system 15530 of the environment 15520) in which the
manufacturing process takes place and reflects a current (or
substantially current) state of the process in real-time.
[0584] In embodiments, the digital representation may include a set
of data structures (e.g., classes) that collectively define a set
of properties of a represented physical object 15522, device 15524,
sensor 15526, or environment 15520 and/or possible behaviors
thereof. For example, the set of properties of a physical object
15522 may include a type of the physical object, the dimensions of
the object, the mass of the object, the density of the object, the
material(s) of the object, the physical properties of the
material(s), the surface of the physical object, the status of the
physical object, a location of the physical object, identifiers of
other digital twins contained within the object, and/or other
suitable properties. Examples of behavior of a physical object may
include a state of the physical object (e.g., a solid, liquid, or
gas), a melting point of the physical object, a density of the
physical object when in a liquid state, a viscosity of the physical
object when in a liquid state, a freezing point of the physical
object, a density of the physical object when in a solid state, a
hardness of the physical object when in a solid state, the
malleability of the physical object, the buoyancy of the physical
object, the conductivity of the physical object, a burning point of
the physical object, the manner by which humidity affects the
physical object, the manner by which water or other liquids affect
the physical object, a terminal velocity of the physical object,
and the like. In another example, the set of properties of a device
may include a type of the device, the dimensions of the device, the
mass of the device, the density of the density of the device, the
material(s) of the device, the physical properties of the
material(s), the surface of the device, the output of the device,
the status of the device, a location of the device, a trajectory of
the device, vibration characteristics of the device, identifiers of
other digital twins that the device is connected to and/or
contains, and the like. Examples of the behaviors of a device may
include a maximum acceleration of a device, a maximum speed of a
device, ranges of motion of a device, a heating profile of a
device, a cooling profile of a device, processes that are performed
by the device, operations that are performed by the device, and the
like. Example properties of an environment may include the
dimensions of the environment, the boundaries of the environment,
the temperature of the environment, the humidity of the
environment, the airflow of the environment, the physical objects
in the environment, currents of the environment (if a body of
water), and the like. Examples of behaviors of an environment may
include scientific laws that govern the environment, processes that
are performed in the environment, rules or regulations that must be
adhered to in the environment, and the like.
[0585] In embodiments, the properties of a digital twin may be
adjusted. For example, the temperature of a digital twin, a
humidity of a digital twin, the shape of a digital twin, the
material of a digital twin, the dimensions of a digital twin, or
any other suitable parameters may be adjusted. As the properties of
the digital twin are adjusted, other properties may be affected as
well. For example, if the temperature of an environment 15520 is
increased, the pressure within the environment may increase as
well, such as a pressure of a gas in accordance with the ideal gas
law. In another example, if a digital twin of a subzero environment
is increased to above freezing temperatures, the properties of an
embedded twin of water in a solid state (i.e., ice) may change into
a liquid state over time.
[0586] Digital twins may be represented in a number of different
forms. In embodiments, a digital twin may be a visual digital twin
that is rendered by a computing device, such that a human user can
view digital representations of an environment 15520 and/or the
physical objects 15522, devices 15524, and/or the sensors 15526
within an environment. In embodiments, the digital twin may be
rendered and output to a display device. In some of these
embodiments, the digital twin may be rendered in a graphical user
interface, such that a user may interact with the digital twin. For
example, a user may "drill down" on a particular element (e.g., a
physical object or device) to view additional information regarding
the element (e.g., a state of a physical object or device,
properties of the physical object or device, or the like). In some
embodiments, the digital twin may be rendered and output in a
virtual reality display. For example, a user may view a 3D
rendering of an environment (e.g., using monitor or a virtual
reality headset). While doing so, the user may view/inspect digital
twins of physical assets or devices in the environment.
[0587] In some embodiments, a data structure of the visual digital
twins (i.e., digital twins that are configured to be displayed in a
2D or 3D manner) may include surfaces (e.g., splines, meshes,
polygons meshes, or the like). In some embodiments, the surfaces
may include texture data, shading information, and/or reflection
data. In this way, a surface may be displayed in a more realistic
manner. In some embodiments, such surfaces may be rendered by a
visualization engine (not shown) when the digital twin is within a
field of view and/or when existing in a larger digital twin (e.g.,
a digital twin of an industrial environment). In these embodiments,
the digital twin system 15500 may render the surfaces of digital
objects, whereby a rendered digital twin may be depicted as a set
of adjoined surfaces.
[0588] In embodiments, a user may provide input that controls one
or more properties of a digital twin via a graphical user
interface. For example, a user may provide input that changes a
property of a digital twin. In response, the digital twin system
15500 can calculate the effects of the changed property and may
update the digital twin and any other digital twins affected by the
change of the property.
[0589] In embodiments, a user may view processes being performed
with respect to one or more digital twins (e.g., manufacturing of a
product, extracting minerals from a mine or well, a livestock
inspection line, and the like). In these embodiments, a user may
view the entire process or specific steps within a process.
[0590] In some embodiments, a digital twin (and any digital twins
embedded therein) may be represented in a non-visual representation
(or "data representation"). In these embodiments, a digital twin
and any embedded digital twins exist in a binary representation but
the relationships between the digital twins are maintained. For
example, in embodiments, each digital twin and/or the components
thereof may be represented by a set of physical dimensions that
define a shape of the digital twin (or component thereof).
Furthermore, the data structure embodying the digital twin may
include a location of the digital twin. In some embodiments, the
location of the digital twin may be provided in a set of
coordinates. For example, a digital twin of an industrial
environment may be defined with respect to a coordinate space
(e.g., a Cartesian coordinate space, a polar coordinate space, or
the like). In embodiments, embedded digital twins may be
represented as a set of one or more ordered triples (e.g., [x
coordinate, y coordinate, z coordinates] or other vector-based
representations). In some of these embodiments, each ordered triple
may represent a location of a specific point (e.g., center point,
top point, bottom point, or the like) on the industrial entity
(e.g., object, device, or sensor) in relation to the environment in
which the industrial entity resides. In some embodiments, a data
structure of a digital twin may include a vector that indicates a
motion of the digital twin with respect to the environment. For
example, fluids (e.g., liquids or gasses) or solids may be
represented by a vector that indicates a velocity (e.g., direction
and magnitude of speed) of the entity represented by the digital
twin. In embodiments, a vector within a twin may represent a
microscopic subcomponent, such as a particle within a fluid, and a
digital twin may represent physical properties, such as
displacement, velocity, acceleration, momentum, kinetic energy,
vibrational characteristics, thermal properties, electromagnetic
properties, and the like.
[0591] In some embodiments, a set of two or more digital twins may
be represented by a graph database that includes nodes and edges
that connect the nodes. In some implementations, an edge may
represent a spatial relationship (e.g., "abuts", "rests upon",
"contains", and the like). In these embodiments, each node in the
graph database represents a digital twin of an entity (e.g., an
industrial entity) and may include the data structure defining the
digital twin. In these embodiments, each edge in the graph database
may represent a relationship between two entities represented by
connected nodes. In some implementations, an edge may represent a
spatial relationship (e.g., "abuts", "rests upon", "interlocks
with", "bears", "contains", and the like). In embodiments, various
types of data may be stored in a node or an edge. In embodiments, a
node may store property data, state data, and/or metadata relating
to a facility, system, subsystem, and/or component. Types of
property data and state data will differ based on the entity
represented by a node. For example, a node representing a robot may
include property data that indicates a material of the robot, the
dimensions of the robot (or components thereof), a mass of the
robot, and the like. In this example, the state data of the robot
may include a current pose of the robot, a location of the robot,
and the like. In embodiments, an edge may store relationship data
and metadata data relating to a relationship between two nodes.
Examples of relationship data may include the nature of the
relationship, whether the relationship is permanent (e.g., a fixed
component would have a permanent relationship with the structure to
which it is attached or resting on), and the like. In embodiments,
an edge may include metadata concerning the relationship between
two entities. For example, if a product was produced on an assembly
line, one relationship that may be documented between a digital
twin of the product and the assembly line may be "created by". In
these embodiments, an example edge representing the "created by"
relationship may include a timestamp indicating a date and time
that the product was created. In another example, a sensor may take
measurements relating to a state of a device, whereby one
relationship between the sensor and the device may include
"measured" and may define a measurement type that is measured by
the sensor. In this example, the metadata stored in an edge may
include a list of N measurements taken and a timestamp of each
respective measurement. In this way, temporal data relating to the
nature of the relationship between two entities may be maintained,
thereby allowing for an analytics engine, machine-learning engine,
and/or visualization engine to leverage such temporal relationship
data, such as by aligning disparate data sets with a series of
points in time, such as to facilitate cause-and-effect analysis
used for prediction systems.
[0592] In some embodiments, a graph database may be implemented in
a hierarchical manner, such that the graph database relates a set
of facilities, systems, and components. For example, a digital twin
of a manufacturing environment may include a node representing the
manufacturing environment. The graph database may further include
nodes representing various systems within the manufacturing
environment, such as nodes representing an HVAC system, a lighting
system, a manufacturing system, and the like, all of which may
connect to the node representing the manufacturing system. In this
example, each of the systems may further connect to various
subsystems and/or components of the system. For example, within the
HVAC system, the HVAC system may connect to a subsystem node
representing a cooling system of the facility, a second subsystem
node representing a heating system of the facility, a third
subsystem node representing the fan system of the facility, and one
or more nodes representing a thermostat of the facility (or
multiple thermostats). Carrying this example further, the subsystem
nodes and/or component nodes may connect to lower level nodes,
which may include subsystem nodes and/or component nodes. For
example, the subsystem node representing the cooling subsystem may
be connected to a component node representing an air conditioner
unit. Similarly, a component node representing a thermostat device
may connect to one or more component nodes representing various
sensors (e.g., temperature sensors, humidity sensors, and the
like).
[0593] In embodiments where a graph database is implemented, a
graph database may relate to a single environment or may represent
a larger enterprise. In the latter scenario, a company may have
various manufacturing and distribution facilities. In these
embodiments, an enterprise node representing the enterprise may
connect to environment nodes of each respective facility. In this
way, the digital twin system 15500 may maintain digital twins for
multiple industrial facilities of an enterprise.
[0594] In embodiments, the digital twin system 15500 may use a
graph database to generate a digital twin that may be rendered and
displayed and/or may be represented in a data representation. In
the former scenario, the digital twin system 15500 may receive a
request to render a digital twin, whereby the request includes one
or more parameters that are indicative of a view that will be
depicted. For example, the one or more parameters may indicate an
industrial environment to be depicted and the type of rendering
(e.g., "real-world view" that depicts the environment as a human
would see it, an "infrared view" that depicts objects as a function
of their respective temperature, an "airflow view" that depicts the
airflow in a digital twin, or the like). In response, the digital
twin system 15500 may traverse a graph database and may determine a
configuration of the environment to be depicted based on the nodes
in the graph database that are related (either directly or through
a lower level node) to the environment node of the environment and
the edges that define the relationships between the related nodes.
Upon determining a configuration, the digital twin system 15500 may
identify the surfaces that are to be depicted and may render those
surfaces. The digital twin system 15500 may then render the
requested digital twin by connecting the surfaces in accordance
with the configuration. The rendered digital twin may then be
output to a viewing device (e.g., VR headset, monitor, or the
like). In some scenarios, the digital twin system 15500 may receive
real-time sensor data from a sensor system 15530 of an environment
15520 and may update the visual digital twin based on the sensor
data. For example, the digital twin system 1550 may receive sensor
data (e.g., vibration data from a vibration sensor 15536) relating
to a motor and its set of bearings. Based on the sensor data, the
digital twin system 15500 may update the visual digital twin to
indicate the approximate vibrational characteristics of the set of
bearings within a digital twin of the motor.
[0595] In scenarios where the digital twin system 15500 is
providing data representations of digital twins (e.g., for dynamic
modeling, simulations, machine learning), the digital twin system
15500 may traverse a graph database and may determine a
configuration of the environment to be depicted based on the nodes
in the graph database that are related (either directly or through
a lower level node) to the environment node of the environment and
the edges that define the relationships between the related nodes.
In some scenarios, the digital twin system 15500 may receive
real-time sensor data from a sensor system 15530 of an environment
15520 and may apply one or more dynamic models to the digital twin
based on the sensor data. In other scenarios, a data representation
of a digital twin may be used to perform simulations, as is
discussed in greater detail throughout the specification.
[0596] In some embodiments, the digital twin system 15500 may
execute a digital ghost that is executed with respect to a digital
twin of an industrial environment. In these embodiments, the
digital ghost may monitor one or more sensors of a sensor system
15530 of an industrial environment to detect anomalies that may
indicate a malicious virus or other security issues.
[0597] As discussed, the digital twin system 15500 may include a
digital twin management system 15502, a digital twin I/O system
15504, a digital twin simulation system 15506, a digital twin
dynamic model system 15508, a cognitive intelligence system 15510,
and/or an environment control system 15512.
[0598] In embodiments, the digital twin management system 15502
creates new digital twins, maintains/updates existing digital
twins, and/or renders digital twins. The digital twin management
system 15502 may receive user input, uploaded data, and/or sensor
data to create and maintain existing digital twins. Upon creating a
new digital twin, the digital twin management system 15502 may
store the digital twin in the digital twin datastore 15516.
Creating, updating, and rendering digital twins are discussed in
greater detail throughout the disclosure.
[0599] In embodiments, the digital twin I/O system 15504 receives
input from various sources and outputs data to various recipients.
In embodiments, the digital twin I/O system receives sensor data
from one or more sensor systems 15530. In these embodiments, each
sensor system 15530 may include one or more IoT sensors that output
respective sensor data. Each sensor may be assigned an IP address
or may have another suitable identifier. Each sensor may output
sensor packets that include an identifier of the sensor and the
sensor data. In some embodiments, the sensor packets may further
include a timestamp indicating a time at which the sensor data was
collected. In some embodiments, the digital twin I/O system 15504
may interface with a sensor system 15530 via the real-time sensor
API 15514. In these embodiments, one or more devices (e.g.,
sensors, aggregators, edge devices) in the sensor system 15530 may
transmit the sensor packets containing sensor data to the digital
twin I/O system 15504 via the API. The digital twin I/O system may
determine the sensor system 15530 that transmitted the sensor
packets and the contents thereof, and may provide the sensor data
and any other relevant data (e.g., time stamp, environment
identifier/sensor system identifier, and the like) to the digital
twin management system 15502.
[0600] In embodiments, the digital twin I/O system 15504 may
receive imported data from one or more sources. For example, the
digital twin system 15500 may provide a portal for users to create
and manage their digital twins. In these embodiments, a user may
upload one or more files (e.g., image files, LIDAR scans,
blueprints, and the like) in connection with a new digital twin
that is being created. In response, the digital twin I/O system
15504 may provide the imported data to the digital twin management
system 15502. The digital twin I/O system 15504 may receive other
suitable types of data without departing from the scope of the
disclosure.
[0601] In some embodiments, the digital twin simulation system
15506 is configured to execute simulations using the digital twin.
For example, the digital twin simulation system 15506 may
iteratively adjust one or more parameters of a digital twin and/or
one or more embedded digital twins. In embodiments, the digital
twin simulation system 15506, for each set of parameters, executes
a simulation based on the set of parameters and may collect the
simulation outcome data resulting from the simulation. Put another
way, the digital twin simulation system 15506 may collect the
properties of the digital twin and the digital twins within or
containing the digital twin used during the simulation as well as
any outcomes stemming from the simulation. For example, in running
a simulation on a digital twin of an indoor agricultural facility,
the digital twin simulation system 15506 can vary the temperature,
humidity, airflow, carbon dioxide and/or other relevant parameters
and can execute simulations that output outcomes resulting from
different combinations of the parameters. In another example, the
digital twin simulation system 15506 may simulate the operation of
a specific machine within an industrial facility that produces an
output given a set of inputs. In some embodiments, the inputs may
be varied to determine an effect of the inputs on the machine and
the output thereof. In another example, the digital twin simulation
system 15506 may simulate the vibration of a machine and/or machine
components. In this example, the digital twin of the machine may
include a set of operating parameters, interfaces, and capabilities
of the machine. In some embodiments, the operating parameters may
be varied to evaluate the effectiveness of the machine. The digital
twin simulation system 15506 is discussed in further detail
throughout the disclosure.
[0602] In embodiments, the digital twin dynamic model system 15508
is configured to model one or more behaviors with respect to a
digital twin of an environment. In embodiments, the digital twin
dynamic model system 15508 may receive a request to model a certain
type of behavior regarding an environment or a process and may
model that behavior using a dynamic model, the digital twin of the
environment or process, and sensor data collected from one or more
sensors that are monitoring the environment or process. For
example, an operator of a machine having bearings may wish to model
the vibration of the machine and bearings to determine whether the
machine and/or bearings can withstand an increase in output. In
this example, the digital twin dynamic model system 15508 may
execute a dynamic model that is configured to determine whether an
increase in output would result in adverse consequences (e.g.,
failures, downtime, or the like). The digital twin dynamic model
system 15508 is discussed in further detail throughout the
disclosure.
[0603] In embodiments, the cognitive processes system 15510
performs machine learning and artificial intelligence related tasks
on behalf of the digital twin system. In embodiments, the cognitive
processes system 15510 may train any suitable type of model,
including but not limited to various types of neural networks,
regression models, random forests, decision trees, Hidden Markov
models, Bayesian models, and the like. In embodiments, the
cognitive processes system 15510 trains machine learned models
using the output of simulations executed by the digital twin
simulation system 15506. In some of these embodiments, the outcomes
of the simulations may be used to supplement training data
collected from real-world environments and/or processes. In
embodiments, the cognitive processes system 15510 leverages machine
learned models to make predictions, identifications,
classifications and provide decision support relating to the
real-world environments and/or processes represented by respective
digital twins.
[0604] For example, a machine-learned prediction model may be used
to predict the cause of irregular vibrational patterns (e.g., a
suboptimal, critical, or alarm vibration fault state) for a bearing
of an engine in an industrial facility. In this example, the
cognitive processes system 15510 may receive vibration sensor data
from one or more vibration sensors disposed on or near the engine
and may receive maintenance data from the industrial facility and
may generate a feature vector based on the vibration sensor data
and the maintenance data. The cognitive processes system 15510 may
input the feature vector into a machine-learned model trained
specifically for the engine (e.g., using a combination simulation
data and real-world data of causes of irregular vibration patterns)
to predict the cause of the irregular vibration patterns. In this
example, the causes of the irregular vibrational patterns could be
a loose bearing, a lack of bearing lubrication, a bearing that is
out of alignment, a worn bearing, the phase of the bearing may be
aligned with the phase of the engine, loose housing, loose bolt,
and the like.
[0605] In another example, a machine-learned model may be used to
provide decision support to bring a bearing of an engine in an
industrial facility operating at a suboptimal vibration fault level
state to a normal operation vibration fault level state. In this
example, the cognitive processes system 15510 may receive vibration
sensor data from one or more vibration sensors disposed on or near
the engine and may receive maintenance data from the industrial
facility and may generate a feature vector based on the vibration
sensor data and the maintenance data. The cognitive processes
system 15510 may input the feature vector into a machine-learned
model trained specifically for the engine (e.g., using a
combination simulation data and real-world data of solutions to
irregular vibration patterns) to provide decision support in
achieving a normal operation fault level state of the bearing. In
this example, the decision support could be a recommendation to
tighten the bearing, lubricate the bearing, re-align the bearing,
order a new bearing, order a new part, collect additional vibration
measurements, change operating speed of the engine, tighten
housings, tighten bolts, and the like.
[0606] In another example, a machine-learned model may be used to
provide decision support relating to vibration measurement
collection by a worker. In this example, the cognitive processes
system 15510 may receive vibration measurement history data from
the industrial facility and may generate a feature vector based on
the vibration measurement history data. The cognitive processes
system 15510 may input the feature vector into a machine-learned
model trained specifically for the engine (e.g., using a
combination simulation data and real-world vibration measurement
history data) to provide decision support in selecting vibration
measurement locations.
[0607] In yet another example, a machine-learned model may be used
to identify vibration signatures associated with machine and/or
machine component problems. In this example, the cognitive
processes system 15510 may receive vibration measurement history
data from the industrial facility and may generate a feature vector
based on the vibration measurement history data. The cognitive
processes system 15510 may input the feature vector into a
machine-learned model trained specifically for the engine (e.g.,
using a combination simulation data and real-world vibration
measurement history data) to identify vibration signatures
associated with a machine and/or machine component. The foregoing
examples are non-limiting examples and the cognitive processes
system 15510 may be used for any other suitable AI/machine-learning
related tasks that are performed with respect to industrial
facilities.
[0608] In embodiments, the environment control system 15512
controls one or more aspects of industrial facilities. In some of
these embodiments, the environment control system 15512 may control
one or more devices within an industrial environment. For example,
the environment control system 15512 may control one or more
machines within an environment, robots within an environment, an
HVAC system of the environment, an alarm system of the environment,
an assembly line in an environment, or the like. In embodiments,
the environment control system 15512 may leverage the digital twin
simulation system 15506, the digital twin dynamic model system
15508, and/or the cognitive processes system 15510 to determine one
or more control instructions. In embodiments, the environment
control system 15512 may implement a rules-based and/or a
machine-learning approach to determine the control instructions. In
response to determining a control instruction, the environment
control system 15512 may output the control instruction to the
intended device within a specific environment via the digital twin
I/O system 15504.
[0609] FIG. 156 illustrates an example digital twin management
system 15502 according to some embodiments of the present
disclosure. In embodiments, the digital twin management system
15502 may include, but is not limited to, a digital twin creation
module 15564, a digital twin update module 15566, and a digital
twin visualization module 15568.
[0610] In embodiments, the digital twin creation module 15564 may
create a set of new digital twins of a set of environments using
input from users, imported data (e.g., blueprints, specifications,
and the like), image scans of the environment, 3D data from a LIDAR
device and/or SLAM sensor, and other suitable data sources. For
example, a user (e.g., a user affiliated with an
organization/customer account) may, via a client application 15570,
provide input to create a new digital twin of an environment. In
doing so, the user may upload 2D or 3D image scans of the
environment and/or a blueprint of the environment. The user may
also upload 3D data, such as taken by a camera, a LIDAR device, an
IR scanner, a set of SLAM sensors, a radar device, an EMF scanner,
or the like. In response to the provided data, the digital twin
creation module 15564 may create a 3D representation of the
environment, which may include any objects that were captured in
the image data/detected in the 3D data. In embodiments, the
cognitive processes system 15572 may analyze input data (e.g.,
blueprints, image scans, 3D data) to classify rooms, pathways,
equipment, and the like to assist in the generation of the 3D
representation. In some embodiments, the digital twin creation
module 15564 may map the digital twin to a 3D coordinate space
(e.g., a Cartesian space having x, y, and z axes).
[0611] In some embodiments, the digital twin creation module 15564
may output the 3D representation of the environment to a graphical
user interface (GUI). In some of these embodiments, a user may
identify certain areas and/or objects and may provide input
relating to the identified areas and/or objects. For example, a
user may label specific rooms, equipment, machines, and the like.
Additionally or alternatively, the user may provide data relating
to the identified objects and/or areas. For example, in identifying
a piece of equipment, the user may provide a make/model number of
the equipment. In some embodiments, the digital twin creation
module 15564 may obtain information from a manufacturer of a
device, a piece of equipment, or machinery. This information may
include one or more properties and/or behaviors of the device,
equipment, or machinery. In some embodiments, the user may, via the
GUI, identify locations of sensors throughout the environment. For
each sensor, the user may provide a type of sensor and related data
(e.g., make, model, IP address, and the like). The digital twin
creation module 15564 may record the locations (e.g., the x, y, z
coordinates of the sensors) in the digital twin of the environment.
In embodiments, the digital twin system 15500 may employ one or
more systems that automate the population of digital twins. For
example, the digital twin system 15500 may employ a machine
vision-based classifier that classifies makes and models of
devices, equipment, or sensors. Additionally or alternatively, the
digital twin system 15500 may iteratively ping different types of
known sensors to identify the presence of specific types of sensors
that are in an environment. Each time a sensor responds to a ping,
the digital twin system 15500 may extrapolate the make and model of
the sensor.
[0612] In some embodiments, the manufacturer may provide or make
available digital twins of their products (e.g., sensors, devices,
machinery, equipment, raw materials, and the like). In these
embodiments, the digital twin creation module 15564 may import the
digital twins of one or more products that are identified in the
environment and may embed those digital twins in the digital twin
of the environment. In embodiments, embedding a digital twin within
another digital twin may include creating a relationship between
the embedded digital twin with the other digital twin. In these
embodiments, the manufacturer of the digital twin may define the
behaviors and/or properties of the respective products. For
example, a digital twin of a machine may define the manner by which
the machine operates, the inputs/outputs of the machine, and the
like. In this way, the digital twin of the machine may reflect the
operation of the machine given a set of inputs.
[0613] In embodiments, a user may define one or more processes that
occur in an environment. In these embodiments, the user may define
the steps in the process, the machines/devices that perform each
step in the process, the inputs to the process, and the outputs of
the process.
[0614] In embodiments, the digital twin creation module 15564 may
create a graph database that defines the relationships between a
set of digital twins. In these embodiments, the digital twin
creation module 15564 may create nodes for the environment, systems
and subsystems of the environment, devices in the environment,
sensors in the environment, workers that work in the environment,
processes that are performed in the environment, and the like. In
embodiments, the digital twin creation module 15564 may write the
graph database representing a set of digital twins to the digital
twin datastore 15516.
[0615] In embodiments, the digital twin creation module 15564 may,
for each node, include any data relating to the entity in the node
representing the entity. For example, in defining a node
representing an environment, the digital twin creation module 15564
may include the dimensions, boundaries, layout, pathways, and other
relevant spatial data in the node. Furthermore, the digital twin
creation module 15564 may define a coordinate space with respect to
the environment. In the case that the digital twin may be rendered,
the digital twin creation module 15564 may include a reference in
the node to any shapes, meshes, splines, surfaces, and the like
that may be used to render the environment. In representing a
system, subsystem, device, or sensor, the digital twin creation
module 15564 may create a node for the respective entity and may
include any relevant data. For example, the digital twin creation
module 15564 may create a node representing a machine in the
environment. In this example, the digital twin creation module
15564 may include the dimensions, behaviors, properties, location,
and/or any other suitable data relating to the machine in the node
representing the machine. The digital twin creation module 15564
may connect nodes of related entities with an edge, thereby
creating a relationship between the entities. In doing so, the
created relationship between the entities may define the type of
relationship characterized by the edge. In representing a process,
the digital twin creation module 15564 may create a node for the
entire process or may create a node for each step in the process.
In some of these embodiments, the digital twin creation module
15564 may relate the process nodes to the nodes that represent the
machinery/devices that perform the steps in the process. In
embodiments, where an edge connects the process step nodes to the
machinery/device that performs the process step, the edge or one of
the nodes may contain information that indicates the input to the
step, the output of the step, the amount of time the step takes,
the nature of processing of inputs to produce outputs, a set of
states or modes the process can undergo, and the like.
[0616] In embodiments, the digital twin update module 15566 updates
sets of digital twins based on a current status of one or more
industrial entities. In some embodiments, the digital twin update
module 15566 receives sensor data from a sensor system 15530 of an
industrial environment and updates the status of the digital twin
of the industrial environment and/or digital twins of any affected
systems, subsystems, devices, workers, processes, or the like. As
discussed, the digital twin I/O system 15504 may receive the sensor
data in one or more sensor packets. The digital twin I/O system
15504 may provide the sensor data to the digital twin update module
15566 and may identify the environment from which the sensor
packets were received and the sensor that provided the sensor
packet. In response to the sensor data, the digital twin update
module 15566 may update a state of one or more digital twins based
on the sensor data. In some of these embodiments, the digital twin
update module 15566 may update a record (e.g., a node in a graph
database) corresponding to the sensor that provided the sensor data
to reflect the current sensor data. In some scenarios, the digital
twin update module 15566 may identify certain areas within the
environment that are monitored by the sensor and may update a
record (e.g., a node in a graph database) to reflect the current
sensor data. For example, the digital twin update module 15566 may
receive sensor data reflecting different vibrational
characteristics of a machine and/or machine components. In this
example, the digital twin update module 15566 may update the
records representing the vibration sensors that provided the
vibration sensor data and/or the records representing the machine
and/or the machine components to reflect the vibration sensor data.
In another example, in some scenarios, workers in an industrial
environment (e.g., manufacturing facility, industrial storage
facility, a mine, a drilling operation, or the like) may be
required to wear wearable devices (e.g., smart watches, smart
helmets, smart shoes, or the like). In these embodiments, the
wearable devices may collect sensor data relating to the worker
(e.g., location, movement, heartrate, respiration rate, body
temperature, or the like) and/or the environment surrounding the
worker and may communicate the collected sensor data to the digital
twin system 15500 (e.g., via the real-time sensor API 15514) either
directly or via an aggregation device of the sensor system. In
response to receiving the sensor data from the wearable device of a
worker, the digital twin update module 15566 may update a digital
twin of a worker to reflect, for example, a location of the worker,
a trajectory of the worker, a health status of the worker, or the
like. In some of these embodiments, the digital twin update module
15566 may update a node representing a worker and/or an edge that
connects the node representing the environment with the collected
sensor data to reflect the current status of the worker.
[0617] In some embodiments, the digital twin update module 15566
may provide the sensor data from one or more sensors to the digital
twin dynamic model system 15508, which may model a behavior of the
environment and/or one or more industrial entities to extrapolate
additional state data.
[0618] In embodiments, the digital twin visualization module 15568
receives requests to view a visual digital twin or a portion
thereof. In embodiments, the request may indicate the digital twin
to be viewed (e.g., an environment identifier). In response, the
digital twin visualization module 15568 may determine the requested
digital twin and any other digital twins implicated by the request.
For example, in requesting to view a digital twin of an
environment, the digital twin visualization module 15568 may
further identify the digital twins of any industrial entities
within the environment. In embodiments, the digital twin
visualization module 15568 may identify the spatial relationships
between the industrial entities and the environment based on, for
example, the relationships defined in a graph database. In these
embodiments, the digital twin visualization module 15568 can
determine the relative location of embedded digital twins within
the containing digital twin, relative locations of adjoining
digital twins, and/or the transience of the relationship (e.g., is
an object fixed to a point or does the object move). The digital
twin visualization module 15568 may render the requested digital
twins and any other implicated digital twin based on the identified
relationships. In some embodiments, the digital twin visualization
module 15568 may, for each digital twin, determine the surfaces of
the digital twin. In some embodiments, the surfaces of a digital
may be defined or referenced in a record corresponding to the
digital twin, which may be provided by a user, determined from
imported images, or defined by a manufacturer of an industrial
entity. In the scenario that an object can take different poses or
shapes (e.g., an industrial robot), the digital twin visualization
module 15568 may determine a pose or shape of the object for the
digital twin. The digital twin visualization module 15568 may embed
the digital twins into the requested digital twin and may output
the requested digital twin to a client application.
[0619] In some of these embodiments, the request to view a digital
twin may further indicate the type of view. As discussed, in some
embodiments, digital twins may be depicted in a number of different
view types. For example, an environment or device may be viewed in
a "real-world" view that depicts the environment or device as they
typically appear, in a "heat" view that depicts the environment or
device in a manner that is indicative of a temperature of the
environment or device, in a "vibration" view that depicts the
machines and/or machine components in an industrial environment in
a manner that is indicative of vibrational characteristics of the
machines and/or machine components, in a "filtered" view that only
displays certain types of objects within an environment or
components of a device (such as objects that require attention
resulting from, for example, recognition of a fault condition, an
alert, an updated report, or other factor), an augmented view that
overlays data on the digital twin, and/or any other suitable view
types. In embodiments, digital twins may be depicted in a number of
different role-based view types. For example, a manufacturing
facility device may be viewed in an "operator" view that depicts
the facility in a manner that is suitable for a facility operator,
a "C-Suite" view that depicts the facility in a manner that is
suitable for executive-level managers, a "marketing" view that
depicts the facility in a manner that is suitable for workers in
sales and/or marketing roles, a "board" view that depicts the
facility in a manner that is suitable for members of a corporate
board, a "regulatory" view that depicts the facility in a manner
that is suitable for regulatory managers, and a "human resources"
view that depicts the facility in a manner that is suitable for
human resources personnel. In response to a request that indicates
a view type, the digital twin visualization module 15568 may
retrieve the data for each digital twin that corresponds to the
view type. For example, if a user has requested a vibration view of
a factory floor, the digital twin visualization module 15568 may
retrieve vibration data for the factory floor (which may include
vibration measurements taken from different machines and/or machine
components and/or vibration measurements that were extrapolated by
the digital twin dynamic model system 15508 and/or simulated
vibration data from digital twin simulation system 15506) as well
as available vibration data for any industrial entities appearing
on the factory floor. In this example, the digital twin
visualization module 15568 may determine colors corresponding to
each machine component on a factory floor that represent a
vibration fault level state (e.g., red for alarm, orange for
critical, yellow for suboptimal, and green for normal operation).
The digital twin visualization module 15568 may then render the
digital twins of the machine components within the environment
based on the determined colors. Additionally or alternatively, the
digital twin visualization module 15568 may render the digital
twins of the machine components within the environment with
indicators having the determined colors. For instance, if the
vibration fault level state of an inbound bearing of a motor is
suboptimal and the outbound bearing of the motor is critical, the
digital twin visualization module 15568 may render the digital twin
of the inbound bearing having an indicator in a shade of yellow
(e.g., suboptimal) and the outbound bearing having an indicator in
a shade of orange (e.g., critical). It is noted that in some
embodiments, the digital twin system 15500 may include an analytics
system (not shown) that determine the manner by which the digital
twin visualization system 15500 presents information to a human
user. For example, the analytics system may track outcomes relating
to human interactions with real-world environments or objects in
response to information presented in a visual digital twin. In some
embodiments, the analytics system may apply cognitive models to
determine the most effective manner to display visualized
information (e.g., what colors to use to denote an alarm condition,
what kind of movements or animations bring attention to an alarm
condition, or the like) or audio information (what sounds to use to
denote an alarm condition) based on the outcome data. In some
embodiments, the analytics system may apply cognitive models to
determine the most suitable manner to display visualized
information based on the role of the user. In embodiments, the
visualization may include display of information related to the
visualized digital twins, including graphical information,
graphical information depicting vibration characteristics,
graphical information depicting harmonic peaks, graphical
information depicting peaks, vibration severity units data,
vibration fault level state data, recommendations from cognitive
intelligence system 15510, predictions from cognitive intelligence
system 15510, probability of failure data, maintenance history
data, time to failure data, cost of downtime data, probability of
downtime data, cost of repair data, cost of machine replace data,
probability of shutdown data, manufacturing KPIs, and the like.
[0620] In another example, a user may request a filtered view of a
digital twin of a process, whereby the digital twin of the process
only shows components (e.g., machine or equipment) that are
involved in the process. In this example, the digital twin
visualization module 15568 may retrieve a digital twin of the
process, as well as any related digital twins (e.g., a digital twin
of the environment and digital twins of any machinery or devices
that impact the process). The digital twin visualization module
15568 may then render each of the digital twins (e.g., the
environment and the relevant industrial entities) and then may
perform the process on the rendered digital twins. It is noted that
as a process may be performed over a period of time and may include
moving items and/or parts, the digital twin visualization module
15568 may generate a series of sequential frames that demonstrate
the process. In this scenario, the movements of the machines and/or
devices implicated by the process may be determined according to
the behaviors defined in the respective digital twins of the
machines and/or devices.
[0621] As discussed, the digital twin visualization module 15568
may output the requested digital twin to a client application
15570. In some embodiments, the client application 15570 is a
virtual reality application, whereby the requested digital twin is
displayed on a virtual reality headset. In some embodiments, the
client application 15570 is an augmented reality application,
whereby the requested digital twin is depicted in an AR-enabled
device. In these embodiments, the requested digital twin may be
filtered such that visual elements and/or text are overlaid on the
display of the AR-enabled device.
[0622] It is noted that while a graph database is discussed, the
digital twin system 15500 may employ other suitable data structures
to store information relating to a set of digital twins. In these
embodiments, the data structures, and any related storage system,
may be implemented such that the data structures provide for some
degree of feedback loops and/or recursion when representing
iteration of flows.
[0623] FIG. 157 illustrates an example of a digital twin I/O system
15504 that interfaces with the environment 15520, the digital twin
system 15500, and/or components thereof to provide bi-directional
transfer of data between coupled components according to some
embodiments of the present disclosure.
[0624] In embodiments, the transferred data includes signals (e.g.,
request signals, command signals, response signals, etc.) between
connected components, which may include software components,
hardware components, physical devices, virtualized devices,
simulated devices, combinations thereof, and the like. The signals
may define material properties (e.g., physical quantities of
temperature, pressure, humidity, density, viscosity, etc.),
measured values (e.g., contemporaneous or stored values acquired by
the device or system), device properties (e.g., device ID or
properties of the device's design specifications, materials,
measurement capabilities, dimensions, absolute position, relative
position, combinations thereof, and the like), set points (e.g.,
targets for material properties, device properties, system
properties, combinations thereof, and the like), and/or critical
points (e.g., threshold values such as minimum or maximum values
for material properties, device properties, system properties,
etc.). The signals may be received from systems or devices that
acquire (e.g., directly measure or generate) or otherwise obtain
(e.g., receive, calculate, look-up, filter, etc.) the data, and may
be communicated to or from the digital twin I/O system 15504 at
predetermined times or in response to a request (e.g., polling)
from the digital twin I/O system 15504. The communications may
occur through direct or indirect connections (e.g., via
intermediate modules within a circuit and/or intermediate devices
between the connected components). The values may correspond to
real-world elements 157302r (e.g., an input or output for a
tangible vibration sensor) or virtual elements 157302v (e.g., an
input or output for a digital twin 157302d and/or a simulated
element 157302s that provide vibration data).
[0625] In embodiments, the real-world elements 157302r may be
elements within the industrial environment 15520. The real-world
elements 157302r may include, for example, non-networked objects
15522, the devices 15524 (smart or non-smart), sensors 15526, and
humans 15528. The real-world elements 151302r may be process or
non-process equipment within the industrial environments 15520. For
example, process equipment may include motors, pumps, mills, fans,
painters, welders, smelters, etc., and non-process equipment may
include personal protective equipment, safety equipment, emergency
stations or devices (e.g., safety showers, eyewash stations, fire
extinguishers, sprinkler systems, etc.), warehouse features (e.g.,
walls, floor layout, etc.), obstacles (e.g., persons or other items
within the environment 15520, etc.), etc.
[0626] In embodiments, the virtual elements 157302v may be digital
representations of or that correspond to contemporaneously existing
real-world elements 157302r. Additionally or alternatively, the
virtual elements 157302v may be digital representations of or that
correspond to real-world elements 157302r that may be available for
later addition and implementation into the environment 15520. The
virtual elements may include, for example, simulated elements
175302s and/or digital twins 157302d. In embodiments, the simulated
elements 157302s may be digital representations of real-world
elements 157302s that are not present within the industrial
environment 15520. The simulated elements 157302s may mimic desired
physical properties which may be later integrated within the
environment 15520 as real-world elements 157302r (e.g., a "black
box" that mimics the dimensions of a real-world elements 157302r).
The simulated elements 157302s may include digital twins of
existing objects (e.g., a single simulated element 151302s may
include one or more digital twins 151302d for existing sensors).
Information related to the simulated elements 157302s may be
obtained, for example, by evaluating behavior of corresponding
real-world elements 157302r using mathematical models or
algorithms, from libraries that define information and behavior of
the simulated elements 131302s (e.g., physics libraries, chemistry
libraries, or the like).
[0627] In embodiments, the digital twin 157302d may be a digital
representation of one or more real-world elements 157302r. The
digital twins 157302d are configured to mimic, copy, and/or model
behaviors and responses of the real-world elements 157302r in
response to inputs, outputs, and/or conditions of the surrounding
or ambient environment. Data related to physical properties and
responses of the real-world elements 157302r may be obtained, for
example, via user input, sensor input, and/or physical modeling
(e.g., thermodynamic models, electrodynamic models, mechanodynamic
models, etc.). Information for the digital twin 157302d may
correspond to and be obtained from the one or more real-world
elements 157302r corresponding to the digital twin 157302d. For
example, in some embodiments, the digital twin 131302d may
correspond to one real-world element 157302r that is a fixed
digital vibration sensor 15536 on a machine component, and
vibration data for the digital twin 131302d may be obtained by
polling or fetching vibration data measured by the fixed digital
vibration sensor on the machine component. In a further example,
the digital twin 157302d may correspond to a plurality of
real-world elements 157302r such that each of the elements can be a
fixed digital vibration sensor on a machine component, and
vibration data for the digital twin 157302d may be obtained by
polling or fetching vibration data measured by each of the fixed
digital vibration sensors on the plurality of real-world elements
157302r. Additionally or alternatively, vibration data of a first
digital twin 157302d may be obtained by fetching vibration data of
a second digital twin 157302d that is embedded within the first
digital twin 157302d, and vibration data for the first digital twin
157302d may include or be derived from vibration data for the
second digital twin 157302d. For example, the first digital twin
may be a digital twin 157302d of an environment 15520
(alternatively referred to as an "environmental digital twin") and
the second digital twin 157302d may be a digital twin 157302d
corresponding to a vibration sensor disposed within the environment
15520 such that the vibration data for the first digital twin
157302d is obtained from or calculated based on data including the
vibration data for the second digital twin 157302d.
[0628] In embodiments, the digital twin system 15500 monitors
properties of the real-world elements 157302r using the sensors
15526 within a respective environment 15520 that is or may be
represented by a digital twin 157302d and/or outputs of models for
one or more simulated elements 157302s. In embodiments, the digital
twin system 15500 may minimize network congestion while maintaining
effective monitoring of processes by extending polling intervals
and/or minimizing data transfer for sensors corresponding that
correspond to affected real-world elements 157302r and performing
simulations (e.g., via the digital-twin simulation system 15506)
during the extended interval using data that was obtained from
other sources (e.g., sensors that are physically proximate to or
have an effect on the affected real-world elements 157302r).
Additionally or alternatively, error checking may be performed by
comparing the collected sensor data with data obtained from the
digital-twin simulation system 15506. For example, consistent
deviations or fluctuations between sensor data obtained from the
real-world element 157302r and the simulated element 157302s may
indicate malfunction of the respective sensor or another fault
condition.
[0629] In embodiments, the digital twin system 15500 may optimize
features of the environment through use of one or more simulated
elements 157302s. For example, the digital twin system 15500 may
evaluate effects of the simulated elements 157302s within a digital
twin of an environment to quickly and efficiently determine costs
and/or benefits flowing from inclusion, exclusion, or substitution
of real-world elements 157302r within the environment 15520. The
costs and benefits may include, for example, increased machinery
costs (e.g., capital investment and maintenance), increased
efficiency (e.g., process optimization to reduce waste or increase
throughput), decreased or altered footprint within the environment
15520, extension or optimization of useful lifespans, minimization
of component faults, minimization of component downtime, etc.
[0630] In embodiments, the digital twin I/O system 15504 may
include one or more software modules that are executed by one or
more controllers of one or more devices (e.g., server devices, user
devices, and/or distributed devices) to affect the described
functions. The digital twin I/O system 15504 may include, for
example, an input module 157304, an output module 157306, and an
adapter module 157308.
[0631] In embodiments, the input module 157304 may obtain or import
data from data sources in communication with the digital twin I/O
system 15504, such as the sensor system 15530 and the digital twin
simulation system 15506. The data may be immediately used by or
stored within the digital twin system 15500. The imported data may
be ingested from data streams, data batches, in response to a
triggering event, combinations thereof, and the like. The input
module 157304 may receive data in a format that is suitable to
transfer, read, and/or write information within the digital twin
system 15500.
[0632] In embodiments, the output module 157306 may output or
export data to other system components (e.g., the digital twin
datastore 15516, the digital twin simulation system 15506, the
cognitive intelligence system 15510, etc.), devices 15524, and/or
the client application 15570. The data may be output in data
streams, data batches, in response to a triggering event (e.g., a
request), combinations thereof, and the like. The output module
157306 may output data in a format that is suitable to be used or
stored by the target element (e.g., one protocol for output to the
client application and another protocol for the digital twin
datastore 15516).
[0633] In embodiments, the adapter module 157308 may process and/or
convert data between the input module 157304 and the output module
157306. In embodiments, the adapter module 157308 may convert
and/or route data automatically (e.g., based on data type) or in
response to a received request (e.g., in response to information
within the data).
[0634] In embodiments, the digital twin system 15500 may represent
a set of industrial workpiece elements in a digital twin, and the
digital twin simulation system 15506 simulates a set of physical
interactions of a worker with the workpiece elements.
[0635] In embodiments, the digital twin simulation system 15506 may
determine process outcomes for the simulated physical interactions
accounting for simulated human factors. For example, variations in
workpiece throughput may be modeled by the digital twin system
15500 including, for example, worker response times to events,
worker fatigue, discontinuity within worker actions (e.g., natural
variations in human-movement speed, differing positioning times,
etc.), effects of discontinuities on downstream processes, and the
like. In embodiments, individualized worker interactions may be
modeled using historical data that is collected, acquired, and/or
stored by the digital twin system 15500. The simulation may begin
based on estimated amounts (e.g., worker age, industry averages,
workplace expectations, etc.). The simulation may also
individualize data for each worker (e.g., comparing estimated
amounts to collected worker-specific outcomes).
[0636] In embodiments, information relating to workers (e.g.,
fatigue rates, efficiency rates, and the like) may be determined by
analyzing performance of specific workers over time and modeling
said performance.
[0637] In embodiments, the digital twin system 15500 includes a
plurality of proximity sensors within the sensor system 15530. The
proximity sensors are or may be configured to detect elements of
the environment 15520 that are within a predetermined area. For
example, proximity sensors may include electromagnetic sensors,
light sensors, and/or acoustic sensors.
[0638] The electromagnetic sensors are or may be configured to
sense objects or interactions via one or more electromagnetic
fields (e.g., emitted electromagnetic radiation or received
electromagnetic radiation). In embodiments, the electromagnetic
sensors include inductive sensors (e.g., radio-frequency
identification sensors), capacitive sensors (e.g., contact and
contactless capacitive sensors), combinations thereof, and the
like.
[0639] The light sensors are or may be configured to sense objects
or interactions via electromagnetic radiation in, for example, the
far-infrared, near-infrared, optical, and/or ultraviolet spectra.
In embodiments, the light sensors may include image sensors (e.g.,
charge-coupled devices and CMOS active-pixel sensors),
photoelectric sensors (e.g., through-beam sensors, retroreflective
sensors, and diffuse sensors), combinations thereof, and the like.
Further, the light sensors may be implemented as part of a system
or subsystem, such as a light detection and ranging ("LIDAR")
sensor.
[0640] The acoustic sensors are or may be configured to sense
objects or interactions via sound waves that are emitted and/or
received by the acoustic sensors. In embodiments, the acoustic
sensors may include infrasonic, sonic, and/or ultrasonic sensors.
Further, the acoustic sensors may be grouped as part of a system or
subsystem, such as a sound navigation and ranging ("SONAR")
sensor.
[0641] In embodiments, the digital twin system 15500 stores and
collects data from a set of proximity sensors within the
environment 15520 or portions thereof. The collected data may be
stored, for example, in the digital twin datastore 15516 for use by
components the digital twin system 15500 and/or visualization by a
user. Such use and/or visualization may occur contemporaneously
with or after collection of the data (e.g., during later analysis
and/or optimization of processes).
[0642] In embodiments, data collection may occur in response to a
triggering condition. These triggering conditions may include, for
example, expiration of a static or a dynamic predetermined
interval, obtaining a value short of or in excess of a static or
dynamic value, receiving an automatically generated request or
instruction from the digital twin system 15500 or components
thereof, interaction of an element with the respective sensor or
sensors (e.g., in response to a worker or machine breaking a beam
or coming within a predetermined distance from the proximity
sensor), interaction of a user with a digital twin (e.g., selection
of an environmental digital twin, a sensor array digital twin, or a
sensor digital twin), combinations thereof, and the like.
[0643] In some embodiments, the digital twin system 15500 collects
and/or stores RFID data in response to interaction of a worker with
a real-world element 157302r. For example, in response to a worker
interaction with a real-world environment, the digital twin will
collect and/or store RFID data from RFID sensors within or
associated with the corresponding environment 15520. Additionally
or alternatively, worker interaction with a sensor-array digital
twin will collect and/or store RFID data from RFID sensors within
or associated with the corresponding sensor array. Similarly,
worker interaction with a sensor digital twin will collect and/or
store RFID data from the corresponding sensor. The RFID data may
include suitable data attainable by RFID sensors such as proximate
RFID tags, RFID tag position, authorized RFID tags, unauthorized
RFID tags, unrecognized RFID tags, RFID type (e.g., active or
passive), error codes, combinations thereof, and the like.
[0644] In embodiments, the digital twin system 15500 may further
embed outputs from one or more devices within a corresponding
digital twin. In embodiments, the digital twin system 15500 embeds
output from a set of individual-associated devices into an
industrial digital twin. For example, the digital twin I/O system
15504 may receive information output from one or more wearable
devices 15554 or mobile devices (not shown) associated with an
individual within an industrial environment. The wearable devices
may include image capture devices (e.g., body cameras or
augmented-reality headwear), navigation devices (e.g., GPS devices,
inertial guidance systems), motion trackers, acoustic capture
devices (e.g., microphones), radiation detectors, combinations
thereof, and the like.
[0645] In embodiments, upon receiving the output information, the
digital twin I/O system 15504 routes the information to the digital
twin creation module 15564 to check and/or update the environment
digital twin and/or associated digital twins within the environment
(e.g., a digital twin of a worker, machine, or robot position at a
given time). Further, the digital twin system 15500 may use the
embedded output to determine characteristics of the environment
15520.
[0646] In embodiments, the digital twin system 15500 embeds output
from a LIDAR point cloud system into an industrial digital twin.
For example, the digital twin I/O system 15504 may receive
information output from one or more Lidar devices 15538 within an
industrial environment. The Lidar devices 15538 is configured to
provide a plurality of points having associated position data
(e.g., coordinates in absolute or relative x, y, and z values).
Each of the plurality of points may include further LIDAR
attributes, such as intensity, return number, total returns, laser
color data, return color data, scan angle, scan direction, etc. The
Lidar devices 15538 may provide a point cloud that includes the
plurality of points to the digital twin system 15500 via, for
example, the digital twin I/O system 15504. Additionally or
alternatively, the digital twin system 15500 may receive a stream
of points and assemble the stream into a point cloud, or may
receive a point cloud and assemble the received point cloud with
existing point cloud data, map data, or three dimensional
(3D)-model data.
[0647] In embodiments, upon receiving the output information, the
digital twin I/O system 15504 routes the point cloud information to
the digital twin creation module 15564 to check and/or update the
environment digital twin and/or associated digital twins within the
environment (e.g., a digital twin of a worker, machine, or robot
position at a given time). In some embodiments, the digital twin
system 15500 is further configured to determine closed-shape
objects within the received LIDAR data. For example, the digital
twin system 15500 may group a plurality of points within the point
cloud as an object and, if necessary, estimate obstructed faces of
objects (e.g., a face of the object contacting or adjacent a floor
or a face of the object contacting or adjacent another object such
as another piece of equipment). The system may use such
closed-shape objects to narrow search space for digital twins and
thereby increase efficiency of matching algorithms (e.g., a
shape-matching algorithm).
[0648] In embodiments, the digital twin system 15500 embeds output
from a simultaneous location and mapping ("SLAM") system in an
environmental digital twin. For example, the digital twin I/O
system 15504 may receive information output from the SLAM system,
such as Slam sensor 15562, and embed the received information
within an environment digital twin corresponding to the location
determined by the SLAM system. In embodiments, upon receiving the
output information from the SLAM system, the digital twin I/O
system 15504 routes the information to the digital twin creation
module 15564 to check and/or update the environment digital twin
and/or associated digital twins within the environment (e.g., a
digital twin of a workpiece, furniture, movable object, or
autonomous object). Such updating provides digital twins of
non-connected elements (e.g., furnishings or persons) automatically
and without need of user interaction with the digital twin system
15500.
[0649] In embodiments, the digital twin system 15500 can leverage
known digital twins to reduce computational requirements for the
Slam sensor 15562 by using suboptimal map-building algorithms. For
example, the suboptimal map-building algorithms may allow for a
higher uncertainty tolerance using simple bounded-region
representations and identifying possible digital twins.
Additionally or alternatively, the digital twin system 15500 may
use a bounded-region representation to limit the number of digital
twins, analyze the group of potential twins for distinguishing
features, then perform higher precision analysis for the
distinguishing features to identify and/or eliminate categories of,
groups of, or individual digital twins and, in the event that no
matching digital twin is found, perform a precision scan of only
the remaining areas to be scanned.
[0650] In embodiments, the digital twin system 15500 may further
reduce compute required to build a location map by leveraging data
captured from other sensors within the environment (e.g., captured
images or video, radio images, etc.) to perform an initial
map-building process (e.g., a simple bounded-region map or other
suitable photogrammetry methods), associate digital twins of known
environmental objects with features of the simple bounded-region
map to refine the simple bounded-region map, and perform more
precise scans of the remaining simple bounded regions to further
refine the map. In some embodiments, the digital twin system 15500
may detect objects within received mapping information and, for
each detected object, determine whether the detected object
corresponds to an existing digital twin of a real-world-element. In
response to determining that the detected object does not
correspond to an existing real-world-element digital twin, the
digital twin system 15500 may use, for example, the digital twin
creation module 15564 to generate a new digital twin corresponding
to the detected object (e.g., a detected-object digital twin) and
add the detected-object digital twin to the real-world-element
digital twins within the digital twin datastore. Additionally or
alternatively, in response to determining that the detected object
corresponds to an existing real-world-element digital twin, the
digital twin system 15500 may update the real-world-element digital
twin to include new information detected by the simultaneous
location and mapping sensor, if any.
[0651] In embodiments, the digital twin system 15500 represents
locations of autonomously or remotely moveable elements and
attributes thereof within an industrial digital twin. Such movable
elements may include, for example, workers, persons, vehicles,
autonomous vehicles, robots, etc. The locations of the moveable
elements may be updated in response to a triggering condition. Such
triggering conditions may include, for example, expiration of a
static or a dynamic predetermined interval, receiving an
automatically generated request or instruction from the digital
twin system 15500 or components thereof, interaction of an element
with a respective sensor or sensors (e.g., in response to a worker
or machine breaking a beam or coming within a predetermined
distance from a proximity sensor), interaction of a user with a
digital twin (e.g., selection of an environmental digital twin, a
sensor array digital twin, or a sensor digital twin), combinations
thereof, and the like.
[0652] In embodiments, the time intervals may be based on
probability of the respective movable element having moved within a
time period. For example, the time interval for updating a worker
location may be relatively shorter for workers expected to move
frequently (e.g., a worker tasked with lifting and carrying objects
within and through the environment 15520) and relatively longer for
workers expected to move infrequently (e.g., a worker tasked with
monitoring a process stream). Additionally or alternatively, the
time interval may be dynamically adjusted based on applicable
conditions, such as increasing the time interval when no movable
elements are detected, decreasing the time interval as or when the
number of moveable elements within an environment increases (e.g.,
increasing number of workers and worker interactions), increasing
the time interval during periods of reduced environmental activity
(e.g., breaks such as lunch), decreasing the time interval during
periods of abnormal environmental activity (e.g., tours,
inspections, or maintenance), decreasing the time interval when
unexpected or uncharacteristic movement is detected (e.g., frequent
movement by a typically sedentary element or coordinated movement,
for example, of workers approaching an exit or moving cooperatively
to carry a large object), combinations thereof, and the like.
Further, the time interval may also include additional, semi-random
acquisitions. For example, occasional mid-interval locations may be
acquired by the digital twin system 15500 to reinforce or evaluate
the efficacy of the particular time interval.
[0653] In embodiments, the digital twin system 15500 may analyze
data received from the digital twin I/O system 15504 to refine,
remove, or add conditions. For example, the digital twin system
15500 may optimize data collection times for movable elements that
are updated more frequently than needed (e.g., multiple consecutive
received positions being identical or within a predetermined margin
of error).
[0654] In embodiments, the digital twin system 15500 may receive,
identify, and/or store a set of states 15840a-n related to the
environment 15520. The states 15840a-n may be, for example, data
structures that include a plurality of attributes 158404a-n and a
set of identifying criteria 158406a-n to uniquely identify each
respective state 15840a-n. In embodiments, the states 15840a-n may
correspond to states where it is desirable for the digital twin
system 15500 to set or alter conditions of real-world elements
157302r and/or the environment 15520 (e.g., increase/decrease
monitoring intervals, alter operating conditions, etc.).
[0655] In embodiments, the set of states 15840a-n may further
include, for example, minimum monitored attributes for each state
15840a-n, the set of identifying criteria 158406a-n for each state
15840a-n, and/or actions available to be taken or recommended to be
taken in response to each state 15840a-n. Such information may be
stored by, for example, the digital twin datastore 15516 or another
datastore. The states 15840a-n or portions thereof may be provided
to, determined by, or altered by the digital twin system 15500.
Further, the set of states 15840a-n may include data from disparate
sources. For example, details to identify and/or respond to
occurrence of a first state may be provided to the digital twin
system 15500 via user input, details to identify and/or respond to
occurrence of a second state may be provided to the digital twin
system 15500 via an external system, details to identify and/or
respond to occurrence of a third state may be determined by the
digital twin system 15500 (e.g., via simulations or analysis of
process data), and details to identify and/or respond to occurrence
of a fourth state may be stored by the digital twin system 15500
and altered as desired (e.g., in response to simulated occurrence
of the state or analysis of data collected during an occurrence of
and response to the state).
[0656] In embodiments, the plurality of attributes 158404a-n
includes at least the attributes 158404a-n needed to identify the
respective state 15840a-n. The plurality of attributes 158404a-n
may further include additional attributes that are or may be
monitored in determining the respective state 15840a-n, but are not
needed to identify the respective state 15840a-n. For example, the
plurality of attributes 158404a-n for a first state may include
relevant information such as rotational speed, fuel level, energy
input, linear speed, acceleration, temperature, strain, torque,
volume, weight, etc.
[0657] The set of identifying criteria 158406a-n may include
information for each of the set of attributes 158404a-n to uniquely
identify the respective state. The identifying criteria 158406a-n
may include, for example, rules, thresholds, limits, ranges,
logical values, conditions, comparisons, combinations thereof, and
the like.
[0658] The change in operating conditions or monitoring may be any
suitable change. For example, after identifying occurrence of a
respective state 158406a-n, the digital twin system 15500 may
increase or decrease monitoring intervals for a device (e.g.,
decreasing monitoring intervals in response to a measured parameter
differing from nominal operation) without altering operation of the
device. Additionally or alternatively, the digital twin system
15500 may alter operation of the device (e.g., reduce speed or
power input) without altering monitoring of the device. In further
embodiments, the digital twin system 15500 may alter operation of
the device (e.g., reduce speed or power input) and alter monitoring
intervals for the device (e.g., decreasing monitoring
intervals).
[0659] FIG. 158 illustrates an example set of identified states
15840a-n related to industrial environments that the digital twin
system 15500 may identify and/or store for access by intelligent
systems (e.g., the cognitive intelligence system 15510) or users of
the digital twin system 15500, according to some embodiments of the
present disclosure. The states 15840a-n may include operational
states (e.g., suboptimal, normal, optimal, critical, or alarm
operation of one or more components), excess or shortage states
(e.g., supply-side or output-side quantities), combinations
thereof, and the like.
[0660] In embodiments, the digital twin system 15500 may monitor
attributes 158404a-n of real-world elements 157302r and/or digital
twins 157302d to determine the respective state 15840a-n. The
attributes 158404a-n may be, for example, operating conditions, set
points, critical points, status indicators, other sensed
information, combinations thereof, and the like. For example, the
attributes 158404a-n may include power input 158404a, operational
speed 158404b, critical speed 158404c, and operational temperature
158404d of the monitored elements. While the illustrated example
illustrates uniform monitored attributes, the monitored attributes
may differ by target device (e.g., the digital twin system 15500
would not monitor rotational speed for an object with no rotatable
components).
[0661] Each of the states 15840a-n includes a set of identifying
criteria 158406a-n meeting particular criteria that are unique
among the group of monitored states 13240a-n. The digital twin
system 15500 may identify the overspeed state 15540a, for example,
in response to the monitored attributes 158404a-n meeting a first
set of identifying criteria 158406a (e.g., operational speed
158404b being higher than the critical speed 158404c, while the
operational temperature 158404d is nominal).
[0662] In response to determining that one or more states 15840a-n
exists or has occurred, the digital twin system 15500 may update
triggering conditions for one or more monitoring protocols, issue
an alert or notification, or trigger actions of subcomponents of
the digital twin system 15500. For example, subcomponents of the
digital twin system 15500 may take actions to mitigate and/or
evaluate impacts of the detected states 15540a-n. When attempting
to take actions to mitigate impacts of the detected states 15540a-n
on real-world elements 157302r, the digital twin system 15500 may
determine whether instructions exist (e.g., are stored in the
digital twin datastore 15516) or should be developed (e.g.,
developed via simulation and cognitive intelligence or via user or
worker input). Further, the digital twin system 15500 may evaluate
impacts of the detected states 15540a-n, for example, concurrently
with the mitigation actions or in response to determining that the
digital twin system 15500 has no stored mitigation instructions for
the detected states 15540a-n.
[0663] In embodiments, the digital twin system 15500 employs the
digital twin simulation system 15506 to simulate one or more
impacts, such as immediate, upstream, downstream, and/or continuing
effects, of recognized states. The digital twin simulation system
15506 may collect and/or be provided with values relevant to the
evaluated states 15540a-n. In simulating the impact of the one or
more states 15540a-n, the digital twin simulation system 15506 may
recursively evaluate performance characteristics of affected
digital twins 157302d until convergence is achieved. The digital
twin simulation system 15506 may work, for example, in tandem with
the cognitive intelligence system 15510 to determine response
actions to alleviate, mitigate, inhibit, and/or prevent occurrence
of the one or more states 15540a-n. For example, the digital twin
simulation system 15506 may recursively simulate impacts of the one
or more states 15540a-n until achieving a desired fit (e.g.,
convergence is achieved), provide the simulated values to the
cognitive intelligence system 15510 for evaluation and
determination of potential actions, receive the potential actions,
evaluate impacts of each of the potential actions for a respective
desired fit (e.g., cost functions for minimizing production
disturbance, preserving critical components, minimizing maintenance
and/or downtime, optimizing system, worker, user, or personal
safety, etc.).
[0664] In embodiments, the digital twin simulation system 15506 and
the cognitive intelligence system 15510 may repeatedly share and
update the simulated values and response actions for each desired
outcome until desired conditions are met (e.g., convergence for
each evaluated cost function for each evaluated action). The
digital twin system 15500 may store the results in the digital twin
datastore 15516 for use in response to determining that one or more
states 15540a-n has occurred. Additionally, simulations and
evaluations by the digital twin simulation system 15506 and/or the
cognitive intelligence system 15510 may occur in response to
occurrence or detection of the event.
[0665] In embodiments, simulations and evaluations are triggered
only when associated actions are not present within the digital
twin system 15500. In further embodiments, simulations and
evaluations are performed concurrently with use of stored actions
to evaluate the efficacy or effectiveness of the actions in real
time and/or evaluate whether further actions should be employed or
whether unrecognized states may have occurred. In embodiments, the
cognitive intelligence system 15510 may also be provided with
notifications of instances of undesired actions with or without
data on the undesired aspects or results of such actions to
optimize later evaluations.
[0666] In embodiments, the digital twin system 15500 evaluates
and/or represents the impact of machine downtime within a digital
twin of a manufacturing facility. For example, the digital twin
system 15500 may employ the digital twin simulation system 15506 to
simulate the immediate, upstream, downstream, and/or continuing
effects of a machine downtime state 15540b. The digital twin
simulation system 15506 may collect or be provided with
performance-related values such as optimal, suboptimal, and minimum
performance requirements for elements (e.g., real-world elements
157302r and/or nested digital twins 157302d) within the affected
digital twins 157302d, and/or characteristics thereof that are
available to the affected digital twins 157302d, nested digital
twins 157302d, redundant systems within the affected digital twins
157302d, combinations thereof, and the like.
[0667] In embodiments, the digital twin system 15500 is configured
to: simulate one or more operating parameters for the real-world
elements in response to the industrial environment being supplied
with given characteristics using the real-world-element digital
twins; calculate a mitigating action to be taken by one or more of
the real-world elements in response to being supplied with the
contemporaneous characteristics; and actuate, in response to
detecting the contemporaneous characteristics, the mitigating
action. The calculation may be performed in response to detecting
contemporaneous characteristics or operating parameters falling
outside of respective design parameters or may be determined via a
simulation prior to detection of such characteristics.
[0668] Additionally or alternatively, the digital twin system 15500
may provide alerts to one or more users or system elements in
response to detecting states.
[0669] In embodiments (FIG. 157), the digital twin I/O system 15504
includes a pathing module 157310. The pathing module 157310 may
ingest navigational data from the elements 157302, provide and/or
request navigational data to components of the digital twin system
15500 (e.g., the digital twin simulation system 15506, the digital
twin behavior system, and/or the cognitive intelligence system
15510), and/or output navigational data to elements 157302 (e.g.,
to the wearable devices 15554). The navigational data may be
collected or estimated using, for example, historical data,
guidance data provided to the elements 157302, combinations
thereof, and the like.
[0670] For example, the navigational data may be collected or
estimated using historical data stored by the digital twin system
15500. The historical data may include or be processed to provide
information such as acquisition time, associated elements 157302,
polling intervals, task performed, laden or unladen conditions,
whether prior guidance data was provided and/or followed,
conditions of the environment 15520, other elements 157302 within
the environment 15520, combinations thereof, and the like. The
estimated data may be determined using one or more suitable pathing
algorithms. For example, the estimated data may be calculated using
suitable order-picking algorithms, suitable path-search algorithms,
combinations thereof, and the like. The order-picking algorithm may
be, for example, a largest gap algorithm, an s-shape algorithm, an
aisle-by-aisle algorithm, a combined algorithm, combinations
thereof, and the like. The path-search algorithms may be, for
example, Dijkstra's algorithm, the A* algorithm, hierarchical
path-finding algorithms, incremental path-finding algorithms, any
angle path-finding algorithms, flow field algorithms, combinations
thereof, and the like.
[0671] Additionally or alternatively, the navigational data may be
collected or estimated using guidance data of the worker. The
guidance data may include, for example, a calculated route provided
to a device of the worker (e.g., a mobile device or the wearable
device 15554). In another example, the guidance data may include a
calculated route provided to a device of the worker that instructs
the worker to collect vibration measurements from one or more
locations on one or more machines along the route. The collected
and/or estimated navigational data may be provided to a user of the
digital twin system 15500 for visualization, used by other
components of the digital twin system 15500 for analysis,
optimization, and/or alteration, provided to one or more elements
157302, combinations thereof, and the like.
[0672] In embodiments, the digital twin system 15500 ingests
navigational data for a set of workers for representation in a
digital twin. Additionally or alternatively, the digital twin
system 15500 ingests navigational data for a set of mobile
equipment assets of an industrial environment into a digital
twin.
[0673] In embodiments, the digital twin system 15500 ingests a
system for modeling traffic of mobile elements in an industrial
digital twin. For example, the digital twin system 15500 may model
traffic patterns for workers or persons within the environment
15520, mobile equipment assets, combinations thereof, and the like.
The traffic patterns may be estimated based on modeling traffic
patterns from and historical data and contemporaneous ingested
data. Further, the traffic patterns may be continuously or
intermittently updated depending on conditions within the
environment 15520 (e.g., a plurality of autonomous mobile equipment
assets may provide information to the digital twin system 15500 at
a slower update interval than the environment 15520 including both
workers and mobile equipment assets).
[0674] The digital twin system 15500 may alter traffic patterns
(e.g., by providing updated navigational data to one or more of the
mobile elements) to achieve one or more predetermined criteria. The
predetermined criteria may include, for example, increasing process
efficiency, decreasing interactions between laden workers and
mobile equipment assets, minimizing worker path length, routing
mobile equipment around paths or potential paths of persons,
combinations thereof, and the like.
[0675] In embodiments, the digital twin system 15500 may provide
traffic data and/or navigational information to mobile elements in
an industrial digital twin. The navigational information may be
provided as instructions or rule sets, displayed path data, or
selective actuation of devices. For example, the digital twin
system 15500 may provide a set of instructions to a robot to direct
the robot to and/or along a desired route for collecting vibration
data from one or more specified locations on one or more specified
machines along the route using a vibration sensor. The robot may
communicate updates to the system including obstructions, reroutes,
unexpected interactions with other assets within the environment
15520, etc.
[0676] In some embodiments, an ant-based system 15574 enables
industrial entities, including robots, to lay a trail with one or
more messages for other industrial entities, including themselves,
to follow in later journeys. In embodiments, the messages include
information related to vibration measurement collection. In
embodiments, the messages include information related to vibration
sensor measurement locations. In some embodiments, the trails may
be configured to fade over time. In some embodiments, the ant-based
trails may be experienced via an augmented reality system. In some
embodiments, the ant-based trails may be experienced via a virtual
reality system. In some embodiments, the ant-based trails may be
experienced via a mixed reality system. In some embodiments,
ant-based tagging of areas can trigger a pain-response and/or
accumulate into a warning signal. In embodiments, the ant-based
trails may be configured to generate an information filtering
response. In some embodiments, the ant-based trails may be
configured to generate an information filtering response wherein
the information filtering response is a heightened sense of visual
awareness. In some embodiments, the ant-based trails may be
configured to generate an information filtering response wherein
the information filtering response is a heightened sense of
acoustic awareness. In some embodiments, the messages include
vectorized data.
[0677] In embodiments, the digital twin system 15500 includes
design specification information for representing a real-world
element 157302r using a digital twin 157302d. The digital may
correspond to an existing real-world element 157302r or a potential
real-world element 157302r. The design specification information
may be received from one or more sources. For example, the design
specification information may include design parameters set by user
input, determined by the digital twin system 15500 (e.g., the via
digital twin simulation system 15506), optimized by users or the
digital twin simulation system 15506, combinations thereof, and the
like. The digital twin simulation system 15506 may represent the
design specification information for the component to users, for
example, via a display device or a wearable device. The design
specification information may be displayed schematically (e.g., as
part of a process diagram or table of information) or as part of an
augmented reality or virtual reality display. The design
specification information may be displayed, for example, in
response to a user interaction with the digital twin system 15500
(e.g., via user selection of the element or user selection to
generally include design specification information within
displays). Additionally or alternatively, the design specification
information may be displayed automatically, for example, upon the
element coming within view of an augmented reality or virtual
reality device. In embodiments, the displayed design specification
information may further include indicia of information source
(e.g., different displayed colors indicate user input versus
digital twin system 15500 determination), indicia of mismatches
(e.g., between design specification information and operational
information), combinations thereof, and the like.
[0678] In embodiments, the digital twin system 15500 embeds a set
of control instructions for a wearable device within an industrial
digital twin, such that the control instructions are provided to
the wearable device to induce an experience for a wearer of the
wearable device upon interaction with an element of the industrial
digital twin. The induced experience may be, for example, an
augmented reality experience or a virtual reality experience. The
wearable device, such as a headset, may be configured to output
video, audio, and/or haptic feedback to the wearer to induce the
experience. For example, the wearable device may include a display
device and the experience may include display of information
related to the respective digital twin. The information displayed
may include maintenance data associated with the digital twin,
vibration data associated with the digital twin, vibration
measurement location data associated with the digital twin,
financial data associated with the digital twin, such as a profit
or loss associated with operation of the digital twin,
manufacturing KPIs associated with the digital twin, information
related to an occluded element (e.g., a sub-assembly) that is at
least partially occluded by a foreground element (e.g., a housing),
a virtual model of the occluded element overlaid on the occluded
element and visible with the foreground element, operating
parameters for the occluded element, a comparison to a design
parameter corresponding to the operating parameter displayed,
combinations thereof, and the like. Comparisons may include, for
example, altering display of the operating parameter to change a
color, size, and/or display period for the operating parameter.
[0679] In some embodiments, the displayed information may include
indicia for removable elements that are or may be configured to
provide access to the occluded element with each indicium being
displayed proximate to or overlying the respective removable
element. Further, the indicia may be sequentially displayed such
that a first indicium corresponding to a first removable element
(e.g., a housing) is displayed, and a second indicium corresponding
to a second removable element (e.g., an access panel within the
housing) is displayed in response to the worker removing the first
removable element. In some embodiments, the induced experience
allows the wearer to see one or more locations on a machine for
optimal vibration measurement collection. In an example, the
digital twin system 15500 may provide an augmented reality view
that includes highlighted vibration measurement collection
locations on a machine and/or instructions related to collecting
vibration measurements. Furthering the example, the digital twin
system 15500 may provide an augmented reality view that includes
instructions related to timing of vibration measurement collection.
Information utilized in displaying the highlighted placement
locations may be obtained using information stored by the digital
twin system 15500. In some embodiments, mobile elements may be
tracked by the digital twin system 15500 (e.g., via observational
elements within the environment 15520 and/or via pathing
information communicated to the digital twin system 15500) and
continually displayed by the wearable device within the occluded
view of the worker. This optimizes movement of elements within the
environment 15520, increases worker safety, and minimizes down time
of elements resulting from damage.
[0680] In some embodiments, the digital twin system 15500 may
provide an augmented reality view that displays mismatches between
design parameters or expected parameters of real-world elements
157302r to the wearer. The displayed information may correspond to
real-world elements 157302r that are not within the view of the
wearer (e.g., elements within another room or obscured by
machinery). This allows the worker to quickly and accurately
troubleshoot mismatches to determine one or more sources for the
mismatch. The cause of the mismatch may then be determined, for
example, by the digital twin system 15500 and corrective actions
ordered. In example embodiments, a wearer may be able to view
malfunctioning subcomponents of machines without removing occluding
elements (e.g., housings or shields). Additionally or
alternatively, the wearer may be provided with instructions to
repair the device, for example, including display of the removal
process (e.g., location of fasteners to be removed), assemblies or
subassemblies that should be transported to other areas for repair
(e.g., dust-sensitive components), assemblies or subassemblies that
need lubrication, and locations of objects for reassembly (e.g.,
storing location that the wearer has placed removed objects and
directing the wearer or another wearer to the stored locations to
expedite reassembly and minimize further disassembly or missing
parts in the reassembled element). This can expedite repair work,
minimize process impact, allow workers to disassemble and
reassemble equipment (e.g., by coordinating disassembly without
direct communication between the workers), increase equipment
longevity and reliability (e.g., by assuring that all components
are properly replaced prior to placing back in service),
combinations thereof, and the like.
[0681] In some embodiments, the induced experience includes a
virtual reality view or an augmented reality view that allows the
wearer to see information related to existing or planned elements.
The information may be unrelated to physical performance of the
element (e.g., financial performance such as asset value, energy
cost, input material cost, output material value, legal compliance,
and corporate operations). One or more wearers may perform a
virtual walkthrough or an augmented walkthrough of the industrial
environment 15520.
[0682] In examples, the wearable device displays compliance
information that expedites inspections or performance of work.
[0683] In further examples, the wearable device displays financial
information that is used to identify targets for alteration or
optimization. For example, a manager or officer may inspect the
environment 15520 for compliance with updated regulations,
including information regarding compliance with former regulations,
"grandfathered," and/or excepted elements. This can be used to
reduce unnecessary downtime (e.g., scheduling upgrades for the
least impactful times, such as during planned maintenance cycles),
prevent unnecessary upgrades (e.g., replacing grandfathered or
excepted equipment), and reduce capital investment.
[0684] Referring back to FIG. 155, in embodiments, the digital twin
system 15500 may include, integrate, integrate with, manage,
handle, link to, take input from, provide output to, control,
coordinate with, or otherwise interact with a digital twin dynamic
model system 15508. The digital twin dynamic model system 15508 can
update the properties of a set of digital twins of a set of
industrial entities and/or environments, including properties of
physical industrial assets, workers, processes, manufacturing
facilities, warehouses, and the like (or any of the other types of
entities or environments described in this disclosure or in the
documents incorporated by reference herein) in such a manner that
the digital twins may represent those industrial entities and
environments, and properties or attributes thereof, in real-time or
very near real-time. In some embodiments, the digital twin dynamic
model system 15508 may obtain sensor data received from a sensor
system 15530 and may determine one or more properties of an
industrial environment or an industrial entity within an
environment based on the sensor data and based on one or more
dynamic models.
[0685] In embodiments, the digital twin dynamic model system 15508
may update/assign values of various properties in a digital twin
and/or one or more embedded digital twins, including, but not
limited to, vibration values, vibration fault level states,
probability of failure values, probability of downtime values, cost
of downtime values, probability of shutdown values, financial
values, KPI values, temperature values, humidity values, heat flow
values, fluid flow values, radiation values, substance
concentration values, velocity values, acceleration values,
location values, pressure values, stress values, strain values,
light intensity values, sound level values, volume values, shape
characteristics, material characteristics, and dimensions.
[0686] In embodiments, a digital twin may be comprised of (e.g.,
via reference) of other embedded digital twins. For example, a
digital twin of a manufacturing facility may include an embedded
digital twin of a machine and one or more embedded digital twins of
one or more respective motors enclosed within the machine. A
digital twin may be embedded, for example, in the memory of an
industrial machine that has an onboard IT system (e.g., the memory
of an Onboard Diagnostic System, control system (e.g., SCADA
system) or the like). Other non-limiting examples of where a
digital twin may be embedded include the following: on a wearable
device of a worker; in memory on a local network asset, such as a
switch, router, access point, or the like; in a cloud computing
resource that is provisioned for an environment or entity; and on
an asset tag or other memory structure that is dedicated to an
entity.
[0687] In one example, the digital twin dynamic model system 15508
can update vibration characteristics throughout an industrial
environment digital twin based on captured vibration sensor data
measured at one or more locations in the industrial environment and
one or more dynamic models that model vibration within the
industrial environment digital twin. The industrial digital twin
may, before updating, already contain information about properties
of the industrial entities and/or environment that can be used to
feed a dynamic model, such as information about materials,
shapes/volumes (e.g., of conduits), positions,
connections/interfaces, and the like, such that vibration
characteristics can be represented for the entities and/or
environment in the digital twin. Alternatively, the dynamic models
may be configured using such information.
[0688] In embodiments, the digital twin dynamic model system 15508
can update the properties of a digital twin and/or one or more
embedded digital twins on behalf of a client application 15570. In
embodiments, a client application 15570 may be an application
relating to an industrial component or environment (e.g.,
monitoring an industrial facility or a component within, simulating
an industrial environment, or the like). In embodiments, the client
application 15570 may be used in connection with both fixed and
mobile data collection systems. In embodiments, the client
application 15570 may be used in connection with Industrial
Internet of Things sensor system 15530.
[0689] In embodiments, the digital twin dynamic model system 15508
leverages digital twin dynamic models 155100 to model the behavior
of an industrial entity and/or environment. Dynamic models 155100
may enable digital twins to represent physical reality, including
the interactions of industrial entities, by using a limited number
of measurements to enrich the digital representation of an
industrial entity and/or environment, such as based on scientific
principles. In embodiments, the dynamic models 155100 are formulaic
or mathematical models. In embodiments, the dynamic models 155100
adhere to scientific laws, laws of nature, and formulas (e.g.,
Newton's laws of motion, second law of thermodynamics, Bernoulli's
principle, ideal gas law, Dalton's law of partial pressures,
Hooke's law of elasticity, Fourier's law of heat conduction,
Archimedes' principle of buoyancy, and the like). In embodiments,
the dynamic models are machine-learned models.
[0690] In embodiments, the digital twin system 15500 may have a
digital twin dynamic model datastore 155102 for storing dynamic
models 155100 that may be represented in digital twins. In
embodiments, digital twin dynamic model datastore can be searchable
and/or discoverable. In embodiments, digital twin dynamic model
datastore can contain metadata that allows a user to understand
what characteristics a given dynamic model can handle, what inputs
are required, what outputs are provided, and the like. In some
embodiments, digital twin dynamic model datastore 155102 can be
hierarchical (such as where a model can be deepened or made more
simple based on the extent of available data and/or inputs, the
granularity of the inputs, and/or situational factors (such as
where something becomes of high interest and a higher fidelity
model is accessed for a period of time).
[0691] In embodiments, a digital twin or digital representation of
an industrial entity or facility may include a set of data
structures that collectively define a set of properties of a
represented physical industrial asset, device, worker, process,
facility, and/or environment, and/or possible behaviors thereof. In
embodiments, the digital twin dynamic model system 15508 may
leverage the dynamic models 155100 to inform the set of data
structures that collectively define a digital twin with real-time
data values. The digital twin dynamic models 155100 may receive one
or more sensor measurements, Industrial Internet of Things device
data, and/or other suitable data as inputs and calculate one or
more outputs based on the received data and one or more dynamic
models 155100. The digital twin dynamic model system 15508 then
uses the one or more outputs to update the digital twin data
structures.
[0692] In one example, the set of properties of a digital twin of
an industrial asset that may be updated by the digital twin dynamic
model system 15508 using dynamic models 155100 may include the
vibration characteristics of the asset, temperature(s) of the
asset, the state of the asset (e.g., a solid, liquid, or gas), the
location of the asset, the displacement of the asset, the velocity
of the asset, the acceleration of the asset, probability of
downtime values associated with the asset, cost of downtime values
associated with the asset, probability of shutdown values
associated with the asset, manufacturing KPIs associated with the
asset, financial information associated with the asset, heat flow
characteristics associated with the asset, fluid flow rates
associated with the asset (e.g., fluid flow rates of a fluid
flowing through a pipe), identifiers of other digital twins
embedded within the digital twin of the asset and/or identifiers of
digital twins embedding the digital twin of the asset, and/or other
suitable properties. Dynamic models 155100 associated with a
digital twin of an asset can be configured to calculate,
interpolate, extrapolate, and/or output values for such asset
digital twin properties based on input data collected from sensors
and/or devices disposed in the industrial setting and/or other
suitable data and subsequently populate the asset digital twin with
the calculated values.
[0693] In some embodiments, the set of properties of a digital twin
of an industrial device that may be updated by the digital twin
dynamic model system 15508 using dynamic models 155100 may include
the status of the device, a location of the device, the
temperature(s) of a device, a trajectory of the device, identifiers
of other digital twins that the digital twin of the device is
embedded within, embeds, is linked to, includes, integrates with,
takes input from, provides output to, and/or interacts with and the
like. Dynamic models 155100 associated with a digital twin of a
device can be configured to calculate or output values for these
device digital twin properties based on input data and subsequently
update the device digital twin with the calculated values.
[0694] In some embodiments, the set of properties of a digital twin
of an industrial worker that may be updated by the digital twin
dynamic model system 15508 using dynamic models 155100 may include
the status of the worker, the location of the worker, a stress
measure for the worker, a task being performed by the worker, a
performance measure for the worker, and the like. Dynamic models
associated with a digital twin of an industrial worker can be
configured to calculate or output values for such properties based
on input data, which then may be used to populate industrial worker
digital twin. In embodiments, industrial worker dynamic models
(e.g., psychometric models) can be configured to predict reactions
to stimuli, such as cues that are given to workers to direct them
to undertake tasks and/or alerts or warnings that are intended to
induce safe behavior. In embodiments, industrial worker dynamic
models may be workflow models (Gantt charts and the like), failure
mode effects analysis models (FMEA), biophysical models (such as to
model worker fatigue, energy utilization, hunger), and the
like.
[0695] Example properties of a digital twin of an industrial
environment that may be updated by the digital twin dynamic model
system 15508 using dynamic models 155100 may include the dimensions
of the industrial environment, the temperature(s) of the industrial
environment, the humidity value(s) of the industrial environment,
the fluid flow characteristics in the industrial environment, the
heat flow characteristics of the industrial environment, the
lighting characteristics of the industrial environment, the
acoustic characteristics of the industrial environment the physical
objects in the environment, processes occurring in the industrial
environment, currents of the industrial environment (if a body of
water), and the like. Dynamic models associated with a digital twin
of an industrial environment can be configured to calculate or
output these properties based on input data collected from sensors
and/or devices disposed in the industrial environment and/or other
suitable data and subsequently populate the industrial environment
digital twin with the calculated values.
[0696] In embodiments, dynamic models 155100 may adhere to physical
limitations that define boundary conditions, constants or variables
for digital twin modeling. For example, the physical
characterization of a digital twin of an industrial entity or
industrial environment may include a gravity constant (e.g., 9.8
m/s2), friction coefficients of surfaces, thermal coefficients of
materials, maximum temperatures of assets, maximum flow capacities,
and the like. Additionally or alternatively, the dynamic models may
adhere to the laws of nature. For example, dynamic models may
adhere to the laws of thermodynamics, laws of motion, laws of fluid
dynamics, laws of buoyancy, laws of heat transfer, laws or
radiation, laws of quantum dynamics, and the like. In some
embodiments, dynamic models may adhere to biological aging theories
or mechanical aging principles. Thus, when the digital twin dynamic
model system 15508 facilitates a real-time digital representation,
the digital representation may conform to dynamic models, such that
the digital representations mimic real world conditions. In some
embodiments, the output(s) from a dynamic model can be presented to
a human user and/or compared against real-world data to ensure
convergence of the dynamic models with the real world. Furthermore,
as dynamic models are based partly on assumptions, the properties
of a digital twin may be improved and/or corrected when a
real-world behavior differs from that of the digital twin. In
embodiments, additional data collection and/or instrumentation can
be recommended based on the recognition that an input is missing
from a desired dynamic model, that a model in operation isn't
working as expected (perhaps due to missing and/or faulty sensor
information), that a different result is needed (such as due to
situational factors that make something of high interest), and the
like.
[0697] Dynamic models may be obtained from a number of different
sources. In some embodiments, a user can upload a model created by
the user or a third party. Additionally or alternatively, the
models may be created on the digital twin system using a graphical
user interface. The dynamic models may include bespoke models that
are configured for a particular environment and/or set of
industrial entities and/or agnostic models that are applicable to
similar types of digital twins. The dynamic models may be
machine-learned models.
[0698] FIG. 159 illustrates example embodiments of a method for
updating a set of properties of a digital twin and/or one or more
embedded digital twins on behalf of client applications 15570. In
embodiments, digital twin dynamic model system 15508 leverages one
or more dynamic models 155100 to update a set of properties of a
digital twin and/or one or more embedded digital twins on behalf of
client application 15570 based on the impact of collected sensor
data from sensor system 15530, data collected from Internet of
Things connected devices 15524, and/or other suitable data in the
set of dynamic models 155100 that are used to enable the industrial
digital twins. In embodiments, the digital twin dynamic model
system 15508 may be instructed to run specific dynamic models using
one or more digital twins that represent physical industrial
assets, devices, workers, processes, and/or industrial environments
that are managed, maintained, and/or monitored by the client
applications 15570.
[0699] In embodiments, the digital twin dynamic model system 15508
may obtain data from other types of external data sources that are
not necessarily industrial-related data sources, but may provide
data that can be used as input data for the dynamic models. For
example, weather data, news events, social media data, and the like
may be collected, crawled, subscribed to, and the like to
supplement sensor data, Industrial Internet of Things device data,
and/or other data that is used by the dynamic models. In
embodiments, the digital twin dynamic model system 15508 may obtain
data from a machine vision system. The machine vision system may
use video and/or still images to provide measurements (e.g.,
locations, statuses, and the like) that may be used as inputs by
the dynamic models.
[0700] In embodiments, the digital twin dynamic model system 15508
may feed this data into one or more of the dynamic models discussed
above to obtain one or more outputs. These outputs may include
calculated vibration fault level states, vibration severity unit
values, vibration characteristics, probability of failure values,
probability of downtime values, probability of shutdown values,
cost of downtime values, cost of shutdown values, time to failure
values, temperature values, pressure values, humidity values,
precipitation values, visibility values, air quality values, strain
values, stress values, displacement values, velocity values,
acceleration values, location values, performance values, financial
values, manufacturing KPI values, electrodynamic values,
thermodynamic values, fluid flow rate values, and the like. The
client application 15570 may then initiate a digital twin
visualization event using the results obtained by the digital twin
dynamic model system 15508. In embodiments, the visualization may
be a heat map visualization.
[0701] In embodiments, the digital twin dynamic model system 15508
may receive requests to update one or more properties of digital
twins of industrial entities and/or environments such that the
digital twins represent the industrial entities and/or environments
in real-time. At 159100, the digital twin dynamic model system
15508 receives a request to update one or more properties of one or
more of the digital twins of industrial entities and/or
environments. For example, the digital twin dynamic model system
15508 may receive the request from a client application 15570 or
from another process executed by the digital twin system 15500
(e.g., a predictive maintenance process). The request may indicate
the one or more properties and the digital twin or digital twins
implicated by the request. Referring to FIG. 159, in step 159102,
the digital twin dynamic model system 15508 determines the one or
more digital twins required to fulfill the request and retrieves
the one or more required digital twins, including any embedded
digital twins, from digital twin datastore 15516. At 159104,
digital twin dynamic model system 15508 determines one or more
dynamic models required to fulfill the request and retrieves the
one or more required dynamic models from digital twin dynamic model
datastore 155102. At 159106, the digital twin dynamic model system
15508 selects one or more sensors from sensor system 15530, data
collected from Internet of Things connected devices 15524, and/or
other data sources from digital twin I/O system 15504 based on
available data sources and the one or more required inputs of the
dynamic model(s). In embodiments, the data sources may be defined
in the inputs required by the one or more dynamic models or may be
selected using a lookup table. At 159108, the digital twin dynamic
model system 15508 retrieves the selected data from digital twin
I/O system 15504. At 159110, digital twin dynamic model system
15508 runs the dynamic model(s) using the retrieved input data
(e.g., vibration sensor data, Industrial Internet of Things device
data, and the like) as inputs and determines one or more output
values based on the dynamic model(s) and the input data. At 159112,
the digital twin dynamic model system 15508 updates the values of
one or more properties of the one or more digital twins based on
the one or more outputs of the dynamic model(s).
[0702] In example embodiments, client application 15570 may be
configured to provide a digital representation and/or visualization
of the digital twin of an industrial entity. In embodiments, the
client application 15570 may include one or more software modules
that are executed by one or more server devices. These software
modules may be configured to quantify properties of the digital
twin, model properties of a digital twin, and/or to visualize
digital twin behaviors. In embodiments, these software modules may
enable a user to select a particular digital twin behavior
visualization for viewing. In embodiments, these software modules
may enable a user to select to view a digital twin behavior
visualization playback. In some embodiments, the client application
15570 may provide a selected behavior visualization to digital twin
dynamic model system 15508.
[0703] In embodiments, the digital twin dynamic model system 15508
may receive requests from client application 15570 to update
properties of a digital twin in order to enable a digital
representation of an industrial entity and/or environment wherein
the real-time digital representation is a visualization of the
digital twin. In embodiments, a digital twin may be rendered by a
computing device, such that a human user can view the digital
representations of real-world industrial assets, devices, workers,
processes and/or environments. For example, the digital twin may be
rendered and outcome to a display device. In embodiments, dynamic
model outputs and/or related data may be overlaid on the rendering
of the digital twin. In embodiments, dynamic model outputs and/or
related information may appear with the rendering of the digital
twin in a display interface. In embodiments, the related
information may include real-time video footage associated with the
real-world entity represented by the digital twin. In embodiments,
the related information may include a sum of each of the vibration
fault level states in the machine. In embodiments, the related
information may be graphical information. In embodiments, the
graphical information may depict motion and/or motion as a function
of frequency for individual machine components. In embodiments,
graphical information may depict motion and/or motion as a function
of frequency for individual machine components, wherein a user is
enabled to select a view of the graphical information in the x, y,
and z dimensions. In embodiments, graphical information may depict
motion and/or motion as a function of frequency for individual
machine components, wherein the graphical information includes
harmonic peaks and peaks. In embodiments, the related information
may be cost data, including the cost of downtime per day data, cost
of repair data, cost of new part data, cost of new machine data,
and the like. In embodiments, related information may be a
probability of downtime data, probability of failure data, and the
like. In embodiments, related information may be time to failure
data.
[0704] In embodiments, the related information may be
recommendations and/or insights. For example, recommendations or
insights received from the cognitive intelligence system related to
a machine may appear with the rendering of the digital twin of a
machine in a display interface.
[0705] In embodiments, clicking, touching, or otherwise interacting
with the digital twin rendered in the display interface can allow a
user to "drill down" and see underlying subsystems or processes
and/or embedded digital twins. For example, in response to a user
clicking on a machine bearing rendered in the digital twin of a
machine, the display interface can allow a user to drill down and
see information related to the bearing, view a 3D visualization of
the bearing's vibration, and/or view a digital twin of the
bearing.
[0706] In embodiments, clicking, touching, or otherwise interacting
with information related to the digital twin rendered in the
display interface can allow a user to "drill down" and see
underlying information.
[0707] FIG. 160 illustrates example embodiments of a display
interface that renders the digital twin of a dryer centrifuge and
other information related to the dryer centrifuge.
[0708] In some embodiments, the digital twin may be rendered and
output in a virtual reality display. For example, a user may view a
3D rendering of an environment (e.g., using a monitor or a virtual
reality headset). The user may also inspect and/or interact with
digital twins of industrial entities. In embodiments, a user may
view processes being performed with respect to one or more digital
twins (e.g., collecting measurements, movements, interactions,
inventorying, loading, packing, shipping, and the like). In
embodiments, a user may provide input that controls one or more
properties of a digital twin via a graphical user interface.
[0709] In some embodiments, the digital twin dynamic model system
15508 may receive requests from client application 15570 to update
properties of a digital twin in order to enable a digital
representation of industrial entities and/or environments wherein
the digital representation is a heat map visualization of the
digital twin. In embodiments, a platform is provided having heat
maps displaying collected data from the sensor system 15530,
Internet of Things connected devices 15524, and data outputs from
dynamic models 155100 for providing input to a display interface.
In embodiments, the heat map interface is provided as an output for
digital twin data, such as for handling and providing information
for visualization of various sensor data, dynamic model output
data, and other data (such as map data, analog sensor data, and
other data), such as to another system, such as a mobile device,
tablet, dashboard, computer, AR/VR device, or the like. A digital
twin representation may be provided in a form factor (e.g., user
device, VR-enabled device, AR-enabled device, or the like) suitable
for delivering visual input to a user, such as the presentation of
a map that includes indicators of levels of analog sensor data,
digital sensor data, and output values from the dynamic models
(such as data indicating vibration fault level states, vibration
severity unit values, probability of downtime values, cost of
downtime values, probability of shutdown values, time to failure
values, probability of failure values, manufacturing KPIs,
temperatures, levels of rotation, vibration characteristics, fluid
flow, heating or cooling, pressure, substance concentrations, and
many other output values). In embodiments, signals from various
sensors or input sources (or selective combinations, permutations,
mixes, and the like) as well as data determined by the digital twin
dynamic model system 15508 may provide input data to a heat map.
Coordinates may include real world location coordinates (such as
geo-location or location on a map of an environment), as well as
other coordinates, such as time-based coordinates, frequency-based
coordinates, or other coordinates that allow for representation of
analog sensor signals, digital signals, dynamic model outputs,
input source information, and various combinations, in a map-based
visualization, such that colors may represent varying levels of
input along the relevant dimensions. For example, among many other
possibilities, if an industrial machine component is at a critical
vibration fault level state, the heat map interface may alert a
user by showing the machine component in orange. In the example of
a heat map, clicking, touching, or otherwise interacting with the
heat map can allow a user to drill down and see underlying sensor,
dynamic model outputs, or other input data that is used as an input
to the heat map display. In other examples, such as ones where a
digital twin is displayed in a VR or AR environment, if an
industrial machine component is vibrating outside of normal
operation (e.g., at a suboptimal, critical, or alarm vibration
fault level), a haptic interface may induce vibration when a user
touches a representation of the machine component, or if a machine
component is operating in an unsafe manner, a directional sound
signal may direct a user's attention toward the machine in digital
twin, such as by playing in a particular speaker of a headset or
other sound system.
[0710] In embodiments, the digital twin dynamic model system 15508
may take a set of ambient environmental data and/or other data and
automatically update a set of properties of a digital twin of an
industrial entity or facility based on the impact of the
environmental data and/or other data in the set of dynamic models
155100 that are used to enable the digital twin. Ambient
environmental data may include temperature data, pressure data,
humidity data, wind data, rainfall data, tide data, storm surge
data, cloud cover data, snowfall data, visibility data, water level
data, and the like. Additionally or alternatively, the digital twin
dynamic model system 15508 may use a set of environmental data
measurements collected by a set of Internet of Things connected
devices 15524 disposed in an industrial setting as inputs for the
set of dynamic models 155100 that are used to enable the digital
twin. For example, digital twin dynamic model system 15508 may feed
the dynamic models 155100 data collected, handled or exchanged by
Internet of Things connected devices 15524, such as cameras,
monitors, embedded sensors, mobile devices, diagnostic devices and
systems, instrumentation systems, telematics systems, and the like,
such as for monitoring various parameters and features of machines,
devices, components, parts, operations, functions, conditions,
states, events, workflows and other elements (collectively
encompassed by the term "states") of industrial environments. Other
examples of Internet of Things connected devices include smart fire
alarms, smart security systems, smart air quality monitors,
smart/learning thermostats, and smart lighting systems.
[0711] FIG. 161 illustrates example embodiments of a method for
updating a set of vibration fault level states for a set of
bearings in a digital twin of a machine. In this example, a client
application 15570, which interfaces with digital twin dynamic model
system 15508, may be configured to provide a visualization of the
fault level states of the bearings in the digital twin of the
machine.
[0712] In this example, the digital twin dynamic model system 15508
may receive requests from client application 15570 to update the
vibration fault level states of the machine digital twin. At
161200, digital twin dynamic model system 15508 receives a request
from client application 15570 to update one or more vibration fault
level states of the machine digital twin. Next, in step 161202,
digital twin dynamic model system 15508 determines the one or more
digital twins required to fulfill the request and retrieves the one
or more required digital twins from digital twin datastore 15516.
In this example, the digital twin dynamic model system 15508 may
retrieve the digital twin of the machine and any embedded digital
twins, such as any embedded motor digital twins and bearing digital
twins, and any digital twins that embed the machine digital twin,
such as the manufacturing facility digital twin. At 161204, digital
twin dynamic model system 15508 determines one or more dynamic
models required to fulfill the request and retrieves the one or
more required dynamic models from the digital twin dynamic model
datastore 155102. At 161206, the digital twin dynamic model system
15508 selects dynamic model input data sources (e.g., one or more
sensors from sensor system 15530, data from Internet of Things
connected devices 15524, and any other suitable data) via digital
twin I/O system 15504 based on available data sources (e.g.,
available sensors from a set of sensors in sensor system 15530) and
the and the one or more required inputs of the dynamic model(s). In
the present example, the retrieved dynamic model(s) 155100 may take
one or more vibration sensor measurements from vibration sensors
15536 as inputs to the dynamic models. In embodiments, vibration
sensors 15536 may be optical vibration sensors, single axis
vibration sensors, tri-axial vibration sensors, and the like. At
161208, digital twin dynamic model system 15508 retrieves one or
more measurements from each of the selected data sources from the
digital twin I/O system 15504. Next, At 161210, digital twin
dynamic model system 15508 runs the dynamic model(s), using the
retrieved vibration sensor measurements as inputs, and calculates
one or more outputs that represent bearing vibration fault level
states. Next, At 161212, the digital twin dynamic model system
15508 updates one or more bearing fault level states of the
manufacturing facility digital twin, machine digital twin, motor
digital twin, and/or bearing digital twins based on the one or more
outputs of the dynamic model(s). The client application 15570 may
obtain vibration fault level states of the bearings and may display
the obtained vibration fault level state associated with each
bearing and/or display colors associated with fault level severity
(e.g., red for alarm, orange for critical, yellow for suboptimal,
green for normal operation) in the rendering of one or more of the
digital twins on a display interface.
[0713] In another example, a client application 15570 may be an
augmented reality application. In some embodiments of this example,
the client application 15570 may obtain vibration fault level
states of bearings in a field of view of an AR-enabled device
(e.g., smart glasses) hosting the client application from the
digital twin of the industrial environment (e.g., via an API of the
digital twin system 15500) and may display the obtained vibration
fault level states on the display of the AR-enabled device, such
that the vibration fault level state displayed corresponds to the
location in the field of view of the AR-enabled device. In this
way, a vibration fault level state may be displayed even if there
are no vibration sensors located within the field of view of the
AR-enabled device.
[0714] FIG. 162 illustrates example embodiments of a method for
updating a set of vibration severity unit values of bearings in a
digital twin of a machine. Vibration severity units may be measured
as displacement, velocity, and acceleration.
[0715] In this example, client application 15570 that interfaces
with the digital twin dynamic model system 15508 may be configured
to provide a visualization of the three-dimensional vibration
characteristics of bearings in a digital twin of a machine.
[0716] In this example, the digital twin dynamic model system 15508
may receive requests from client application 15570 to update the
vibration severity unit values for bearings in the digital twin of
a machine. At 162300, digital twin dynamic model system 15508
receives a request from client application 15570 to update one or
more vibration severity unit value(s) of the manufacturing facility
digital twin. Next, in step 162302, digital twin dynamic model
system 15508 determines the one or more digital twins required to
fulfill the request and retrieves the one or more required digital
twins from digital twin datastore 15516. In this example, the
digital twin dynamic model system 15508 may retrieve the digital
twin of the machine and any embedded digital twins (e.g., digital
twins of bearings and other components). At 162304, digital twin
dynamic model system 15508 determines one or more dynamic models
required to fulfill the request and retrieves the one or more
required dynamic models from dynamic model datastore 155102. At
162306, the digital twin dynamic model system 15508 selects dynamic
model input data sources (e.g., one or more sensors from sensor
system 15530, data from Internet of Things connected devices 15524,
and any other suitable data) via digital twin I/O system 15504
based on available data sources (e.g., available sensors from a set
of sensors in sensor system 15530) and the one or more required
inputs of the dynamic model(s). In the present example, the
retrieved dynamic models may be configured to take one or more
vibration sensor measurements as inputs and provide severity unit
values for bearings in the machine. At 162308, digital twin dynamic
model system 15508 retrieves one or more measurements from each of
the selected sensors. In the present example, the digital twin
dynamic model system 15508 retrieves measurements from vibration
sensors 15536 via digital twin I/O system 15504. At 162310, digital
twin dynamic model system 15508 runs the dynamic model(s) using the
retrieved vibration measurements as inputs and calculates one or
more output values that represent vibration severity unit values
for bearings in the machine. Next, at 162312, the digital twin
dynamic model system 15508 updates one or more vibration severity
unit values of the bearings in the machine digital twin and all
other embedded digital twins or digital twins that embed the
machine digital twin based on the one or more values output by the
dynamic model(s).
[0717] FIG. 163 illustrates example embodiments of a method for
updating a set of probability of failure values for machine
components in the digital twin of a machine.
[0718] In this example, the digital twin dynamic model system 15508
may receive requests from client application 15570 to update the
probability of failure values for components in a machine digital
twin. At 156400, digital twin dynamic model system 15508 receives a
request from client application 15570 to update one or more
probability of failure value(s) of the machine digital twin, any
embedded component digital twins, and any digital twins that embed
the machine digital twin such as a manufacturing facility digital
twin. Next, in step 163402, digital twin dynamic model system 15508
determines the one or more digital twins required to fulfill the
request and retrieves the one or more required digital twins. In
this example, the digital twin dynamic model system 15508 may
retrieve the digital twin of the manufacturing facility, the
digital twin of the machine, and the digital twins of machine
components from digital twin datastore 15516. At 163404, digital
twin dynamic model system 15508 determines one or more dynamic
models required to fulfill the request and retrieves the one or
more required dynamic models from dynamic model datastore 155102.
At 163406, the digital twin dynamic model system 15508 selects, via
digital twin I/O system 15504, dynamic model input data sources
(e.g., one or more sensors from sensor system 15530, data from
Internet of Things connected devices 15524, and any other suitable
data) based on available data sources (e.g., available sensors from
a set of sensors in sensor system 15530) and the and the one or
more required inputs of the dynamic model(s). In the present
example, the retrieved dynamic models may take one or more
vibration measurements from vibration sensors 15536 and historical
failure data as dynamic model inputs and output probability of
failure values for the machine components in the digital twin of
the machine. At 163408, digital twin dynamic model system 15508
retrieves data from each of the selected sensors and/or Internet of
Things connected devices via digital twin I/O system 15504. At
163410, digital twin dynamic model system 15508 runs the dynamic
model(s) using the retrieved vibration data and historical failure
data as inputs and calculates one or more outputs that represent
probability of failure values for bearings in the machine digital
twin. Next, At 163412, the digital twin dynamic model system 15508
updates one or more probability of failure values of the bearings
in the machine digital twin, all embedded digital twins, and all
digital twins that embed the machine digital twin based on the
output of the dynamic model(s).
[0719] FIG. 164 illustrates example embodiments of a method for
updating a set of probability of downtime for machines in the
digital twin of a manufacturing facility.
[0720] In this example, client application 15570, which interfaces
with the digital twin dynamic model system 15508, may be configured
to provide a visualization of the probability of downtime values of
a manufacturing facility in the digital twin of the manufacturing
facility.
[0721] In this example, the digital twin dynamic model system 15508
may receive requests from client application 15570 to assign
probability of downtime values to machines in a manufacturing
facility digital twin. At 164500, digital twin dynamic model system
16208 receives a request from client application 15570 to update
one or more probability of downtime values of machines in the
manufacturing facility digital twin and any embedded digital twins
such as the individual machine digital twins. Next, in step 164502,
digital twin dynamic model system 15508 determines the one or more
digital twins required to fulfill the request and retrieves the one
or more required digital twins from digital twin datastore 15516.
In this example, the digital twin dynamic model system 15508 may
retrieve the digital twin of the manufacturing facility and any
embedded digital twins from digital twin datastore 15516. At
164504, digital twin dynamic model system 15508 determines one or
more dynamic models required to fulfill the request and retrieves
the one or more required dynamic models from dynamic model
datastore 155102. At 164506, the digital twin dynamic model system
15508 selects dynamic model input data sources (e.g., one or more
sensors from sensor system 15530, data from Internet of Things
connected devices 15524, and any other suitable data) based on
available data sources (e.g., available sensors from a set of
sensors in sensor system 15530) and the and the one or more
required inputs of the dynamic model(s) via digital twin I/O system
15504. In the present example, the dynamic model(s) may be
configured to take vibration measurements from vibration sensors
and historical downtime data as inputs and output probability of
downtime values for different machines throughout the manufacturing
facility. At 164508, digital twin dynamic model system 15508
retrieves one or more measurements from each of the selected
sensors via digital twin I/O system 15504. At 164510, digital twin
dynamic model system 15508 runs the dynamic model(s) using the
retrieved vibration measurements and historical downtime data as
inputs and calculates one or more outputs that represent
probability of downtime values for machines in the manufacturing
facility. Next, At 164512, the digital twin dynamic model system
15508 updates one or more probability of downtime values for
machines in the manufacturing facility digital twins and all
embedded digital twins based on the one or more outputs of the
dynamic models.
[0722] FIG. 165 illustrates example embodiments of a method for
updating one or more probability of shutdown values in the digital
twin of an enterprise having a set of manufacturing facilities.
[0723] In the present example, the digital twin dynamic model
system 15508 may receive requests from client application 15570 to
update the probability of shutdown values for the set of
manufacturing facilities within an enterprise digital twin. At
165600, digital twin dynamic model system 15508 receives a request
from client application 15570 to update one or more probability of
shutdown values of the enterprise digital twin and any embedded
digital twins. Next, in step 165602, digital twin dynamic model
system 15508 determines the one or more digital twins required to
fulfill the request and retrieves the one or more required digital
twins from digital twin datastore 15516. In this example, the
digital twin dynamic model system 15508 may retrieve the digital
twin of the enterprise and any embedded digital twins. At 165604,
digital twin dynamic model system 15508 determines one or more
dynamic models required to fulfill the request and retrieves the
one or more required dynamic models from dynamic model datastore
155102. At 165606, the digital twin dynamic model system 15508
selects dynamic model input data sources (e.g., one or more sensors
from sensor system 15530, data from Internet of Things connected
devices 15524, and any other suitable data) based on available data
sources (e.g., available sensors from a set of sensors in sensor
system 15530) and the and the one or more required inputs of the
dynamic model(s) via digital twin I/O system 15504. In the present
example, the retrieved dynamic models may be configured to take one
or more vibration measurements from vibration sensors 15536 and/or
other suitable data as inputs and output probability of shutdown
values for each manufacturing entity in the enterprise digital
twin. At 165608, digital twin dynamic model system 15508 retrieves
one or more vibration measurements from each of the selected
vibration sensors 15536 from digital twin I/O system 15504. At
165610, digital twin dynamic model system 15508 runs the dynamic
model(s) using the retrieved vibration measurements and historical
shut down data as inputs and calculates one or more outputs that
represent probability of shutdown values for manufacturing
facilities within the enterprise digital twin. Next, at 165612, the
digital twin dynamic model system 15508 updates one or more
probability of shutdown values of the enterprise digital twin and
all embedded digital twins based on the one or more outputs of the
dynamic model(s).
[0724] FIG. 166 illustrates example embodiments of a method for
updating a set of cost of downtime values in machines in the
digital twin of a manufacturing facility. In the present example,
the digital twin dynamic model system 15508 may receive requests
from a client application 15570 to populate real-time cost of
downtime values associated with machines in a manufacturing
facility digital twin. At 166700, digital twin dynamic model system
15508 receives a request from the client application 15570 to
update one or more cost of downtime values of the manufacturing
facility digital twin and any embedded digital twins (e.g.,
machines, machine parts, and the like) from the client application
15570. Next, in step 166702, the digital twin dynamic model system
15508 determines the one or more digital twins required to fulfill
the request and retrieves the one or more required digital twins.
In this example, the digital twin dynamic model system 15508 may
retrieve the digital twins of the manufacturing facility, the
machines, the machine parts, and any other embedded digital twins
from digital twin datastore 15516. At 166704, digital twin dynamic
model system 15508 determines one or more dynamic models required
to fulfill the request and retrieves the one or more required
dynamic models from dynamic model datastore 155102. At 166706, the
digital twin dynamic model system 15508 selects dynamic model input
data sources (e.g., one or more sensors from sensor system 15530,
data from Internet of Things connected devices 15524, and any other
suitable data) based on available data sources (e.g., available
sensors from a set of sensors in sensor system 15530) and the and
the one or more required inputs of the dynamic model(s) via digital
twin I/O system 15504. In the present example, the retrieved
dynamic model(s) may be configured to take historical downtime data
and operational data as inputs and output data representing cost of
downtime per day for machines in the manufacturing facility. At
166708, digital twin dynamic model system 15508 retrieves
historical downtime data and operational data from digital twin I/O
system 15504. At 166710, digital twin dynamic model system 15508
runs the dynamic model(s) using the retrieved data as input and
calculates one or more outputs that represent cost of downtime per
day for machines in the manufacturing facility. Next, at 166712,
the digital twin dynamic model system 15508 updates one or more
cost of downtime values of the manufacturing facility digital twins
and machine digital twins based on the one or more outputs of the
dynamic model(s).
[0725] FIG. 167 illustrates example embodiments of a method for
updating a set of manufacturing KPI values in the digital twin of a
manufacturing facility. In embodiments, the manufacturing KPI is
selected from the set of uptime, capacity utilization, on standard
operating efficiency, overall operating efficiency, overall
equipment effectiveness, machine downtime, unscheduled downtime,
machine set up time, inventory turns, inventory accuracy, quality
(e.g., percent defective), first pass yield, rework, scrap, failed
audits, on-time delivery, customer returns, training hours,
employee turnover, reportable health & safety incidents,
revenue per employee, and profit per employee, schedule attainment,
total cycle time, throughput, changeover time, yield, planned
maintenance percentage, availability, and customer return rate.
[0726] In the present example, the digital twin dynamic model
system 15508 may receive requests from a client application 15570
to populate real-time manufacturing KPI values in a manufacturing
facility digital twin. At 167800, digital twin dynamic model system
15508 receives a request from the client application 15570 to
update one or more KPI values of the manufacturing facility digital
twin and any embedded digital twins (e.g., machines, machine parts,
and the like) from the client application 15570. Next, in step
167802, the digital twin dynamic model system 15508 determines the
one or more digital twins required to fulfill the request and
retrieves the one or more required digital twins. In this example,
the digital twin dynamic model system 15508 may retrieve the
digital twins of the manufacturing facility, the machines, the
machine parts, and any other embedded digital twins from digital
twin datastore 15516. At 167804, digital twin dynamic model system
15508 determines one or more dynamic models required to fulfill the
request and retrieves the one or more required dynamic models from
dynamic model datastore 155102. At 167806, the digital twin dynamic
model system 15508 selects dynamic model input data sources (e.g.,
one or more sensors from sensor system 15530, data from Internet of
Things connected devices 15524, and any other suitable data) based
on available data sources (e.g., available sensors from a set of
sensors in sensor system 15530) and the and the one or more
required inputs of the dynamic model(s) via digital twin I/O system
15504. In the present example, the retrieved dynamic model(s) may
be configured to take one or more vibration measurements obtained
from vibration sensors 15536 and other operational data as inputs
and output one or more manufacturing KPIs for the facility. At
167808, digital twin dynamic model system 15508 retrieves one or
more vibration measurements from each of the selected vibration
sensors 15536 and operational data from digital twin I/O system
15504. At 167810, digital twin dynamic model system 15508 runs the
dynamic model(s) using the retrieved vibration measurements and
operational data as inputs and calculates one or more outputs that
represent manufacturing KPIs for the manufacturing facility. Next,
At 167812, the digital twin dynamic model system 15508 updates one
or more KPI values of the manufacturing facility digital twins,
machine digital twins, machine part digital twins, and all other
embedded digital twins based on the one or more outputs of the
dynamic model(s).
[0727] Further embodiments may include the following examples. FIG.
155 illustrates an example environment of a digital twin system
15500. In embodiments, the digital twin system 15500 generates a
set of digital twins of a set of industrial environments 15520
and/or industrial entities within the set of industrial
environments. In embodiments, the digital twin system 15500
maintains a set of states of the respective industrial environments
15520, such as using sensor data obtained from respective sensor
systems 15530 that monitor the industrial environments 15520. In
embodiments, the digital twin system 15500 may include a digital
twin management system 15502, a digital twin I/O system 15504, a
digital twin simulation system 15506, a digital twin dynamic model
system 15508, a cognitive intelligence system 15510, and/or an
environment control module 15512. In embodiments, the digital twin
system 15500 may provide a real time sensor API that provides a set
of capabilities for enabling a set of interfaces for the sensors of
the respective sensor systems 15530. In embodiments, the digital
twin system 15500 may include and/or employ other suitable APIs,
brokers, connectors, bridges, gateways, hubs, ports, routers,
switches, data integration systems, peer-to-peer systems, and the
like to facilitate the transferring of data to and from the digital
twin system 15500. In these embodiments, these connective
components may allow an IoT sensor or an intermediary device (e.g.,
a relay, an edge device, a switch, or the like) within a sensor
system 15530 to communicate data to the digital twin system 155300
and/or to receive data (e.g., configuration data, control data, or
the like) from the digital twin system 15500 or another external
system. In embodiments, the digital twin system 15500 may further
include a digital twin datastore 15516 that stores digital twins
15518 of various industrial environments 15520 and the objects
15522, devices 15524, sensors 15526, and/or humans 15528 in the
environment 15520.
[0728] A digital twin may refer to a digital representation of one
or more industrial entities, such as an industrial environment
15520, a physical object 15522, a device 15524, a sensor 15526, a
human 15528, or any combination thereof. Examples of industrial
environments 15520 include, but are not limited to, a factory, a
power plant, a food production facility (which may include an
inspection facility), a commercial kitchen, an indoor growing
facility, a natural resources excavation site (e.g., a mine, an oil
field, etc.), and the like. Depending on the type of environment,
the types of objects, devices, and sensors that are found in the
environments will differ. Non-limiting examples of physical objects
15522 include raw materials, manufactured products, excavated
materials, containers (e.g., boxes, dumpsters, cooling towers,
vats, pallets, barrels, palates, bins, and the like), furniture
(e.g., tables, counters, workstations, shelving, etc.), and the
like. Non-limiting examples of devices 15524 include robots,
computers, vehicles (e.g., cars, trucks, tankers, trains,
forklifts, cranes, etc.), machinery/equipment (e.g., tractors,
tillers, drills, presses, assembly lines, conveyor belts, etc.),
and the like. The sensors 15526 may be any sensor devices and/or
sensor aggregation devices that are found in a sensor system 15530
within an environment. Non-limiting examples of sensors 15526 that
may be implemented in a sensor system 15530 may include temperature
sensors 15532, humidity sensors 15534, vibration sensors 15536,
LIDAR sensors 15538, motion sensors 15540, chemical sensors 15542,
audio sensors 15544, pressure sensors 15546, weight sensors 15548,
radiation sensors 15550, video sensors 15552, wearable devices
15554, relays 15556, edge devices 15558, crosspoint switches 15560,
and/or any other suitable sensors. Examples of different types of
physical objects 15522, devices 15524, sensors 15526, and
environments 15520 are referenced throughout the disclosure.
[0729] In some embodiments, on-device sensor fusion and data
storage for industrial IoT devices is supported, including
on-device sensor fusion and data storage for an industrial IoT
device, where data from multiple sensors is multiplexed at the
device for storage of a fused data stream. For example, pressure
and temperature data may be multiplexed into a data stream that
combines pressure and temperature in a time series, such as in a
byte-like structure (where time, pressure, and temperature are
bytes in a data structure, so that pressure and temperature remain
linked in time, without requiring separate processing of the
streams by outside systems), or by adding, dividing, multiplying,
subtracting, or the like, such that the fused data can be stored on
the device. Any of the sensor data types described throughout this
disclosure, including vibration data, can be fused in this manner
and stored in a local data pool, in storage, or on an IoT device,
such as a data collector, a component of a machine, or the
like.
[0730] In some embodiments, a set of digital twins may represent an
entire organization, such as energy production organizations, oil
and gas organizations, renewable energy production organizations,
aerospace manufacturers, vehicle manufacturers, heavy equipment
manufacturers, mining organizations, drilling organizations,
offshore platform organizations, and the like. In these examples,
the digital twins may include digital twins of one or more
industrial facilities of the organization.
[0731] In embodiments, the digital twin management system 15502
generates digital twins. A digital twin may be comprised of (e.g.,
via reference) other digital twins. In this way, a discrete digital
twin may be comprised of a set of other discrete digital twins. For
example, a digital twin of a machine may include digital twins of
sensors on the machine, digital twins of components that make up
the machine, digital twins of other devices that are incorporated
in or integrated with the machine (such as systems that provide
inputs to the machine or take outputs from it), and/or digital
twins of products or other items that are made by the machine.
Taking this example one step further, a digital twin of an
industrial facility (e.g., a factory) may include a digital twin
representing the layout of the industrial facility, including the
arrangement of physical assets and systems in or around the
facility, as well as digital assets of the assets within the
facility (e.g., the digital twin of the machine), as well as
digital twins of storage areas in the facility, digital twins of
humans collecting vibration measurements from machines throughout
the facility, and the like. In this second example, the digital
twin of the industrial facility may reference the embedded digital
twins, which may then reference other digital twins embedded within
those digital twins.
[0732] In some embodiments, a digital twin may represent abstract
entities, such as workflows and/or processes, including inputs,
outputs, sequences of steps, decision points, processing loops, and
the like that make up such workflows and processes. For example, a
digital twin may be a digital representation of a manufacturing
process, a logistics workflow, an agricultural process, a mineral
extraction process, or the like. In these embodiments, the digital
twin may include references to the industrial entities that are
included in the workflow or process. The digital twin of the
manufacturing process may reflect the various stages of the
process. In some of these embodiments, the digital twin system
15500 receives real-time data from the industrial facility (e.g.,
from a sensor system 15530 of the environment 15520) in which the
manufacturing process takes place and reflects a current (or
substantially current) state of the process in real-time.
[0733] In embodiments, the digital representation may include a set
of data structures (e.g., classes) that collectively define a set
of properties of a represented physical object 15522, device 15524,
sensor 15526, or environment 15520 and/or possible behaviors
thereof. For example, the set of properties of a physical object
15522 may include a type of the physical object, the dimensions of
the object, the mass of the object, the density of the object, the
material(s) of the object, the physical properties of the
material(s), the surface of the physical object, the status of the
physical object, a location of the physical object, identifiers of
other digital twins contained within the object, and/or other
suitable properties. Examples of behavior of a physical object may
include a state of the physical object (e.g., a solid, liquid, or
gas), a melting point of the physical object, a density of the
physical object when in a liquid state, a viscosity of the physical
object when in a liquid state, a freezing point of the physical
object, a density of the physical object when in a solid state, a
hardness of the physical object when in a solid state, the
malleability of the physical object, the buoyancy of the physical
object, the conductivity of the physical object, a burning point of
the physical object, the manner by which humidity affects the
physical object, the manner by which water or other liquids affect
the physical object, a terminal velocity of the physical object,
and the like. In another example, the set of properties of a device
may include a type of the device, the dimensions of the device, the
mass of the device, the density of the density of the device, the
material(s) of the device, the physical properties of the
material(s), the surface of the device, the output of the device,
the status of the device, a location of the device, a trajectory of
the device, vibration characteristics of the device, identifiers of
other digital twins that the device is connected to and/or
contains, and the like. Examples of the behaviors of a device may
include a maximum acceleration of a device, a maximum speed of a
device, ranges of motion of a device, a heating profile of a
device, a cooling profile of a device, processes that are performed
by the device, operations that are performed by the device, and the
like. Example properties of an environment may include the
dimensions of the environment, the boundaries of the environment,
the temperature of the environment, the humidity of the
environment, the airflow of the environment, the physical objects
in the environment, currents of the environment (if a body of
water), and the like. Examples of behaviors of an environment may
include scientific laws that govern the environment, processes that
are performed in the environment, rules or regulations that must be
adhered to in the environment, and the like.
[0734] In embodiments, the properties of a digital twin may be
adjusted. For example, the temperature of a digital twin, a
humidity of a digital twin, the shape of a digital twin, the
material of a digital twin, the dimensions of a digital twin, or
any other suitable parameters may be adjusted. As the properties of
the digital twin are adjusted, other properties may be affected as
well. For example, if the temperature of an environment 15520 is
increased, the pressure within the environment may increase as
well, such as a pressure of a gas in accordance with the ideal gas
law. In another example, if a digital twin of a subzero environment
is increased to above freezing temperatures, the properties of an
embedded twin of water in a solid state (i.e., ice) may change into
a liquid state over time.
[0735] Digital twins may be represented in a number of different
forms. In embodiments, a digital twin may be a visual digital twin
that is rendered by a computing device, such that a human user can
view digital representations of an environment 15520 and/or the
physical objects 15522, devices 15524, and/or the sensors 15526
within an environment. In embodiments, the digital twin may be
rendered and output to a display device. In some of these
embodiments, the digital twin may be rendered in a graphical user
interface, such that a user may interact with the digital twin. For
example, a user may "drill down" on a particular element (e.g., a
physical object or device) to view additional information regarding
the element (e.g., a state of a physical object or device,
properties of the physical object or device, or the like). In some
embodiments, the digital twin may be rendered and output in a
virtual reality display. For example, a user may view a 3D
rendering of an environment (e.g., using monitor or a virtual
reality headset). While doing so, the user may view/inspect digital
twins of physical assets or devices in the environment.
[0736] In some embodiments, a data structure of the visual digital
twins (i.e., digital twins that are configured to be displayed in a
2D or 3D manner) may include surfaces (e.g., splines, meshes,
polygons meshes, or the like). In some embodiments, the surfaces
may include texture data, shading information, and/or reflection
data. In this way, a surface may be displayed in a more realistic
manner. In some embodiments, such surfaces may be rendered by a
visualization engine (not shown) when the digital twin is within a
field of view and/or when existing in a larger digital twin (e.g.,
a digital twin of an industrial environment). In these embodiments,
the digital twin system 15500 may render the surfaces of digital
objects, whereby a rendered digital twin may be depicted as a set
of adjoined surfaces.
[0737] In embodiments, a user may provide input that controls one
or more properties of a digital twin via a graphical user
interface. For example, a user may provide input that changes a
property of a digital twin. In response, the digital twin system
15500 can calculate the effects of the changed property and may
update the digital twin and any other digital twins affected by the
change of the property.
[0738] In embodiments, a user may view processes being performed
with respect to one or more digital twins (e.g., manufacturing of a
product, extracting minerals from a mine or well, a livestock
inspection line, and the like). In these embodiments, a user may
view the entire process or specific steps within a process.
[0739] In some embodiments, a digital twin (and any digital twins
embedded therein) may be represented in a non-visual representation
(or "data representation"). In these embodiments, a digital twin
and any embedded digital twins exist in a binary representation but
the relationships between the digital twins are maintained. For
example, in embodiments, each digital twin and/or the components
thereof may be represented by a set of physical dimensions that
define a shape of the digital twin (or component thereof).
Furthermore, the data structure embodying the digital twin may
include a location of the digital twin. In some embodiments, the
location of the digital twin may be provided in a set of
coordinates. For example, a digital twin of an industrial
environment may be defined with respect to a coordinate space
(e.g., a Cartesian coordinate space, a polar coordinate space, or
the like). In embodiments, embedded digital twins may be
represented as a set of one or more ordered triples (e.g., [x
coordinate, y coordinate, z coordinates] or other vector-based
representations). In some of these embodiments, each ordered triple
may represent a location of a specific point (e.g., center point,
top point, bottom point, or the like) on the industrial entity
(e.g., object, device, or sensor) in relation to the environment in
which the industrial entity resides. In some embodiments, a data
structure of a digital twin may include a vector that indicates a
motion of the digital twin with respect to the environment. For
example, fluids (e.g., liquids or gasses) or solids may be
represented by a vector that indicates a velocity (e.g., direction
and magnitude of speed) of the entity represented by the digital
twin. In embodiments, a vector within a twin may represent a
microscopic subcomponent, such as a particle within a fluid, and a
digital twin may represent physical properties, such as
displacement, velocity, acceleration, momentum, kinetic energy,
vibrational characteristics, thermal properties, electromagnetic
properties, and the like.
[0740] In some embodiments, a set of two or more digital twins may
be represented by a graph database that includes nodes and edges
that connect the nodes. In some implementations, an edge may
represent a spatial relationship (e.g., "abuts", "rests upon",
"contains", and the like). In these embodiments, each node in the
graph database represents a digital twin of an entity (e.g., an
industrial entity) and may include the data structure defining the
digital twin. In these embodiments, each edge in the graph database
may represent a relationship between two entities represented by
connected nodes. In some implementations, an edge may represent a
spatial relationship (e.g., "abuts", "rests upon", "interlocks
with", "bears", "contains", and the like). In embodiments, various
types of data may be stored in a node or an edge. In embodiments, a
node may store property data, state data, and/or metadata relating
to a facility, system, subsystem, and/or component. Types of
property data and state data will differ based on the entity
represented by a node. For example, a node representing a robot may
include property data that indicates a material of the robot, the
dimensions of the robot (or components thereof), a mass of the
robot, and the like. In this example, the state data of the robot
may include a current pose of the robot, a location of the robot,
and the like. In embodiments, an edge may store relationship data
and metadata data relating to a relationship between two nodes.
Examples of relationship data may include the nature of the
relationship, whether the relationship is permanent (e.g., a fixed
component would have a permanent relationship with the structure to
which it is attached or resting on), and the like. In embodiments,
an edge may include metadata concerning the relationship between
two entities. For example, if a product was produced on an assembly
line, one relationship that may be documented between a digital
twin of the product and the assembly line may be "created by". In
these embodiments, an example edge representing the "created by"
relationship may include a timestamp indicating a date and time
that the product was created. In another example, a sensor may take
measurements relating to a state of a device, whereby one
relationship between the sensor and the device may include
"measured" and may define a measurement type that is measured by
the sensor. In this example, the metadata stored in an edge may
include a list of N measurements taken and a timestamp of each
respective measurement. In this way, temporal data relating to the
nature of the relationship between two entities may be maintained,
thereby allowing for an analytics engine, machine-learning engine,
and/or visualization engine to leverage such temporal relationship
data, such as by aligning disparate data sets with a series of
points in time, such as to facilitate cause-and-effect analysis
used for prediction systems.
[0741] In some embodiments, a graph database may be implemented in
a hierarchical manner, such that the graph database relates a set
of facilities, systems, and components. For example, a digital twin
of a manufacturing environment may include a node representing the
manufacturing environment. The graph database may further include
nodes representing various systems within the manufacturing
environment, such as nodes representing an HVAC system, a lighting
system, a manufacturing system, and the like, all of which may
connect to the node representing the manufacturing system. In this
example, each of the systems may further connect to various
subsystems and/or components of the system. For example, within the
HVAC system, the HVAC system may connect to a subsystem node
representing a cooling system of the facility, a second subsystem
node representing a heating system of the facility, a third
subsystem node representing the fan system of the facility, and one
or more nodes representing a thermostat of the facility (or
multiple thermostats). Carrying this example further, the subsystem
nodes and/or component nodes may connect to lower level nodes,
which may include subsystem nodes and/or component nodes. For
example, the subsystem node representing the cooling subsystem may
be connected to a component node representing an air conditioner
unit. Similarly, a component node representing a thermostat device
may connect to one or more component nodes representing various
sensors (e.g., temperature sensors, humidity sensors, and the
like).
[0742] In embodiments where a graph database is implemented, a
graph database may relate to a single environment or may represent
a larger enterprise. In the latter scenario, a company may have
various manufacturing and distribution facilities. In these
embodiments, an enterprise node representing the enterprise may
connect to environment nodes of each respective facility. In this
way, the digital twin system 15500 may maintain digital twins for
multiple industrial facilities of an enterprise.
[0743] In embodiments, the digital twin system 15500 may use a
graph database to generate a digital twin that may be rendered and
displayed and/or may be represented in a data representation. In
the former scenario, the digital twin system 15500 may receive a
request to render a digital twin, whereby the request includes one
or more parameters that are indicative of a view that will be
depicted. For example, the one or more parameters may indicate an
industrial environment to be depicted and the type of rendering
(e.g., "real-world view" that depicts the environment as a human
would see it, an "infrared view" that depicts objects as a function
of their respective temperature, an "airflow view" that depicts the
airflow in a digital twin, or the like). In response, the digital
twin system 15500 may traverse a graph database and may determine a
configuration of the environment to be depicted based on the nodes
in the graph database that are related (either directly or through
a lower level node) to the environment node of the environment and
the edges that define the relationships between the related nodes.
Upon determining a configuration, the digital twin system 15500 may
identify the surfaces that are to be depicted and may render those
surfaces. The digital twin system 15500 may then render the
requested digital twin by connecting the surfaces in accordance
with the configuration. The rendered digital twin may then be
output to a viewing device (e.g., VR headset, monitor, or the
like). In some scenarios, the digital twin system 15500 may receive
real-time sensor data from a sensor system 15530 of an environment
15520 and may update the visual digital twin based on the sensor
data. For example, the digital twin system 15500 may receive sensor
data (e.g., vibration data from a vibration sensor 15536) relating
to a motor and its set of bearings. Based on the sensor data, the
digital twin system 15500 may update the visual digital twin to
indicate the approximate vibrational characteristics of the set of
bearings within a digital twin of the motor.
[0744] In scenarios where the digital twin system 15500 is
providing data representations of digital twins (e.g., for dynamic
modeling, simulations, machine learning), the digital twin system
15500 may traverse a graph database and may determine a
configuration of the environment to be depicted based on the nodes
in the graph database that are related (either directly or through
a lower level node) to the environment node of the environment and
the edges that define the relationships between the related nodes.
In some scenarios, the digital twin system 15500 may receive
real-time sensor data from a sensor system 15530 of an environment
15520 and may apply one or more dynamic models to the digital twin
based on the sensor data. In other scenarios, a data representation
of a digital twin may be used to perform simulations, as is
discussed in greater detail throughout the specification.
[0745] In some embodiments, the digital twin system 15500 may
execute a digital ghost that is executed with respect to a digital
twin of an industrial environment. In these embodiments, the
digital ghost may monitor one or more sensors of a sensor system
15530 of an industrial environment to detect anomalies that may
indicate a malicious virus or other security issues.
[0746] As discussed, the digital twin system 15500 may include a
digital twin management system 15502, a digital twin I/O system
15504, a digital twin simulation system 15506, a digital twin
dynamic model system 15508, a cognitive intelligence system 15510,
and/or an environment control module 15512.
[0747] In embodiments, the digital twin management system 15502
creates new digital twins, maintains/updates existing digital
twins, and/or renders digital twins. The digital twin management
system 15502 may receive user input, uploaded data, and/or sensor
data to create and maintain existing digital twins. Upon creating a
new digital twin, the digital twin management system 15502 may
store the digital twin in the digital twin datastore 15516.
Creating, updating, and rendering digital twins are discussed in
greater detail throughout the disclosure.
[0748] In embodiments, the digital twin I/O system 15504 receives
input from various sources and outputs data to various recipients.
In embodiments, the digital twin I/O system receives sensor data
from one or more sensor systems 15530. In these embodiments, each
sensor system 15530 may include one or more IoT sensors that output
respective sensor data. Each sensor may be assigned an IP address
or may have another suitable identifier. Each sensor may output
sensor packets that include an identifier of the sensor and the
sensor data. In some embodiments, the sensor packets may further
include a timestamp indicating a time at which the sensor data was
collected. In some embodiments, the digital twin I/O system 15504
may interface with a sensor system 15530 via the real-time sensor
API 15514. In these embodiments, one or more devices (e.g.,
sensors, aggregators, edge devices) in the sensor system 15530 may
transmit the sensor packets containing sensor data to the digital
twin I/O system 15504 via the API. The digital twin I/O system may
determine the sensor system 15530 that transmitted the sensor
packets and the contents thereof, and may provide the sensor data
and any other relevant data (e.g., time stamp, environment
identifier/sensor system identifier, and the like) to the digital
twin management system 15502.
[0749] In embodiments, the digital twin I/O system 15504 may
receive imported data from one or more sources. For example, the
digital twin system 15500 may provide a portal for users to create
and manage their digital twins. In these embodiments, a user may
upload one or more files (e.g., image files, LIDAR scans,
blueprints, and the like) in connection with a new digital twin
that is being created. In response, the digital twin I/O system
15504 may provide the imported data to the digital twin management
system 15502. The digital twin I/O system 15504 may receive other
suitable types of data without departing from the scope of the
disclosure.
[0750] In some embodiments, the digital twin simulation system
15506 is configured to execute simulations using the digital twin.
For example, the digital twin simulation system 15506 may
iteratively adjust one or more parameters of a digital twin and/or
one or more embedded digital twins. In embodiments, the digital
twin simulation system 15506, for each set of parameters, executes
a simulation based on the set of parameters and may collect the
simulation outcome data resulting from the simulation. Put another
way, the digital twin simulation system 15506 may collect the
properties of the digital twin and the digital twins within or
containing the digital twin used during the simulation as well as
any outcomes stemming from the simulation. For example, in running
a simulation on a digital twin of an indoor agricultural facility,
the digital twin simulation system 15506 can vary the temperature,
humidity, airflow, carbon dioxide and/or other relevant parameters
and can execute simulations that output outcomes resulting from
different combinations of the parameters. In another example, the
digital twin simulation system 15506 may simulate the operation of
a specific machine within an industrial facility that produces an
output given a set of inputs. In some embodiments, the inputs may
be varied to determine an effect of the inputs on the machine and
the output thereof. In another example, the digital twin simulation
system 15506 may simulate the vibration of a machine and/or machine
components. In this example, the digital twin of the machine may
include a set of operating parameters, interfaces, and capabilities
of the machine. In some embodiments, the operating parameters may
be varied to evaluate the effectiveness of the machine. The digital
twin simulation system 15506 is discussed in further detail
throughout the disclosure.
[0751] In embodiments, the digital twin dynamic model system 15508
is configured to model one or more behaviors with respect to a
digital twin of an environment. In embodiments, the digital twin
dynamic model system 15508 may receive a request to model a certain
type of behavior regarding an environment or a process and may
model that behavior using a dynamic model, the digital twin of the
environment or process, and sensor data collected from one or more
sensors that are monitoring the environment or process. For
example, an operator of a machine having bearings may wish to model
the vibration of the machine and bearings to determine whether the
machine and/or bearings can withstand an increase in output. In
this example, the digital twin dynamic model system 15508 may
execute a dynamic model that is configured to determine whether an
increase in output would result in adverse consequences (e.g.,
failures, downtime, or the like). The digital twin dynamic model
system 15508 is discussed in further detail throughout the
disclosure.
[0752] In embodiments, the cognitive processes system 15510
performs machine learning and artificial intelligence related tasks
on behalf of the digital twin system. In embodiments, the cognitive
processes system 15510 may train any suitable type of model,
including but not limited to various types of neural networks,
regression models, random forests, decision trees, Hidden Markov
models, Bayesian models, and the like. In embodiments, the
cognitive processes system 15510 trains machine learned models
using the output of simulations executed by the digital twin
simulation system 15506. In some of these embodiments, the outcomes
of the simulations may be used to supplement training data
collected from real-world environments and/or processes. In
embodiments, the cognitive processes system 15510 leverages machine
learned models to make predictions, identifications,
classifications and provide decision support relating to the
real-world environments and/or processes represented by respective
digital twins.
[0753] For example, a machine-learned prediction model may be used
to predict the cause of irregular vibrational patterns (e.g., a
suboptimal, critical, or alarm vibration fault state) for a bearing
of an engine in an industrial facility. In this example, the
cognitive processes system 15510 may receive vibration sensor data
from one or more vibration sensors disposed on or near the engine
and may receive maintenance data from the industrial facility and
may generate a feature vector based on the vibration sensor data
and the maintenance data. The cognitive processes system 15510 may
input the feature vector into a machine-learned model trained
specifically for the engine (e.g., using a combination simulation
data and real-world data of causes of irregular vibration patterns)
to predict the cause of the irregular vibration patterns. In this
example, the causes of the irregular vibrational patterns could be
a loose bearing, a lack of bearing lubrication, a bearing that is
out of alignment, a worn bearing, the phase of the bearing may be
aligned with the phase of the engine, loose housing, loose bolt,
and the like.
[0754] In another example, a machine-learned model may be used to
provide decision support to bring a bearing of an engine in an
industrial facility operating at a suboptimal vibration fault level
state to a normal operation vibration fault level state. In this
example, the cognitive processes system 15510 may receive vibration
sensor data from one or more vibration sensors disposed on or near
the engine and may receive maintenance data from the industrial
facility and may generate a feature vector based on the vibration
sensor data and the maintenance data. The cognitive processes
system 15510 may input the feature vector into a machine-learned
model trained specifically for the engine (e.g., using a
combination simulation data and real-world data of solutions to
irregular vibration patterns) to provide decision support in
achieving a normal operation fault level state of the bearing. In
this example, the decision support could be a recommendation to
tighten the bearing, lubricate the bearing, re-align the bearing,
order a new bearing, order a new part, collect additional vibration
measurements, change operating speed of the engine, tighten
housings, tighten bolts, and the like.
[0755] In another example, a machine-learned model may be used to
provide decision support relating to vibration measurement
collection by a worker. In this example, the cognitive processes
system 15510 may receive vibration measurement history data from
the industrial facility and may generate a feature vector based on
the vibration measurement history data. The cognitive processes
system 15510 may input the feature vector into a machine-learned
model trained specifically for the engine (e.g., using a
combination simulation data and real-world vibration measurement
history data) to provide decision support in selecting vibration
measurement locations.
[0756] In yet another example, a machine-learned model may be used
to identify vibration signatures associated with machine and/or
machine component problems. In this example, the cognitive
processes system 15510 may receive vibration measurement history
data from the industrial facility and may generate a feature vector
based on the vibration measurement history data. The cognitive
processes system 15510 may input the feature vector into a
machine-learned model trained specifically for the engine (e.g.,
using a combination simulation data and real-world vibration
measurement history data) to identify vibration signatures
associated with a machine and/or machine component. The foregoing
examples are non-limiting examples and the cognitive processes
system 15510 may be used for any other suitable AI/machine-learning
related tasks that are performed with respect to industrial
facilities.
[0757] In embodiments, the environment control system 15512
controls one or more aspects of industrial facilities. In some of
these embodiments, the environment control system 15512 may control
one or more devices within an industrial environment. For example,
the environment control system 15512 may control one or more
machines within an environment, robots within an environment, an
HVAC system of the environment, an alarm system of the environment,
an assembly line in an environment, or the like. In embodiments,
the environment control system 15512 may leverage the digital twin
simulation system 15506, the digital twin dynamic model system
15508, and/or the cognitive processes system 15510 to determine one
or more control instructions. In embodiments, the environment
control system 15512 may implement a rules-based and/or a
machine-learning approach to determine the control instructions. In
response to determining a control instruction, the environment
control system 15512 may output the control instruction to the
intended device within a specific environment via the digital twin
I/O system 15504.
[0758] FIG. 156 illustrates an example digital twin management
system 15502 according to some embodiments of the present
disclosure. In embodiments, the digital twin management system
15502 may include, but is not limited to, a digital twin creation
module 15564, a digital twin update module 15566, and a digital
twin visualization module 15568.
[0759] In embodiments, the digital twin creation module 15564 may
create a set of new digital twins of a set of environments using
input from users, imported data (e.g., blueprints, specifications,
and the like), image scans of the environment, 3D data from a LIDAR
device and/or SLAM sensor, and other suitable data sources. For
example, a user (e.g., a user affiliated with an
organization/customer account) may, via a client application 15570,
provide input to create a new digital twin of an environment. In
doing so, the user may upload 2D or 3D image scans of the
environment and/or a blueprint of the environment. The user may
also upload 3D data, such as taken by a camera, a LIDAR device, an
IR scanner, a set of SLAM sensors, a radar device, an EMF scanner,
or the like. In response to the provided data, the digital twin
creation module 15564 may create a 3D representation of the
environment, which may include any objects that were captured in
the image data/detected in the 3D data. In embodiments, the
cognitive processes system 15572 may analyze input data (e.g.,
blueprints, image scans, 3D data) to classify rooms, pathways,
equipment, and the like to assist in the generation of the 3D
representation. In some embodiments, the digital twin creation
module 15564 may map the digital twin to a 3D coordinate space
(e.g., a Cartesian space having x, y, and z axes).
[0760] In some embodiments, the digital twin creation module 15564
may output the 3D representation of the environment to a graphical
user interface (GUI). In some of these embodiments, a user may
identify certain areas and/or objects and may provide input
relating to the identified areas and/or objects. For example, a
user may label specific rooms, equipment, machines, and the like.
Additionally or alternatively, the user may provide data relating
to the identified objects and/or areas. For example, in identifying
a piece of equipment, the user may provide a make/model number of
the equipment. In some embodiments, the digital twin creation
module 15564 may obtain information from a manufacturer of a
device, a piece of equipment, or machinery. This information may
include one or more properties and/or behaviors of the device,
equipment, or machinery. In some embodiments, the user may, via the
GUI, identify locations of sensors throughout the environment. For
each sensor, the user may provide a type of sensor and related data
(e.g., make, model, IP address, and the like). The digital twin
creation module 15564 may record the locations (e.g., the x, y, z
coordinates of the sensors) in the digital twin of the environment.
In embodiments, the digital twin system 15500 may employ one or
more systems that automate the population of digital twins. For
example, the digital twin system 15500 may employ a machine
vision-based classifier that classifies makes and models of
devices, equipment, or sensors. Additionally or alternatively, the
digital twin system 15500 may iteratively ping different types of
known sensors to identify the presence of specific types of sensors
that are in an environment. Each time a sensor responds to a ping,
the digital twin system 15500 may extrapolate the make and model of
the sensor.
[0761] In some embodiments, the manufacturer may provide or make
available digital twins of their products (e.g., sensors, devices,
machinery, equipment, raw materials, and the like). In these
embodiments, the digital twin creation module 15564 may import the
digital twins of one or more products that are identified in the
environment and may embed those digital twins in the digital twin
of the environment. In embodiments, embedding a digital twin within
another digital twin may include creating a relationship between
the embedded digital twin with the other digital twin. In these
embodiments, the manufacturer of the digital twin may define the
behaviors and/or properties of the respective products. For
example, a digital twin of a machine may define the manner by which
the machine operates, the inputs/outputs of the machine, and the
like. In this way, the digital twin of the machine may reflect the
operation of the machine given a set of inputs.
[0762] In embodiments, a user may define one or more processes that
occur in an environment. In these embodiments, the user may define
the steps in the process, the machines/devices that perform each
step in the process, the inputs to the process, and the outputs of
the process.
[0763] In embodiments, the digital twin creation module 15564 may
create a graph database that defines the relationships between a
set of digital twins. In these embodiments, the digital twin
creation module 15564 may create nodes for the environment, systems
and subsystems of the environment, devices in the environment,
sensors in the environment, workers that work in the environment,
processes that are performed in the environment, and the like. In
embodiments, the digital twin creation module 15564 may write the
graph database representing a set of digital twins to the digital
twin datastore 15516.
[0764] In embodiments, the digital twin creation module 15564 may,
for each node, include any data relating to the entity in the node
representing the entity. For example, in defining a node
representing an environment, the digital twin creation module 15564
may include the dimensions, boundaries, layout, pathways, and other
relevant spatial data in the node. Furthermore, the digital twin
creation module 15564 may define a coordinate space with respect to
the environment. In the case that the digital twin may be rendered,
the digital twin creation module 15564 may include a reference in
the node to any shapes, meshes, splines, surfaces, and the like
that may be used to render the environment. In representing a
system, subsystem, device, or sensor, the digital twin creation
module 15564 may create a node for the respective entity and may
include any relevant data. For example, the digital twin creation
module 15564 may create a node representing a machine in the
environment. In this example, the digital twin creation module
15564 may include the dimensions, behaviors, properties, location,
and/or any other suitable data relating to the machine in the node
representing the machine. The digital twin creation module 15564
may connect nodes of related entities with an edge, thereby
creating a relationship between the entities. In doing so, the
created relationship between the entities may define the type of
relationship characterized by the edge. In representing a process,
the digital twin creation module 15564 may create a node for the
entire process or may create a node for each step in the process.
In some of these embodiments, the digital twin creation module
15564 may relate the process nodes to the nodes that represent the
machinery/devices that perform the steps in the process. In
embodiments, where an edge connects the process step nodes to the
machinery/device that performs the process step, the edge or one of
the nodes may contain information that indicates the input to the
step, the output of the step, the amount of time the step takes,
the nature of processing of inputs to produce outputs, a set of
states or modes the process can undergo, and the like.
[0765] In embodiments, the digital twin update module 15566 updates
sets of digital twins based on a current status of one or more
industrial entities. In some embodiments, the digital twin update
module 15566 receives sensor data from a sensor system 15530 of an
industrial environment and updates the status of the digital twin
of the industrial environment and/or digital twins of any affected
systems, subsystems, devices, workers, processes, or the like. As
discussed, the digital twin I/O system 15504 may receive the sensor
data in one or more sensor packets. The digital twin I/O system
15504 may provide the sensor data to the digital twin update module
15566 and may identify the environment from which the sensor
packets were received and the sensor that provided the sensor
packet. In response to the sensor data, the digital twin update
module 15566 may update a state of one or more digital twins based
on the sensor data. In some of these embodiments, the digital twin
update module 15566 may update a record (e.g., a node in a graph
database) corresponding to the sensor that provided the sensor data
to reflect the current sensor data. In some scenarios, the digital
twin update module 15566 may identify certain areas within the
environment that are monitored by the sensor and may update a
record (e.g., a node in a graph database) to reflect the current
sensor data. For example, the digital twin update module 15566 may
receive sensor data reflecting different vibrational
characteristics of a machine and/or machine components. In this
example, the digital twin update module 15566 may update the
records representing the vibration sensors that provided the
vibration sensor data and/or the records representing the machine
and/or the machine components to reflect the vibration sensor data.
In another example, in some scenarios, workers in an industrial
environment (e.g., manufacturing facility, industrial storage
facility, a mine, a drilling operation, or the like) may be
required to wear wearable devices (e.g., smart watches, smart
helmets, smart shoes, or the like). In these embodiments, the
wearable devices may collect sensor data relating to the worker
(e.g., location, movement, heartrate, respiration rate, body
temperature, or the like) and/or the environment surrounding the
worker and may communicate the collected sensor data to the digital
twin system 15500 (e.g., via the real-time sensor API 15514) either
directly or via an aggregation device of the sensor system. In
response to receiving the sensor data from the wearable device of a
worker, the digital twin update module 15566 may update a digital
twin of a worker to reflect, for example, a location of the worker,
a trajectory of the worker, a health status of the worker, or the
like. In some of these embodiments, the digital twin update module
15566 may update a node representing a worker and/or an edge that
connects the node representing the environment with the collected
sensor data to reflect the current status of the worker.
[0766] In some embodiments, the digital twin update module 15566
may provide the sensor data from one or more sensors to the digital
twin dynamic model system 15508, which may model a behavior of the
environment and/or one or more industrial entities to extrapolate
additional state data.
[0767] In embodiments, the digital twin visualization module 15568
receives requests to view a visual digital twin or a portion
thereof. In embodiments, the request may indicate the digital twin
to be viewed (e.g., an environment identifier). In response, the
digital twin visualization module 15568 may determine the requested
digital twin and any other digital twins implicated by the request.
For example, in requesting to view a digital twin of an
environment, the digital twin visualization module 15568 may
further identify the digital twins of any industrial entities
within the environment. In embodiments, the digital twin
visualization module 15568 may identify the spatial relationships
between the industrial entities and the environment based on, for
example, the relationships defined in a graph database. In these
embodiments, the digital twin visualization module 15568 can
determine the relative location of embedded digital twins within
the containing digital twin, relative locations of adjoining
digital twins, and/or the transience of the relationship (e.g., is
an object fixed to a point or does the object move). The digital
twin visualization module 15568 may render the requested digital
twins and any other implicated digital twin based on the identified
relationships. In some embodiments, the digital twin visualization
module 15568 may, for each digital twin, determine the surfaces of
the digital twin. In some embodiments, the surfaces of a digital
may be defined or referenced in a record corresponding to the
digital twin, which may be provided by a user, determined from
imported images, or defined by a manufacturer of an industrial
entity. In the scenario that an object can take different poses or
shapes (e.g., an industrial robot), the digital twin visualization
module 15568 may determine a pose or shape of the object for the
digital twin. The digital twin visualization module 15568 may embed
the digital twins into the requested digital twin and may output
the requested digital twin to a client application.
[0768] In some of these embodiments, the request to view a digital
twin may further indicate the type of view. As discussed, in some
embodiments, digital twins may be depicted in a number of different
view types. For example, an environment or device may be viewed in
a "real-world" view that depicts the environment or device as they
typically appear, in a "heat" view that depicts the environment or
device in a manner that is indicative of a temperature of the
environment or device, in a "vibration" view that depicts the
machines and/or machine components in an industrial environment in
a manner that is indicative of vibrational characteristics of the
machines and/or machine components, in a "filtered" view that only
displays certain types of objects within an environment or
components of a device (such as objects that require attention
resulting from, for example, recognition of a fault condition, an
alert, an updated report, or other factors), an augmented view that
overlays data on the digital twin, and/or any other suitable view
types. In embodiments, digital twins may be depicted in a number of
different role-based view types. For example, a manufacturing
facility device may be viewed in an "operator" view that depicts
the facility in a manner that is suitable for a facility operator,
a "C-Suite" view that depicts the facility in a manner that is
suitable for executive-level managers, a "marketing" view that
depicts the facility in a manner that is suitable for workers in
sales and/or marketing roles, a "board" view that depicts the
facility in a manner that is suitable for members of a corporate
board, a "regulatory" view that depicts the facility in a manner
that is suitable for regulatory managers, and a "human resources"
view that depicts the facility in a manner that is suitable for
human resources personnel. In response to a request that indicates
a view type, the digital twin visualization module 15568 may
retrieve the data for each digital twin that corresponds to the
view type. For example, if a user has requested a vibration view of
a factory floor, the digital twin visualization module 15568 may
retrieve vibration data for the factory floor (which may include
vibration measurements taken from different machines and/or machine
components and/or vibration measurements that were extrapolated by
the digital twin dynamic model system 15508 and/or simulated
vibration data from digital twin simulation system 15506) as well
as available vibration data for any industrial entities appearing
on the factory floor. In this example, the digital twin
visualization module 15568 may determine colors corresponding to
each machine component on a factory floor that represent a
vibration fault level state (e.g., red for alarm, orange for
critical, yellow for suboptimal, and green for normal operation).
The digital twin visualization module 15568 may then render the
digital twins of the machine components within the environment
based on the determined colors. Additionally or alternatively, the
digital twin visualization module 15568 may render the digital
twins of the machine components within the environment with
indicators having the determined colors. For instance, if the
vibration fault level state of an inbound bearing of a motor is
suboptimal and the outbound bearing of the motor is critical, the
digital twin visualization module 15568 may render the digital twin
of the inbound bearing having an indicator in a shade of yellow
(e.g., suboptimal) and the outbound bearing having an indicator in
a shade of orange (e.g., critical). It is noted that in some
embodiments, the digital twin system 15500 may include an analytics
system (not shown) that determine the manner by which the digital
twin visualization system 15568 presents information to a human
user. For example, the analytics system may track outcomes relating
to human interactions with real-world environments or objects in
response to information presented in a visual digital twin. In some
embodiments, the analytics system may apply cognitive models to
determine the most effective manner to display visualized
information (e.g., what colors to use to denote an alarm condition,
what kind of movements or animations bring attention to an alarm
condition, or the like) or audio information (what sounds to use to
denote an alarm condition) based on the outcome data. In some
embodiments, the analytics system may apply cognitive models to
determine the most suitable manner to display visualized
information based on the role of the user. In embodiments, the
visualization may include display of information related to the
visualized digital twins, including graphical information,
graphical information depicting vibration characteristics,
graphical information depicting harmonic peaks, graphical
information depicting peaks, vibration severity units data,
vibration fault level state data, recommendations from cognitive
intelligence system 15510, predictions from cognitive intelligence
system 15510, probability of failure data, maintenance history
data, time to failure data, cost of downtime data, probability of
downtime data, cost of repair data, cost of machine replace data,
probability of shutdown data, manufacturing KPIs, and the like.
[0769] In another example, a user may request a filtered view of a
digital twin of a process, whereby the digital twin of the process
only shows components (e.g., machine or equipment) that are
involved in the process. In this example, the digital twin
visualization module 15568 may retrieve a digital twin of the
process, as well as any related digital twins (e.g., a digital twin
of the environment and digital twins of any machinery or devices
that impact the process). The digital twin visualization module
15568 may then render each of the digital twins (e.g., the
environment and the relevant industrial entities) and then may
perform the process on the rendered digital twins. It is noted that
as a process may be performed over a period of time and may include
moving items and/or parts, the digital twin visualization module
15568 may generate a series of sequential frames that demonstrate
the process. In this scenario, the movements of the machines and/or
devices implicated by the process may be determined according to
the behaviors defined in the respective digital twins of the
machines and/or devices.
[0770] As discussed, the digital twin visualization module 15568
may output the requested digital twin to a client application
15570. In some embodiments, the client application 15570 is a
virtual reality application, whereby the requested digital twin is
displayed on a virtual reality headset. In some embodiments, the
client application 15570 is an augmented reality application,
whereby the requested digital twin is depicted in an AR-enabled
device. In these embodiments, the requested digital twin may be
filtered such that visual elements and/or text are overlaid on the
display of the AR-enabled device.
[0771] It is noted that while a graph database is discussed, the
digital twin system 15500 may employ other suitable data structures
to store information relating to a set of digital twins. In these
embodiments, the data structures, and any related storage system,
may be implemented such that the data structures provide for some
degree of feedback loops and/or recursion when representing
iteration of flows.
[0772] FIG. 131 illustrates an example of a digital twin I/O system
15504 that interfaces with the environment 15520, the digital twin
system 15500, and/or components thereof to provide bi-directional
transfer of data between coupled components according to some
embodiments of the present disclosure.
[0773] In embodiments, the transferred data includes signals (e.g.,
request signals, command signals, response signals, etc.) between
connected components, which may include software components,
hardware components, physical devices, virtualized devices,
simulated devices, combinations thereof, and the like. The signals
may define material properties (e.g., physical quantities of
temperature, pressure, humidity, density, viscosity, etc.),
measured values (e.g., contemporaneous or stored values acquired by
the device or system), device properties (e.g., device ID or
properties of the device's design specifications, materials,
measurement capabilities, dimensions, absolute position, relative
position, combinations thereof, and the like), set points (e.g.,
targets for material properties, device properties, system
properties, combinations thereof, and the like), and/or critical
points (e.g., threshold values such as minimum or maximum values
for material properties, device properties, system properties,
etc.). The signals may be received from systems or devices that
acquire (e.g., directly measure or generate) or otherwise obtain
(e.g., receive, calculate, look-up, filter, etc.) the data, and may
be communicated to or from the digital twin I/O system 15504 at
predetermined times or in response to a request (e.g., polling)
from the digital twin I/O system 15504. The communications may
occur through direct or indirect connections (e.g., via
intermediate modules within a circuit and/or intermediate devices
between the connected components). The values may correspond to
real-world elements 131302r (e.g., an input or output for a
tangible vibration sensor) or virtual elements 131302v (e.g., an
input or output for a digital twin 131302d and/or a simulated
element 131302s that provide vibration data).
[0774] In embodiments, the real-world elements 131302r may be
elements within the industrial environment 15520. The real-world
elements 131302r may include, for example, non-networked objects
15522, the devices 15524 (smart or non-smart), sensors 15526, and
humans 15528. The real-world elements 131302r may be process or
non-process equipment within the industrial environments 15520. For
example, process equipment may include motors, pumps, mills, fans,
painters, welders, smelters, etc., and non-process equipment may
include personal protective equipment, safety equipment, emergency
stations or devices (e.g., safety showers, eyewash stations, fire
extinguishers, sprinkler systems, etc.), warehouse features (e.g.,
walls, floor layout, etc.), obstacles (e.g., persons or other items
within the environment 15520, etc.), etc.
[0775] In embodiments, the virtual elements 131302v may be digital
representations of or that correspond to contemporaneously existing
real-world elements 131302r. Additionally or alternatively, the
virtual elements 131302v may be digital representations of or that
correspond to real-world elements 131302r that may be available for
later addition and implementation into the environment 15520. The
virtual elements may include, for example, simulated elements
131302s and/or digital twins 131302d. In embodiments, the simulated
elements 131302s may be digital representations of real-world
elements 131302s that are not present within the industrial
environment 15520. The simulated elements 131302s may mimic desired
physical properties which may be later integrated within the
environment 15520 as real-world elements 131302r (e.g., a "black
box" that mimics the dimensions of a real-world elements 131302r).
The simulated elements 131302s may include digital twins of
existing objects (e.g., a single simulated element 131302s may
include one or more digital twins 131302d for existing sensors).
Information related to the simulated elements 131302s may be
obtained, for example, by evaluating behavior of corresponding
real-world elements 131302r using mathematical models or
algorithms, from libraries that define information and behavior of
the simulated elements 131302s (e.g., physics libraries, chemistry
libraries, or the like).
[0776] In embodiments, the digital twin 131302d may be a digital
representation of one or more real-world elements 131302r. The
digital twins 131302d are configured to mimic, copy, and/or model
behaviors and responses of the real-world elements 131302r in
response to inputs, outputs, and/or conditions of the surrounding
or ambient environment. Data related to physical properties and
responses of the real-world elements 131302r may be obtained, for
example, via user input, sensor input, and/or physical modeling
(e.g., thermodynamic models, electrodynamic models, mechanodynamic
models, etc.). Information for the digital twin 131302d may
correspond to and be obtained from the one or more real-world
elements 131302r corresponding to the digital twin 131302d. For
example, in some embodiments, the digital twin 131302d may
correspond to one real-world element 131302r that is a fixed
digital vibration sensor 15536 on a machine component, and
vibration data for the digital twin 131302d may be obtained by
polling or fetching vibration data measured by the fixed digital
vibration sensor on the machine component. In a further example,
the digital twin 131302d may correspond to a plurality of
real-world elements 131302r such that each of the elements can be a
fixed digital vibration sensor on a machine component, and
vibration data for the digital twin 131302d may be obtained by
polling or fetching vibration data measured by each of the fixed
digital vibration sensors on the plurality of real-world elements
131302r. Additionally or alternatively, vibration data of a first
digital twin 131302d may be obtained by fetching vibration data of
a second digital twin 157302d that is embedded within the first
digital twin 157302d, and vibration data for the first digital twin
157302d may include or be derived from vibration data for the
second digital twin 157302d. For example, the first digital twin
may be a digital twin 157302d of an environment 15520
(alternatively referred to as an "environmental digital twin") and
the second digital twin 157302d may be a digital twin 157302d
corresponding to a vibration sensor disposed within the environment
15520 such that the vibration data for the first digital twin
157302d is obtained from or calculated based on data including the
vibration data for the second digital twin 157302d.
[0777] In embodiments, the digital twin system 15500 monitors
properties of the real-world elements 157302r using the sensors
15526 within a respective environment 15520 that is or may be
represented by a digital twin 157302d and/or outputs of models for
one or more simulated elements 157302s. In embodiments, the digital
twin system 15500 may minimize network congestion while maintaining
effective monitoring of processes by extending polling intervals
and/or minimizing data transfer for sensors corresponding that
correspond to affected real-world elements 157302r and performing
simulations (e.g., via the digital-twin simulation system 15506)
during the extended interval using data that was obtained from
other sources (e.g., sensors that are physically proximate to or
have an effect on the affected real-world elements 157302r).
Additionally or alternatively, error checking may be performed by
comparing the collected sensor data with data obtained from the
digital-twin simulation system 15506. For example, consistent
deviations or fluctuations between sensor data obtained from the
real-world element 157302r and the simulated element 157302s may
indicate malfunction of the respective sensor or another fault
condition.
[0778] In embodiments, the digital twin system 15500 may optimize
features of the environment through use of one or more simulated
elements 157302s. For example, the digital twin system 15500 may
evaluate effects of the simulated elements 157302s within a digital
twin of an environment to quickly and efficiently determine costs
and/or benefits flowing from inclusion, exclusion, or substitution
of real-world elements 157302r within the environment 15520. The
costs and benefits may include, for example, increased machinery
costs (e.g., capital investment and maintenance), increased
efficiency (e.g., process optimization to reduce waste or increase
throughput), decreased or altered footprint within the environment
15520, extension or optimization of useful lifespans, minimization
of component faults, minimization of component downtime, etc.
[0779] In embodiments, the digital twin I/O system 15504 may
include one or more software modules that are executed by one or
more controllers of one or more devices (e.g., server devices, user
devices, and/or distributed devices) to affect the described
functions. The digital twin I/O system 15504 may include, for
example, an input module 157304, an output module 157306, and an
adapter module 157308.
[0780] In embodiments, the input module 157304 may obtain or import
data from data sources in communication with the digital twin I/O
system 15504, such as the sensor system 15530 and the digital twin
simulation system 15506. The data may be immediately used by or
stored within the digital twin system 15500. The imported data may
be ingested from data streams, data batches, in response to a
triggering event, combinations thereof, and the like. The input
module 157304 may receive data in a format that is suitable to
transfer, read, and/or write information within the digital twin
system 15500.
[0781] In embodiments, the output module 157306 may output or
export data to other system components (e.g., the digital twin
datastore 15516, the digital twin simulation system 15506, the
cognitive intelligence system 15510, etc.), devices 15524, and/or
the client application 15570. The data may be output in data
streams, data batches, in response to a triggering event (e.g., a
request), combinations thereof, and the like. The output module
157306 may output data in a format that is suitable to be used or
stored by the target element (e.g., one protocol for output to the
client application and another protocol for the digital twin
datastore 15516).
[0782] In embodiments, the adapter module 157308 may process and/or
convert data between the input module 157304 and the output module
157306. In embodiments, the adapter module 157308 may convert
and/or route data automatically (e.g., based on data type) or in
response to a received request (e.g., in response to information
within the data).
[0783] In embodiments, the digital twin system 15500 may represent
a set of industrial workpiece elements in a digital twin, and the
digital twin simulation system 15506 simulates a set of physical
interactions of a worker with the workpiece elements.
[0784] In embodiments, the digital twin simulation system 15506 may
determine process outcomes for the simulated physical interactions
accounting for simulated human factors. For example, variations in
workpiece throughput may be modeled by the digital twin system
15500 including, for example, worker response times to events,
worker fatigue, discontinuity within worker actions (e.g., natural
variations in human-movement speed, differing positioning times,
etc.), effects of discontinuities on downstream processes, and the
like. In embodiments, individualized worker interactions may be
modeled using historical data that is collected, acquired, and/or
stored by the digital twin system 15500. The simulation may begin
based on estimated amounts (e.g., worker age, industry averages,
workplace expectations, etc.). The simulation may also
individualize data for each worker (e.g., comparing estimated
amounts to collected worker-specific outcomes).
[0785] In embodiments, information relating to workers (e.g.,
fatigue rates, efficiency rates, and the like) may be determined by
analyzing performance of specific workers over time and modeling
said performance.
[0786] In embodiments, the digital twin system 15500 includes a
plurality of proximity sensors within the sensor system 15530. The
proximity sensors are or may be configured to detect elements of
the environment 15520 that are within a predetermined area. For
example, proximity sensors may include electromagnetic sensors,
light sensors, and/or acoustic sensors.
[0787] The electromagnetic sensors are or may be configured to
sense objects or interactions via one or more electromagnetic
fields (e.g., emitted electromagnetic radiation or received
electromagnetic radiation). In embodiments, the electromagnetic
sensors include inductive sensors (e.g., radio-frequency
identification sensors), capacitive sensors (e.g., contact and
contactless capacitive sensors), combinations thereof, and the
like.
[0788] The light sensors are or may be configured to sense objects
or interactions via electromagnetic radiation in, for example, the
far-infrared, near-infrared, optical, and/or ultraviolet spectra.
In embodiments, the light sensors may include image sensors (e.g.,
charge-coupled devices and CMOS active-pixel sensors),
photoelectric sensors (e.g., through-beam sensors, retroreflective
sensors, and diffuse sensors), combinations thereof, and the like.
Further, the light sensors may be implemented as part of a system
or subsystem, such as a light detection and ranging ("LIDAR")
sensor.
[0789] The acoustic sensors are or may be configured to sense
objects or interactions via sound waves that are emitted and/or
received by the acoustic sensors. In embodiments, the acoustic
sensors may include infrasonic, sonic, and/or ultrasonic sensors.
Further, the acoustic sensors may be grouped as part of a system or
subsystem, such as a sound navigation and ranging ("SONAR")
sensor.
[0790] In embodiments, the digital twin system 15500 stores and
collects data from a set of proximity sensors within the
environment 15520 or portions thereof. The collected data may be
stored, for example, in the digital twin datastore 15516 for use by
components the digital twin system 15500 and/or visualization by a
user. Such use and/or visualization may occur contemporaneously
with or after collection of the data (e.g., during later analysis
and/or optimization of processes).
[0791] In embodiments, data collection may occur in response to a
triggering condition. These triggering conditions may include, for
example, expiration of a static or a dynamic predetermined
interval, obtaining a value short of or in excess of a static or
dynamic value, receiving an automatically generated request or
instruction from the digital twin system 15500 or components
thereof, interaction of an element with the respective sensor or
sensors (e.g., in response to a worker or machine breaking a beam
or coming within a predetermined distance from the proximity
sensor), interaction of a user with a digital twin (e.g., selection
of an environmental digital twin, a sensor array digital twin, or a
sensor digital twin), combinations thereof, and the like.
[0792] In some embodiments, the digital twin system 15500 collects
and/or stores RFID data in response to interaction of a worker with
a real-world element 157302r. For example, in response to a worker
interaction with a real-world environment, the digital twin will
collect and/or store RFID data from RFID sensors within or
associated with the corresponding environment 15520. Additionally
or alternatively, worker interaction with a sensor-array digital
twin will collect and/or store RFID data from RFID sensors within
or associated with the corresponding sensor array. Similarly,
worker interaction with a sensor digital twin will collect and/or
store RFID data from the corresponding sensor. The RFID data may
include suitable data attainable by RFID sensors such as proximate
RFID tags, RFID tag position, authorized RFID tags, unauthorized
RFID tags, unrecognized RFID tags, RFID type (e.g., active or
passive), error codes, combinations thereof, and the like.
[0793] In embodiments, the digital twin system 15500 may further
embed outputs from one or more devices within a corresponding
digital twin. In embodiments, the digital twin system 15500 embeds
output from a set of individual-associated devices into an
industrial digital twin. For example, the digital twin I/O system
15504 may receive information output from one or more wearable
devices 15554 or mobile devices (not shown) associated with an
individual within an industrial environment. The wearable devices
may include image capture devices (e.g., body cameras or
augmented-reality headwear), navigation devices (e.g., GPS devices,
inertial guidance systems), motion trackers, acoustic capture
devices (e.g., microphones), radiation detectors, combinations
thereof, and the like.
[0794] In embodiments, upon receiving the output information, the
digital twin I/O system 15504 routes the information to the digital
twin creation module 15564 to check and/or update the environment
digital twin and/or associated digital twins within the environment
(e.g., a digital twin of a worker, machine, or robot position at a
given time). Further, the digital twin system 15500 may use the
embedded output to determine characteristics of the environment
15520.
[0795] In embodiments, the digital twin system 15500 embeds output
from a LIDAR point cloud system into an industrial digital twin.
For example, the digital twin I/O system 15504 may receive
information output from one or more Lidar devices 15538 within an
industrial environment. The Lidar devices 15538 are configured to
provide a plurality of points having associated position data
(e.g., coordinates in absolute or relative x, y, and z values).
Each of the plurality of points may include further LIDAR
attributes, such as intensity, return number, total returns, laser
color data, return color data, scan angle, scan direction, etc. The
Lidar devices 15538 may provide a point cloud that includes the
plurality of points to the digital twin system 15500 via, for
example, the digital twin I/O system 15504. Additionally or
alternatively, the digital twin system 15500 may receive a stream
of points and assemble the stream into a point cloud, or may
receive a point cloud and assemble the received point cloud with
existing point cloud data, map data, or three dimensional
(3D)-model data.
[0796] In embodiments, upon receiving the output information, the
digital twin I/O system 15504 routes the point cloud information to
the digital twin creation module 15564 to check and/or update the
environment digital twin and/or associated digital twins within the
environment (e.g., a digital twin of a worker, machine, or robot
position at a given time). In some embodiments, the digital twin
system 15500 is further configured to determine closed-shape
objects within the received LIDAR data. For example, the digital
twin system 15500 may group a plurality of points within the point
cloud as an object and, if necessary, estimate obstructed faces of
objects (e.g., a face of the object contacting or adjacent a floor
or a face of the object contacting or adjacent another object such
as another piece of equipment). The system may use such
closed-shape objects to narrow search space for digital twins and
thereby increase efficiency of matching algorithms (e.g., a
shape-matching algorithm).
[0797] In embodiments, the digital twin system 15500 embeds output
from a simultaneous location and mapping ("SLAM") system in an
environmental digital twin. For example, the digital twin I/O
system 15504 may receive information output from the SLAM system,
such as Slam sensor 15562, and embed the received information
within an environment digital twin corresponding to the location
determined by the SLAM system. In embodiments, upon receiving the
output information from the SLAM system, the digital twin I/O
system 15504 routes the information to the digital twin creation
module 15564 to check and/or update the environment digital twin
and/or associated digital twins within the environment (e.g., a
digital twin of a workpiece, furniture, movable object, or
autonomous object). Such updating provides digital twins of
non-connected elements (e.g., furnishings or persons) automatically
and without need of user interaction with the digital twin system
15500.
[0798] In embodiments, the digital twin system 15500 can leverage
known digital twins to reduce computational requirements for the
Slam sensor 15562 by using suboptimal map-building algorithms. For
example, the suboptimal map-building algorithms may allow for a
higher uncertainty tolerance using simple bounded-region
representations and identifying possible digital twins.
Additionally or alternatively, the digital twin system 15500 may
use a bounded-region representation to limit the number of digital
twins, analyze the group of potential twins for distinguishing
features, then perform higher precision analysis for the
distinguishing features to identify and/or eliminate categories of,
groups of, or individual digital twins and, in the event that no
matching digital twin is found, perform a precision scan of only
the remaining areas to be scanned.
[0799] In embodiments, the digital twin system 15500 may further
reduce compute required to build a location map by leveraging data
captured from other sensors within the environment (e.g., captured
images or video, radio images, etc.) to perform an initial
map-building process (e.g., a simple bounded-region map or other
suitable photogrammetry methods), associate digital twins of known
environmental objects with features of the simple bounded-region
map to refine the simple bounded-region map, and perform more
precise scans of the remaining simple bounded regions to further
refine the map. In some embodiments, the digital twin system 15500
may detect objects within received mapping information and, for
each detected object, determine whether the detected object
corresponds to an existing digital twin of a real-world-element. In
response to determining that the detected object does not
correspond to an existing real-world-element digital twin, the
digital twin system 15500 may use, for example, the digital twin
creation module 15564 to generate a new digital twin corresponding
to the detected object (e.g., a detected-object digital twin) and
add the detected-object digital twin to the real-world-element
digital twins within the digital twin datastore. Additionally or
alternatively, in response to determining that the detected object
corresponds to an existing real-world-element digital twin, the
digital twin system 15500 may update the real-world-element digital
twin to include new information detected by the simultaneous
location and mapping sensor, if any.
[0800] In embodiments, the digital twin system 15500 represents
locations of autonomously or remotely moveable elements and
attributes thereof within an industrial digital twin. Such movable
elements may include, for example, workers, persons, vehicles,
autonomous vehicles, robots, etc. The locations of the moveable
elements may be updated in response to a triggering condition. Such
triggering conditions may include, for example, expiration of a
static or a dynamic predetermined interval, receiving an
automatically generated request or instruction from the digital
twin system 15500 or components thereof, interaction of an element
with a respective sensor or sensors (e.g., in response to a worker
or machine breaking a beam or coming within a predetermined
distance from a proximity sensor), interaction of a user with a
digital twin (e.g., selection of an environmental digital twin, a
sensor array digital twin, or a sensor digital twin), combinations
thereof, and the like.
[0801] In embodiments, the time intervals may be based on
probability of the respective movable element having moved within a
time period. For example, the time interval for updating a worker
location may be relatively shorter for workers expected to move
frequently (e.g., a worker tasked with lifting and carrying objects
within and through the environment 15520) and relatively longer for
workers expected to move infrequently (e.g., a worker tasked with
monitoring a process stream). Additionally or alternatively, the
time interval may be dynamically adjusted based on applicable
conditions, such as increasing the time interval when no movable
elements are detected, decreasing the time interval as or when the
number of moveable elements within an environment increases (e.g.,
increasing number of workers and worker interactions), increasing
the time interval during periods of reduced environmental activity
(e.g., breaks such as lunch), decreasing the time interval during
periods of abnormal environmental activity (e.g., tours,
inspections, or maintenance), decreasing the time interval when
unexpected or uncharacteristic movement is detected (e.g., frequent
movement by a typically sedentary element or coordinated movement,
for example, of workers approaching an exit or moving cooperatively
to carry a large object), combinations thereof, and the like.
Further, the time interval may also include additional, semi-random
acquisitions. For example, occasional mid-interval locations may be
acquired by the digital twin system 15500 to reinforce or evaluate
the efficacy of the particular time interval.
[0802] In embodiments, the digital twin system 15500 may analyze
data received from the digital twin I/O system 15504 to refine,
remove, or add conditions. For example, the digital twin system
15500 may optimize data collection times for movable elements that
are updated more frequently than needed (e.g., multiple consecutive
received positions being identical or within a predetermined margin
of error).
[0803] In embodiments, the digital twin system 15500 may receive,
identify, and/or store a set of states 15540a-n related to the
environment 15520. The states 15540a-n may be, for example, data
structures that include a plurality of attributes 158404a-n and a
set of identifying criteria 158406a-n to uniquely identify each
respective state 15540a-n. In embodiments, the states 15540a-n may
correspond to states where it is desirable for the digital twin
system 15500 to set or alter conditions of real-world elements
157302r and/or the environment 15520 (e.g., increase/decrease
monitoring intervals, alter operating conditions, etc.).
[0804] In embodiments, the set of states 15540a-n may further
include, for example, minimum monitored attributes for each state
15540a-n, the set of identifying criteria 158406a-n for each state
15540a-n, and/or actions available to be taken or recommended to be
taken in response to each state 15540a-n. Such information may be
stored by, for example, the digital twin datastore 15516 or another
datastore. The states 15540a-n or portions thereof may be provided
to, determined by, or altered by the digital twin system 15500.
Further, the set of states 15540a-n may include data from disparate
sources. For example, details to identify and/or respond to
occurrence of a first state may be provided to the digital twin
system 15500 via user input, details to identify and/or respond to
occurrence of a second state may be provided to the digital twin
system 15500 via an external system, details to identify and/or
respond to occurrence of a third state may be determined by the
digital twin system 15500 (e.g., via simulations or analysis of
process data), and details to identify and/or respond to occurrence
of a fourth state may be stored by the digital twin system 15500
and altered as desired (e.g., in response to simulated occurrence
of the state or analysis of data collected during an occurrence of
and response to the state).
[0805] In embodiments, the plurality of attributes 158404a-n
includes at least the attributes 158404a-n needed to identify the
respective state 15540a-n. The plurality of attributes 158404a-n
may further include additional attributes that are or may be
monitored in determining the respective state 15540a-n, but are not
needed to identify the respective state 15540a-n. For example, the
plurality of attributes 158404a-n for a first state may include
relevant information such as rotational speed, fuel level, energy
input, linear speed, acceleration, temperature, strain, torque,
volume, weight, etc.
[0806] The set of identifying criteria 158406a-n may include
information for each of the set of attributes 158404a-n to uniquely
identify the respective state. The identifying criteria 158406a-n
may include, for example, rules, thresholds, limits, ranges,
logical values, conditions, comparisons, combinations thereof, and
the like.
[0807] The change in operating conditions or monitoring may be any
suitable change. For example, after identifying occurrence of a
respective state 158406a-n, the digital twin system 15500 may
increase or decrease monitoring intervals for a device (e.g.,
decreasing monitoring intervals in response to a measured parameter
differing from nominal operation) without altering operation of the
device. Additionally or alternatively, the digital twin system
15500 may alter operation of the device (e.g., reduce speed or
power input) without altering monitoring of the device. In further
embodiments, the digital twin system 15500 may alter operation of
the device (e.g., reduce speed or power input) and alter monitoring
intervals for the device (e.g., decreasing monitoring
intervals).
[0808] FIG. 151 illustrates an example set of identified states
15540a-n related to industrial environments that the digital twin
system 15500 may identify and/or store for access by intelligent
systems (e.g., the cognitive intelligence system 15510) or users of
the digital twin system 15500, according to some embodiments of the
present disclosure. The states 15540a-n may include operational
states (e.g., suboptimal, normal, optimal, critical, or alarm
operation of one or more components), excess or shortage states
(e.g., supply-side or output-side quantities), combinations
thereof, and the like.
[0809] In embodiments, the digital twin system 15500 may monitor
attributes 151404a-n of real-world elements 157302r and/or digital
twins 157302d to determine the respective state 15540a-n. The
attributes 151404a-n may be, for example, operating conditions, set
points, critical points, status indicators, other sensed
information, combinations thereof, and the like. For example, the
attributes 151404a-n may include power input 151404a, operational
speed 151404b, critical speed 151404c, and operational temperature
151404d of the monitored elements. While the illustrated example
illustrates uniform monitored attributes, the monitored attributes
may differ by target device (e.g., the digital twin system 15500
would not monitor rotational speed for an object with no rotatable
components).
[0810] Each of the states 15540a-n includes a set of identifying
criteria 151406a-n meeting particular criteria that are unique
among the group of monitored states 15540a-n. The digital twin
system 15500 may identify the overspeed state 15540a, for example,
in response to the monitored attributes 151404a-n meeting a first
set of identifying criteria 151406a (e.g., operational speed
151404b being higher than the critical speed 151404c, while the
operational temperature 151404d is nominal).
[0811] In response to determining that one or more states 15540a-n
exists or has occurred, the digital twin system 15500 may update
triggering conditions for one or more monitoring protocols, issue
an alert or notification, or trigger actions of subcomponents of
the digital twin system 15500. For example, subcomponents of the
digital twin system 15500 may take actions to mitigate and/or
evaluate impacts of the detected states 15540a-n. When attempting
to take actions to mitigate impacts of the detected states 15540a-n
on real-world elements 157302r, the digital twin system 15500 may
determine whether instructions exist (e.g., are stored in the
digital twin datastore 15516) or should be developed (e.g.,
developed via simulation and cognitive intelligence or via user or
worker input). Further, the digital twin system 15500 may evaluate
impacts of the detected states 15540a-n, for example, concurrently
with the mitigation actions or in response to determining that the
digital twin system 15500 has no stored mitigation instructions for
the detected states 15540a-n.
[0812] In embodiments, the digital twin system 15500 employs the
digital twin simulation system 15506 to simulate one or more
impacts, such as immediate, upstream, downstream, and/or continuing
effects, of recognized states. The digital twin simulation system
15506 may collect and/or be provided with values relevant to the
evaluated states 15540a-n. In simulating the impact of the one or
more states 15540a-n, the digital twin simulation system 15506 may
recursively evaluate performance characteristics of affected
digital twins 157302d until convergence is achieved. The digital
twin simulation system 15506 may work, for example, in tandem with
the cognitive intelligence system 15510 to determine response
actions to alleviate, mitigate, inhibit, and/or prevent occurrence
of the one or more states 15540a-n. For example, the digital twin
simulation system 15506 may recursively simulate impacts of the one
or more states 15540a-n until achieving a desired fit (e.g.,
convergence is achieved), provide the simulated values to the
cognitive intelligence system 15510 for evaluation and
determination of potential actions, receive the potential actions,
evaluate impacts of each of the potential actions for a respective
desired fit (e.g., cost functions for minimizing production
disturbance, preserving critical components, minimizing maintenance
and/or downtime, optimizing system, worker, user, or personal
safety, etc.).
[0813] In embodiments, the digital twin simulation system 15506 and
the cognitive intelligence system 15510 may repeatedly share and
update the simulated values and response actions for each desired
outcome until desired conditions are met (e.g., convergence for
each evaluated cost function for each evaluated action). The
digital twin system 15500 may store the results in the digital twin
datastore 15516 for use in response to determining that one or more
states 15540a-n has occurred. Additionally, simulations and
evaluations by the digital twin simulation system 15506 and/or the
cognitive intelligence system 15510 may occur in response to
occurrence or detection of the event.
[0814] In embodiments, simulations and evaluations are triggered
only when associated actions are not present within the digital
twin system 15500. In further embodiments, simulations and
evaluations are performed concurrently with use of stored actions
to evaluate the efficacy or effectiveness of the actions in real
time and/or evaluate whether further actions should be employed or
whether unrecognized states may have occurred. In embodiments, the
cognitive intelligence system 15510 may also be provided with
notifications of instances of undesired actions with or without
data on the undesired aspects or results of such actions to
optimize later evaluations.
[0815] In embodiments, the digital twin system 15500 evaluates
and/or represents the impact of machine downtime within a digital
twin of a manufacturing facility. For example, the digital twin
system 15500 may employ the digital twin simulation system 15506 to
simulate the immediate, upstream, downstream, and/or continuing
effects of a machine downtime state 15540b. The digital twin
simulation system 15506 may collect or be provided with
performance-related values such as optimal, suboptimal, and minimum
performance requirements for elements (e.g., real-world elements
157302r and/or nested digital twins 157302d) within the affected
digital twins 157302d, and/or characteristics thereof that are
available to the affected digital twins 157302d, nested digital
twins 157302d, redundant systems within the affected digital twins
157302d, combinations thereof, and the like.
[0816] In embodiments, the digital twin system 15500 is configured
to: simulate one or more operating parameters for the real-world
elements in response to the industrial environment being supplied
with given characteristics using the real-world-element digital
twins; calculate a mitigating action to be taken by one or more of
the real-world elements in response to being supplied with the
contemporaneous characteristics; and actuate, in response to
detecting the contemporaneous characteristics, the mitigating
action. The calculation may be performed in response to detecting
contemporaneous characteristics or operating parameters falling
outside of respective design parameters or may be determined via a
simulation prior to detection of such characteristics.
[0817] Additionally or alternatively, the digital twin system 15500
may provide alerts to one or more users or system elements in
response to detecting states.
[0818] In embodiments, the digital twin I/O system 15504 includes a
pathing module 157310. The pathing module 157310 may ingest
navigational data from the elements 157302, provide and/or request
navigational data to components of the digital twin system 15500
(e.g., the digital twin simulation system 15506, the digital twin
behavior system, and/or the cognitive intelligence system 15510),
and/or output navigational data to elements 157302 (e.g., to the
wearable devices 15554). The navigational data may be collected or
estimated using, for example, historical data, guidance data
provided to the elements 157302, combinations thereof, and the
like.
[0819] For example, the navigational data may be collected or
estimated using historical data stored by the digital twin system
15500. The historical data may include or be processed to provide
information such as acquisition time, associated elements 157302,
polling intervals, task performed, laden or unladen conditions,
whether prior guidance data was provided and/or followed,
conditions of the environment 15520, other elements 157302 within
the environment 15520, combinations thereof, and the like. The
estimated data may be determined using one or more suitable pathing
algorithms. For example, the estimated data may be calculated using
suitable order-picking algorithms, suitable path-search algorithms,
combinations thereof, and the like. The order-picking algorithm may
be, for example, a largest gap algorithm, an s-shape algorithm, an
aisle-by-aisle algorithm, a combined algorithm, combinations
thereof, and the like. The path-search algorithms may be, for
example, Dijkstra's algorithm, the A* algorithm, hierarchical
path-finding algorithms, incremental path-finding algorithms, any
angle path-finding algorithms, flow field algorithms, combinations
thereof, and the like.
[0820] Additionally or alternatively, the navigational data may be
collected or estimated using guidance data of the worker. The
guidance data may include, for example, a calculated route provided
to a device of the worker (e.g., a mobile device or the wearable
device 15554). In another example, the guidance data may include a
calculated route provided to a device of the worker that instructs
the worker to collect vibration measurements from one or more
locations on one or more machines along the route. The collected
and/or estimated navigational data may be provided to a user of the
digital twin system 15500 for visualization, used by other
components of the digital twin system 15500 for analysis,
optimization, and/or alteration, provided to one or more elements
157302, combinations thereof, and the like.
[0821] In embodiments, the digital twin system 15500 ingests
navigational data for a set of workers for representation in a
digital twin. Additionally or alternatively, the digital twin
system 15500 ingests navigational data for a set of mobile
equipment assets of an industrial environment into a digital
twin.
[0822] In embodiments, the digital twin system 15500 ingests a
system for modeling traffic of mobile elements in an industrial
digital twin. For example, the digital twin system 15500 may model
traffic patterns for workers or persons within the environment
15520, mobile equipment assets, combinations thereof, and the like.
The traffic patterns may be estimated based on modeling traffic
patterns from and historical data and contemporaneous ingested
data. Further, the traffic patterns may be continuously or
intermittently updated depending on conditions within the
environment 15520 (e.g., a plurality of autonomous mobile equipment
assets may provide information to the digital twin system 15500 at
a slower update interval than the environment 15520 including both
workers and mobile equipment assets).
[0823] The digital twin system 15500 may alter traffic patterns
(e.g., by providing updated navigational data to one or more of the
mobile elements) to achieve one or more predetermined criteria. The
predetermined criteria may include, for example, increasing process
efficiency, decreasing interactions between laden workers and
mobile equipment assets, minimizing worker path length, routing
mobile equipment around paths or potential paths of persons,
combinations thereof, and the like.
[0824] In embodiments, the digital twin system 15500 may provide
traffic data and/or navigational information to mobile elements in
an industrial digital twin. The navigational information may be
provided as instructions or rule sets, displayed path data, or
selective actuation of devices. For example, the digital twin
system 15500 may provide a set of instructions to a robot to direct
the robot to and/or along a desired route for collecting vibration
data from one or more specified locations on one or more specified
machines along the route using a vibration sensor. The robot may
communicate updates to the system including obstructions, reroutes,
unexpected interactions with other assets within the environment
15520, etc.
[0825] In some embodiments, an ant-based system 15574 enables
industrial entities, including robots, to lay a trail with one or
more messages for other industrial entities, including themselves,
to follow in later journeys. In embodiments, the messages include
information related to vibration measurement collection. In
embodiments, the messages include information related to vibration
sensor measurement locations. In some embodiments, the trails may
be configured to fade over time. In some embodiments, the ant-based
trails may be experienced via an augmented reality system. In some
embodiments, the ant-based trails may be experienced via a virtual
reality system. In some embodiments, the ant-based trails may be
experienced via a mixed reality system. In some embodiments,
ant-based tagging of areas can trigger a pain-response and/or
accumulate into a warning signal. In embodiments, the ant-based
trails may be configured to generate an information filtering
response. In some embodiments, the ant-based trails may be
configured to generate an information filtering response wherein
the information filtering response is a heightened sense of visual
awareness. In some embodiments, the ant-based trails may be
configured to generate an information filtering response wherein
the information filtering response is a heightened sense of
acoustic awareness. In some embodiments, the messages include
vectorized data.
[0826] In embodiments, the digital twin system 15500 includes
design specification information for representing a real-world
element 157302r using a digital twin 157302d. The digital may
correspond to an existing real-world element 157302r or a potential
real-world element 157302r. The design specification information
may be received from one or more sources. For example, the design
specification information may include design parameters set by user
input, determined by the digital twin system 15500 (e.g., the via
digital twin simulation system 15506), optimized by users or the
digital twin simulation system 15506, combinations thereof, and the
like. The digital twin simulation system 15506 may represent the
design specification information for the component to users, for
example, via a display device or a wearable device. The design
specification information may be displayed schematically (e.g., as
part of a process diagram or table of information) or as part of an
augmented reality or virtual reality display. The design
specification information may be displayed, for example, in
response to a user interaction with the digital twin system 15500
(e.g., via user selection of the element or user selection to
generally include design specification information within
displays). Additionally or alternatively, the design specification
information may be displayed automatically, for example, upon the
element coming within view of an augmented reality or virtual
reality device. In embodiments, the displayed design specification
information may further include indicia of information source
(e.g., different displayed colors indicate user input versus
digital twin system 15500 determination), indicia of mismatches
(e.g., between design specification information and operational
information), combinations thereof, and the like.
[0827] In embodiments, the digital twin system 15500 embeds a set
of control instructions for a wearable device within an industrial
digital twin, such that the control instructions are provided to
the wearable device to induce an experience for a wearer of the
wearable device upon interaction with an element of the industrial
digital twin. The induced experience may be, for example, an
augmented reality experience or a virtual reality experience. The
wearable device, such as a headset, may be configured to output
video, audio, and/or haptic feedback to the wearer to induce the
experience. For example, the wearable device may include a display
device and the experience may include display of information
related to the respective digital twin. The information displayed
may include maintenance data associated with the digital twin,
vibration data associated with the digital twin, vibration
measurement location data associated with the digital twin,
financial data associated with the digital twin, such as a profit
or loss associated with operation of the digital twin,
manufacturing KPIs associated with the digital twin, information
related to an occluded element (e.g., a sub-assembly) that is at
least partially occluded by a foreground element (e.g., a housing),
a virtual model of the occluded element overlaid on the occluded
element and visible with the foreground element, operating
parameters for the occluded element, a comparison to a design
parameter corresponding to the operating parameter displayed,
combinations thereof, and the like. Comparisons may include, for
example, altering display of the operating parameter to change a
color, size, and/or display period for the operating parameter.
[0828] In some embodiments, the displayed information may include
indicia for removable elements that are or may be configured to
provide access to the occluded element with each indicium being
displayed proximate to or overlying the respective removable
element. Further, the indicia may be sequentially displayed such
that a first indicium corresponding to a first removable element
(e.g., a housing) is displayed, and a second indicium corresponding
to a second removable element (e.g., an access panel within the
housing) is displayed in response to the worker removing the first
removable element. In some embodiments, the induced experience
allows the wearer to see one or more locations on a machine for
optimal vibration measurement collection. In an example, the
digital twin system 15500 may provide an augmented reality view
that includes highlighted vibration measurement collection
locations on a machine and/or instructions related to collecting
vibration measurements. Furthering the example, the digital twin
system 15500 may provide an augmented reality view that includes
instructions related to timing of vibration measurement collection.
Information utilized in displaying the highlighted placement
locations may be obtained using information stored by the digital
twin system 15500. In some embodiments, mobile elements may be
tracked by the digital twin system 15500 (e.g., via observational
elements within the environment 15520 and/or via pathing
information communicated to the digital twin system 15500) and
continually displayed by the wearable device within the occluded
view of the worker. This optimizes movement of elements within the
environment 15520, increases worker safety, and minimizes down time
of elements resulting from damage.
[0829] In some embodiments, the digital twin system 15500 may
provide an augmented reality view that displays mismatches between
design parameters or expected parameters of real-world elements
157302r to the wearer. The displayed information may correspond to
real-world elements 157302r that are not within the view of the
wearer (e.g., elements within another room or obscured by
machinery). This allows the worker to quickly and accurately
troubleshoot mismatches to determine one or more sources for the
mismatch. The cause of the mismatch may then be determined, for
example, by the digital twin system 15500 and corrective actions
ordered. In example embodiments, a wearer may be able to view
malfunctioning subcomponents of machines without removing occluding
elements (e.g., housings or shields). Additionally or
alternatively, the wearer may be provided with instructions to
repair the device, for example, including display of the removal
process (e.g., location of fasteners to be removed), assemblies or
subassemblies that should be transported to other areas for repair
(e.g., dust-sensitive components), assemblies or subassemblies that
need lubrication, and locations of objects for reassembly (e.g.,
storing location that the wearer has placed removed objects and
directing the wearer or another wearer to the stored locations to
expedite reassembly and minimize further disassembly or missing
parts in the reassembled element). This can expedite repair work,
minimize process impact, allow workers to disassemble and
reassemble equipment (e.g., by coordinating disassembly without
direct communication between the workers), increase equipment
longevity and reliability (e.g., by assuring that all components
are properly replaced prior to placing back in service),
combinations thereof, and the like.
[0830] In some embodiments, the induced experience includes a
virtual reality view or an augmented reality view that allows the
wearer to see information related to existing or planned elements.
The information may be unrelated to physical performance of the
element (e.g., financial performance such as asset value, energy
cost, input material cost, output material value, legal compliance,
and corporate operations). One or more wearers may perform a
virtual walkthrough or an augmented walkthrough of the industrial
environment 15520.
[0831] In examples, the wearable device displays compliance
information that expedites inspections or performance of work.
[0832] In further examples, the wearable device displays financial
information that is used to identify targets for alteration or
optimization. For example, a manager or officer may inspect the
environment 15520 for compliance with updated regulations,
including information regarding compliance with former regulations,
"grandfathered," and/or excepted elements. This can be used to
reduce unnecessary downtime (e.g., scheduling upgrades for the
least impactful times, such as during planned maintenance cycles),
prevent unnecessary upgrades (e.g., replacing grandfathered or
excepted equipment), and reduce capital investment.
[0833] Referring back to FIG. 155, in embodiments, the digital twin
system 15500 may include, integrate, integrate with, manage,
handle, link to, take input from, provide output to, control,
coordinate with, or otherwise interact with a digital twin dynamic
model system 15508. The digital twin dynamic model system 15508 can
update the properties of a set of digital twins of a set of
industrial entities and/or environments, including properties of
physical industrial assets, workers, processes, manufacturing
facilities, warehouses, and the like (or any of the other types of
entities or environments described in this disclosure or in the
documents incorporated by reference herein) in such a manner that
the digital twins may represent those industrial entities and
environments, and properties or attributes thereof, in real-time or
very near real-time. In some embodiments, the digital twin dynamic
model system 15508 may obtain sensor data received from a sensor
system 15530 and may determine one or more properties of an
industrial environment or an industrial entity within an
environment based on the sensor data and based on one or more
dynamic models.
[0834] In embodiments, the digital twin dynamic model system 15508
may update/assign values of various properties in a digital twin
and/or one or more embedded digital twins, including, but not
limited to, vibration values, vibration fault level states,
probability of failure values, probability of downtime values, cost
of downtime values, probability of shutdown values, financial
values, KPI values, temperature values, humidity values, heat flow
values, fluid flow values, radiation values, substance
concentration values, velocity values, acceleration values,
location values, pressure values, stress values, strain values,
light intensity values, sound level values, volume values, shape
characteristics, material characteristics, and dimensions.
[0835] In embodiments, a digital twin may be comprised of (e.g.,
via reference) of other embedded digital twins. For example, a
digital twin of a manufacturing facility may include an embedded
digital twin of a machine and one or more embedded digital twins of
one or more respective motors enclosed within the machine. A
digital twin may be embedded, for example, in the memory of an
industrial machine that has an onboard IT system (e.g., the memory
of an Onboard Diagnostic System, control system (e.g., SCADA
system) or the like). Other non-limiting examples of where a
digital twin may be embedded include the following: on a wearable
device of a worker; in memory on a local network asset, such as a
switch, router, access point, or the like; in a cloud computing
resource that is provisioned for an environment or entity; and on
an asset tag or other memory structure that is dedicated to an
entity.
[0836] In one example, the digital twin dynamic model system 15508
can update vibration characteristics throughout an industrial
environment digital twin based on captured vibration sensor data
measured at one or more locations in the industrial environment and
one or more dynamic models that model vibration within the
industrial environment digital twin. The industrial digital twin
may, before updating, already contain information about properties
of the industrial entities and/or environment that can be used to
feed a dynamic model, such as information about materials,
shapes/volumes (e.g., of conduits), positions,
connections/interfaces, and the like, such that vibration
characteristics can be represented for the entities and/or
environment in the digital twin. Alternatively, the dynamic models
may be configured using such information.
[0837] In embodiments, the digital twin dynamic model system 15508
can update the properties of a digital twin and/or one or more
embedded digital twins on behalf of a client application 15570. In
embodiments, a client application 15570 may be an application
relating to an industrial component or environment (e.g.,
monitoring an industrial facility or a component within, simulating
an industrial environment, or the like). In embodiments, the client
application 15570 may be used in connection with both fixed and
mobile data collection systems. In embodiments, the client
application 15570 may be used in connection with Industrial
Internet of Things sensor system 15530.
[0838] In embodiments, the digital twin dynamic model system 15508
leverages digital twin dynamic models 155100 to model the behavior
of an industrial entity and/or environment. Dynamic models 155100
may enable digital twins to represent physical reality, including
the interactions of industrial entities, by using a limited number
of measurements to enrich the digital representation of an
industrial entity and/or environment, such as based on scientific
principles. In embodiments, the dynamic models 155100 are formulaic
or mathematical models. In embodiments, the dynamic models 155100
adhere to scientific laws, laws of nature, and formulas (e.g.,
Newton's laws of motion, second law of thermodynamics, Bernoulli's
principle, ideal gas law, Dalton's law of partial pressures,
Hooke's law of elasticity, Fourier's law of heat conduction,
Archimedes' principle of buoyancy, and the like). In embodiments,
the dynamic models are machine-learned models.
[0839] In embodiments, the digital twin system 15500 may have a
digital twin dynamic model datastore 155102 for storing dynamic
models 155100 that may be represented in digital twins. In
embodiments, digital twin dynamic model datastore can be searchable
and/or discoverable. In embodiments, digital twin dynamic model
datastore can contain metadata that allows a user to understand
what characteristics a given dynamic model can handle, what inputs
are required, what outputs are provided, and the like. In some
embodiments, digital twin dynamic model datastore 155102 can be
hierarchical (such as where a model can be deepened or made more
simple based on the extent of available data and/or inputs, the
granularity of the inputs, and/or situational factors (such as
where something becomes of high interest and a higher fidelity
model is accessed for a period of time).
[0840] In embodiments, a digital twin or digital representation of
an industrial entity or facility may include a set of data
structures that collectively define a set of properties of a
represented physical industrial asset, device, worker, process,
facility, and/or environment, and/or possible behaviors thereof. In
embodiments, the digital twin dynamic model system 15508 may
leverage the dynamic models 155100 to inform the set of data
structures that collectively define a digital twin with real-time
data values. The digital twin dynamic models 155100 may receive one
or more sensor measurements, Industrial Internet of Things device
data, and/or other suitable data as inputs and calculate one or
more outputs based on the received data and one or more dynamic
models 155100. The digital twin dynamic model system 15508 then
uses the one or more outputs to update the digital twin data
structures.
[0841] In one example, the set of properties of a digital twin of
an industrial asset that may be updated by the digital twin dynamic
model system 15508 using dynamic models 155100 may include the
vibration characteristics of the asset, temperature(s) of the
asset, the state of the asset (e.g., a solid, liquid, or gas), the
location of the asset, the displacement of the asset, the velocity
of the asset, the acceleration of the asset, probability of
downtime values associated with the asset, cost of downtime values
associated with the asset, probability of shutdown values
associated with the asset, manufacturing KPIs associated with the
asset, financial information associated with the asset, heat flow
characteristics associated with the asset, fluid flow rates
associated with the asset (e.g., fluid flow rates of a fluid
flowing through a pipe), identifiers of other digital twins
embedded within the digital twin of the asset and/or identifiers of
digital twins embedding the digital twin of the asset, and/or other
suitable properties. Dynamic models 155100 associated with a
digital twin of an asset can be configured to calculate,
interpolate, extrapolate, and/or output values for such asset
digital twin properties based on input data collected from sensors
and/or devices disposed in the industrial setting and/or other
suitable data and subsequently populate the asset digital twin with
the calculated values.
[0842] In some embodiments, the set of properties of a digital twin
of an industrial device that may be updated by the digital twin
dynamic model system 15508 using dynamic models 155100 may include
the status of the device, a location of the device, the
temperature(s) of a device, a trajectory of the device, identifiers
of other digital twins that the digital twin of the device is
embedded within, embeds, is linked to, includes, integrates with,
takes input from, provides output to, and/or interacts with and the
like. Dynamic models 155100 associated with a digital twin of a
device can be configured to calculate or output values for these
device digital twin properties based on input data and subsequently
update the device digital twin with the calculated values.
[0843] In some embodiments, the set of properties of a digital twin
of an industrial worker that may be updated by the digital twin
dynamic model system 15508 using dynamic models 155100 may include
the status of the worker, the location of the worker, a stress
measure for the worker, a task being performed by the worker, a
performance measure for the worker, and the like. Dynamic models
associated with a digital twin of an industrial worker can be
configured to calculate or output values for such properties based
on input data, which then may be used to populate industrial worker
digital twin. In embodiments, industrial worker dynamic models
(e.g., psychometric models) can be configured to predict reactions
to stimuli, such as cues that are given to workers to direct them
to undertake tasks and/or alerts or warnings that are intended to
induce safe behavior. In embodiments, industrial worker dynamic
models may be workflow models (Gantt charts and the like), failure
mode effects analysis models (FMEA), biophysical models (such as to
model worker fatigue, energy utilization, hunger), and the
like.
[0844] Example properties of a digital twin of an industrial
environment that may be updated by the digital twin dynamic model
system 15508 using dynamic models 155100 may include the dimensions
of the industrial environment, the temperature(s) of the industrial
environment, the humidity value(s) of the industrial environment,
the fluid flow characteristics in the industrial environment, the
heat flow characteristics of the industrial environment, the
lighting characteristics of the industrial environment, the
acoustic characteristics of the industrial environment the physical
objects in the environment, processes occurring in the industrial
environment, currents of the industrial environment (if a body of
water), and the like. Dynamic models associated with a digital twin
of an industrial environment can be configured to calculate or
output these properties based on input data collected from sensors
and/or devices disposed in the industrial environment and/or other
suitable data and subsequently populate the industrial environment
digital twin with the calculated values.
[0845] In embodiments, dynamic models 155100 may adhere to physical
limitations that define boundary conditions, constants or variables
for digital twin modeling. For example, the physical
characterization of a digital twin of an industrial entity or
industrial environment may include a gravity constant (e.g., 9.8
m/s2), friction coefficients of surfaces, thermal coefficients of
materials, maximum temperatures of assets, maximum flow capacities,
and the like. Additionally or alternatively, the dynamic models may
adhere to the laws of nature. For example, dynamic models may
adhere to the laws of thermodynamics, laws of motion, laws of fluid
dynamics, laws of buoyancy, laws of heat transfer, laws of
radiation, laws of quantum dynamics, and the like. In some
embodiments, dynamic models may adhere to biological aging theories
or mechanical aging principles. Thus, when the digital twin dynamic
model system 15508 facilitates a real-time digital representation,
the digital representation may conform to dynamic models, such that
the digital representations mimic real world conditions. In some
embodiments, the output(s) from a dynamic model can be presented to
a human user and/or compared against real-world data to ensure
convergence of the dynamic models with the real world. Furthermore,
as dynamic models are based partly on assumptions, the properties
of a digital twin may be improved and/or corrected when a
real-world behavior differs from that of the digital twin. In
embodiments, additional data collection and/or instrumentation can
be recommended based on the recognition that an input is missing
from a desired dynamic model, that a model in operation isn't
working as expected (perhaps due to missing and/or faulty sensor
information), that a different result is needed (such as due to
situational factors that make something of high interest), and the
like.
[0846] Dynamic models may be obtained from a number of different
sources. In some embodiments, a user can upload a model created by
the user or a third party. Additionally or alternatively, the
models may be created on the digital twin system using a graphical
user interface. The dynamic models may include bespoke models that
are configured for a particular environment and/or set of
industrial entities and/or agnostic models that are applicable to
similar types of digital twins. The dynamic models may be
machine-learned models.
[0847] FIG. 159 illustrates example embodiments of a method for
updating a set of properties of a digital twin and/or one or more
embedded digital twins on behalf of client applications 15570. In
embodiments, digital twin dynamic model system 15508 leverages one
or more dynamic models 155100 to update a set of properties of a
digital twin and/or one or more embedded digital twins on behalf of
client application 15570 based on the impact of collected sensor
data from sensor system 15530, data collected from Internet of
Things connected devices 15524, and/or other suitable data in the
set of dynamic models 155100 that are used to enable the industrial
digital twins. In embodiments, the digital twin dynamic model
system 15508 may be instructed to run specific dynamic models using
one or more digital twins that represent physical industrial
assets, devices, workers, processes, and/or industrial environments
that are managed, maintained, and/or monitored by the client
applications 15570.
[0848] In embodiments, the digital twin dynamic model system 15508
may obtain data from other types of external data sources that are
not necessarily industrial-related data sources, but may provide
data that can be used as input data for the dynamic models. For
example, weather data, news events, social media data, and the like
may be collected, crawled, subscribed to, and the like to
supplement sensor data, Industrial Internet of Things device data,
and/or other data that is used by the dynamic models. In
embodiments, the digital twin dynamic model system 15508 may obtain
data from a machine vision system. The machine vision system may
use video and/or still images to provide measurements (e.g.,
locations, statuses, and the like) that may be used as inputs by
the dynamic models.
[0849] In embodiments, the digital twin dynamic model system 15508
may feed this data into one or more of the dynamic models discussed
above to obtain one or more outputs. These outputs may include
calculated vibration fault level states, vibration severity unit
values, vibration characteristics, probability of failure values,
probability of downtime values, probability of shutdown values,
cost of downtime values, cost of shutdown values, time to failure
values, temperature values, pressure values, humidity values,
precipitation values, visibility values, air quality values, strain
values, stress values, displacement values, velocity values,
acceleration values, location values, performance values, financial
values, manufacturing KPI values, electrodynamic values,
thermodynamic values, fluid flow rate values, and the like. The
client application 15570 may then initiate a digital twin
visualization event using the results obtained by the digital twin
dynamic model system 15508. In embodiments, the visualization may
be a heat map visualization.
[0850] In embodiments, the digital twin dynamic model system 15508
may receive requests to update one or more properties of digital
twins of industrial entities and/or environments such that the
digital twins represent the industrial entities and/or environments
in real-time. At 159100, the digital twin dynamic model system
15508 receives a request to update one or more properties of one or
more of the digital twins of industrial entities and/or
environments. For example, the digital twin dynamic model system
15508 may receive the request from a client application 15570 or
from another process executed by the digital twin system 15500
(e.g., a predictive maintenance process). The request may indicate
the one or more properties and the digital twin or digital twins
implicated by the request. In step 159102, the digital twin dynamic
model system 15508 determines the one or more digital twins
required to fulfill the request and retrieves the one or more
required digital twins, including any embedded digital twins, from
digital twin datastore 15516. At 159104, digital twin dynamic model
system 15508 determines one or more dynamic models required to
fulfill the request and retrieves the one or more required dynamic
models from digital twin dynamic model store 155102. At 159106, the
digital twin dynamic model system 15508 selects one or more sensors
from sensor system 15530, data collected from Internet of Things
connected devices 15524, and/or other data sources from digital
twin I/O system 15504 based on available data sources and the one
or more required inputs of the dynamic model(s). In embodiments,
the data sources may be defined in the inputs required by the one
or more dynamic models or may be selected using a lookup table. At
159108, the digital twin dynamic model system 15508 retrieves the
selected data from digital twin I/O system 15504. At 159110,
digital twin dynamic model system 15508 runs the dynamic model(s)
using the retrieved input data (e.g., vibration sensor data,
Industrial Internet of Things device data, and the like) as inputs
and determines one or more output values based on the dynamic
model(s) and the input data. At 159112, the digital twin dynamic
model system 15508 updates the values of one or more properties of
the one or more digital twins based on the one or more outputs of
the dynamic model(s).
[0851] In example embodiments, client application 15570 may be
configured to provide a digital representation and/or visualization
of the digital twin of an industrial entity. In embodiments, the
client application 15570 may include one or more software modules
that are executed by one or more server devices. These software
modules may be configured to quantify properties of the digital
twin, model properties of a digital twin, and/or to visualize
digital twin behaviors. In embodiments, these software modules may
enable a user to select a particular digital twin behavior
visualization for viewing. In embodiments, these software modules
may enable a user to select to view a digital twin behavior
visualization playback. In some embodiments, the client application
15570 may provide a selected behavior visualization to digital twin
dynamic model system 15508.
[0852] In embodiments, the digital twin dynamic model system 15508
may receive requests from client application 15570 to update
properties of a digital twin in order to enable a digital
representation of an industrial entity and/or environment wherein
the real-time digital representation is a visualization of the
digital twin. In embodiments, a digital twin may be rendered by a
computing device, such that a human user can view the digital
representations of real-world industrial assets, devices, workers,
processes and/or environments. For example, the digital twin may be
rendered and outcome to a display device. In embodiments, dynamic
model outputs and/or related data may be overlaid on the rendering
of the digital twin. In embodiments, dynamic model outputs and/or
related information may appear with the rendering of the digital
twin in a display interface. In embodiments, the related
information may include real-time video footage associated with the
real-world entity represented by the digital twin. In embodiments,
the related information may include a sum of each of the vibration
fault level states in the machine. In embodiments, the related
information may be graphical information. In embodiments, the
graphical information may depict motion and/or motion as a function
of frequency for individual machine components. In embodiments,
graphical information may depict motion and/or motion as a function
of frequency for individual machine components, wherein a user is
enabled to select a view of the graphical information in the x, y,
and z dimensions. In embodiments, graphical information may depict
motion and/or motion as a function of frequency for individual
machine components, wherein the graphical information includes
harmonic peaks and peaks. In embodiments, the related information
may be cost data, including the cost of downtime per day data, cost
of repair data, cost of new part data, cost of new machine data,
and the like. In embodiments, related information may be a
probability of downtime data, probability of failure data, and the
like. In embodiments, related information may be time to failure
data.
[0853] In embodiments, the related information may be
recommendations and/or insights. For example, recommendations or
insights received from the cognitive intelligence system related to
a machine may appear with the rendering of the digital twin of a
machine in a display interface.
[0854] In embodiments, clicking, touching, or otherwise interacting
with the digital twin rendered in the display interface can allow a
user to "drill down" and see underlying subsystems or processes
and/or embedded digital twins. For example, in response to a user
clicking on a machine bearing rendered in the digital twin of a
machine, the display interface can allow a user to drill down and
see information related to the bearing, view a 3D visualization of
the bearing's vibration, and/or view a digital twin of the
bearing.
[0855] In embodiments, clicking, touching, or otherwise interacting
with information related to the digital twin rendered in the
display interface can allow a user to "drill down" and see
underlying information.
[0856] FIG. 160 illustrates example embodiments of a display
interface that renders the digital twin of a dryer centrifuge and
other information related to the dryer centrifuge.
[0857] In some embodiments, the digital twin may be rendered and
output in a virtual reality display. For example, a user may view a
3D rendering of an environment (e.g., using a monitor or a virtual
reality headset). The user may also inspect and/or interact with
digital twins of industrial entities. In embodiments, a user may
view processes being performed with respect to one or more digital
twins (e.g., collecting measurements, movements, interactions,
inventorying, loading, packing, shipping, and the like). In
embodiments, a user may provide input that controls one or more
properties of a digital twin via a graphical user interface.
[0858] In some embodiments, the digital twin dynamic model system
15508 may receive requests from client application 15570 to update
properties of a digital twin in order to enable a digital
representation of industrial entities and/or environments wherein
the digital representation is a heat map visualization of the
digital twin. In embodiments, a platform is provided having heat
maps displaying collected data from the sensor system 15530,
Internet of Things connected devices 15524, and data outputs from
dynamic models 155100 for providing input to a display interface.
In embodiments, the heat map interface is provided as an output for
digital twin data, such as for handling and providing information
for visualization of various sensor data, dynamic model output
data, and other data (such as map data, analog sensor data, and
other data), such as to another system, such as a mobile device,
tablet, dashboard, computer, AR/VR device, or the like. A digital
twin representation may be provided in a form factor (e.g., user
device, VR-enabled device, AR-enabled device, or the like) suitable
for delivering visual input to a user, such as the presentation of
a map that includes indicators of levels of analog sensor data,
digital sensor data, and output values from the dynamic models
(such as data indicating vibration fault level states, vibration
severity unit values, probability of downtime values, cost of
downtime values, probability of shutdown values, time to failure
values, probability of failure values, manufacturing KPIs,
temperatures, levels of rotation, vibration characteristics, fluid
flow, heating or cooling, pressure, substance concentrations, and
many other output values). In embodiments, signals from various
sensors or input sources (or selective combinations, permutations,
mixes, and the like) as well as data determined by the digital twin
dynamic model system 15508 may provide input data to a heat map.
Coordinates may include real world location coordinates (such as
geo-location or location on a map of an environment), as well as
other coordinates, such as time-based coordinates, frequency-based
coordinates, or other coordinates that allow for representation of
analog sensor signals, digital signals, dynamic model outputs,
input source information, and various combinations, in a map-based
visualization, such that colors may represent varying levels of
input along the relevant dimensions. For example, among many other
possibilities, if an industrial machine component is at a critical
vibration fault level state, the heat map interface may alert a
user by showing the machine component in orange. In the example of
a heat map, clicking, touching, or otherwise interacting with the
heat map can allow a user to drill down and see underlying sensor,
dynamic model outputs, or other input data that is used as an input
to the heat map display. In other examples, such as ones where a
digital twin is displayed in a VR or AR environment, if an
industrial machine component is vibrating outside of normal
operation (e.g., at a suboptimal, critical, or alarm vibration
fault level), a haptic interface may induce vibration when a user
touches a representation of the machine component, or if a machine
component is operating in an unsafe manner, a directional sound
signal may direct a user's attention toward the machine in digital
twin, such as by playing in a particular speaker of a headset or
other sound system.
[0859] In embodiments, the digital twin dynamic model system 15508
may take a set of ambient environmental data and/or other data and
automatically update a set of properties of a digital twin of an
industrial entity or facility based on the impact of the
environmental data and/or other data in the set of dynamic models
155100 that are used to enable the digital twin. Ambient
environmental data may include temperature data, pressure data,
humidity data, wind data, rainfall data, tide data, storm surge
data, cloud cover data, snowfall data, visibility data, water level
data, and the like. Additionally or alternatively, the digital twin
dynamic model system 15508 may use a set of environmental data
measurements collected by a set of Internet of Things connected
devices 15524 disposed in an industrial setting as inputs for the
set of dynamic models 155100 that are used to enable the digital
twin. For example, digital twin dynamic model system 15508 may feed
the dynamic models 155100 data collected, handled or exchanged by
Internet of Things connected devices 15524, such as cameras,
monitors, embedded sensors, mobile devices, diagnostic devices and
systems, instrumentation systems, telematics systems, and the like,
such as for monitoring various parameters and features of machines,
devices, components, parts, operations, functions, conditions,
states, events, workflows and other elements (collectively
encompassed by the term "states") of industrial environments. Other
examples of Internet of Things connected devices include smart fire
alarms, smart security systems, smart air quality monitors,
smart/learning thermostats, and smart lighting systems.
[0860] FIG. 161 illustrates example embodiments of a method for
updating a set of vibration fault level states for a set of
bearings in a digital twin of a machine. In this example, a client
application 15570, which interfaces with digital twin dynamic model
system 15508, may be configured to provide a visualization of the
fault level states of the bearings in the digital twin of the
machine.
[0861] In this example, the digital twin dynamic model system 15508
may receive requests from client application 15570 to update the
vibration fault level states of the machine digital twin. At
161200, digital twin dynamic model system 15508 receives a request
from client application 15570 to update one or more vibration fault
level states of the machine digital twin. Next, in step 161202,
digital twin dynamic model system 15508 determines the one or more
digital twins required to fulfill the request and retrieves the one
or more required digital twins from digital twin datastore 15516.
In this example, the digital twin dynamic model system 15508 may
retrieve the digital twin of the machine and any embedded digital
twins, such as any embedded motor digital twins and bearing digital
twins, and any digital twins that embed the machine digital twin,
such as the manufacturing facility digital twin. At 161204, digital
twin dynamic model system 15508 determines one or more dynamic
models required to fulfill the request and retrieves the one or
more required dynamic models from the digital twin dynamic model
datastore 155102. At 161206, the digital twin dynamic model system
15508 selects dynamic model input data sources (e.g., one or more
sensors from sensor system 15530, data from Internet of Things
connected devices 15524, and any other suitable data) via digital
twin I/O system 15504 based on available data sources (e.g.,
available sensors from a set of sensors in sensor system 15530) and
the and the one or more required inputs of the dynamic model(s). In
the present example, the retrieved dynamic model(s) 155100 may take
one or more vibration sensor measurements from vibration sensors
15536 as inputs to the dynamic models. In embodiments, vibration
sensors 15536 may be optical vibration sensors, single axis
vibration sensors, tri-axial vibration sensors, and the like. At
161208, digital twin dynamic model system 15508 retrieves one or
more measurements from each of the selected data sources from the
digital twin I/O system 15504. Next, At 161210, digital twin
dynamic model system 15508 runs the dynamic model(s), using the
retrieved vibration sensor measurements as inputs, and calculates
one or more outputs that represent bearing vibration fault level
states. Next, At 161212, the digital twin dynamic model system
15508 updates one or more bearing fault level states of the
manufacturing facility digital twin, machine digital twin, motor
digital twin, and/or bearing digital twins based on the one or more
outputs of the dynamic model(s). The client application 15570 may
obtain vibration fault level states of the bearings and may display
the obtained vibration fault level state associated with each
bearing and/or display colors associated with fault level severity
(e.g., red for alarm, orange for critical, yellow for suboptimal,
green for normal operation) in the rendering of one or more of the
digital twins on a display interface.
[0862] In another example, a client application 15570 may be an
augmented reality application. In some embodiments of this example,
the client application 15570 may obtain vibration fault level
states of bearings in a field of view of an AR-enabled device
(e.g., smart glasses) hosting the client application from the
digital twin of the industrial environment (e.g., via an API of the
digital twin system 15500) and may display the obtained vibration
fault level states on the display of the AR-enabled device, such
that the vibration fault level state displayed corresponds to the
location in the field of view of the AR-enabled device. In this
way, a vibration fault level state may be displayed even if there
are no vibration sensors located within the field of view of the
AR-enabled device.
[0863] FIG. 155 illustrates example embodiments of a method for
updating a set of vibration severity unit values of bearings in a
digital twin of a machine. Vibration severity units may be measured
as displacement, velocity, and acceleration.
[0864] In this example, client application 15570 that interfaces
with the digital twin dynamic model system 15508 may be configured
to provide a visualization of the three-dimensional vibration
characteristics of bearings in a digital twin of a machine.
[0865] In this example, the digital twin dynamic model system 15508
may receive requests from client application 15570 to update the
vibration severity unit values for bearings in the digital twin of
a machine. At 155300, digital twin dynamic model system 15508
receives a request from client application 15570 to update one or
more vibration severity unit value(s) of the manufacturing facility
digital twin. Next, in step 155302, digital twin dynamic model
system 15508 determines the one or more digital twins required to
fulfill the request and retrieves the one or more required digital
twins from digital twin datastore 15516. In this example, the
digital twin dynamic model system 15508 may retrieve the digital
twin of the machine and any embedded digital twins (e.g., digital
twins of bearings and other components). At 155304, digital twin
dynamic model system 15508 determines one or more dynamic models
required to fulfill the request and retrieves the one or more
required dynamic models from dynamic model datastore 155102. At
155306, the digital twin dynamic model system 15508 selects dynamic
model input data sources (e.g., one or more sensors from sensor
system 15530, data from Internet of Things connected devices 15524,
and any other suitable data) via digital twin I/O system 15504
based on available data sources (e.g., available sensors from a set
of sensors in sensor system 15530) and the one or more required
inputs of the dynamic model(s). In the present example, the
retrieved dynamic models may be configured to take one or more
vibration sensor measurements as inputs and provide severity unit
values for bearings in the machine. At 155308, digital twin dynamic
model system 15508 retrieves one or more measurements from each of
the selected sensors. In the present example, the digital twin
dynamic model system 15508 retrieves measurements from vibration
sensors 15536 via digital twin I/O system 15504. At 155310, digital
twin dynamic model system 15508 runs the dynamic model(s) using the
retrieved vibration measurements as inputs and calculates one or
more output values that represent vibration severity unit values
for bearings in the machine. Next, At 155312, the digital twin
dynamic model system 15508 updates one or more vibration severity
unit values of the bearings in the machine digital twin and all
other embedded digital twins or digital twins that embed the
machine digital twin based on the one or more values output by the
dynamic model(s).
[0866] FIG. 163 illustrates example embodiments of a method for
updating a set of probability of failure values for machine
components in the digital twin of a machine.
[0867] In this example, the digital twin dynamic model system 15508
may receive requests from client application 15570 to update the
probability of failure values for components in a machine digital
twin. At 163400, digital twin dynamic model system 15508 receives a
request from client application 15570 to update one or more
probability of failure value(s) of the machine digital twin, any
embedded component digital twins, and any digital twins that embed
the machine digital twin such as a manufacturing facility digital
twin. Next, in step 163402, digital twin dynamic model system 15508
determines the one or more digital twins required to fulfill the
request and retrieves the one or more required digital twins. In
this example, the digital twin dynamic model system 15508 may
retrieve the digital twin of the manufacturing facility, the
digital twin of the machine, and the digital twins of machine
components from digital twin datastore 15516. At 163404, digital
twin dynamic model system 15508 determines one or more dynamic
models required to fulfill the request and retrieves the one or
more required dynamic models from dynamic model datastore 155102.
At 163406, the digital twin dynamic model system 15508 selects, via
digital twin I/O system 15504, dynamic model input data sources
(e.g., one or more sensors from sensor system 15530, data from
Internet of Things connected devices 15524, and any other suitable
data) based on available data sources (e.g., available sensors from
a set of sensors in sensor system 15530) and the and the one or
more required inputs of the dynamic model(s). In the present
example, the retrieved dynamic models may take one or more
vibration measurements from vibration sensors 15536 and historical
failure data as dynamic model inputs and output probability of
failure values for the machine components in the digital twin of
the machine. At 163408, digital twin dynamic model system 15508
retrieves data from each of the selected sensors and/or Internet of
Things connected devices via digital twin I/O system 15504. At
163410, digital twin dynamic model system 15508 runs the dynamic
model(s) using the retrieved vibration data and historical failure
data as inputs and calculates one or more outputs that represent
probability of failure values for bearings in the machine digital
twin. Next, At 163412, the digital twin dynamic model system 15508
updates one or more probability of failure values of the bearings
in the machine digital twin, all embedded digital twins, and all
digital twins that embed the machine digital twin based on the
output of the dynamic model(s).
[0868] FIG. 164 illustrates example embodiments of a method for
updating a set of probability of downtime for machines in the
digital twin of a manufacturing facility.
[0869] In this example, client application 15570, which interfaces
with the digital twin dynamic model system 15508, may be configured
to provide a visualization of the probability of downtime values of
a manufacturing facility in the digital twin of the manufacturing
facility.
[0870] In this example, the digital twin dynamic model system 15508
may receive requests from client application 15570 to assign
probability of downtime values to machines in a manufacturing
facility digital twin. At 164500, digital twin dynamic model system
15508 receives a request from client application 15570 to update
one or more probability of downtime values of machines in the
manufacturing facility digital twin and any embedded digital twins
such as the individual machine digital twins. Next, in step 164502,
digital twin dynamic model system 15508 determines the one or more
digital twins required to fulfill the request and retrieves the one
or more required digital twins from digital twin datastore 15516.
In this example, the digital twin dynamic model system 15508 may
retrieve the digital twin of the manufacturing facility and any
embedded digital twins from digital twin datastore 15516. At
164504, digital twin dynamic model system 15508 determines one or
more dynamic models required to fulfill the request and retrieves
the one or more required dynamic models from dynamic model
datastore 155102. At 164506, the digital twin dynamic model system
15508 selects dynamic model input data sources (e.g., one or more
sensors from sensor system 15530, data from Internet of Things
connected devices 15524, and any other suitable data) based on
available data sources (e.g., available sensors from a set of
sensors in sensor system 15530) and the and the one or more
required inputs of the dynamic model(s) via digital twin I/O system
15504. In the present example, the dynamic model(s) may be
configured to take vibration measurements from vibration sensors
and historical downtime data as inputs and output probability of
downtime values for different machines throughout the manufacturing
facility. At 164508, digital twin dynamic model system 15508
retrieves one or more measurements from each of the selected
sensors via digital twin I/O system 15504. At 164510, digital twin
dynamic model system 15508 runs the dynamic model(s) using the
retrieved vibration measurements and historical downtime data as
inputs and calculates one or more outputs that represent
probability of downtime values for machines in the manufacturing
facility. Next, At 164512, the digital twin dynamic model system
15508 updates one or more probability of downtime values for
machines in the manufacturing facility digital twins and all
embedded digital twins based on the one or more outputs of the
dynamic models.
[0871] FIG. 165 illustrates example embodiments of a method for
updating one or more probability of shutdown values in the digital
twin of an enterprise having a set of manufacturing facilities.
[0872] In the present example, the digital twin dynamic model
system 15508 may receive requests from client application 15570 to
update the probability of shutdown values for the set of
manufacturing facilities within an enterprise digital twin. At
165600, digital twin dynamic model system 15508 receives a request
from client application 15570 to update one or more probability of
shutdown values of the enterprise digital twin and any embedded
digital twins. Next, in step 165602, digital twin dynamic model
system 15508 determines the one or more digital twins required to
fulfill the request and retrieves the one or more required digital
twins from digital twin datastore 15516. In this example, the
digital twin dynamic model system 15508 may retrieve the digital
twin of the enterprise and any embedded digital twins. At 165604,
digital twin dynamic model system 15508 determines one or more
dynamic models required to fulfill the request and retrieves the
one or more required dynamic models from dynamic model datastore
155102. At 165606, the digital twin dynamic model system 15508
selects dynamic model input data sources (e.g., one or more sensors
from sensor system 15530, data from Internet of Things connected
devices 15524, and any other suitable data) based on available data
sources (e.g., available sensors from a set of sensors in sensor
system 15530) and the and the one or more required inputs of the
dynamic model(s) via digital twin I/O system 15504. In the present
example, the retrieved dynamic models may be configured to take one
or more vibration measurements from vibration sensors 15536 and/or
other suitable data as inputs and output probability of shutdown
values for each manufacturing entity in the enterprise digital
twin. At 165608, digital twin dynamic model system 15508 retrieves
one or more vibration measurements from each of the selected
vibration sensors 15536 from digital twin I/O system 15504. At
165610, digital twin dynamic model system 15508 runs the dynamic
model(s) using the retrieved vibration measurements and historical
shut down data as inputs and calculates one or more outputs that
represent probability of shutdown values for manufacturing
facilities within the enterprise digital twin. Next, At 165612, the
digital twin dynamic model system 15508 updates one or more
probability of shutdown values of the enterprise digital twin and
all embedded digital twins based on the one or more outputs of the
dynamic model(s).
[0873] FIG. 159 illustrates example embodiments of a method for
updating a set of cost of downtime values in machines in the
digital twin of a manufacturing facility. In embodiments, the
manufacturing
[0874] In the present example, the digital twin dynamic model
system 15508 may receive requests from a client application 15570
to populate real-time cost of downtime values associated with
machines in a manufacturing facility digital twin. At 159700,
digital twin dynamic model system 15508 receives a request from the
client application 15570 to update one or more cost of downtime
values of the manufacturing facility digital twin and any embedded
digital twins (e.g., machines, machine parts, and the like) from
the client application 15570. Next, in step 159702, the digital
twin dynamic model system 15508 determines the one or more digital
twins required to fulfill the request and retrieves the one or more
required digital twins. In this example, the digital twin dynamic
model system 15508 may retrieve the digital twins of the
manufacturing facility, the machines, the machine parts, and any
other embedded digital twins from digital twin datastore 15516. At
159704, digital twin dynamic model system 15508 determines one or
more dynamic models required to fulfill the request and retrieves
the one or more required dynamic models from dynamic model
datastore 155102. At 159706, the digital twin dynamic model system
15508 selects dynamic model input data sources (e.g., one or more
sensors from sensor system 15530, data from Internet of Things
connected devices 15524, and any other suitable data) based on
available data sources (e.g., available sensors from a set of
sensors in sensor system 15530) and the and the one or more
required inputs of the dynamic model(s) via digital twin I/O system
15504. In the present example, the retrieved dynamic model(s) may
be configured to take historical downtime data and operational data
as inputs and output data representing cost of downtime per day for
machines in the manufacturing facility. At 159708, digital twin
dynamic model system 15508 retrieves historical downtime data and
operational data from digital twin I/O system 15504. At 159710,
digital twin dynamic model system 15508 runs the dynamic model(s)
using the retrieved data as input and calculates one or more
outputs that represent cost of downtime per day for machines in the
manufacturing facility. Next, At 159712, the digital twin dynamic
model system 15508 updates one or more cost of downtime values of
the manufacturing facility digital twins and machine digital twins
based on the one or more outputs of the dynamic model(s).
[0875] FIG. 160 illustrates example embodiments of a method for
updating a set of manufacturing KPI values in the digital twin of a
manufacturing facility. In embodiments, the manufacturing KPI is
selected from the set of uptime, capacity utilization, on standard
operating efficiency, overall operating efficiency, overall
equipment effectiveness, machine downtime, unscheduled downtime,
machine set up time, inventory turns, inventory accuracy, quality
(e.g., percent defective), first pass yield, rework, scrap, failed
audits, on-time delivery, customer returns, training hours,
employee turnover, reportable health & safety incidents,
revenue per employee, and profit per employee, schedule attainment,
total cycle time, throughput, changeover time, yield, planned
maintenance percentage, availability, and customer return rate.
[0876] In the present example, the digital twin dynamic model
system 15508 may receive requests from a client application 15570
to populate real-time manufacturing KPI values in a manufacturing
facility digital twin. At 159700, digital twin dynamic model system
15508 receives a request from the client application 15570 to
update one or more KPI values of the manufacturing facility digital
twin and any embedded digital twins (e.g., machines, machine parts,
and the like) from the client application 15570. Next, in step
159702, the digital twin dynamic model system 15508 determines the
one or more digital twins required to fulfill the request and
retrieves the one or more required digital twins. In this example,
the digital twin dynamic model system 15508 may retrieve the
digital twins of the manufacturing facility, the machines, the
machine parts, and any other embedded digital twins from digital
twin datastore 15516. At 159704, digital twin dynamic model system
15508 determines one or more dynamic models required to fulfill the
request and retrieves the one or more required dynamic models from
dynamic model datastore 155102. At 159706, the digital twin dynamic
model system 15508 selects dynamic model input data sources (e.g.,
one or more sensors from sensor system 15530, data from Internet of
Things connected devices 15524, and any other suitable data) based
on available data sources (e.g., available sensors from a set of
sensors in sensor system 15530) and the and the one or more
required inputs of the dynamic model(s) via digital twin I/O system
15504. In the present example, the retrieved dynamic model(s) may
be configured to take one or more vibration measurements obtained
from vibration sensors 15536 and other operational data as inputs
and output one or more manufacturing KPIs for the facility. At
167708, digital twin dynamic model system 15508 retrieves one or
more vibration measurements from each of the selected vibration
sensors 15536 and operational data from digital twin I/O system
15504. At 159710, digital twin dynamic model system 15508 runs the
dynamic model(s) using the retrieved vibration measurements and
operational data as inputs and calculates one or more outputs that
represent manufacturing KPIs for the manufacturing facility. Next,
At 159712, the digital twin dynamic model system 15508 updates one
or more KPI values of the manufacturing facility digital twins,
machine digital twins, machine part digital twins, and all other
embedded digital twins based on the one or more outputs of the
dynamic model(s).
[0877] With the proliferation of vibration sensors and other
Industrial Internet of Things (IIoT) sensors, there are vast
amounts of data available relating to industrial environments. This
data is useful in predicting the need for maintenance and for
classifying potential issues in the industrial environments. There
are, however, many unexplored uses for vibration sensor data and
other IIoT sensor data that can improve the operation and uptime of
the industrial environments and provide industrial entities with
agility in responding to problems before the problems become
catastrophic.
[0878] Industrial enterprises that rely on industrial experts
struggle to capture the knowledge of these experts when they move
on to another enterprise or leave the workforce. There exists a
need in the art to capture industrial expertise and to use the
captured industrial expertise in guiding newer workers or mobile
electronic industrial entities to perform industrial-related
tasks.
[0879] A knowledge distribution process and related technologies
now will be described more fully hereinafter with reference to the
accompanying drawings, in which illustrative embodiments are shown.
The knowledge distribution process and technologies may, however,
be embodied in many different forms and should not be construed as
limited to the embodiments set forth herein; rather, these
embodiments are provided so that this disclosure will be thorough
and complete, and will fully convey the scope of the disclosure and
inventions to those skilled in the art. The knowledge distribution
process may use a knowledge distribution platform or system that
utilizes blockchain technology for storing digital knowledge and
providing convenient and secure control of the digital
knowledge.
[0880] Where digital knowledge may be cryptographically secured,
there can be a number of practical obstacles to the sharing of
knowledge, such as the absence of trust between parties that could
potentially benefit from sharing of the knowledge. For example, a
manufacturer might benefit from having a supplier access trade
secrets of the manufacturer in order to make components or
materials on behalf of the manufacturer, but sharing the trade
secrets creates a risk that the supplier may use the trade secrets
on its own behalf or on behalf of competitors. Similarly, an
engineer may be willing to share valuable code or instruction sets
with others but be fearful of the misuse of that code. A need
exists for a digital knowledge distribution system that facilitates
orchestration of the sharing of knowledge by providing a high
degree of control over the extent to which counterparties can
access shared knowledge.
[0881] Even where knowledge is secure and well-controlled, some
types of knowledge are so sensitive that an owner may be unwilling
to share the entire set of knowledge with a single counterparty.
For example, a proprietary process may be divided among different
suppliers in order to keep any one supplier from deducing or
reverse engineering the entire process. However, dividing knowledge
presents operational challenges, as the owner may orchestrate a
series of secure interactions with all involved parties in order to
assure that the full set of knowledge may be maintained and
accurately implemented. A need exists for a digital knowledge
distribution system that facilitates handling and control of
subsets of knowledge, including automated handling of aggregation
of knowledge, or related outputs, that result from division of
knowledge subsets.
[0882] Referring to FIG. 168, a knowledge distribution system 16802
is configured to facilitate management of digital knowledge 16804
by one or more users via a distributed ledger 16808. The digital
knowledge 16804 may include any suitable knowledge that is
conveyable from one party to another, such as in a digital format.
Users and/or parties may include one or more knowledge providers
16806 and/or one or more knowledge recipients 16818. The knowledge
providers 16806 are parties that provide knowledge to be managed
via the knowledge distribution system 16802, such as by uploading
one or more instances of the digital knowledge 16804 to the
knowledge distribution system 16802 and/or the distributed ledger
16808. Uploading one or more instances of the digital knowledge
16804 and/or hosting one or more instances of the digital knowledge
16804 on the distributed ledger 16808 may include uploading an
instance of the digital knowledge 16804 itself to the distributed
ledger 16808 (e.g., which may be tokenized, contained in the smart
contract, and/or stored in an associated database) and/or providing
a reference to an accessible location of the instance of the
digital knowledge 16804 and any other information required to
retrieve the digital knowledge from the accessible location. When
reference is made to receiving digital knowledge 16804, the digital
knowledge 16804 itself or a reference thereto may be received. The
term knowledge recipients 16818 may refer to parties that receive
knowledge from the knowledge providers 16806 via the knowledge
distribution system 16802 (including via reference or link to
digital knowledge 16804) and/or via a distributed ledger 16808 that
stores digital knowledge 16804. In some embodiments, the knowledge
distribution system 16802 may facilitate management of digital
knowledge 16804 by facilitating establishment of a chain of work,
possession, and/or title of one or more instances of digital
knowledge, such as by serving as a log of ownership of an instance
of knowledge. The log of ownership may include a chain of log
entries including an indication of a set of owners and/or
contributors. For example, in some embodiments the knowledge
distribution system 16802 may facilitate establishment of a chain
of work, a chain of possession, and/or a chain of title
corresponding to a 3D-printing instruction set for 3D printing an
object (e.g., a custom-designed part, a replacement part, a toy, a
medical device, a tool, or the like). In some embodiments, the
knowledge distribution system 16802 may facilitate establishment of
a seller database where the schematic may be stored prior to one or
more of sale, transfer of the schematic to a buyer database, entry
of a serial number of the custom part into a clause of a smart
contract 16840, and printing of the custom part by a 3D printer
owned by a buyer.
[0883] In some embodiments the knowledge distribution system 16802
may facilitate management of digital knowledge 16804 by managing
aggregation of instances of digital knowledge 16804, such as where
component instances of digital knowledge 16804 are aggregated to
form larger instances of digital knowledge (e.g., where chapter
instances are concatenated to form book instances, where component
instances are linked schematically to form a system, where element
instances are linked diagrammatically to generate a workflow, where
partial instances are coupled to form a whole instance (e.g., the
necessary parts of a formula), where related instances are
topically linked to form a cluster, and via many other forms of
aggregation).
[0884] In some embodiments, the knowledge distribution system 16802
may facilitate management of digital knowledge 16804 by
facilitating verification of one or more sources of the digital
knowledge and/or providing a chain of origination for a digital or
physical item by virtue of related knowledge. For example, in
embodiments, the knowledge distribution system 16802 may log a
digital signature of a steel manufacturer in a distributed ledger
that certifies a quality grade of steel provided by the steel
manufacturer to a factory owner and may link the digital signature
to serial numbers of each part produced by a factory owned by the
factory owner that used the steel provided by the steel
manufacturer.
[0885] In some embodiments, the knowledge distribution system 16802
may facilitate control of digital knowledge 16804 by facilitating
collaboration of a plurality of knowledge providers 16806 such that
information related to one or more instances of digital knowledge
from one or more of disparate parties, disparate knowledge
providers 16806, and disparate distributed ledgers 16808 may be
tracked and/or combined into one or more consolidated distributed
ledgers.
[0886] In some embodiments, instances of the digital knowledge
16804 may include, for example, instruction sets such as process
steps and other methodologies in food production, transportation,
executable algorithmic logic such as computer programs, a firmware
program, an instructions set for a field-programmable gate array
(FPGA), serverless code logic, a crystal fabrication
system/process, a polymer production process, a chemical synthesis
process, a biological production process, part schematics, and/or
production records (e.g., production records of aircraft parts,
spaceship parts, nuclear engine parts, etc.), a process and/or
instruction set for semiconductor fabrication such as silicon
etching and/or doping, an instruction set for a 3D printer such as
for printing a medical device, an automobile part, an airplane
part, a piece of furniture or component thereof, a replacement part
for an industrial robot or machine, algorithmic logic such as an
instruction set for use in an application, AI logic and/or
definitions, machine learning logic and/or definitions,
cryptography logic, serverless code logic, trade secrets and/or
other intellectual property such as know-how, patented material,
and works of authorship, food preparation instructions (e.g., for
industrial food preparation), coating process instructions,
biological production process instructions, chemical synthesis
instructions, polymer production instructions, smart contract
instructions, data sets and/or sensor information defining and/or
populating a set of digital twins (such as digital twins that
embody digital knowledge about one or more physical entities,
including knowledge about configurations, operating modes,
instruction sets, capabilities, defects, performance parameters,
and many others), and/or any other suitable type of digitally
transmittable knowledge. In these embodiments, the instruction sets
may be consumed/leveraged by a computing device, a special purpose
device, or a combination of devices (e.g., factory equipment). In
some embodiments, instances of the digital knowledge 16804 may
include personal and/or professional knowledge relating to one or
more organizations and/or individuals, such as a professional
resume and/or professional history tracking information. In some
embodiments, the personal and/or professional knowledge may include
one or more records of professional credentials such as academic
degrees and/or certificates. In some embodiments, the personal
and/or professional knowledge may include one or more verifications
of professional positions held by the one or more individuals. In
some embodiments, the personal and/or professional knowledge may
include professional feedback for and/or verification of work
performed for and/or by one or more third parties. The personal
and/or professional knowledge may include personal and/or business
financial history, personal life achievements as verified by one or
more third parties. The knowledge provider 16806 may be any party
that at least partially provides one or more instances of the
digital knowledge 16804, such as a manufacturer, a seller, a
customer, a wholesaler, a user, a manager, a notary, a factory
owner, a maintenance worker, or any other suitable provider of the
digital knowledge 16804.
[0887] In embodiments, the distributed ledger 16808 may be any
suitable type of electronic ledger 16808, such as a blockchain
(e.g., Hyperledger, Solidity, Ethereum, and the like). The
distributed ledger 16808 may be centralized, decentralized, or a
hybrid configuration where the knowledge distribution system 16802
stores a copy of a distributed ledger 16808 in addition to any
number of participant nodes 16916 that store copies of the
distributed ledger 16808. When referring to the distributed ledger
16808, the term "distributed ledger" (and/or any logs, records,
smart contracts, blocks, tokens, and/or data stored thereon) may
refer to a specific instance of a copy of the distributed ledger
16808 (and/or any logs, records, smart contracts, blocks, tokens,
and/or data stored thereon) and/or the collection of local copies
of the distributed ledger 16808-L stored across any number of nodes
(which may include the knowledge distribution system 16802), unless
specifically indicated otherwise.
[0888] In some embodiments, a private network of authorized
participants, such as one or more of the knowledge providers and/or
nodes, may establish cryptography-based consensus on one or more
items, such that the knowledge distribution system 16802 may
provide security, transparency, auditability, immutability, and
non-repudiation to transactions for digital knowledge. In some
embodiments, a trusted authority (e.g., the knowledge distribution
system 16802 or another suitable authority) may issue private key
and public key pairs to each registered user of the knowledge
distribution system 16802. The private key and public key pairs may
be used to encrypt and decrypt data (e.g., messages, files,
documents, etc.) and/or to perform operations with respect to the
distributed ledger 16808. In some embodiments, the knowledge
distribution system 16802 (or another trusted authority) may
provide two or more levels of access to users. In some embodiments,
the knowledge distribution system 16802 may define one or more
classes of users, where each of the classes of users is granted a
respective level of access. In some of these embodiments, the
knowledge distribution system 16802 may issue one or more access
keys to one or more classes of users, where the one or more access
keys each correspond to a respective level of access, thereby
providing users of different levels of access via their respective
issued access keys. In embodiments, possession of certain access
keys may be used to determine a level of access to the distributed
ledger 16808. For example, in some embodiments, a first class of
users may be granted full viewing access of a block, while a second
class of users may be granted both viewing access of blocks and an
ability to verify and/or certify one or more instances of digital
knowledge contained within a block, and while a third class of
users may be granted viewing access of blocks, an ability to verify
and/or certify one or more instances of digital knowledge contained
within a block, and an ability to modify the one or more instances
of digital knowledge contained within the block. In some
embodiments, a class of users may be verified as being a legitimate
user of the distributed ledger 16808 in one or more roles and
allowed related permissions with respect to the distributed ledger
and content stored therein. A user may be verified, for example, as
a bona fide knowledge provider 16806 that uses a knowledge provider
device 16890, knowledge recipient 16818 that uses a knowledge
recipient device 16894, and/or crowdsourcer 16836 that uses a
crowdsourcer device 16892. There may be any number of each device
16890, 16892, 16894. As shown in FIG. 168, there is one knowledge
provider device 16890, two crowdsourcer devices 16892, and one
knowledge recipient device 16894. In other examples, as it is
understood, there may be one, two, three, or more of any device
type 16890, 16892, 16894, in any combination. In other examples,
there may be one of each device type (e.g., one knowledge provider
device 16890, one crowdsourcer device 16892, and one knowledge
recipient device 16894). In other embodiments, these devices 16890,
16892, 16894 may be implemented as one or more computing devices
and/or server devices (e.g., as part of a server farm).
[0889] In some embodiments, the knowledge distribution system 16802
may include a ledger management system 16910. In some embodiments,
the ledger management system 16910 manages one or more distributed
ledgers (also referred to as "ledgers"). In some embodiments, the
ledger management system 16910 may instantiate a distributed ledger
for a particular knowledge provider 16806 or group of knowledge
providers 16806, such as by instantiating a distributed ledger
16808 that stores instances of digital knowledge 16804 provided by
the knowledge provider 16806 or group of knowledge providers 16806.
The knowledge distribution system 16802 may allow only the
particular knowledge provider 16806 or particular group of
knowledge providers 16806 to host instances of digital knowledge
16804 (e.g., by using knowledge provider device 16890) on the
related distributed ledger 16808 and/or for each instance of
digital knowledge 16804, such that each distributed ledger 16808 is
specific to a respective knowledge provider 16806 and/or an
instance of digital knowledge 16804. In some embodiments, the
ledger management system 16910 may instantiate a plurality of
distributed ledgers 16808, one or more of the distributed ledgers
16808 being configured to facilitate hosting, sharing, buying,
selling, licensing, or otherwise managing a category of digital
knowledge 16804. Categories of digital knowledge may be related to,
for example, one or more industries such as automotive and/or
financial, or one or more types of digital knowledge, such as 3D
printing schematics. In some embodiments, the ledger management
system 16910 may maintain a distributed ledger that facilitates
management of some or all of the instances of digital knowledge
16808 and/or the knowledge providers 16806 for which related data
is stored by the knowledge distribution system 16802.
[0890] In some embodiments, a distributed ledger 16808 is any
suitable type of blockchain. Any other suitable types of
distributed ledgers may be used, however, without departing from
the scope of the disclosure. The distributed ledger may be public
or private. In embodiments, where the distributed ledger is
private, reading from the ledger and/or validation privileges by a
user such as the knowledge provider 16806 (e.g., using knowledge
provider device 16890) may be restricted to invitees, users with
one or more accounts/passwords, or by any other suitable method of
restricting access to the distributed ledger 16808. In some
embodiments, the distributed ledger 16808 may be at least partially
centralized, such that a plurality of nodes of the distributed
ledger is stored by the knowledge distribution system 16802. In
some embodiments, the distributed ledgers are federated distributed
ledgers, as the distributed ledgers may be stored on pre-selected
or pre-approved nodes that are associated with the parties to a
management of digital knowledge 16804 via the knowledge
distribution system 16802. The techniques described herein may be
applied, however, to publicly distributed ledgers as well. In a
publicly distributed ledger, any suitably configured computing
device (personal computers, user devices, servers) or set of
devices (e.g., a server farm) may act as a node 16916 and may store
a local copy of a distributed ledger 16808-L, whether the owner of
the node otherwise participates in the transactions facilitated by
the knowledge distribution system 16802. In these embodiments, such
nodes 16916 may add validate/deny new blocks, save new blocks to
the distributed ledger 16808 (if validated) to maintain a full copy
(or nearly full copy) of the transaction history relating to the
distributed ledger 16808, and broadcast the transaction history to
other participating nodes 16916.
[0891] In some embodiments, the ledger management system 16910
(and/or the collection of participant nodes 16916) may be
configured to leverage a distributed ledger 16808 to create an
immutable log establishing of a chain of work, possession, and/or
title of one or more instances of digital knowledge 16804,
establishing verification of 16810 may utilize a distributed ledger
to manage a set of permission keys that provide access to one or
more instances of the digital knowledge 16804 and/or services
associated with the knowledge distribution system 16802. In some
embodiments, the distributed ledger 16808 provides provable access
to the digital knowledge 16804, such as by one or more
cryptographic proofs and/or techniques. In some embodiments, the
distributed ledger 16808 may provide provable access to the digital
knowledge 16804 by one or more zero-knowledge proof techniques. In
some embodiments, the ledger management system 16910 may manage the
distributed ledger to facilitate cooperation and/or collaboration
between two or more knowledge providers 16806 with regard to one or
more instances of digital knowledge 16804.
[0892] FIG. 169 illustrates an exemplary embodiment of the
distributed ledger 16808, the distributed ledger 16808 being
distributed over a ledger network 16970. The ledger network 16970
may include the distributed ledger 16808 and a set of node
computing devices 16916-1, 1691602, 1691603, 16916-N that
communicate via one or more communication networks 16814. In some
embodiments, the communication network 16814 may include the
Internet, private networks, cellular networks, and/or the like. In
embodiments, the nodes 16916 may all host a copy of the distributed
ledger 16808 (or a portion thereof). For example, the ledger
network 16970 may include a first node 16916-1, a second node
16916-2, a third node 16916-3 . . . and an Nth node 16916-N that
communicate with the knowledge distribution system 16802 and with
other nodes 16916 in the ledger network 16970. In some embodiments,
the knowledge distribution system 16802 is configured to execute
the ledger management system 16910 and may store and manage a local
copy of a distributed ledger 16808 that is used in connection with
facilitating management of one or more instances of the digital
knowledge 16804 via the knowledge distribution system 16802. In
some embodiments, the knowledge distribution system 16802 (or the
ledger management system 16910 executed thereon) may also be
thought of and referred to as a node of the ledger network 16970.
In some embodiments, the ledger management system 16910 may also
generate and assign private key and public key pairs to users such
as one or more of the knowledge providers 16806 and/or one or more
knowledge recipients 16818 of the digital knowledge 16804 (also
referred to as "knowledge recipients") and/or to each node 16916 in
the ledger network 16970, such that the private key and public key
pairs are used to encrypt data transmitted between nodes 16916 in
the ledger network 16970.
[0893] In some embodiments, each of the nodes 16916 of the ledger
network 16970 (other than the knowledge distribution system 16802)
may be a computing device or a set of connected computing devices
that are associated with the knowledge providers 16806 and/or
knowledge recipients 16818. In some embodiments, the nodes 16916
may include computing devices of parties that are not involved in
the providing or receipt of knowledge (e.g., parties that are
associated with neither the knowledge providers 16806 nor any of
the knowledge recipients 16818). In some embodiments, each of the
nodes 16916 may store a respective local copy 16808-L of the
distributed ledger 16808. In some embodiments, one or more nodes
may store a partial copy of the distributed ledger 16808. In some
embodiments, each of the nodes 16916, 16916-1, 16916-2, 16916-3,
16916-N may execute a respective agent 16920, 16920-1, 16920-2,
16920-3, 16920-N. An agent 16920 may be configured to perform one
or more of managing the local copy 16808-L of the distributed
ledger 16808 associated with the node 16916 that executed the agent
16920, helping verify blocks that were previously stored on the
ledger 16808, helping verify requests from other nodes 16916 to
store new blocks on the ledger 16808, requesting permission to
perform operations relating to the digital knowledge or management
thereof on behalf of a user associated with the node 16916 on which
the agent resides, and/or facilitating collaboration between one or
more of the knowledge providers 16806 and/or one or more of the
knowledge recipients 16818 (e.g., using knowledge provider
device(s) 16890 and/or knowledge recipient device(s) 16894,
respectively), such as by assisting with validation and/or transfer
of one or more instances of the digital knowledge 16804 and/or
executing one or more clauses of one or more smart contracts 16840.
It is understood that nodes may perform additional or alternative
tasks without departing from the scope of the disclosure.
[0894] In some embodiments, a knowledge recipient 16818 may receive
one or more in 16894 may be any device that is configured to
receive and/or use the digital knowledge 16804 from the distributed
ledger 16808, such as a computing device, and/or may be devices for
using the digital knowledge 16804, such as a 3D printer, a
manufacturing device or system, and the like. In some scenarios,
knowledge recipient 16818 may employ a plurality of knowledge
recipient devices 16894, such as a server or computing device
configured to download one or more instances of digital knowledge
16804 from the distributed ledger 16808 and transmit the one or
more instances of digital knowledge to a 3D printer, a factory
machine, a manufacturing system, or some other suitable device for
using the one or more instances of the digital knowledge 16804. For
example, a knowledge provider 16806 may upload a link (e.g., using
a knowledge provider device 16890) of a computer-aided design (CAD)
file of a 3D printable airplane part to the distributed ledger
16808. In embodiments, the knowledge provider 16806 may use e.g.,
the knowledge provider device 16890 to define or otherwise provide
a smart contract that governs the use of the digital knowledge
(e.g., the design file for the airplane part), including a cost of
a use of the CAD file of the airplane part. A knowledge recipient
16818 may transfer funds (e.g., using knowledge recipient device
16894) to the knowledge provider 16806 (e.g., knowledge provider
device 16890) (e.g., via the smart contract) in exchange for access
to the CAD file via the distributed ledger 16808. A knowledge
recipient device 16894 may then download the CAD file, which may
then be used to 3D print the part. For example, the knowledge
recipient device 16894 may be a business computer in communication
with a 3D printer or a smart 3D printer itself. In the former
scenario, the business computer may transfer the CAD file to the 3D
printer. Upon receiving the CAD file, the 3D printer may 3D print
the airplane part. In some embodiments, the digital knowledge
itself (e.g., the CAD file) may be contained in the smart contract,
such that the smart contract provides the digital knowledge to the
knowledge recipient device 16894 upon verifying that the knowledge
recipient 16818 has satisfied the conditions of release of the
digital knowledge 16804 (e.g., deposited a requisite amount of
currency). In some embodiments, each time that an instance of
knowledge is used by a knowledge recipient 16818, a smart contract,
the knowledge distribution system 16802, an agent 16920, and/or the
knowledge recipient device 16894 may update the distributed ledger
16808 with a block indicating that the knowledge recipient used the
instance of digital knowledge 16804.
[0895] In some embodiments, the knowledge distribution system 16802
may be configured to facilitate participation in management of
digital knowledge 16804 by one or more crowdsourcers 16836, such as
by allowing a crowdsourcer 16836 to verify one or more aspects of
an instance of digital knowledge 16804 (e.g., using crowdsourcer
device 16892). In embodiments, a crowdsourcer 16836 may be granted
crowdsourcing permissions, thereby allowing the crowdsourcer 16836
to view/inspect the digital knowledge and to provide a verification
vote 16926 and/or opinion. In embodiments, non-limiting examples of
crowdsourcing permissions may include one or more of reviewing an
instance of digital knowledge 16804, signing an instance of digital
knowledge 16804, verifying an instance of digital knowledge 16804,
and the like. Examples of crowdsourcers 16836 include certifying
entities, domain experts, customers, manufacturers, wholesalers,
and any other suitable party capable of verifying an instance of
digital knowledge. In embodiments, certifying entities or domain
experts may certify an instance of digital knowledge 16804 as being
authentic, accurate, and/or reliable, and/or as coming from an
authentic, accurate, and/or reliable source. In embodiments,
customers may review an instance of digital knowledge 16804, such
as to indicate that the digital knowledge 16804 is in working order
and/or of expected quality. In embodiments, manufacturers and/or
wholesalers may sign an instance of digital knowledge 16804, such
as by applying a serial number to a piece of digital knowledge
16804 before the piece of digital knowledge is transmittable to a
knowledge recipient 16818 (e.g., via knowledge recipient device
16894). Certifications, reviews, signatures, and/or any other
validation indicia made by crowdsourcers 16836 may be recorded in
the distributed ledger 16808, such as by adding one or more new
blocks 16922 to the distributed ledger 16808 that indicate the
certification, review, signature, or other validation indicia. In
some embodiments, the new blocks 16922 may include data related to
the certifications, reviews, signatures, and/or other validation
indicia made by the one or more crowdsourcers 16836 (e.g., an
identifier of the crowdsources, a timestamp, a location, and/or the
like), e.g., using crowdsourcer devices 16892. In some examples,
the knowledge distribution system 16802 may be paired with a
crowdsourcing system (e.g., crowdsourcer devices 16892).
Specifically, in examples, the crowdsourcing system (e.g.,
crowdsourcer devices 16892) may communicate with and engage with
the smart contract 16840 such that upon crowdsourcing an element of
the digital knowledge 16804 via the smart contract 16840, the
digital knowledge 16804 may be embodied (e.g., recorded) in the
distributed ledger 16808. The knowledge distribution system 16802
may use the smart contract 16840 to facilitate management of the
digital knowledge 16804, such as by allowing the smart contract
16840 and crowdsourcers 16836 to verify (and/or contribute to) one
or more aspects of an instance of digital knowledge 16804. For
example, a software developer may provide a crowdsource request for
a module or function in the smart contract 16840. This crowdsource
request may be embedded in open source code as a request for a code
element (e.g., where a first supplier of working code may get a
share of proceeds (or a credit, or a token, etc.)) of a product.
For this example, crowdsourcers 16836 may use the crowdsourcing
system (e.g., crowdsource devices 16892) to respond to the
crowdsource request by viewing/inspecting the digital knowledge
(e.g., open source code) and may provide collaboration in the form
of verifications, opinions, corrections, and/or contributions to
the open source code which may relate to improvements to the open
source code (e.g., improve accuracy and/or reliability of
software). These verifications, opinions, corrections, and/or
contributions indicia provided by the crowdsourcers 16836 may be
recorded in the distributed ledger 16808 by adding one or more new
blocks 16922 to the distributed ledger 16808 that indicate the
indicia. The crowdsourcers 16836 may be compensated (e.g., via the
smart contract 16840) based on their percentage of contribution to
the open source code such that the original software developer may
share the proceeds (or credits, or tokens, etc.) of the software
product with the crowdsources. The percentage contribution may be
based on the amount of code written and/or the impact of each
crowdsourcer's contribution on the resulting open source code's
functionality.
[0896] In some embodiments, the digital knowledge 16804 may be
tokenized (e.g., at least partially converted to/wrapped in a
knowledge token 17038). In embodiments, tokenizing the digital
knowledge 16804 may include wrapping the digital knowledge into a
knowledge token 17038 and/or wrapping access, licensing, ownership,
and/or other suitable rights related to the digital knowledge 16804
such that the access, licensing, ownership and/or other suitable
rights managed by one or more of the knowledge tokens 17038. By
tokenizing digital knowledge 16804, the digital knowledge 16804 may
reside in and be distributed via a distributed ledger 16808 and
smart contracts 16840. In some embodiments, the knowledge
distribution system 16802 may define permissions and/or operations
associated with the knowledge tokens 17038. For example, the
knowledge token 17038 may allow the tokenized digital knowledge
16804 to be viewed, edited, copied, bought, sold, and/or licensed
based on permissions set at a time of tokenization by the knowledge
distribution system 16802. In embodiments, the knowledge
distribution system 16802 may provide for orchestration of a
marketplace or exchange for digital knowledge 16804, such as where
bodies or instances of digital knowledge 16804 may be exchanged,
such as, without limitation, through sets of knowledge tokens 17038
that are optionally governed by smart contracts that may be
configured by a host of a knowledge exchange or marketplace and/or
by knowledge providers 16806 (e.g., using knowledge provider
devices 16890) or knowledge recipients 16818 (e.g., using knowledge
recipient devices 16894). For example, an exchange or marketplace
may host exchanges for specific categories of know-how, expertise,
instruction sets, trade secrets, insight, or other elements of
knowledge described or referenced herein, where knowledge is
categorized by subject matter of interest, where transaction terms
are pre-defined and/or configurable (such as with configurable
smart contracts that enable various transaction models, including
bid/ask models, auction models, donation models, reverse auction
models, fixed price models, variable price models, contingent
pricing models and others), where metadata is collected and/or
represented about categories of knowledge exchange, and where
relevant content is presented, including market pricing data,
substantive content about knowledge areas, content about providers,
and the like. Such an exchange may facilitate monetization of
tokenized knowledge represented in knowledge tokens 17038. In
embodiments, a knowledge exchange, as described herein, may be
integrated with or within another exchange, such as a
domain-specific exchange, a geography-specific exchange, or the
like, where the knowledge exchange may facilitate exchange of
valuable or sensitive knowledge related to the subject matter of
the other exchange. The other exchange may be a stock exchange, a
commodities exchange, a derivatives exchange, a futures exchange,
an advertising exchange, an energy exchange, a renewable energy
credits exchange, a cryptocurrency exchange, a bonds exchange, a
currency exchange, a precious metals exchange, a petroleum
exchange, an exchange for goods, an exchange for services, or any
of a wide variety of others. This may include integration by APIs,
connectors, ports, brokers, and other interfaces, as well as
integration by extraction, transformation and loading (ETL)
technologies, smart contracts, wrappers, containers, or other
capabilities.
[0897] In some embodiments, the knowledge distribution system 16802
may be configured to create and issue one or more currency tokens
associated with the distributed ledger 16808. The currency tokens
may be digital objects such as cryptographic tokens, cryptographic
currency, and the like, that may be purchased, mined, assigned,
and/or distributed to users of the distributed ledger 16808. In
some embodiments, the currency tokens may represent fiat currency
(e.g., US Dollars, British Pounds, Euros, or the like), such that
the value of the token is pegged to the fiat currency. In
embodiments, the currency tokens may be used to transact digital
knowledge. For example, in embodiments, smart contracts may be used
to receive and verify that a knowledge recipient 16818 has paid the
requisite amount of funds before releasing the digital knowledge
16804 to a knowledge recipient device 16894. Additionally or
alternatively, knowledge recipients 16818 may use traditional
payment methods (e.g., credit card payments) to transact for
instances of knowledge. In some embodiments, the currency tokens
may function as digital currency. For example, the currency tokens
may be paid by knowledge recipients to knowledge providers in
exchange for digital knowledge 16804 and/or paid to crowdsources
(e.g., certifiers or experts) for verifying one or more aspects of
digital knowledge 16804. In some embodiments, one or more users may
be awarded currency tokens as a reward for discovering, or
"mining", one or more new blocks 16922 of the distributed ledger
16808. In some embodiments, currency tokens may be asset-backed
tokens, such as tokens backed by one or more other currencies
(e.g., fiat currencies), securities, ownership rights of property,
ownership rights of intellectual property, licensing rights of
property and/or intellectual property, and the like. In some
embodiments, the knowledge distribution system 16802 may be
configured to track access rights and/or ownership rights of one or
more of the currency tokens, such as by logging contents and/or
balances of digital wallets of users. In some embodiments, the
knowledge distribution system 16802 may be configured to issue a
wallet passcode to a user, the wallet passcode being necessary to
access, view, transfer, and otherwise manage the currency tokens
owned (or at least partially owned) by the user to which the wallet
passcode has been issued.
[0898] In some embodiments, the knowledge distribution system 16802
may include a smart contract system 16868 configured to generate
smart contracts 16840 and deploy the smart contracts 16840 to the
distributed ledger 16808. In embodiments, a smart contract 16840
may refer to a piece of software stored on the distributed ledger
16808 and configured to manage one or more rights associated with
one or more instances of the digital knowledge 16804 and/or one or
more knowledge tokens 17038. In embodiments, the smart contract may
be a computer protocol that assists with negotiation and/or
performance of terms in an agreement (e.g., distributed on
blockchain such as Ethereum blockchain). The smart contract may be
used in banking, government, management, supply chain, automobiles,
real estate, health care, insurance, etc. In some embodiments, the
smart contract 16840 may be contained and/or executed in a virtual
machine or a container (e.g., a Docker container). In some
embodiments, one or more of the nodes 16916 of the ledger network
16970 may provide an execution environment for the smart contract
16840. In embodiments, a smart contract 16840 may include
information, data, and/or logic related to an instance of digital
knowledge 16804, one or more triggering events, one or more smart
contract actions to be executed in response to detection of one or
more of the triggering events, and the like. In embodiments, the
triggering events may define conditions that may be satisfied by
events performable by one or more users, such as the knowledge
provider 16806, the knowledge recipient 16818, and/or the
crowdsourcer 16836, or by one or more third parties. Examples of
the triggering events include payment of one party by another
party, adherence or lack of adherence to one or more terms of a
sales, licensing, insurance, or other agreement made by one or more
parties, meeting of one or more thresholds or ranges of properties
of one or more pieces of the digital knowledge 16804, such as
value, user rating, production amount, or any other suitable
property, passage of time, or any other suitable triggering event.
Additionally or alternatively, the triggering events defined in a
smart contract 16840 may include conditions that may be satisfied
independently of action or inaction of a human. For example, a
triggering event may be when a certain date is reached, when a
stock price reaches a certain threshold, when patent rights expire,
when a copyright expires, when a natural event occurs (e.g., a
hurricane, a tornado, a drought, or the like), etc. Triggering
events may be defined as different types of triggers. For example,
triggers or triggering events may refer to changing states (e.g.,
state change event) such as where the smart contract is active upon
a set of data states (e.g., state change events). In other
examples, triggers or triggering events may refer to events that
occur such that users may need to passively wait for the events to
occur and the knowledge distribution system 16802 may need to
monitor for these events.
[0899] Referring to FIG. 170, the knowledge distribution system
16802 includes details of the smart contract 16840 and the smart
contract system 17068. In embodiments, smart contract actions 17086
may include, for example, monitoring events from a defined data
source, verifying fulfillment of obligations of one or more users
and/or third parties according to one or more conditions 17084
defined in the smart contract 16840, verifying payment and/or
transfer of tokens, property, other goods, or services, between one
or more users and/or third parties, transferring the digital
knowledge 16804 between parties or to one or more users, logging
one or more transactions in the distributed ledger 16808,
performing one or more operations with respect to the distributed
ledger 16808, creating one or more new blocks 16922 in the
distributed ledger 16808, and the like. In some embodiments, a
smart contract 16840 may include an event listener 17080 that is
configured to monitor one or more data sources (e.g., databases,
data feeds, data lakes, public data sources, or the like) for
detecting events to determine whether one or more conditions 17084
are met. For example, an event listener 17080 may listen to an
application programming interface (API) that provides a connection
between the knowledge distribution system 16802 and a printer, such
that a smart contract may trigger an obligation of a user to make a
payment when a printing instruction set governed by the knowledge
distribution set (such as a tokenized instruction set in a
knowledge token 17038) is used to print an item using the
instruction set. Thus, when a predefined set of conditions 17084 is
met, then a smart contract action 17086 may be triggered. This may
include triggering a payment process (such as initiating an
authorization of a payment on a credit card), closing out a
contract (such as when a prepaid number of uses of a knowledge set
has been reached), determining a price (such as by initiating a
reference to current pricing data in a marketplace or exchange),
reporting on an outcome (such as reporting a workflow or event), or
the like. In response to being triggered, the smart contract may
automatically execute the smart contract action 17086. In some
embodiments, the smart contracts are Ethereum smart contracts and
may be defined in accordance with the Ethereum specification, which
may be accessed at https://github.com/ethereum, the contents of
which are incorporated by reference. In other embodiments, the
smart contract system 17068 may include the event listener
17080.
[0900] In some embodiments, the smart contract 16840 may be
configured to "wrap" one or more instances of the digital knowledge
16804 in a smart contract wrapper (e.g., a "smart wrapper"). Once
wrapped, an instance of digital knowledge may be handled and/or
accessed differently than when unwrapped, such as by only being
readable, editable, and/or transferrable according to terms,
conditions, and/or operations of the smart contract 16840. The
smart contract 16840 may wrap the digital knowledge 16804 such that
in order to be accessed by the knowledge recipient 16818, the
digital knowledge 16804 must first be "unwrapped," (e.g., reverted
to a pre-wrapped form). In some embodiments, the pre-wrapped form
may be the tokenized form. The smart contract 16840, the
distributed ledger 16808, and/or the knowledge distribution system
16802 may unwrap one or more tokens and/or instances of the digital
knowledge 16804 in response to one or more triggering events. In
some embodiments, the knowledge distribution system 16802, or
another suitable system, may store a plurality of smart contract
templates from which the smart contract 16840 may be generated. In
some embodiments, the smart contract system 17068 may include a
smart contract (SC) generator 17082 that may parameterize at least
one smart contract template (from the plurality of smart contract
templates) based on the information provided by a user and any
conditions 17084 and/or actions 17086 defined by the user. For
example, the smart contract template may correspond to a type of
digital knowledge that is to be tokenized. The contract template
may include parameters based on the type of the digital knowledge.
These parameters may include: financial parameters for use of the
tokenized digital knowledge (e.g., financial parameters), royalty
rate parameters for intellectual property (e.g., royalty
parameters), number of times an instruction set can be used
parameters (e.g., usage parameters), output amount parameters that
may be produced using an instruction set (e.g., output produced
parameters), allocation of consideration among parties parameters
to the smart contract and designated beneficiaries of the smart
contract (e.g., allocation of consideration parameters), identity
parameters that may have permission to access the distributed
ledger 16808 and/or the digital knowledge (e.g., identity
parameters), and/or access condition parameters for the distributed
ledger 16808 and/or the digital knowledge (e.g., access condition
parameters). In some embodiments, the smart contract 16840 may be
configured to manage a wrapped token based on an aggregated set of
instructions defined in the smart contract 16840.
[0901] In some embodiments, the distributed ledger 16808 may store
smart contracts 16840 configured to facilitate licensing of one or
more intellectual property rights corresponding to an instance of
digital knowledge, such as know-how, patented material, trademarks,
works of authorship (e.g., copyrights), and/or trade secrets. In
embodiments, the knowledge distribution system 16802 may be
configured to allow one or more of the knowledge providers 16806 to
engage in a licensing agreement with one or more of the knowledge
recipients 16818 via a smart contract 16840 (e.g., using one or
more knowledge provider devices 16890 and/or one or more knowledge
recipient devices 16894). In embodiments, a smart contract 16840
may be configured to embed licensing terms for the intellectual
property in one or more of the blocks 16922 of the distributed
ledger 16808, including scopes of use, waivers, indemnifications,
limitations of use, geographical limitations, and/or the like. In
embodiments, one or more copies of and/or references to the one or
more pieces of intellectual property may be stored on the
distributed ledger 16808, and access to the one or more pieces of
intellectual property may be governed by terms of the smart
contract 16840. Upon execution of the smart contract 16840, the
knowledge distribution system 16802 may automatically transfer
access and licensing rights to the intellectual property to the
knowledge recipient 16818 (e.g., knowledge recipient device 16894
of knowledge recipient 16818) according to terms and/or operations
set forth in the smart contract 16840. In some embodiments, the
knowledge distribution system may be configured to verify assignee
rights with resources such as public patent assignee logs prior to
transferring access and/or licensing rights. In embodiments, the
smart contract 16840 may contain one or more operations to be
performed with respect to the distributed ledger 16808 to
facilitate an execution defined by the smart contract 16840. In
some embodiments, the smart contract 16840 may be configured to
automatically allocate royalties in transfers between one or more
knowledge providers 16806 and knowledge recipients 16818 (e.g.,
using knowledge provider devices 16890 and knowledge recipient
devices 16894) involving transfer of access to, ownership of,
and/or licensing rights to intellectual property. For example, if
the owner of the digital knowledge pays licensing fees to a
third-party patent owner of one or more aspects of the digital
knowledge (e.g., the inventor of a particular product design), the
smart contract may allocate a set percentage or amount of the
transaction price of the digital knowledge 16804 to the licensor,
such that the license to make, sell, use, and/or otherwise transact
for is transferred to the recipient of the digital knowledge 16804.
In embodiments, the operations for allocating royalties may be
performed according to one or more terms of one or more of the
smart contracts 16840 and may have related smart contract actions
17086.
[0902] In some embodiments, the knowledge distribution system 16802
may be configured to aggregate intellectual property licensing
terms. The distributed ledger 16808 may be configured to store an
aggregate stack of instances of the digital knowledge 16804, where
one or more aspects of the digital knowledge 16804 are restricted
in accordance with an intellectual property right of a party (e.g.,
a patent, copyright, trademark, or trade secret of the knowledge
provider or any other party). In embodiments, the ledger management
system 16910 may facilitate adding one or more instances of the
intellectual property to the aggregate stack, thereby associating
the added instance of intellectual property to the stack of
intellectual property to which the instance of intellectual
property is added. Operations such as transfer of control, edit,
viewing, ownership, and/or licensing rights may be performed on an
entire stack of intellectual property by the knowledge distribution
system 16802, such as according to terms of one or more smart
contracts 16840. Access to, ownership of, and/or sublicensing
rights to the aggregate stack of intellectual property may be
transferred from one or more of the knowledge providers 16806 to
one or more of the knowledge recipients 16818 via the knowledge
distribution system 16802 (e.g., using knowledge provider devices
16890 and knowledge recipient devices 16894). In some embodiments,
a smart contract may be configured to transfer rights to the
aggregate stack of intellectual property associated with an
instance of digital knowledge (e.g., the right to use, sell, offer
for sale, export, import, a product or process associated with the
intellectual property stack) or to transfer the intellectual
property stack in its entirety to the digital knowledge recipient.
In the latter scenario, the smart contract may be configured to
facilitate the assignment of the intellectual property stack to the
digital knowledge recipient (e.g., populating assignment forms that
are submitted to patent, trademark, or copyright offices of one or
more jurisdictions and to electronically file the assignment
documents). In some embodiments, the assignment of intellectual
property rights may be recorded in the distributed ledger 16808 as
well.
[0903] In some embodiments, the ledger management system 16910 may
define one or more operations that may handle or process
commitments of one or more parties to the smart contract 16840
and/or terms thereof. When a set of parties (e.g., knowledge
providers 16806, knowledge recipients 16818, crowdsourcers 16836
and/or third parties) commit to the terms of a smart contract 16840
to a term of a smart contract governing the transfer of digital
knowledge 16804, the knowledge distribution system 16802 (and/or
the smart contract 16840 itself) may handle or process commitments
of the parties and/or identifiers of the parties to one or more
portions (e.g., terms) of the smart contracts 16840. In
embodiments, upon a set of parties committing to a smart contract
16840, the smart contract 16840 and/or the knowledge distribution
system 16802 may link one or more of the parties to one or more of
the triggering events defined in the smart contract 16840, begin
monitoring one or more data sources to determine whether any
conditions 17084 defined trigger events are met, and/or
automatically perform operations/actions defined in the smart
contract (e.g., in response to the occurrence of a triggering
event). For example, a knowledge provider 16806 may upload a smart
contract 16840 (e.g., using knowledge provider device 16890) to the
distributed ledger 16808 and/or customize a smart contract 16840
using a smart contract template in connection with uploading an
instance of the digital knowledge 16804. In embodiments, the
knowledge provider 16806, a knowledge recipient 16818, or some
other party may indicate (e.g., via the knowledge distribution
system 16802, the distributed ledger 16808, and/or the smart
contract 16840) terms of an agreement between the knowledge
provider 16806 and the knowledge recipient 16818 upon an agreement
being formed between the knowledge provider 16806 and the knowledge
recipient 16818. In some embodiments, the smart contract 16840 may
include one or more rights, terms, and/or obligations provided by
the knowledge provider 16806 and/or a third party prior to
identification of and/or dealing with the knowledge recipient
16818. The knowledge recipient 16818 may agree to be bound by
rights, terms, and/or obligations defined via the smart contract
16840 upon agreeing to receive the digital knowledge 16804 (e.g.,
using knowledge recipient device 16894). The knowledge recipient
16818 may be a user who is willing to transact (e.g., buy, license,
or otherwise make a deal with the knowledge provider 16806) for the
digital knowledge 16804. The smart contract 16840 may commit or
otherwise bind (or process commitments) the knowledge provider
16806, the knowledge recipient 16818, and/or other parties to the
agreement to terms and/or conditions 17084 of the smart contract
16840 in response to receiving indication via the knowledge
distribution system 16802 and/or the distributed ledger 16808.
[0904] In some embodiments, the knowledge distribution system 16802
may include an account management system. In embodiments, the
account management system 16846 may facilitate creation and/or
storage of user accounts related to users of the knowledge
distribution system 16802, the knowledge distribution system 16802,
and/or the distributed ledger 16808. For example, the account
management system 16846 may be configured to facilitate
registration of one or more of the knowledge providers 16806, the
knowledge recipients 16818, the crowdsourcers 16836, and/or other
third parties that may be associated with the knowledge
distribution system 16802, the knowledge distribution system 16802,
and/or the distributed ledger 16808. In some embodiments, the
account management system 16846 may be configured to, together with
the ledger management system 16910, facilitate intake of data from
registered users of the distributed ledger 16808, such as name,
address, company affiliation, financial account information (e.g.,
bank account numbers and/or routing numbers), digital identifiers
(e.g., IP addresses, MAC addresses, and the like), and any other
suitable information related to the registered users.
[0905] The account management system 16846 may update the user
account of the registered user with data taken in and related to
the registered user. In some embodiments, the account management
system may facilitate generation and/or distribution of one or more
of the permission keys 16932 to one or more of the registered
users. The permission keys 16932, 16932-1, 16932-2, 16932-3,
16932-N may provide the registered user with access to one or more
instances of the digital knowledge 16804 and/or services associated
with the knowledge distribution system 16802.
[0906] In some embodiments, the knowledge distribution system 16802
may include a user interface system 16850 configured to present a
user interface. The user interface may be configured to facilitate
uploading of digital knowledge 16804, generation and/or uploading
of a smart contract 16840, and viewing of the digital knowledge
16804 and/or the smart contract 16840 (and statuses thereof). The
user interface may be a graphical user interface. Information
presented to users of the knowledge distribution system 16802 via
the user interface may include descriptions of one or more
instances of the digital knowledge 16804, ownership and/or
licensing information related to the one or more instances of the
digital knowledge 16804, information related to the user viewing
the user interface and/or other users of the knowledge distribution
system 16802, price information related to one or more instances of
the digital knowledge 16804, statistics and/or metrics related to
the distributed ledger 16808 and/or contents thereof, such as node
count, payouts for generation of additional nodes, and any other
suitable information. In some embodiments, users may view contents
of their digital wallets via the user interface, such as a balance
of one or more types of currency tokens.
[0907] In some embodiments, the user interface may be configured to
allow one or more users to perform one or more of the operations
related to the digital knowledge 16804 and/or the distributed
ledger 16808, such as buying, selling, verifying, and/or reviewing
the digital knowledge 16804 and/or performing other operations
related to the distributed ledger 16808 discussed herein. For
example, the knowledge provider 16806 may select a computer file
(such as a 3D printer schematic file) to upload to the distributed
ledger 16808 via the user interface (e.g., using knowledge provider
device 16890). The user interface may present the knowledge
provider 16806 with one or more options related to uploading the
digital knowledge 16804, such as an ability to configure a smart
contract 16840 and related terms for wrapping and/or tokenizing the
digital knowledge 16804. Other options may include privacy options,
such as options pertaining to one or more users or classes of users
who may and/or may not view, buy, sell, license, rate, verify,
review, or otherwise manage or interact with the digital knowledge
16804.
[0908] In some embodiments, the user interface system 16850 may
include a marketplace system 16854 configured to establish and
maintain a digital marketplace 16856. In embodiments, the digital
marketplace 16856 provides an environment that allows knowledge
providers and potential recipients to engage in commerce relating
to the transfer of digital knowledge 16804. For example, the
digital marketplace may be configured to allow one or more users
and/or third parties to search for one or more pieces of digital
knowledge 16804 similar to a digital storefront, transact for one
or more pieces of the digital knowledge 16804 (e.g., buy, sell,
license, lease, bid on, and/or give away the digital knowledge),
receive recommendations for digital knowledge 16804, review one or
more pieces of the digital knowledge 16804, verify source
information and/or other information related to one or more pieces
of the digital knowledge 16804, transact for one or more pieces of
the digital knowledge 16804 (e.g., buy, license, bid on one or more
pieces of the digital knowledge, or the like), and/or perform any
other suitable marketplace interaction with the digital knowledge
16804, one or more of the knowledge providers 16806, the
distributed ledger 16808, one or more of the knowledge recipients
16818, one or more crowdsourcers 16836, or any other user or third
party. In some embodiments, the digital marketplace 16856 may be
configured to allow users to edit user accounts associated with
themselves and view user accounts associated with other users. In
some embodiments, the digital marketplace 16856 user interface may
allow users to make reviews and/or ratings of other users.
[0909] In embodiments, the knowledge distribution system 16802 may
include one or more datastores 16858. FIG. 171 illustrates an
example set of datastores 16858 of the knowledge distribution
system 16802. In some embodiments, the knowledge distribution
system 16802 may include one or more datastores 16858 configured to
store data related to the digital knowledge 16804, the distributed
ledger 16808, the knowledge providers 16806, the knowledge
recipients 16818, the crowdsourcers 16836, the knowledge tokens
17038, the smart contracts 16840, the account management system
16846, the marketplace system 16854, or any other suitable type of
data. A datastore may store folders, files, documents, databases,
data lakes, structured data, unstructured data, or any other
suitable data.
[0910] In some embodiments, the datastores 16858 may include a
knowledge datastore 17160 configured to store data. The knowledge
datastore 17160 may be in communication with the user interface
system 16850. The user interface system 16850 may be configured to
populate the user interface with data stored in the knowledge
datastore 17160. In some embodiments, data stored in the knowledge
datastore 17160 may include knowledge related to the digital
knowledge 16804 such as source, reviews, price, ownership,
licensing, related knowledge providers 16806, related knowledge
recipients 16818, serial numbers, related crowdsourcers 16836, or
any other suitable information. For example, the knowledge
datastore 17160 may contain information related to a 3D printer
schematic such as origin, date of creation, names of one or more
contributing individuals, groups, and/or companies, pricing, market
trends for related schematics, serial numbers and/or part
identifiers, and any other suitable type of data related to the 3D
printer schematic.
[0911] In some embodiments, the datastores 16858 may include a
client datastore 17162 (e.g., may include user datastore), the
client datastore 17162 being configured to store data relating to
users of the knowledge distribution system 16802. The client
datastore 17162 may be in communication with the account management
system 16846 and may be populated with user accounts related to one
or more of the user accounts, data contained in one or more of the
user accounts, data related to the one or more user accounts,
and/or a combination thereof.
[0912] In some embodiments, as shown in FIGS. 170 and 171, the
datastores 16858 may include a smart contract datastore 17164. In
embodiments, the smart contract datastore 17064 is configured to
store data related to one or more of the smart contracts 16840
and/or smart contract templates (from which smart contracts 16840
may be parameterized and instantiated). In embodiments, the smart
contract datastore 17064 may be in communication with the ledger
management system 16910. Data stored in the smart contract
datastore may include, for example, smart contract templates, one
or more smart contracts 16840, data related to instances of the
digital knowledge 16804 related to one or more of the smart
contracts 16840, data related to parties to one or more of the
smart contracts 16840, and any other suitable data. The smart
contract datastore 17064 may be configured to store completed smart
contracts that have already been executed. The smart contract
datastore 17064 may be configured to store smart contracts that
have not yet been uploaded to the distributed ledger 16808.
[0913] Referring to FIG. 168, in some embodiments, the knowledge
distribution system 16802 may include an analytics system 16866
configured to analyze one or more tokenized instances of the
digital knowledge 16804, such as the knowledge token 17038, and
report an analytic result. The analytics system may analyze the
tokenized instance of the digital knowledge 16804 in order to
determine one or more properties and/or metrics of the tokenized
instance of the digital knowledge 16804. Properties of tokenized
digital knowledge 16804 may include, for example, source, reviews,
price, ownership, licensing, related knowledge providers 16806,
related knowledge recipients 16818, serial numbers, related
crowdsourcers 16836, or any other suitable information. The
analytics system 16866 may be configured to determine one or more
trends, metrics, and/or predictions related to the properties. In
some embodiments, the analytics system 16866 may include a machine
learning module configured to perform predictions and/or analyses
of the properties related to the digital knowledge 16804 via one or
more machine learning techniques.
[0914] In some embodiments, properties of tokenized intellectual
property may be analyzed by the analytics system 16866. For
example, the analytics system 16866 may be configured to analyze
tokenized digital knowledge 16804 including intellectual property.
Analyzed properties of tokenized intellectual property may include,
for example, transaction history including changes and/or
assignments in ownership and/or licensing rights of the
intellectual property, litigation history including lawsuits
involving the intellectual property and data related to the
lawsuits, information pulled from one or more databases of
intellectual property related to the intellectual property, and any
other suitable property of the tokenized intellectual property or
any other token or suitable instance of the digital knowledge
16804. Metrics related to tokenized intellectual property may
include, for example, value, age, strength, efficacy, or any other
suitable metric related to the tokenized intellectual property or
any other knowledge token 17038 or suitable instance of the digital
knowledge 16804.
[0915] In some embodiments, the knowledge distribution system 16802
may be configured to perform an aggregation operation that
aggregates a set of operations and/or instructions included in one
or more instances of the digital knowledge 16804. Aggregation may
be employed where component instances of digital knowledge 16804
are aggregated to form larger instances of digital knowledge. In
embodiments, aggregation occurs where component instances are
concatenated to form larger instances, such as by adding component
instances as additional (optionally tokenized) blocks to a chain,
or by adding references or links to component knowledge blocks.
Examples of concatenation aggregation include where chapter
instances are concatenated to form book instances, where sentence
instances are concatenated to form paragraphs, where word instances
are concatenated to form sentence instances, and the like. In
embodiments, aggregation occurs where component instances are
linked schematically to form a system, such as where knowledge
representing physical component parts of a machine are linked to
form the machine, where component steps in a process are linked to
form the complete process, or the like. In embodiments, aggregation
of knowledge involves linking elements in a flow, such as where
instances are linked diagrammatically to generate a workflow, such
as where ingredients and process steps are linked to generate a
recipe or formulation process, where workflow steps and materials
are linked to describe a work process (such as one involving
expertise or know how) or the like. In embodiments, aggregation of
knowledge involves coupling of partial instances to form a whole
instance, such as where two or more sub-parts of a formula are
joined to form a complete formula (e.g., a chemical,
pharmaceutical, biological, materials science, physical, or other
formula), where two or more sub-parts of an instruction set (e.g.,
computer code) are joined to form the entire instruction set, or
the like. In embodiments, aggregation may involve linking related
instances of knowledge in a cluster, such as where instances of
knowledge are topically linked to form a cluster, such as a cluster
of related subjects in a knowledge domain (such as a scientific
domain, a domain of the humanities, a social science domain, a
commercial domain, a business domain, or many others). In
embodiments, aggregation may involve hierarchical aggregation of
knowledge, such as by representing knowledge according to one or
more defined hierarchies, such as an organizational hierarchy (such
as an organizational chart or reporting structure), an industry
hierarchy, a topical hierarchy, a physical hierarchy, or the like.
Many other examples of aggregation may be envisioned. The ledger
management system 16910 may be configured to perform the
aggregation operation. The aggregation operation adds at least one
instruction to a preexisting set of instructions, thereby yielding
a modified set of instructions. The modified set of instructions
may then be stored in the distributed ledger 16808 and may be
tokenized, manipulated, and/or managed similarly to any instance of
the digital knowledge 16804. In some embodiments, the aggregation
operation may be performed by the smart contract 16840, according
to one or more terms of the smart contract 16840, and/or in
reaction to triggering of one or more triggering events of the
smart contract 16840. In some embodiments, the aggregation
operation may be performed at request of a user of the knowledge
distribution system 16802.
[0916] In some embodiments, the ledger network 16970 is a federated
network, such that the ledger management system 16910 of the
knowledge distribution system 16802 may act as an arbiter to
simplify the consensus mechanism. Some or all of the nodes 16916
may be preselected or preapproved to act as nodes 16916 with
respect to the management of blocks of the distributed ledger 16808
and/or data contained therein, such as one or more instances of the
digital knowledge 16804. The ledger management system 16910 may
ease computational burdens on the other nodes 16916 in the ledger
network 16970. In some embodiments, the distributed ledger 16808 is
distributed, such that the participating nodes 16916 may each store
a respective local copy 16808-L of the distributed ledger 16808,
where each local copy 16808-L may include the entire distributed
ledger 16808 or a portion thereof.
[0917] In the illustrated example, the knowledge distribution
system 16802 stores a copy of the distributed ledger 16808, the
copy of the distributed ledger 16808 being local to the knowledge
distribution system 16802, and each node 16916 stores a distributed
local copy 16808-L of the distributed ledger 16808. In some
embodiments, however, the knowledge distribution system 16802 does
not store a local copy of the distributed ledger 16808 such that
the distributed ledger 16808 is maintained wholly by participant
nodes 16916. A distributed copy (e.g., copy 16808-L) of the
distributed ledger 16808 may contain the entire distributed ledger
16808 or only a portion of the distributed ledger 16808. In
general, each copy of the distributed ledger 16808 stores a set of
blocks 16922. In some embodiments, each respective block may store
information relating to a respective state change event as a hash
value and may further store a block identifier of a "parent" block
that was added to the distributed ledger 16808 prior to the
respective block. In some embodiments, the ledger management system
16910 may select the block that was most recently added to the
ledger to act as the parent block, whereby the ledger management
system 16910 includes the block identifier of the most recently
added block to the state change event record.
[0918] A state change event may refer to any change of state
relating to the digital knowledge 16804 and/or management of one or
more instances of the digital knowledge 16804. Non-exhaustive
examples of state change events may include creating a new instance
of the digital knowledge 16804, registering a new user of the
knowledge distribution system 16802, such as a new knowledge
provider 16806 (and/or registering a new knowledge provider device
16890) or a new knowledge recipient 16818 (and/or registering a new
knowledge recipient device 16894), granting the new user permission
to perform a specific operation, modifying certification and/or
validation of one or more instances of the digital knowledge 16804
at the request of a user, transmitting one or more instances of the
digital knowledge 16804 to one or more knowledge recipients 16816
(e.g., knowledge recipient devices 16894), recordation of a use of
an instance of digital knowledge 16804 by a knowledge recipient,
and the like. In some embodiments, the ledger management system
16910 may create a state change event record that indicates the
state change event, e.g., the operation that was performed, for
each state change event that occurs. A state change event record
may further be information/metadata relating to the event, such as
one or more user identifiers of one or more respective users
associated with the state change event, a timestamp corresponding
to the state change event, a device identifier of the device that
requested or performed an operation, an IP address corresponding to
the device that requested or performed the operation, and/or any
other relevant data. In some embodiments, the ledger management
system 16910 may include a block identifier of a previous block
that was previously stored on the ledger 16808 in the state change
event record, such that the previous block may be a "parent" to a
new block that will be generated based on the state change event
record, as the state change event record references the previously
stored block, but the previously stored block will not reference
the new block. In some embodiments, the block identifier may be the
hash value of a previously generated block.
[0919] In some embodiments, the ledger management system 16910
and/or one or more of the nodes 16916 may be configured to generate
a state change event record for each event occurring with respect
to management of digital knowledge 16804 via the knowledge
distribution system 16802. In embodiments, the ledger management
system 16910 and/or one or more of the nodes 16916 may generate a
new block 16922 corresponding to the state change event record by
generating a cryptographic hash (or "hash value") of the state
change event record by inputting the state change event record into
a hashing function to obtain the cryptographic hash. The resultant
hash value is a unique value (or substantially unique value with a
very low likelihood of collisions) that represents the contents of
the state change event record, such that the resultant hash value
is a unique identifier that identifies the new block and that also
encodes the contents thereof, including a block identifier of the
parent block. Thus, when the new block is "solved" (solving a block
in this context may refer to the process of determining the
original contents of the state change event record encoded in the
hash value), the solution of the new block indicates the contents
of the state change event record, including the block identifier of
the parent block. As such, the hash value of the preceding state
change event record may be used to verify the authenticity of the
current state change event record by way of verification. While the
above description describes blocks that store only one state change
event record, in some embodiments, the ledger management system
16910 and/or one or more of the nodes 16916 may encode two or more
state change event records in a single block. The ledger management
system 16910 and/or one or more of the nodes 16916 may include the
two or more state change event records in the body of a new block
data structure and may include the block identifier of the previous
block in a block header of the new block data structure. The ledger
management system 16910 and/or one or more of the nodes 16916 may
then input the new block data structure into the hashing function,
which outputs the new block 16922. In these embodiments, the new
block 16922 may be a cryptographic hash that represents the two or
more state change event records and the block identifier of the
previous block (i.e., the parent block to the new block). In this
way, when the new block is solved, the solution to the block is the
two or more state change event records and the block identifier of
the parent block, where the block identifier of the parent block
can be used to validate the authenticity and accuracy of the new
block.
[0920] In embodiments, the ledger management system 16910 and/or
one or more of the nodes 16916 may request verification of a block
16922. In some embodiments, verification of a block 16922 may
include broadcasting a request 16924 to verify a block 16922
(referred to as "the block 16922 to be verified") to the other
nodes 16916 in the ledger network 16970 (which may include the
ledger management system 16910 if the ledger management system
16910 is not issuing the request 16924). In some embodiments, the
request 16924 may include or be broadcasted with the block 16922 to
be verified. Verification may further include one of the other
nodes 16916 that received the request 16924 (or potentially the
ledger management system 16910) solving the block 16922 to be
verified. A node 16916 may determine it has solved the block 16922,
when the solution to the block contains a valid block
identifier--that is, a block identifier that references one of the
other blocks 16922 stored on the distributed ledger 16808. Once the
solver has determined the solution to the block 16922, the solver
broadcasts a "proof of work" 16928 to the other nodes 16916. In
some embodiments, the proof of work 16928 may be the block
identifier of the previous block 16922. In some embodiments, each
of the non-solving nodes 16916 (potentially including the ledger
management system 16910), may receive the proof of work and may
validate the proof of work based on the copy of the distributed
ledger 16808 that is stored at the node 16916. In these
embodiments, each node 16916 may determine whether the block
identifier contained in the proof of work corresponds to (e.g.,
matches) a block identifier of a block stored on the local copy of
the distributed ledger 16808.
[0921] In some embodiments, the ledger management system 16910, in
conjunction with the other nodes 16916 in the ledger network 16970,
maintains an immutable record of any operations performed with
respect to a management of digital knowledge 16804 via the
knowledge distribution system 16802 using the knowledge
distribution system 16802. In these embodiments, any time a user
performs an operation with respect to management of digital
knowledge 16804 via the knowledge distribution system 16802 hosted
on the knowledge distribution system 16802, the ledger management
system 16910 may: generate a new event state record corresponding
to the operation; encode the new event state record into a new
block data structure and a block identifier of a previous block
(e.g., the most recently added block) into a block header of the
new block data structure; and hash the new block data structure
using a hashing function to obtain the new block. Furthermore, in
some embodiments, the ledger management system 16910 may transmit a
request 169240 to verify the new block 16922 to the other nodes
16922 in the network 16814. In some embodiments, one of the nodes
16916 may attempt to determine a solution to the new block 16922.
If a valid solution is determined, the solver node 16916 may
transmit a proof of work 16928 to the other nodes 16916 in the
ledger network 16970, and the other nodes 16916 may attempt to
validate the proof of work 16928.
[0922] In some embodiments, the ledger management system 16910
utilizes a distributed ledger 16808 to manage permissions of
different users of the knowledge distribution system 16802, such as
one or more of the knowledge providers 16806 (and/or one or more
knowledge provider devices 16890) and/or one or more of the
knowledge recipients 16818 (and/or one or more knowledge recipient
devices 16894). In some embodiments, permissions may be granted
with respect to an instance of the digital knowledge 16804 or set
of instances of the digital knowledge 16804. For example,
permissions may include permission for a user to upload an instance
of the digital knowledge 16804 or set of instances of the digital
knowledge 16804, permission for a user to view an instance of the
digital knowledge 16804 or set of instances of the digital
knowledge 16804, permission for a user to edit an instance of the
digital knowledge 16804 or set of instances of the digital
knowledge 16804, permission for a user to delete an instance of the
digital knowledge 16804 or set of instances of the digital
knowledge 16804, permission for a user to download an instance of
the digital knowledge 16804 or set of instances of the digital
knowledge 16804, permission for a user to print (e.g.,
print-to-paper or 3D print) an instance of the digital knowledge
16804 or set of instances of the digital knowledge 16804, and the
like. Permissions may additionally or alternatively relate to
services 16930 that are offered by the knowledge distribution
system 16802. For example, permissions may include permission for a
user to access a full-text search functionality on the knowledge
distribution system 16802, permission for the user to use a virus
scanner offered by the knowledge distribution system 16802,
permission for the user to have the knowledge distribution system
16802 generate a machine-generated instance of the digital
knowledge 16804 or set of instances of the digital knowledge 16804,
and the like. Permissions may also include the permission to
perform an operation granted to one or more other users.
Permissions may be applied to one or more users by default, applied
to one or more classes of users, such as users holding one or more
of the permission keys 16932, automatically be applied to one or
more categories of users such as knowledge provider 16806,
knowledge recipient 16818, and/or crowdsourcer 16836, and/or may be
manually given to one or more users by administrators and/or
managers of the knowledge distribution system.
[0923] In some embodiments, the ledger management system 16910 may
manage individual participant's access to respective services 16930
by generating one or more unique service-specific permission keys
16932 for a respective service 16930 and issuing each respective
unique service-specific permission key 16932 to a respective
participant that has been granted access to the respective service
16930. In some of these embodiments, the ledger management system
16910 may utilize the distributed ledger 16808 to store proof of
the service-specific permission keys 16932 and to manage
permissions to the services 16930. In these embodiments, the ledger
management system 16910 may: receive an instruction to grant a user
permission to access a particular service; generate a
service-specific permission key 16932 corresponding to the
particular service and assign the key 16932 to the user; encode a
user ID of the user and the service-specific permission key 16932
in a state change event record; generate a new block based on the
state change event record and a block identifier of a previously
stored block; store the new block on its local copy of the
distributed ledger 16808, and broadcast the new block to other
nodes 16916 in the network for storage on the respective copies of
the distributed ledger 16808 stored at the nodes. In some
embodiments, the ledger management system 16910 may validate the
new block prior to storing and transmitting the block for storage
at the other nodes 16916. The ledger management system 16910 may
further transmit the new block to a computing device associated
with the user (which may or may not be a participating node),
whereby an agent 16920 on the computing device may store the new
block. In this way, the agent 16920 may use the new block to obtain
access to the particular service, when the user attempts to access
the particular service 16930 from the computing device. When the
user attempts to access the particular service 16930, the agent may
communicate the block containing the permission key 16932
corresponding to the particular service 16930 to the ledger
management system 16910. The ledger management system 16910 may
solve the received block and/or validate the received block, in the
manner described above. If the ledger management system 16910 is
able to validate the received block, the ledger management system
16910 grants the computing device of the user access to the service
16930, whereby the user may begin using the service 16930. In some
embodiments, permissions and access to an instance of the digital
knowledge 16804 or set of instances of the digital knowledge 16804
that are stored in an organizational structure may be managed in a
similar way, where users are granted permission keys 16932, where
these permission keys 16932 correspond to specific operations that
a user may perform on the instance of the digital knowledge 16804
or set of instances of the digital knowledge 16804 with which the
permission keys 16932 are associated.
[0924] In some embodiments, the ledger management system 16910
and/or one or more computing node computing devices 16916 having
requisite processing resources may generate an immutable log of a
transaction based on the distributed ledger 16808. In these
embodiments, the ledger management system 16910 and/or the nodes
16916 (referred collectively as the solving nodes 16916) may begin
solving the most recent blocks 16922 in the distributed ledger
16808. Each time a block is solved, the solving node 16916 may
transmit the proof of work 16928 to other nodes 16916, which may
then verify the accuracy of the solution. The solving nodes 16916
may iteratively solve each of the blocks 16922 in the distributed
ledger 16808 in this manner until the entire distributed ledger
16808 is solved, thereby resulting in an operation log of the
management of digital knowledge 16804 via the knowledge
distribution system 16802. The operational log may define the
actions or operations that were performed using the knowledge
distribution system 16802. By creating, validating, and solving the
blocks 16922 of the distributed ledger 16808 in the manners
described above, the distributed ledger 16808 is generated in a
transparent and secure manner. The resultant operational log is
stored in an encrypted manner until it is solved, and once solved,
the operational log is auditable and immutable. The operational log
may indicate each time a user was allowed to access the distributed
ledger 16808 and/or management of digital knowledge 16804 via the
knowledge distribution system 16802, the permissions that each user
was granted, the requests to perform operations or use services
16930 that each user initiated, the operations that were performed,
the user that performed the operation, and the like.
[0925] In some embodiments, the solving nodes 16916 may optimize
the solving of the ledger 16808 by solving different blocks 16922
in a distributed manner. For example, if the distributed ledger
16808 includes one or more forks (e.g., when more than one child
block points 16922 to the same parent block 16922), the distributed
ledger 16808 may be said to fork at the parent block 16922. In this
example, each chain originating at the fork may have a final block
16922 (or leaf block 16922). In this scenario, different solving
nodes 16916 may begin solving the ledger 16808 at different leaf
blocks 16922 in a breadth-first or depth-first manner, thereby
increasing the speed at which the ledger 16808 is solved.
[0926] In some embodiments, the ledger management system 16910 may
be configured to facilitate collaboration between one or more of
the knowledge providers 16806, one or more of the knowledge
recipients 16818, or a combination thereof, by assisting in the
execution of management of digital knowledge 16804 via the
knowledge distribution system 16802 using, for example, smart
contracts. In these embodiments, the ledger management system 16910
may provide management of digital knowledge 16804 via the knowledge
distribution system 16802 that is defined to facilitate a
respective type of management of digital knowledge 16804. For
example, for a transfer of one or more instances of digital
knowledge to a knowledge recipient 16818 (e.g., using knowledge
recipient device 16894) in exchange for funds transmitted to a
knowledge provider 16806 (e.g., knowledge provider device 16890) in
accordance with a sale, license, or rental agreement and/or a smart
contract, the ledger management system 16910 define various tasks
that must be completed before a next step can be performed in sale,
license, or rental of the digital knowledge 16804. In this example,
the knowledge distribution system 16802 may require that an
instance of the digital knowledge 16804 or a link and/or reference
thereto must be uploaded to the distributed ledger 16808 prior to a
transfer of funds from the knowledge recipient 16818 to the
knowledge provider 16806. Another condition may be that one or more
parties having adequate permission to sign a document must
electronically execute a document before engaging in a transfer of
one or more instances of the digital knowledge 16804. The knowledge
distribution system 16802, the ledger management system 16910,
and/or the distributed ledger 16808 may be preconfigured based on
the type of management of digital knowledge 16804 to be executed
via the knowledge distribution system 16802 and/or may be
customized by one or more parties associated with the management of
digital knowledge 16804 via the knowledge distribution system
16802. In some embodiments, each operation and/or management of
digital knowledge 16804 via the knowledge distribution system 16802
may be encoded in a smart contract, whereby the smart contract may
manage the phases of the workflow when the smart contract
determines that one or more required conditions are met. In some
embodiments, copies of a smart contract are stored and executed by
the agents 16920 of one or more respective nodes 16916. The agent
16920 may facilitate the performance of operations that are defined
in the smart contract (including validating permissions to perform
the operations using the distributed ledger 16808), the reporting
and recording of the performance of the operations (e.g., by
generating blocks or requesting generation of blocks from the
ledger management system 16910), and/or verifying that one or more
conditions defined in the smart contract are met. Once a consensus
is achieved with respect to one or more required conditions, the
management of digital knowledge 16804 via the knowledge
distribution system 16802 may progress to a next phase in the
workflow. In this way, the ledger network 16970 (e.g., the ledger
management system 16910 and the participating nodes 16916) may
facilitate collaboration between parties in the management of
digital knowledge 16804 via the knowledge distribution system 16802
by assisting in the execution of the workflow associated with the
management of digital knowledge 16804 via the knowledge
distribution system 16802 by validating pre-closing and closing
work and/or providing a framework for the management of digital
knowledge 16804 via the knowledge distribution system 16802 by way
of smart contracts.
[0927] FIG. 172 illustrates a method 17200 of deploying a knowledge
token 17038 and related smart contract 16840 via the knowledge
distribution system 16802.
[0928] At 17210, the knowledge distribution system 16802 receives
an instance of the digital knowledge 16804, such as from a user. In
embodiments, the user may be affiliated with an organization (e.g.,
an organization that owns the digital knowledge) or an unaffiliated
individual (e.g., a person who created the digital knowledge on
their own or in collaboration with other unaffiliated individuals).
The user may provide the instance of the digital knowledge 16804
via a graphical user interface. For example, the user may upload
the digital knowledge via the graphical user interface. In
embodiments, the digital knowledge may be an instruction set that
may be performed by a device or set of devices. The user may upload
the digital knowledge by providing the knowledge itself or a
reference to the digital knowledge (e.g., an address from which the
digital knowledge may be accessed/retrieved electronically).
[0929] In embodiments, the user may provide additional information,
such as a type of the digital knowledge, a description of the
digital knowledge, a price to be charged to access the digital
knowledge, and the like. In some embodiments, the user may provide
licensing data, such as any patent, trademark, copyright rights
that are licensed or otherwise conveyed to a knowledge recipient, a
length of the license(s) (e.g., when each license expires), a scope
of the license(s) (e.g., limitations on use/sale/transferability or
geographical limitations), and the like. In embodiments, the user
may define validation information, such as
certifications/validations of the digital knowledge. In
embodiments, the user may also define limitations on the
distribution of the digital knowledge (e.g., a total number of
knowledge tokens that may be generated).
[0930] In embodiments, the user may define a set of conditions
and/or actions that are used to generate a smart contract governing
transactions for the digital knowledge. Examples of conditions may
include a time period when the smart contract is valid,
requirements for a recipient device (e.g., certain specifications
on a device, such as a type of 3D printer, a minimum amount of
processing power, required machinery to perform certain processes,
or the like) that must be verified before release of the digital
knowledge, requirements for a knowledge recipient (e.g.,
definitions of certain types of data that must be provided to
ensure the knowledge recipient is eligible to receive the digital
knowledge), or any other suitable conditions. In some embodiments,
the user may define a set of actions that may be performed in
response to certain conditions being triggered. Some of the actions
that are performed by a smart contract may be default conditions,
such as writing a record of the transaction to the distributed
ledger or releasing the digital knowledge. In some embodiments, a
user may define custom actions, such as defining allocations of
funds to third-party rights holders, generating a serial number for
a product that is produced by the digital knowledge, digitally
signing a product that is produced by the digital knowledge,
exposing an API to a knowledge recipient, or the like.
[0931] At 17212, the knowledge distribution system tokenizes the
digital knowledge 16804, thereby creating a knowledge token 17038.
In embodiments, the ledger management system may tokenize the
digital knowledge by wrapping a smart contract around the digital
knowledge to obtain the knowledge token. In some embodiments, the
ledger management system may retrieve a smart contract template
from the smart contract datastore, such that the smart contract
template corresponds to a type of the digital knowledge that is to
be tokenized. In some of these embodiments, the ledger management
system may parameterize the smart contract template based on the
information provided by the user and any conditions and/or actions
defined by the user. For example, the smart contract may be
parameterized for the instance of digital knowledge (or a reference
thereto), any licenses that are granted, the price to be paid, any
conditions that are to be met, and any actions to be performed. In
some embodiments, the ledger management system may include any
libraries in the smart contract that are needed to support any of
the functions defined in the smart contract. In some embodiments,
the ledger management system may configure one or more event
listeners that allow the smart contract to monitor one or more data
sources. In these embodiments, the ledger management system may
define the data source(s) to be monitored, whereby the event
listener obtains and/or processes the data from the data source(s)
which is then used to determine whether a certain condition or set
of conditions is met. Additional examples of tokenization may be
found in the Ethereum specification, which may be accessed at
https://github.com/ethereum. In embodiments, the knowledge
distribution system may generate a set number of knowledge tokens,
whereby each knowledge token may be used to facilitate a different
transaction for the instance of digital knowledge.
[0932] At 17214, the knowledge distribution system stores the
knowledge token(s) 17038. In embodiments, the ledger management
system may store the knowledge token(s) by deploying the knowledge
token(s) to a distributed ledger 16808. In embodiments, the ledger
management system may initially assign the ownership of the
knowledge token(s) to the knowledge provider. In embodiments, the
knowledge distribution system may also store information relating
to the instance of digital knowledge in the knowledge datastore,
which may be used to populate a marketplace site where potential
knowledge recipients can view information relating to the digital
knowledge.
[0933] FIG. 173 illustrates a method 17300 of performing high level
process flow of a smart contract that distributes digital
knowledge. In embodiments, the smart contract may be a knowledge
token that is stored on the distributed ledger and that is executed
by one or more nodes that host the distributed ledger. In some of
these embodiments, the smart contract may be executed on a virtual
machine or in a container.
[0934] At 17310, the smart contract monitors one or more of the
conditions defined in the smart contract. In some embodiments, an
event listener obtains data (either passively or actively) from one
or more data sources defined in the smart contract 16840. As the
event listener obtains data from the one or more data sources, the
smart contract may determine whether certain conditions are met,
and if so, may perform an action that is triggered by the met
conditions.
[0935] At 17312, the smart contract verifies conditions for
transaction of digital knowledge and at 17314, the smart contract
initiates transfer of the digital knowledge 16804. In embodiments,
the smart contract may include an event listener that determines
whether a requisite amount of funds have been deposited to the
smart contract. Once a party has deposited the requisite amount of
funds (e.g., a predefined amount of cryptocurrency or fiat
currency), the smart contract may initiate the transfer of the
digital knowledge to the knowledge recipient (e.g., the party that
deposited the requisite amount of funds). In embodiments, this may
include updating the distributed ledger with a block that indicates
the change in ownership of the token to the knowledge recipient and
providing any required keys to the knowledge recipient. Once the
ownership of the knowledge token has been changed, the knowledge
recipient may access the digital knowledge contained therein (and
in accordance with any restrictions defined in the smart contract,
such as using a particular type of device).
[0936] As discussed, the techniques described herein may be applied
to facilitate transactions for different types of instruction sets.
In some embodiments, the knowledge distribution system may be used
to distribute instruction sets for 3D-printing specific products
(e.g., replacement parts, medical devices, custom products,
manufacturing parts, and the like). In operation, the knowledge
distribution system 16802 may present a graphical user interface to
a user, whereby the user may provide an instance of digital
knowledge, as well as a user provider (e.g., a knowledge provider)
may upload an instruction set for printing a 3D item to the
knowledge distribution system 16802. In embodiments, the 3D
printing instruction set may include a file (e.g., a CAD file
and/or an STL file) and any accompanying instructions for printing
the product defined in the file. In some embodiments, the user may
also define a transaction price that defines an amount of currency
(fiat currency and/or cryptocurrency) that must be paid to purchase
a knowledge token containing the 3D printing instruction set.
Additionally, the user may provide a description of the product and
any requirements for printing (e.g., required materials and/or
device types or minimum specifications needed to 3D print the
product). The user may also provide additional information, such as
photographs of a printed product, certifications made with respect
to the product, and the like.
[0937] In embodiments, the user may define any intellectual
property rights that are being licensed or otherwise conveyed to a
knowledge recipient with the digital knowledge (also referred to as
an intellectual property stack) with the transaction for the 3D
printing instruction set. In some embodiments, the user may define
an allocation schedule that defines how royalties are divided
amongst one or more licensors. For example, if the product that is
printed from the instance of digital knowledge is licensed under
one or more patents, design patents, copyrights, and/or trademarks,
a portion of the transaction price for each printed product may be
allocated to the licensors as royalty payments. In this example,
the user may identify the licensor(s) that collect the royalties
and may assign a percentage or amount of the royalties that go to
each respective licensor. In embodiments, the user may define any
geographical limitations on the digital knowledge. For example, the
user may define countries, regions, jurisdictions, or other
geographical areas to which the digital knowledge may or may not be
distributed. In embodiments, the user may further define other
types of permissions or restrictions, including 3D printer
requirements (e.g., a set of 3D printer types, makes and models
that can print the product, serial numbers of 3D printers that can
print the product, material types that must be used to print the 3D
product, and the like), a time period during which the item can be
3D printed, whether the digital token may be transferred to a
downstream recipient, or the like. In embodiments, the user may
define actions that are performed in connection with 3D printing an
object, such as assigning a serial number to the product (which may
or may not be printed to the object), and/or the like. In
embodiments, the user may further define any warranties,
disclaimers, indemnifications, and/or the like associated with the
3D-printed product.
[0938] In embodiments, the smart contract system 17068 may generate
knowledge tokens 17038 that contain the digital knowledge (or a
reference thereto). In some embodiments, the smart contract system
17068 may tokenize the digital knowledge by wrapping the digital
knowledge (e.g., the 3D printing instruction set or a reference to
the instruction set) with a smart contract wrapper. In some
embodiments, the smart contract system 17068 may obtain a smart
contract template and may parameterize the smart contract using
some of the information entered in by the user, such as price,
license fee allocations, geographic restrictions, other
restrictions, custom actions (e.g., assigning serial numbers),
if/when the token expires, 3D printer requirements, and the like.
In some embodiments, each knowledge token that is generated for the
3D printer instruction set may be assigned a different serial
number, such that each 3D-printed product may be identified by its
serial number and associated with the token from which it was
printed. In this way, the product may be verified and tied to a
particular record in the distributed ledger. In embodiments, the
smart contract system 17068 may output the generated knowledge
tokens to the ledger management system 16910.
[0939] In embodiments, the ledger management system 16910 may
upload the knowledge tokens on the distributed ledger. In some
embodiments, the ledger management system 16910 may generate a
block containing the knowledge tokens and may broadcast the block
to the distributed ledger 16808, whereby a knowledge recipient may
then transact for one or more of the knowledge tokens (e.g., to
print one or more respective products using the 3D printing
instruction set). In some embodiments, one or more of the recipient
nodes may execute the smart contracts that wrap the digital tokens,
whereby the smart contract listens for one or more triggering
conditions (e.g., receiving an amount of currency equal to the
transaction price of the knowledge token). Additionally or
alternatively, the ledger management system 16910 may execute the
smart contracts (e.g., in containers) and may record the
transaction for the knowledge token to the distributed ledger.
[0940] In embodiments, the knowledge distribution system 16802 may
provide or connect to a digital knowledge marketplace, whereby
potential recipients may purchase knowledge tokens corresponding to
respective 3D-printing instruction sets. For example, the
marketplace may display items that may be 3D printed, such as
airplane parts, car parts, machinery parts, other types of
replacement parts, toys, medical devices, and/or the like. A
potential recipient may enter into a transaction for a particular
3D printing instruction set. In embodiments, the potential
recipient may select one of the items. In response, the knowledge
distribution system may present the price of each token, the
restrictions associated with the knowledge token (e.g., any device
requirements, geographical restrictions, use limitations, and/or
the like), warranties, disclaimers, indemnifications,
certifications, and/or the like to the potential recipient. The
potential recipient may then choose to accept the terms of the
transaction (e.g., agree to buy the token). The potential recipient
may then commit a defined amount of currency to the transaction. In
response, the smart contract may listen for additional conditions
(if so defined) before completing the transaction and/or releasing
the digital knowledge. For example, the smart contract may request
the potential recipient to verify that the printer requirements are
met or may connect to the 3D printer to verify the requirements. If
all the conditions required to complete the transaction are met,
the smart contract may provide the currency to the knowledge
provider (and any other licensors) and may perform any other
actions, such as releasing the digital knowledge to the 3D printer
(or another device), broadcasting a block to the distributed ledger
verifying the transaction and/or recording the serial number in the
distributed ledger. The 3D printer may receive the 3D printing
instructions and may print the product in accordance with the 3D
printing instruction set.
[0941] In embodiments, the knowledge distribution system 16802 may
be deployed on or integrated with or within a set of infrastructure
capabilities, such as cloud computing infrastructure,
platform-as-a-service infrastructure, Internet of Things platform
capabilities, distributed database capabilities, data management
platform infrastructure, enterprise database resources (including
cloud and on premises resources), and the like. In embodiments, the
knowledge distribution system 16802 may use or integrate with or
within various services, such as identity management services,
information management services, digital rights management
services, information rights management services, cryptographic
services, key management services, distributed database services,
and many others.
[0942] Referring to FIG. 174, in embodiments, the knowledge
distribution system 16802 may provide one or more collaboration
APIs 17474 for facilitating collaboration between users. The
collaboration APIs may be configured to allow users to provide and
share information to establish a shared set of data resources for
collaboration, such as to provide a shared "ground truth" as to
underlying facts, to establish a set of alternative views regarding
the underlying facts (e.g., to identify where there may be
disagreement as to the ground truth or the absence of information
that is needed to establish shared understanding), to facilitate
management of a set of scenarios with respect to which
collaboration is desired, to facilitate a set of simulations
relating to topics of interest for collaborators, to facilitate
controlled access to shared and non-shared knowledge elements,
and/or to allow users to provide, verify, and/or share information
outside of enterprise firewalls. The collaboration API 17474 may be
configured to allow users and/or parties to provide, receive,
share, and/or verify information, such as the digital knowledge
16804, information related to the digital knowledge 16804,
information related to transactions performed via the distributed
ledger 16808, via one or more smart contracts 16840, via the
marketplace system 17454, and the like. The APIs may be configured
to allow for sharing of information privately, publicly, or a
combination thereof. Information shared via the APIs, or events or
transactions relating thereto, may be stored on the distributed
ledger 16808 and thereby be distributed across the nodes 16916 of
the distributed ledger. The users may include the knowledge
providers 16806, the knowledge recipients 16818, the crowdsources
16836, the users and/or parties to the distributed ledger 16808
and/or the digital marketplace 17456, and the like.
[0943] In some embodiments, the collaboration API 17474 may include
operational and/or situational knowledge that may be captured by
the knowledge distribution system 16802. The collaboration API
17474 may be configured to process the situational knowledge and
transmit the situational knowledge and/or interpretations of the
situational knowledge to the ledger management system 16910. The
ledger management system 16910 may be configured to store the
situational knowledge and/or interpretations of the situational
knowledge on the distributed ledger 16808. An example of
situational knowledge is data regarding current condition, state,
and/or location of a piece of collateral related to an instance of
the digital knowledge 16804 and/or related to a smart contract
16840. Another example of situational knowledge is a state of
completion of a work-in-progress that is subject to a transaction,
a term of payment and/or lending triggered by completion of an item
(e.g., an instance of the digital knowledge 16804) to a certain
stage of completion.
[0944] In embodiments, the smart contract system 16868 may include
one or more transaction frameworks 17476 configured to facilitate
managing transactions via the smart contracts 16840. The
transaction frameworks 17476 may include one or more data
structures, routines, subroutines, and the like configured to
assist in management of transactions, such as by automatically
importing, exporting, sorting, configuring, handling, or otherwise
processing data related to transactions handled via the knowledge
distribution system 16802. The smart contract system 17068 may be
configured to include one or more transaction frameworks 17476
related to billing, payments, reporting, auditing, reconciliation,
and/or the like.
[0945] In embodiments, each of the transaction frameworks 17476 may
be configured to facilitate management of one or more particular
types of transactions. Examples of types of transactions and
related data of which one or more of the transaction frameworks
17476 may be configured to facilitate management include
purchase/sale, lending/leasing, rental, licensing, resource/time
sharing, service contracts, maintenance/repair, warranty, guaranty,
insurance, profit/revenue sharing, manufacturing (optionally
tiered), resale/distribution (optionally tiered), demand
aggregation, forward market/futures transactions, and
conditional/contingent contracts, among others, including any of
the many types describe in this disclosure and the documents
incorporated herein by reference. For example, in a tiered
distribution contract framework, the transaction framework 17476
may be configured to use the distributed ledger 16808 and the smart
contract 16840 to allocate one or more of payments, commissions,
and costs in a granular manner. In another example, in a contingent
contract framework, the transaction framework 17476 may be
configured to use the distributed ledger 16808 and the smart
contract 16840 to manage one or more of options, futures, emergent
events, and the like. Other examples of smart contract frameworks
17476 include those configured to manage commissions, incentive
payments, payments for milestones (e.g., partial work, delivery
partway through a supply chain, etc.), and escrows.
[0946] In embodiments, one or more of the transaction frameworks
17476 may be configured to facilitate management of the smart
contracts 16840 in situations in which there are issues with
performance by one or more parties to an agreement. Issues with
performance may include, for example, breach of contract, failure
to pay, late payment, poor performance, poor quality goods, failure
to perform services, and the like. Remedies for issues that may be
encoded in the transactional frameworks 17476 may include pulling
functionality, loss of license, ramping down of performance,
financial penalties (e.g., loss of tokens or currency) and the
like.
[0947] In embodiments, the transaction framework 17476 may
facilitate using the distributed ledger 16808 and the smart
contract 16840 to allocate risk and liability in a granular manner.
The knowledge distribution system 16802 may be configured to import
sensor data from one or more sensors, such as IoT sensors. The
sensor data may include single sensor data, multiple sensor data,
fused sensor data (e.g., where results from two or more sensors are
joined, such as by multiplexing, by computation, or the like), raw
sensor data, normalized sensor data (such as to allow comparison to
a scale, such as a quality scale, a condition scale, or the like),
calibrated sensor data (such as to allow comparison to other
sensors on an accurate basis), and others. In embodiments, the
sensor data may indicate the state or condition of a physical item
or its environment at a point in time or over a period of time,
such as its temperature, the ambient temperature of the environment
in which it is located, environmental humidity, movements of the
item (such as resulting from impacts, vibration, transport, or the
like), exposure to heat, exposure to radiation, exposure to
chemicals (including particulates, toxins, and the like), bearing
of loads, bearing of weight, exposure to stress, exposure to
strain, exposure to impacts, damage (such as dents, deformations,
deflections, disconnects, breaks, cracks, shatters, tears and many
others), exposure to biological factors (including pathogens),
extent of progressive damage (such as rust), and other factors. In
embodiments, the sensor data may indicate the presence or absence
of activities or workflows related to an item, such as where sensor
data indicates fluid levels (e.g., oil or other lubrication, fuels,
antifreeze, and other fluids, which indicate whether required
maintenance, such as an oil change, has been timely performed),
levels of particulates or other matter (such as dirt, grime, sand
particles, and many others, which may indicate whether required
cleaning has occurred), levels of rust, and many others. In various
embodiments, the imported sensor data may allow the smart contract
system 17068 to allocate related to performance, lack of
performance, utilization, deterioration, wear-and-tear, damage,
maintenance activity, or other relevant factors that may be
attributed to individual parts of a tiered manufacturing system
(including individual machines, equipment items, devices, component
parts, or the like) to particular related parties via the
transaction framework 17476. As one example among many, a series of
parties in possession of an item may be allocated responsibility
for depreciation, deterioration, or other reduction in its value
based on their measured activities with respect to its caretaking,
such as the environmental conditions in which it was stored and the
presence or absence of required maintenance activities, such as
fluid changes, cleaning, and the like. For example, a party that
stored the item in pristine conditions at specified temperatures
and replaced fluids according to a defined schedule might be
assigned a moderate number of points (or other metrics), while a
party that stored the item outdoors in poor conditions might be
assigned a much higher number of points, in automatically
allocating responsibility for the replacement of the item by a
smart contract. Similarly, a party whose actions or lack of actions
can be directly measured as causing damage (e.g., the item was
dropped and dented while in the party's possession), may be
automatically allocated responsibility for the damage. Thus, a
sensor-enabled smart contract may track and allocate responsibility
for conditions and activities involving a physical item across its
lifetime, including among parties that share or transfer possession
of the item, share use of the item, or the like. Shared use or
possession over the lifetime may include situations of tiered
manufacturing, such as where component parts are progressively
configured into an overall system by a set of parties. In such a
situation, a smart contract may use sensor data collected
throughout manufacturing to determine responsibility for a failure
of an item (e.g., a manufacturing defect) based on what part of the
item failed and/or why an item failed (such as due to a problem in
the manufacturing chain). Shared use of possession may also include
"shared economy" situations, such as shared use of property
(including rooms, office space, homes, apartments, real estate,
vehicles, electronic devices, and many others), where a smart
contract may allocate responsibility for damage, maintenance (or
lack thereof), cleaning, and other factors based on sensor data
collected over the lifetime of the shared item. In embodiments, the
smart contract framework 17476 may also provide for inclusion of
indemnity clauses and more complex causes allocating liability
and/or limitation thereof (including exceptions to the same) which
may include factors related to the sensor data collected over time
as noted above. For example, a smart contract may limit a
manufacturer's liability for defects to a period (e.g., ninety
days, one year, or the like) but the smart contract may embody an
exception for hidden defects (e.g., ones that were present but did
not manifest during the warranty period). Sensor data may indicate
whether a defect was manifested or not during the base warranty
period and automatically determine whether a warranty claim
asserted after the period is valid. In embodiments, such a smart
contract may further allocate, and optionally execute a transfer of
value, such as currency, upon determination of the ultimate
responsibilities among parties, such as where one party has
indemnified another for a type of liability. In embodiments, a
smart contract may be configured to perform a computation and
allocation of net liability among multiple parties to a contract
that involves indemnification by one or more parties of another. In
embodiments, such a smart contract may consume sensor data that is
used to determine the extent of liability to be allocated to each
party (e.g., where a party's actions or inactions may result in
sensed changes of the condition of an item that may trigger greater
responsibility for indemnification of others). In embodiments, such
a smart contract may automatically credit or debit an account,
trigger a transfer of value, or the like.
[0948] In embodiments, the transaction framework 17476 may
facilitate using the distributed ledger 16808 and the smart
contract 16840 to allocate payments, commissions, costs, and the
like in a granular manner. The transaction framework 17476 may
facilitate inclusion in the smart contract 16840 and management by
the smart contract 16840 of one or more parties to a set of
distribution agreements, value added reseller agreements,
manufacturer agreements, sub-distributor agreements, sub-licensee
agreements, payment agreements, servicing agreements, maintenance
agreements, update agreements, upgrade agreements, rental
agreements, resource sharing agreements, item-sharing agreements,
warranty agreements, insurance agreements, lending agreements,
indemnification agreements, guarantee agreements, and the like.
[0949] In embodiments, the transaction framework 17476 may
facilitate management and/or execution of contingent contracts via
the distributed ledger 16808 and the smart contract 16840. The
contingent contracts may include clauses whereby a provision of a
good, service, payment, and the like is contingent on one or more
triggering events taking place. For example, admission tickets for
a sporting event may be sold to a fans of a plurality of sports
teams, with validity of the ticket and/or the related transaction
being contingent on the team of which the fan is a fan of being
eligible to participate in the sporting event. In embodiments, the
transaction framework 17476 may have one or more data collection
facilities, such as web crawlers, spiders, clustering systems,
sensor data collection systems, services, APIs, or the like that
collect data indicating the presence or absence of a triggering
condition for the contingent contract, such as, in the example of
an event-triggered contract, a system that searches for the
existence of an event involving a particular performer, player, or
team (among others) in an event, and the smart contract may
automatically handle the allocation of rights that are triggered by
the occurrence of the event. For example, where the right to attend
the Super Bowl (or other game) is triggered by a particular team's
presence in the game (or in similar examples where an attendance
right is triggered by emergence or realization of a desired
instance of a type of event), the smart contract transaction
framework 17476 may automatically determine (such as via a search
engine or other capability operating on news data sources) the
triggering event (e.g., that a given team won a conference
championship game resulting in becoming a participant in the league
championship, or other similar example, or the a particular
performer or group has announced a date and place for a concert
tour or other performance). Further, the smart contract transaction
framework 17476 may trigger a set of actions upon the automatic
determination of the instance of the triggering event, such as
transferring ticket rights to parties for whom the rights vest upon
the event, informing other parties that their contracts are closed
(i.e., that there remains no possibility of the event occurring
under the defined conditions for those parties, such as holders of
rights related to teams that did not make it to the championship
games), allocating consolation prizes to losing parties, triggering
other smart contracts (such as smart contracts that allocate
provision of related goods and services, such as travel and
transportation services (e.g., automatically securing airline
tickets based on the location of the ticket holder and the location
of the game, automatically securing a rental car, and the like),
hospitality services (e.g., automatically securing a hotel
reservation in the city of the game for a fan that does not live in
the city, automatically securing reservations for meals, and the
like).
[0950] In embodiments a smart contract for an event embodying
automatic detection of triggers for contingencies related to the
emergence or realization of an instance of an event, and embodying
automatic allocation of rights (such as attendance rights, travel
and hospitality rights, and the like) based on the triggers may
include, take input from, use, connect with, link to, and/or
integrate with a set of intelligent agents, such as using any of
the artificial intelligence, machine learning, deep learning, and
other techniques described herein or in the documents incorporated
by reference herein, including robotic process automation trained
and/or supervised by human experts. The set of intelligent agents
may include ones that are trained, for example, (a) to determine
and manage a set of possible events (such as what teams could be
involved in what games at what locations and what points in time
across a set of leagues, sports, locations and the like), including
expanding or pruning the list based on game results and other
factors (e.g., where teams fall out of contention for playoff
spots, rendering previously possible games impossible); (b) to
forecast probabilities of likelihood of instances of event based on
current and historical data (e.g., the likelihood that a game will
occur between two particular teams, the likelihood that a performer
will hold a concert at a given location (or within a geofence)
during a date range, or the like); (c) to generate and configure a
smart contract that governs allocation of rights subject to
contingencies, including setting parameters for the launch (e.g.,
by auction, by lottery, by "drop" or the like) of a marketplace or
other venue by which parties may enter into the smart contract for
the contingent event; (d) to forecast demand for an instance of a
contract (e.g., demand for Final Four tickets in New Orleans if
University of Kentucky is playing; or demand for Elton John tickets
in Paris during Q3 of a given year; among many others) based on a
given type of contingent event, such as based on historical demand
for similar events (optionally using various clustering and
similarity techniques operating on historical attendance data,
secondary market ticket data and other data sets), expressed demand
(including demand expressed in demand aggregation contracts, such
as where some users have purchased options for the event or similar
events), historical data on contingent smart contracts for similar
items or services, secondary indicators of demand (such as search
engine metrics, social media metrics and the like) and many others;
(e) to set initial pricing for events, including based on the
forecast demand and historical pricing data for underlying events
(e.g., ticket prices, secondary market prices and activity levels,
time required to sell out tickets and the like) and for other
contingent event contracts; (f) to manage allocation of smart
contracts, such as in tranches of release; (g) to collect and
manage party-specific factors and user profiles, such as
understanding location factors (e.g., place of residence, place of
work), affinity and loyalty factors (favorite teams, favorite
restaurants, favorite airlines, favorite hotel chains, favorite
types of food, and others), and others; (h) to manage matching of
party-specific factors and user profiles to contingent events (such
as to find and present smart contract opportunities that fit a
user's profile, such as ones involving possibilities of attending
events with a favorite team, player or performer involved); and (i)
to manage discovery and presentation of, and configuration of
parameters for, smart contracts that embody other goods and
services that may be paired with a contingent event smart contract
(such as automatically finding and matching an appropriate airline
flight, train reservation, bus ticket or the like and configuring a
contingent smart contract for the same between the transportation
provider and the prospective event ticket holder; automatically
finding and matching an appropriate hotel reservation and
configuration a smart contract hotel reservation between the
prospective ticket holder and the hotel provider, or creating
similar contingent smart contracts among providers and prospective
ticket holders for other travel, accommodations and hospitality
packages, such as restaurant reservations, rental cars, and many
others); among others. Thus, the set of AI-enabled intelligent
agents may provide automation of various capabilities for enabling
the creation, hosting, provisioning and resolution of a marketplace
for contingent event smart contracts.
[0951] In embodiments, the transaction framework 17476 may
facilitate demand aggregation via the distributed ledger 16808 and
the smart contract 16840. The transaction framework 17476 may
aggregate demand for one or more products and/or services
accumulated via analytics, commitments, options, or any other
suitable source. Upon accumulation of demand, such as by a demand
metric meeting a demand threshold, the smart contract 16840 may
trigger to begin design, manufacturing, distribution, and/or the
like of the related product and/or service. In embodiments, a set
of intelligent agents, using various AI capabilities noted above,
may be configured facilitate demand aggregation, including agents
that may (a) forecast demand for an instance or type of product,
such as based on various secondary indicators of demand, such as
search engine metrics, chat activity (e.g., in relevant forums),
event information (such as attendance at relevant industry events),
social media information (such as numbers of posts), product sales,
historical selling times (e.g., time from product launch to selling
out of a product), and many others; (b) aggregate demand, such as
by configuring a set of smart contracts by which parties commit to
purchasing an item upon its instantiation, such as during a given
time window within a given price range and determining a total
aggregate demand; (c) projects the cost a demand-aggregated
offering, such as based on a model (optionally itself managed
and/or created by an intelligent agent) that indicates the
projected cost of an item at various volumes of production,
optionally based on a projection or model of the likely component
parts and cost thereof, as well as other costs, such as assembly,
transportation, financing, warranty and the like; (d) projects the
price of the demand-aggregated item at various volumes of offering,
such as based on the forecast demand and historical pricing; (e)
forecasts the profit likely to be associated with offering the
demand-aggregated item at various volumes of production and/or at
various points in time, such as using the forecasted demand, cost
and pricing information; and the others. Thus a demand aggregation
marketplace may be enabled and/or supported by automation
capabilities provided by the set of intelligent agents.
[0952] In embodiments, the smart contract system 16868 may be
configured to import patterns of implementation and/or systems
building knowledge into one or more of the transaction frameworks
17476. The patterns of implementation and/or systems building
knowledge may include, for example, knowledge systems, workflow,
product management, support calls, human interaction, social media,
redundant systems, data storage, and implementation patterns at
scale. The smart contract system 16868 may automatically configure
the smart contracts 16840 to implement the imported patterns of
implementation and/or systems building knowledge. The imported
patterns of implementation and/or systems building knowledge may be
stored in the datastore 16858.
[0953] In some embodiments, the knowledge distribution system 16802
may include an artificial intelligence (AI) system 17480 in
communication with the ledger management system 16910 and/or the
smart contract system 17468 and configured to perform AI-related
tasks according to a machine learned model. The AI system 17480 may
be configured to perform actions with respect to the knowledge
distribution system 16802 to manage the digital knowledge 16804.
The AI system 17480 may have permission and access rights to
manage, use, and interact with systems of the knowledge
distribution system 16802 similarly to a user.
[0954] In embodiments, the AI system 17480 may be trained by one or
more transaction experts to develop the machine learned model by
which the AI system 17480 operates to perform AI-related functions.
Examples of transaction experts that may at least partially train
the AI system 17480 include agents, brokers, traders, attorneys,
financial advisors, auditors, accountants, bankers, marketers,
advertisers, exchange operators, buyers, sellers, distributors, and
manufacturers/developers. The AI system 17480 may be trained by any
suitable machine learning algorithm, and by any suitable training
data set. Examples of machine learning algorithms include
supervised learning, unsupervised learning, semi-supervised
learning, reinforcement learning, self-learning, feature learning,
sparse dictionary learning, anomaly detection, robot learning, and
association rules. The machine learned model may be any suitable
type of model, such as an artificial neural network, a decision
tree, a support vector machine, a regression analysis model, a
Bayesian network, or a genetic algorithm.
[0955] In embodiments, the AI system 17480 may be trained to
identify opportunities for smart contracts. Examples of
opportunities include exchange opportunities and arbitrage
opportunities.
[0956] In embodiments, the AI system 17480 may be trained to
configure market contract terms and conditions.
[0957] In embodiments, the AI system 17480 may be trained to
monitor market conditions.
[0958] In embodiments, the AI system 17480 may be trained to
monitor and manage contract terms and conditions. Monitoring and
managing contract terms and conditions may include monitoring goods
and/or observing services.
[0959] In embodiments, the AI system 17480 may be trained to
monitor and manage transaction processes. For example, the AI
system 17480 may be trained to recognize release of funds from an
escrow account.
[0960] In embodiments, the AI system 17480 may be trained to
monitor counter-party information. Examples of counter-party
information include solvency, and status of performance, and
quality of performance.
[0961] In embodiments, the AI system 17480 may be trained to
identify transaction opportunities. Examples of transaction
opportunities include instances of exchange and arbitration
opportunities.
[0962] In embodiments, the AI system 17480 may be trained to
negotiate on behalf of parties to transactions involving digital
knowledge.
[0963] In embodiments, the AI system 17480 may be configured to
configure and execute auctions. The AI system 17480 may perform
auction-related actions such as selecting a type of auction
suitable for a transaction and/or settings rules and parameters for
an auction to be at least partially carried out on the distributed
ledger 16808. The auctions selected may be any suitable type of
auction for being at least partially carried out on the distributed
ledger 16808, such as a Dutch auction or a reverse auction.
[0964] In embodiments, the AI system 17480 may be configured to
distribute currency tokens and/or tokenized digital knowledge
16804.
[0965] In embodiments, the AI system 17480 may be configured to
configure and manage exchange of the digital knowledge 16804 across
different marketplaces and exchanges. The AI system 17480 may set
exchange rates between native currencies of exchanges and/or may
tokenize the digital knowledge 16804 and set exchange rates between
instances of the tokenized digital knowledge 16804.
[0966] In embodiments, the AI system 17480 may be configured to
establish, monitor, and/or negotiate payment, leasing, and/or
lending options related to management of the digital knowledge
16804. The lending options may include payment plans, trust-less
scenarios, and/or non-trust-less scenarios.
[0967] In embodiments, the knowledge distribution system 16802 may
include a robotic process automation (RPA) system 17482 in
communication with the AI system 17480 and configured to improve
one or more functions of the AI system 17480. The RPA system 17482
may use robotic process automation techniques to allow the AI
system 17480 to interface with one or more of systems of the
knowledge distribution system 16802, the distributed ledger 16808,
and systems, marketplaces, and/or exchanges and the like external
to the knowledge distribution system 16802 by performing actions in
one or more graphical user interfaces of the knowledge distribution
system 16802, the distributed ledger 16808, and systems,
marketplaces, and/or exchanges and the like external to the
knowledge distribution system 16802.
[0968] In embodiments, the knowledge distribution system 16802 may
include a rights management system 17484 configured to manage
rights of users apart from an exchange or marketplace.
[0969] In embodiments, the knowledge distribution system 16802 may
include a market management system 17486 configured to establish a
market for selling and/or reselling currency tokens and/or
instances of the digital knowledge 16804. The market may be
configured such that wrapped and/or tokenized instances of the
digital knowledge 16804 may be resold without being unwrapped. The
market may be established and configured as a spot market, a
secondary market, and/or a futures/derivatives market. Futures and
derivatives resold on the futures/derivatives market may include
options, futures, and other derivatives. The knowledge distribution
system 16802 may establish and/or monitor secondary markets,
ancillary markets, forward markets, and the like in addition to
resale markets for digital currency and instances of the digital
knowledge 16804.
[0970] In embodiments, the market management system 17486 may be
configured to monitor metrics of users, buyers, and/or sellers
participating in one or more markets established by the market
management system 17486. The metrics may include, for example,
metrics indicating how, where, or how often an instance of the
digital knowledge 16804 is used. The metrics may alternatively or
additionally include metrics regarding creation of the digital
knowledge 16804, duration during which a given type of digital
knowledge 16804 remains relevant or valuable, and/or metrics
regarding transaction patterns. Examples of transaction patterns
include size of transaction, transaction pricing and trends
thereof, profile information of buyers, sellers, consumers, users,
and/or creators of the digital knowledge 16804, and the like.
Another metric additionally or alternatively monitored by the
markets system may include metrics indicative of gaming and/or
misconduct by users of the market.
[0971] In embodiments, the digital knowledge 16804 may include
instruction sets such as: process steps in food production or food
preparation instructions (e.g., for industrial food preparation),
additive manufacturing/3D printing instructions, instruction sets
for surgical robots and human/robot interfaces generally, crystal
fabrication system instructions, crystal fabrication process
instructions, polymer production process instructions, chemical
synthesis process instructions, coating process instructions,
semiconductor fabrication process instructions, silicon etching
instructions, doping instructions, chemical vapor deposition
instructions, biological production process instructions, smart
contract instructions, and/or instructions for establishing,
updating, and/or verifying a chain of work, possession, title, and
the like.
[0972] In embodiments, the digital knowledge 16804 may include
code, software, and/or logic, such as: algorithmic logic,
instruction sets for use in an application, executable algorithmic
logic, computer programs, firmware programs, instruction sets for
field-programmable gate arrays, instruction sets for complex
programmable logic devices, serverless code logic, cryptography
logic, AI logic, AI definitions, machine learning logic and/or
definitions, and/or quantum algorithms.
[0973] In embodiments, the digital knowledge 16804 may include
digital documents, such as digital documents relating to: part
schematics, production records (e.g., for aircraft parts, spaceship
parts, nuclear engine parts, and the any/or any other suitable
part), automobile parts, airplane parts, pieces of furniture or
components thereof, replacement parts for industrial robots or
machines, trade secrets, and/or other intellectual property such as
know-how, patented material, and/or works of authorship.
[0974] In embodiments, the digital knowledge 16804 may include 3D
printing schematics, such as schematics for printing medical
devices, automobile parts, airplane parts, furniture, furniture
components, and/or replacement parts for industrial robots or
machines.
[0975] In embodiments, the digital knowledge 16804 may include
personal and/or professional knowledge relating to one or more
organizations and/or individuals. The personal and/or professional
knowledge may include: professions resumes, professional history
tracking information, records of professional credentials, academic
degrees, professional certificates, verifications of professional
positions held by one or more individuals, professional feedback,
verification of work performed by one or more individuals and/or
parties, personal financial history, business financial history,
and/or personal life achievements as verified by one or more third
parties.
[0976] In some embodiments, the digital knowledge 16804 may include
data sets and/or sensor information defining and/or population a
set of digital twins. The digital twins may embody one or more
instances of the digital knowledge 16804 relating to one or more
physical entities. The one or more instances of the digital
knowledge 16804 may include knowledge related to one or more of
configurations, operating modes, instructions sets, capabilities,
defects, performance parameters, and the like.
[0977] In embodiments, the knowledge distribution system 16802 may
be configured to transmit instances of the digital knowledge 16804
to and/or receive instances of the digital knowledge 16804 from one
or more external knowledge exchanges and/or knowledge databases.
The external knowledge exchanges and/or knowledge databases include
domain-specific exchanges, geography-specific exchanges, and the
like. The knowledge distribution system 16802 may be configured to
facilitate exchange of valuable or sensitive instances of the
digital knowledge 16804 related to the subject matter of the
external knowledge exchange and/or knowledge database. Additional
or alternative examples of external knowledge exchanges and/or
databases may include stock exchanges, commodities exchanges,
derivative exchanges, futures exchanges, advertising exchanges,
energy exchanges, renewable energy credits exchanges,
cryptocurrency exchanges, bonds exchanges, currency exchanges,
precious metals exchanges, petroleum exchanges, goods exchanges,
services exchanges, or any other suitable type of exchange and/or
database. The knowledge distribution system 16802 may integrate
and/or communicate with interfaces of external knowledge exchanges
and/or databases, such as APIs connectors, ports, brokers. The
integration and/or communication may be facilitated via one or more
of extraction, transformation, and loading (ETL) technologies,
smart contracts, wrappers, tokens, containers, and the like.
[0978] In embodiments, the knowledge distribution system 16802 may
be deployed on and/or integrated with or within a set of
infrastructure capabilities. Examples of infrastructure
capabilities include cloud computing infrastructure,
platform-as-a-service infrastructure, Internet of Things platform
capabilities, distributed database capabilities, data management
platform infrastructure, enterprise database resources (including
cloud and on premises resources), and the like.
[0979] In embodiments, the knowledge distribution system 16802 may
use and/or integrate with or within various services. Examples of
services with which or within which the knowledge distribution
system 16802 may integrate include identify management services,
information management services, digital rights management
services, information rights management services, cryptographic
services, key management services, distributed databased services,
and the like.
[0980] Referring to FIG. 175, knowledge distribution system 17500
for controlling rights related to digital knowledge is depicted.
The knowledge distribution system 17500 may include an input system
17802, a tokenization system 17812, a ledger management system
17818, and a smart contract system 17824. In some embodiments the
knowledge distribution system 17500 may further include a smart
contract generator 17858, an execution system 17510, a reporting
system 17514, and a crowdsourcing module 17516.
[0981] The input system 17802 receives digital knowledge 17808 from
a user 17502 and the tokenization system 17812 may tokenize the
received digital knowledge 17808 resulting in a token/tokenized
digital knowledge 17814 that is manipulable as a token.
[0982] The ledger management system 17818 creates and manages one
or more distributed ledgers 17820, where a distributed ledger may
include a plurality of cryptographically linked blocks distributed
over a plurality of nodes of a network 17848 as described elsewhere
herein. The ledger management system 17818 may then store a smart
contract(s) 17822 and the tokenized digital knowledge 17814 in a
distributed ledger 17820 (FIG. 188).
[0983] A smart contract system 17824 may implement and manage a
smart contract 17822 which may include the tokenized digital
knowledge 17814, a triggering event 17828, and a smart contract
action 17830. Upon occurrence of a triggering event 17828, the
smart contract system 17824 may perform the smart contract action
17830. The smart contract system 17824 may process commitment(s)
17832 of parties 17532 to the smart contract 17822. The smart
contract system 17824 may manage rights 17540 including control
rights 17840 over the tokenized digital knowledge 17814 and access
rights 17842 regarding who can view, edit, access, or use the
tokenized digital knowledge 17814. The smart contract 17822 further
comprises a smart contract wrapper 17503. The knowledge
distribution system 17500 further comprises an account management
system 17505, a user interface system 17507, and a marketplace
system 17509.
[0984] As depicted in FIG. 180, the tokenized digital knowledge
17814 may include executable algorithmic logic 18002, a 3D printer
instruction set 18004, an instruction set for a coating process
18008, an instructions set for a semiconductor fabrication process
18010, a firmware program 18012, an instruction set for a
field-programmable gate array (FPGA) 18014, serverless code logic
18018, an instructions set for a crystal fabrication system 18020,
an instruction set for a food preparation process 18022, an
instruction set for a polymer production process 18024, an
instruction set for a biological production process 18030, a data
set for a digital twin 18032, an instruction set to perform a trade
secret 18034, intellectual property 18038, an instruction set
18040, or the like. In embodiments, where the tokenized digital
knowledge 17814 includes intellectual property 18038, the smart
contract system 17824 may embed intellectual property licensing
term(s) 18802 for the intellectual property 18038 in the
distributed ledger and, in response to a triggering event 17828,
update the access rights 17842 to provide access to the
intellectual property 18038 or process a commitment 17832 of a
party 17532 to the smart contract 17822 and the corresponding
intellectual property licensing term(s) 18802.
[0985] In embodiments, the smart contract 17822 may include a smart
contract wrapper 17503 which may add intellectual property 18038 to
a stack of intellectual property which may be on the distributed
ledger 17820 and commitment 17832 by one or more parties 17532 to:
an apportionment of royalties for the added intellectual property
18804. The smart contract wrapper 17503 may record, in a
distributed ledger, a commitment 17832 by one or more parties 17532
to: an apportionment of royalties for the aggregate stack of
intellectual property 18804, or a contract term 18810.
[0986] In embodiments the ledger management system 17818 may
include a logging system 17512 to store logged data in the
distributed ledger 17820. In embodiments, the digital knowledge
17808 may be an instruction set and the ledger management system
17818 may provide provable access to the instruction set and
execute the instruction set on a system. Providing provable access
may include logging or recording data in at least one of the
plurality of cryptographically linked blocks. Provable access may
include: aggregating views of a trade secret into a chain that
records which knowledge recipients have viewed the trade secret
18814 on the distributed ledger; recording a party who has
contributed to the digital knowledge, by logging data related to
the party, logging access transactions to an instance of digital
knowledge 18830; recording, a source of an instance of the digital
information by storing data related to the source,
[0987] The knowledge distribution system 17500 may include a
reporting system 17514 to report analytic data or an analytic
response(s) 18834 based on a plurality of operations performed on
the distributed ledger, or on the tokenized digital knowledge. The
reporting system 17514 may also analyzed the tokenized digital
knowledge 17814 and report the analytic result 18832.
[0988] In an embodiment, the smart contract system 17824 may
aggregate a set of instructions into an instructions set 18040, add
an instruction to pre-existing instructions set to provide a
modified instruction set 18040, manage allocation of instruction
subsets 18042 to the distributed ledger, manage access to the
instruction to the instruction sets based on access rights 17842,
or the like.
[0989] In embodiments the ledger management system 17818 may
include a crowdsourcing module 17516 to obtain crowdsourced
information 18602 which may then be stored in the distributed
ledger. crowdsourced information 18602 may include: a review of an
instance of the digital knowledge 18824, a signature related to an
instance of crowdsourced information 18826, a verification of an
instance of the digital knowledge 18828, and the like.
[0990] In embodiments, the knowledge distribution system 17500 may
include a private network system to enable a private network and
allow authorized parties to establish a cryptography-based
consensus requirement for verification of new cryptographically
linked blocks to be added to the plurality of cryptographically
linked block. In embodiments, the ledger management may establish
cryptocurrency tokens designed to be tradeable among users of the
distributed ledger.
[0991] In embodiments, the knowledge distribution system 17500 may
include an account management system 17505 to facilitate creation
and management of a plurality of user accounts 19094 corresponding
to a plurality of users 17502, 19004 of a knowledge distribution
system 17500. The user account data may be stored on the
distributed ledger.
[0992] In embodiments, the knowledge distribution system may
include a user interface system 17507, 19074 to present a user
interface 19093 to a user(s) 17502, 19004 which enables the user to
view data related to an instance of the digital knowledge.
[0993] In embodiments, the knowledge distribution system may
include a marketplace system 17509 to establish and maintain a
digital marketplace 19090 and visually present data corresponding
to an instance of the digital knowledge to a user of the knowledge
distribution system.
[0994] In embodiments, the knowledge distribution system may
include a datastore in communication with the distributed ledger
where the datastore may include a knowledge datastore configured to
store data related to the digital knowledge, client datastore is
configured to store data related to a plurality of users of the
knowledge distribution system, smart contract datastore is
configured to store data related to the smart contract, and the
like.
[0995] In embodiments, the knowledge distribution system 17500 may
include a smart contract generator 17858 to generate a smart
contract using a parameterizable smart contract template. Smart
contract parameters may be based on a type of digital knowledge to
be tokenized and may include a financial parameter, a royalty
parameter, a usage parameter, an output produced parameter, an
allocation of consideration parameter, an identity parameter, an
access condition parameter, or the like.
[0996] Referring to FIG. 176, a computer-implemented method for
controlling rights related to digital knowledge is depicted. In
embodiments, a distributed ledger is created and managed (Step
17602) where the distributed ledger may include a plurality of
blocks linked via cryptography distributed over a plurality of
nodes of a network as shown elsewhere herein. A smart contact may
be implemented and managed (step 17604). A smart contract may
include a triggering event, a corresponding smart contract action,
and the like. The smart contract may be stored on the distributed
ledger. An instance of digital knowledge may be received (step
17608) The digital knowledge may be tokenized (step 17610) and the
resulting tokenized digital knowledge stored via the distributed
ledger (step 17612). Commitments of a plurality of parties to the
smart contract may be processed (step 17614) and rights of control
or and access to the tokenized digital knowledge may be managed
according to the smart contract (step 17618). In response to an
occurrence of the triggering event, the corresponding smart
contract action may be performed with respect to the tokenized
digital knowledge (step 17620).
[0997] Referring to FIG. 177, an embodiment of a
computer-implemented method for controlling rights related to
digital knowledge is depicted. The computer-implemented method may
further include orchestrating, based on the smart contract, an
exchange of new digital knowledge for the tokenized digital
knowledge (step 17702). The method may also include integrating the
knowledge exchange with a separate exchange, wherein the knowledge
exchange facilitates an exchange of at least one of valuable and
sensitive knowledge related to a subject matter of the separate
exchange (step 17704).
[0998] Referring to FIG. 178, a knowledge distribution system 17800
for controlling rights related to digital knowledge is depicted. In
embodiments, the knowledge distribution system 17800 may include an
input system 17802, a tokenization system 17812, a ledger
management system 17818, a smart contract system 17824, an event
monitoring module 17850, and a smart contract generator 17858. The
input system 17802 receives information 17862 and digital knowledge
17808 from a knowledge provider device 17804 and the tokenization
system 17812 may tokenize the digital knowledge 17808 resulting in
a token/tokenized digital knowledge 17814 that is manipulable as a
token.
[0999] As depicted in FIG. 180, the tokenized digital knowledge
17814 may include executable algorithmic logic 18002, a 3D printer
instruction set 18004, an instruction set for a coating process
18008, an instructions set for a semiconductor fabrication process
18010, a firmware program 18012, an instruction set for a
field-programmable gate array (FPGA) 18014, serverless code logic
18018, an instructions set for a crystal fabrication system 18020,
an instruction set for a food preparation process 18022, an
instruction set for a polymer production process 18024, an
instruction set for a biological production process 18030, a data
set for a digital twin 18032, an instruction set to perform a trade
secret 18034, intellectual property 18038, an instruction set
18040, and the like.
[1000] In some embodiments, the digital knowledge may include a 3D
printer instruction set for 3D printing an object such as a custom
part, a custom product, a manufacturing part, a replacement part, a
toy, a medical device, a tool, or the like. As depicted in FIG.
179, the 3D printer instruction set for 3D printing an object 17810
may include a 3D printing schematic 17902, an origin 17904, a date
of creation 17908, a name of a contributing individual 17910, name
of a contributing group 17912, name of a contributing company
17914, a price 17918, a market trend for a related schematic 17920,
a serial number 17922, a part identifier 17924, or the like.
[1001] The ledger management system 17818 creates and manages one
or more distributed ledgers 17820, where a distributed ledger may
include a plurality of a series of cryptographically linked blocks
distributed over a plurality of nodes of a network 17848 as
described elsewhere herein. The ledger management system 17818 may
then store smart contracts 17822 and the tokenized digital
knowledge 17814 in a distributed ledger 17820.
[1002] The smart contract system 17824 may implement and manage a
smart contract 17822 where the smart contract 17822 may include one
or more triggering events 17828 and corresponding smart contract
actions 17830. The smart contract system may manage rights 17861,
such as control rights 17840 and access rights 17842, to the
tokenized digital knowledge 17814 according to the smart contract
17822. The smart contract system may process commitments of the
knowledge provider 17834 and a knowledge recipient 17838 of the 3D
printer instruction set for 3D printing an object 17810.
[1003] In response to an occurrence of a triggering event 17828,
the smart contract system 17824 may perform the corresponding smart
contract action 17830 and manage the smart contract action 17830
according to a condition 17844 and the triggering event 17828. The
triggering event may be a transfer of the 3D printer instructions
or use of the 3D printer instructions and the smart contract action
may, based on the rights of control 17840 and access rights 17842,
modify on the distributed ledger, when the 3D printer instruction
set is purchased, downloaded, or used. As depicted in FIG. 181, a
smart contract action 17830 may include: assigning a serial number
to the object that is 3D printed 18108, monitoring for the
triggering event 18110, verifying fulfillment of an obligation
based on the condition 18112, verifying payment or transfer of the
tokenized digital knowledge 18114, logging one or more transactions
in the distributed ledger 18118, transferring the tokenized digital
knowledge, performing one or more operations with respect to the
distributed ledger 18120, creating new or more new blocks in the
distributed ledger 18122, verifying that the condition is met
18124, generating a payment request of the knowledge recipient
18128, modifying on the distributed ledger when the 3D printer
instruction set is purchased, downloaded, or used 18130, or the
like. A smart contract action 17830 may include: receiving a
purchase request from a knowledge recipient device 18102,
fulfilling a purchase request from a knowledge recipient device
18104, verifying that the conditions is met when the condition is a
printer requirement, a payment received, a currency transferred
from a knowledge recipient device or the knowledge recipient, a
transfer of the tokenized digital knowledge to the knowledge
recipient device, and the like. As depicted in FIG. 182, a
condition 17844 may include a printer requirement 18202, a payment
received 18204, a currency transferred from a knowledge recipient
or knowledge recipient device 18208, a transfer of the tokenized
digital knowledge to the knowledge recipient or knowledge recipient
device 18210, or the like.
[1004] Referring to FIG. 183, possible rights 17861 of control of
and access to the tokenized digital knowledge may include at least
one of permission for a user to 3D print using multiple instances
of the 3D printer instruction set 18302, a 3D printer requirement
18304, a time period during which the object can be 3D printed
18308, whether the tokenized digital knowledge is transferred to a
downstream knowledge recipient 18310, warranty 18312, disclaimer
18314, indemnification 18318, or certification with respect to the
object 18320.
[1005] Referring to FIG. 184, possible triggering events 17828 may
include transfer of the 3D printer instructions 18402 or us of the
3D printer instructions 18404.
[1006] Referring to FIG. 185, a computer-implemented method 18500
for controlling rights related to digital knowledge is depicted. In
embodiments, the method may include creating and managing a
distributed ledger, wherein the distributed ledger comprises a
plurality of blocks linked via cryptography distributed over a
plurality of nodes of a network (step 18502). A smart contract may
be implemented and subsequently managed (step 18502). The smart
contract may include a triggering event and be stored in the
distributed ledger. In response to an occurrence of the triggering
event, a smart contract action may be performed with respect to the
digital knowledge (step 18506). The method 18500 may further
include receiving, from a knowledge provider device, an instance of
the digital knowledge that comprises a three-dimensional (3D)
printer instruction set for 3D printing an object (step 18508),
tokenizing the digital knowledge (step 18510), and storing the
tokenized digital knowledge via the distributed ledger (step
18512). The method 18500 may further include processing commitments
of the knowledge provider and a knowledge recipient of the 3D
printer instruction set to the smart contract (step 18514),
managing, according to the smart contract, rights of control of and
access to the tokenized digital knowledge (step 18516), and
managing the smart contract action according to a condition and the
triggering event (step 18518).
[1007] In embodiments, and with reference to FIG. 186 through FIG.
188, a computer-implemented method 18600 for controlling rights
related to digital knowledge is depicted. The computer-implemented
method 18600 may include crowdsourcing information regarding the
digital knowledge (step 18602) where the crowdsourced information
may include: an element of the instance of the digital knowledge
18702, information regarding an element of the instance of the
digital knowledge 18704, information regarding the knowledge
provider 18708, information regarding the knowledge recipient
18710, and the like. The computer-implemented method 18600 may
further include updating the smart contract in response to the
crowdsourced information (step 18604) or updating a condition (step
18608).
[1008] With reference to FIG. 190, a knowledge distribution system
19000 for controlling rights related to digital knowledge is
depicted. In embodiments, the knowledge distribution system 19000
may include an input system 19002, a tokenization system 19012, a
ledger management system 19018, and a smart contract system 19024.
The input system 19002 receives digital knowledge 19008 and the
tokenization system 19012 may tokenize the digital knowledge 19008
resulting in a tokenized digital knowledge 19014 that is
manipulable as a token.
[1009] The ledger management system 19018 may create and manage a
distributed ledgers 19020, where the distributed ledger may include
a plurality of cryptographically linked blocks distributed over a
plurality of nodes of a network as described elsewhere herein. The
ledger management system 19018 may store a smart contract(s) 19022
and the tokenized digital knowledge 19014 in a distributed ledger
19020. The ledger management system 19018 may provide provable
access to the digital knowledge 19008 by recording and storing
access transactions 19048 in the distributed ledger. Other methods
of providing provable access are described elsewhere herein.
[1010] A smart contract system 19024 may implement and manage a
smart contract 1902 which may include the tokenized digital
knowledge 19014, and a triggering event 19028. Upon occurrence of a
triggering event 19028, the smart contract system 19024 may perform
a smart 19062 including rights of control 19040 over the tokenized
digital knowledge 19014 and rights of access 19042 regarding who
can view, edit, access, or use the digital knowledge 19008. The
smart contract 19022 may process commitments 19032 of parties to
the smart contract 19034.
[1011] In embodiments, the smart contract 19022 may include a smart
contract wrapper 19064 which may perform an operation on the
distributed ledger to: add intellectual property 18038, add
intellectual property 18038 to a stack of intellectual property,
add commitment 17832 by one or more parties 17532 to: an
apportionment of royalties for the added intellectual property
18804.
[1012] In embodiments, the knowledge distribution system 19000 may
include an account management system 19072, in communication with a
distributed ledger, to facilitate creation and management of a
plurality of user accounts 19094 corresponding to a plurality of
users 19004 of a knowledge distribution system 19500. The knowledge
distribution system 19000 may include a user interface system 19074
to present a user interface 19093 to a user(s) 19004 which enables
the user to view data related to an instance of the digital
knowledge 19008.
[1013] In embodiments, the knowledge distribution system 19000 may
include a marketplace system 19078 to establish and maintain a
digital marketplace 19090 and visually present data corresponding
to an instance of the digital knowledge 19008 to a user 19004 of
the knowledge distribution system 19000.
[1014] In embodiments, the knowledge distribution system 19000 may
include a datastore in communication with the distributed ledger
where the datastore may include a knowledge datastore 19082
configured to store data related to the digital knowledge 19008,
client datastore 19084 configured to store data related to a
plurality of users 19004 of the knowledge distribution system,
smart contract datastore 17164 configured to store data related to
the smart contract 19022, and the like.
[1015] The knowledge distribution system 19000 may include a
reporting system 19080 analyze the tokenized digital knowledge
19014 and report the analytic result 19098.
[1016] In embodiments, the knowledge distribution system 19000 may
include a smart contract generator 19088 to generate a smart
contract 19022 using a parameterizable smart contract template
19060. Referring to FIG. 189, smart contract parameters 17522 may
be based on a type of digital knowledge to be tokenized and may
include a financial parameter 18902, a royalty parameter 18904, a
usage parameter 18906, and output produced parameter 18908, and
allocation of consideration parameter 18910, an identity parameter
18912, an access condition parameter 18914, or the like
[1017] With reference to FIG. 191, an illustrative and non-limiting
example method for controlling rights related to digital knowledge
is depicted. The method may include creating and managing a
distributed ledger 19102, wherein the distributed ledger comprises
a plurality of blocks linked via cryptography distributed over a
plurality of nodes of a network; tokenizing the digital knowledge
19104; storing the tokenized digital knowledge via the distributed
ledger 19108; implementing and managing a smart contract 19110,
wherein the smart contract comprises a triggering event, the
tokenized knowledge, and a corresponding smart contract action and
is stored in the distributed ledger; receiving an instance of the
digital knowledge 19112; processing commitments of a plurality of
parties to the smart contract 19114; managing, according to the
smart contract, rights of control of and access to the tokenized
digital knowledge 19118; performing, in response to an occurrence
of the triggering event, the corresponding smart contract action
with respect to the tokenized digital knowledge 19120; and managing
the smart contract action in response to the triggering event
19122. In some embodiments, and with reference to FIG. 192, the
method may further include crowdsourcing information regarding an
element of the instance of the digital knowledge 19224 and updating
the smart contract in response to the crowdsourced information
19228. In some embodiments, and with reference to FIG. 193, the
method may further include adding intellectual property to the
distributed ledger 19324, committing parties to an apportionment of
royalties for the added intellectual property 19328, and processing
a commitment of a party to a contract term 19330. In some
embodiments, and with reference to FIG. 194, the method may further
include creating a user account 19424, receiving a request from a
user account to display data related to an instance of the digital
knowledge 19428, confirming access to the instance of the digital
knowledge allowed for the user account 19430, and presenting a user
interface configured to display the data related to an instance of
the digital knowledge 19432. In some embodiments, and with
reference to FIG. 195, the method may further include buying or
selling the digital knowledge 19524. In some embodiments, and with
reference to FIG. 196, the method may further include creating and
issuing a currency token associated with the distributed ledger
19624.
[1018] With reference to FIG. 190, a knowledge distribution system
19000 for controlling rights related to digital knowledge is
depicted. In embodiments, the knowledge distribution system 19000
may include an input system 19002, a tokenization system 19012, a
ledger management system 19018, and a smart contract system 19024.
The input system 19002 receives digital knowledge 19008 and the
tokenization system 19012 may tokenize the digital knowledge 19008
resulting in a tokenized digital knowledge 19014 that is
manipulable as a token.
[1019] The ledger management system 19018 may create and manage a
distributed ledgers 19020, where the distributed ledger may include
a plurality of cryptographically linked blocks distributed over a
plurality of nodes of a network as described elsewhere herein. The
ledger management system 19018 may store a smart contract(s) 19022
and the tokenized digital knowledge 19014 in a distributed ledger
19020. The ledger management system 19018 may provide provable
access to the digital knowledge 19008 by recording and storing
access transactions 19048 in the distributed ledger. Other methods
of providing provable access are described elsewhere herein.
[1020] A smart contract system 19024 may implement and manage a
smart contract 1902 which may include the tokenized digital
knowledge 19014, and a triggering event 19028. Upon occurrence of a
triggering event 19028, the smart contract system 19024 may perform
a smart 19062 including rights of control 19040 over the tokenized
digital knowledge 19014 and rights of access 19042 regarding who
can view, edit, access, or use the digital knowledge 19008. The
smart contract 19024 may process commitments 19032 of parties to
the smart contract 19034.
[1021] In embodiments, the smart contract 19022 may include a smart
contract wrapper 19064 which may perform an operation on the
distributed ledger to: add intellectual property 18038, add
intellectual property 18038 to a stack of intellectual property,
add commitment 17832 by one or more parties 17532 to: an
apportionment of royalties for the added intellectual property
18804.
[1022] In embodiments, the knowledge distribution system 19000 may
include an account management system 19072, in communication with a
distributed ledger, to facilitate creation and management of a
plurality of user accounts 19094 corresponding to a plurality of
users 19004 of a knowledge distribution system 19500. The knowledge
distribution system 19000 may include a user interface system 19074
to present a user interface 19093 to a user(s) 19004 which enables
the user to view data related to an instance of the digital
knowledge 19008.
[1023] In embodiments, the knowledge distribution system 19000 may
include a marketplace system 19078 to establish and maintain a
digital marketplace 19090 and visually present data corresponding
to an instance of the digital knowledge 19008 to a user 19004 of
the knowledge distribution system 19000.
[1024] In embodiments, the knowledge distribution system 19000 may
include a datastore in communication with the distributed ledger
where the datastore may include a knowledge datastore 19082
configured to store data related to the digital knowledge 19008,
client datastore 19084 configured to store data related to a
plurality of users 19004 of the knowledge distribution system,
smart contract datastore 17164 configured to store data related to
the smart contract 19022, and the like.
[1025] The knowledge distribution system 19000 may include a
reporting system 19080 analyze the tokenized digital knowledge
19014 and report the analytic result 19098.
[1026] In embodiments, the knowledge distribution system 19000 may
include a smart contract generator 19088 to generate a smart
contract 19022 using a parameterizable smart contract template
19060. Smart contract parameters may be based on a type of digital
knowledge to be tokenized and may include a financial parameter, a
royalty parameter, a usage parameter, and output produced
parameter, and allocation of consideration parameter, an identity
parameter, an access condition parameter, or the like
Workflow Management Systems
[1027] In embodiments, the workflow management system may support
various workflows associated with a facility, such as including
interfaces of the platform by which a facility manager may review
various analytic results, status information, and the like. In
embodiments, the workflow management system tracks the operation of
a post-action follow-up module to ensure that the correct follow-up
messages are automatically, or under control of a facility agent
using the platform, sent to appropriate individuals, systems and/or
services.
[1028] In the various embodiments, various elements are included
for a workflow for each of an energy project, a compute project
(e.g., cryptocurrency and/or AI) and hybrids. In embodiments,
provided herein is an information technology system for providing
data to an intelligent energy and compute facility resource
management system having a system for learning on a training set of
facility outcomes, facility parameters, and data collected from
data sources to train an artificial intelligence/machine learning
system to at least one of predict a likelihood of a facility
production outcome, predict a facility production outcome, optimize
provisioning and allocation of energy and compute resources to
produce a favorable facility resource utilization profile among a
set of available profiles, optimize provisioning and allocation of
energy and compute resources to produce a favorable facility
resource output selection among a set of available outputs,
optimize requisition and provisioning of available energy and
compute resources to produce a favorable facility input resource
profile among a set of available profiles, optimize configuration
of available energy and compute resources to produce a favorable
facility resource configuration profile among a set of available
profiles, optimize selection and configuration of an artificial
intelligence system to produce a favorable facility output profile
among a set of available artificial intelligence systems and
configurations, or generate an indication that a current or
prospective customer should be contacted about an output that can
be provided by the facility.
Management Application Platform
[1029] Referring to FIG. 33, a transactional, financial and
marketplace enablement system 3300 is illustrated, including a set
of systems, applications, processes, modules, services, layers,
devices, components, machines, products, sub-systems, interfaces,
connections, and other elements working in coordination to enable
intelligent management of a set of financial and transactional
entities 3330 that may occur, operate, transact or the like within,
or own, operate, support or enable, one or more platform-operated
marketplaces 3327 or external marketplaces 3390 or that may
otherwise be part of, integrated with, linked to, or operated on by
the platform 3300. Platform marketplaces 3327 and external
marketplaces 3390 may include a wide variety of marketplaces and
exchanges for physical goods, services, virtual goods, digital
content, advertising, credits (such as renewable energy credits,
pollution abatement credits and the like), currencies, commodities,
cryptocurrencies, loyalty points, physical resources, human
resources, attention resources, information technology resources,
storage resources, energy resources, options, futures, derivatives,
securities, rights of access, tickets, licenses (including seat
licenses, private or government-issued licenses or permissions to
undertake regulated activities, medallions, badges and others), and
many others. Financial and transactional entities 3330 may include
any of the wide variety of assets, systems, devices, machines,
facilities, individuals or other entities mentioned throughout this
disclosure or in the documents incorporated herein by reference,
such as, without limitation: financial machines 3352 and their
components (e.g., automated teller machines, point of sale
machines, vending machines, kiosks, smart-card-enabled machines,
and many others); financial and transactional processes 3350 (such
as lending processes, software processes (including applications,
programs, services, and others), production processes, banking
processes (e.g., lending processes, underwriting processes,
investing processes, and many others), financial service processes,
diagnostic processes, security processes, safety processes and many
others); wearable and portable devices 3348 (such as mobile phones,
tablets, dedicated portable devices for financial applications,
data collectors (including mobile data collectors), sensor-based
devices, watches, glasses, hearables, head-worn devices,
clothing-integrated devices, arm bands, bracelets, neck-worn
devices, AR/VR devices, headphones, and many others); workers 3344
(such as banking workers, financial service personnel, managers,
engineers, floor managers, vault workers, inspectors, delivery
personnel, currency handling workers, process supervisors, security
personnel, safety personnel and many others); robotic systems 3342
(e.g., physical robots, collaborative robots (e.g., "cobots"),
software bots and others); and operating facilities 3340 (such as
currency production facilities, storage facilities, vaults, bank
branches, office buildings, banking facilities, financial services
facilities, cryptocurrency mining facilities, data centers, trading
floors, high frequency trading operations, and many others), which
may include, without limitation, among many others, storage and
financial services facilities 3338 (such as for financial services
inventory, components, packaging materials, goods, products,
machinery, equipment, and other items); insurance facilities 3334
(such as branches, offices, storage facilities, data centers,
underwriting operations and others); and banking facilities 3332
(such as for commercial banking, investing, consumer banking,
lending and many other banking activities).
[1030] In embodiments, the platform 3300 may include a set of data
handling layers 3308 each of which is configured to provide a set
of capabilities that facilitate development and deployment of
intelligence, such as for facilitating automation, machine
learning, applications of artificial intelligence, intelligent
transactions, state management, event management, process
management, and many others, for a wide variety of financial and
transactional applications and end uses. In embodiments, the data
handling layers 3308 include a financial and transactional
monitoring systems layer 3306, a financial and transactional
entity-oriented data storage systems layer 3310 (referred to in
some cases herein for convenience simply as a data storage layer
3310), an adaptive intelligent systems layer 3304 and a financial
and transactional management application platform layer 3302. Each
of the data handling layers 3308 may include a variety of services,
programs, applications, workflows, systems, components and modules,
as further described herein and in the documents incorporated
herein by reference. In embodiments, each of the data handling
layers 3308 (and optionally the platform 3300 as a whole) is
configured such that one or more of its elements can be accessed as
a service by other layers 3308 or by other systems (e.g., being
configured as a platform-as-a-service deployed on a set of cloud
infrastructure components in a microservices architecture). For
example, a data handling layer 3308 may have a set of interfaces
3316, such as application programming interfaces (APIs), brokers,
services, connectors, wired or wireless communication links, ports,
human-accessible interfaces, software interfaces or the like by
which data may be exchanged between the data handling layer 3308
and other layers, systems or sub-systems of the platform 3300, as
well as with other systems, such as financial entities 3330 or
external systems, such as cloud-based or on-premises enterprise
systems (e.g., accounting systems, resource management systems, CRM
systems, supply chain management systems and many others. Each of
the data handling layers 3308 may include a set of services (e.g.,
microservices), for data handling, including facilities for data
extraction, transformation and loading; data cleansing and
deduplication facilities; data normalization facilities; data
synchronization facilities; data security facilities; computational
facilities (e.g., for performing pre-defined calculation operations
on data streams and providing an output stream); compression and
de-compression facilities; analytic facilities (such as providing
automated production of data visualizations) and others.
[1031] In embodiments, each data handling layer 3308 has a set of
application programming interfaces 3316 for automating data
exchange with each of the other data handling layers 3308. These
may include data integration capabilities, such as for extracting,
transforming, loading, normalizing, compression, decompressing,
encoding, decoding, and otherwise processing data packets, signals,
and other information as it exchanged among the layers and/or the
applications 3312, such as transforming data from one format or
protocol to another as needed in order for one layer to consume
output from another. In embodiments, the data handling layers 3308
are configured in a topology that facilitates shared data
collection and distribution across multiple applications and uses
within the platform 3300 by the financial monitoring systems layer
3306. The financial monitoring systems layer 3306 may include,
integrate with, and/or cooperate with various data collection and
management systems 3318, referred to for convenience in some cases
as data collection systems 3318, for collecting and organizing data
collected from or about financial and transactional entities 3330,
as well as data collected from or about the various data layers
3308 or services or components thereof. For example, a stream of
physiological data from a wearable device worn by a worker
undertaking a task or a consumer engaged in an activity can be
distributed via the monitoring systems layer 3306 to multiple
distinct applications in the management application platform layer
3302, such as one that facilitates monitoring the physiological,
psychological, performance level, attention, or other state of a
worker and another that facilitates operational efficiency and/or
effectiveness. In embodiments, the monitoring systems layer 3306
facilitates alignment, such as time-synchronization, normalization,
or the like of data that is collected with respect to one or more
entities 3330. For example, one or more video streams or other
sensor data collected of or with respect to a worker 3344 or other
entity in a transactional or financial environment, such as from a
set of camera-enabled IoT devices, may be aligned with a common
clock, so that the relative timing of a set of videos or other data
can be understood by systems that may process the videos, such as
machine learning systems that operate on images in the videos, on
changes between images in different frames of the video, or the
like. In such an example, the monitoring systems layer 3306 may
further align a set of videos, camera images, sensor data, or the
like, with other data, such as a stream of data from wearable
devices, a stream of data produced by financial or transactional
systems (such as point-of-sale systems, ATMs, kiosks, handheld
transaction systems, card readers, and the like), a stream of data
collected by mobile data collectors, and the like. Configuration of
the monitoring systems layer 3306 as a common platform, or set of
microservices, that are accessed across many applications, may
dramatically reduce the number of interconnections required by an
enterprise in order to have a growing set of applications
monitoring a growing set of IoT devices and other systems and
devices that are under its control.
[1032] In embodiments, the data handling layers 3308 are configured
in a topology that facilitates shared or common data storage across
multiple applications and uses of the platform 3300 by the
financial and transactional entity and transaction-oriented data
storage systems layer 3310, referred to herein for convenience in
some cases simply as the data storage layer 3310 or storage layer
3310. For example, various data collected about the financial
entities 3330, as well as data produced by the other data handling
layers 3308, may be stored in the data storage layer 3310, such
that any of the services, applications, programs, or the like of
the various data handling layers 3308 can access a common data
source (which may comprise a single logical data source that is
distributed across disparate physical and/or virtual storage
locations). This may facilitate a dramatic reduction in the amount
of data storage required to handle the enormous amount of data
produced by or about entities 3330 as applications of the financial
and transactional IoT proliferate. For example, a supply chain or
inventory management application in the management application
platform layer 3302, such as one for ordering replacement parts for
a financial or transactional machine or item of equipment, or for
reordering currency or other inventory, may access the same data
set about what parts have been replaced for a set of machines as a
predictive maintenance application that is used to predict whether
a machine is likely to require replacement parts. Similarly,
prediction may be used with respect to resupply of currency or
other items. In embodiments, the data storage systems layer 3310
may provide an extremely rich environment for collection of data
that can be used for extraction of features or inputs for
intelligence systems, such as expert systems, artificial
intelligence systems, robotic process automation systems, machine
learning systems, deep learning systems, supervised learning
systems, or other intelligent systems as disclosed throughout this
disclosure and the documents incorporated herein by reference. As a
result, each application in the management application platform
layer 3302 and each adaptive intelligent system in the adaptive
intelligent systems layer 3304 can benefit from the data collected
or produced by or for each of the others. A wide range of data
types may be stored in the storage layer 3310 using various storage
media and data storage types and formats, including, without
limitation: asset and facility data 3320 (such as asset identity
data, operational data, transactional data, event data, state data,
workflow data, maintenance data, pricing data, ownership data,
transferability data, and many other types of data relating to an
asset (which may be a physical asset, digital asset, virtual asset,
financial asset, securities asset, or other asset); worker data
3322 (including identity data, role data, task data, workflow data,
health data, attention data, mood data, stress data, physiological
data, performance data, quality data and many other types); event
data 3324 (including process events, transaction events, exchange
events, pricing events, promotion events, discount events, rebate
events, reward events, point utilization events, financial events,
output events, input events, state-change events, operating events,
repair events, maintenance events, service events, damage events,
injury events, replacement events, refueling events, recharging
events, supply events, and many others); claims data 3354 (such as
relating to insurance claims, such as for business interruption
insurance, product liability insurance, insurance on goods,
facilities, or equipment, flood insurance, insurance for
contract-related risks, and many others, as well as claims data
relating to product liability, general liability, workers
compensation, injury and other liability claims and claims data
relating to contracts, such as supply contract performance claims,
product delivery requirements, contract claims, claims for damages,
claims to redeem points or rewards, claims of access rights,
warranty claims, indemnification claims, energy production
requirements, delivery requirements, timing requirements,
milestones, key performance indicators and others); accounting data
3358 (such as data relating to debits, credits, costs, prices,
profits, margins, rates of return, valuation, write-offs, and many
others); underwriting data 3360 (such as data relating to
identities of prospective and actual parties involved insurance and
other transactions, actuarial data, data relating to probability of
occurrence and/or extent of risk associated with activities, data
relating to observed activities and other data used to underwrite
or estimate risk); access data 3362 (such as data relating to
rights of access, tickets, tokens, licenses and other access rights
described throughout this disclosure, including data structures
representing access rights; pricing data 3364 (including spot
market pricing, forward market pricing, pricing discount
information, promotional pricing, and other information relating to
the cost or price of items in any of the platform operated
marketplaces 3327 and/or external marketplaces 3390); as well as
other types of data not shown, such as production data (such as
data relating to production of physical or digital goods, services,
events, content, and the like, as well as data relating to energy
production found in databases of public utilities or independent
services organizations that maintain energy infrastructure, data
relating to outputs of banking, data related to outputs of mining
and energy extraction facilities, outputs of drilling and pipeline
facilities and many others); and supply chain data (such as
relating to items supplied, amounts, pricing, delivery, sources,
routes, customs information and many others).
[1033] In embodiments, the data handling layers 3308 are configured
in a topology that facilitates shared adaptation capabilities,
which may be provided, managed, mediated and the like by one or
more of a set of services, components, programs, systems, or
capabilities of the adaptive intelligent systems layer 3304,
referred to in some cases herein for convenience as the adaptive
intelligence layer 3304. The adaptive intelligence systems layer
3304 may include a set of data processing, artificial intelligence
and computational systems 3314 that are described in more detail
elsewhere throughout this disclosure. Thus, use of various
resources, such as computing resources (such as available
processing cores, available servers, available edge computing
resources, available on-device resources (for single devices or
peered networks), and available cloud infrastructure, among
others), data storage resources (including local storage on
devices, storage resources in or on financial entities or
environments (including on-device storage, storage on asset tags,
local area network storage and the like), network storage
resources, cloud-based storage resources, database resources and
others), networking resources (including cellular network spectrum,
wireless network resources, fixed network resources and others),
energy resources (such as available battery power, available
renewable energy, fuel, grid-based power, and many others) and
others may be optimized in a coordinated or shared way on behalf of
an operator, enterprise, or the like, such as for the benefit of
multiple applications, programs, workflows, or the like. For
example, the adaptive intelligence layer 3304 may manage and
provision available network resources for both a financial
analytics application and for a financial remote control
application (among many other possibilities), such that low latency
resources are used for remote control and longer latency resources
are used for the analytics application. As described in more detail
throughout this disclosure and the documents incorporated herein by
reference, a wide variety of adaptations may be provided on behalf
of the various services and capabilities across the various layers
3308, including ones based on application requirements, quality of
service, budgets, costs, pricing, risk factors, operational
objectives, efficiency objectives, optimization parameters, returns
on investment, profitability, uptime/downtime, worker utilization,
and many others.
[1034] The management application platform layer 3302, referred to
in some cases herein for convenience as the platform layer 3302,
may include a set of financial and transactional processes,
workflows, activities, events and applications 3312 (referred to
collectively, except where context indicates otherwise, as
applications 3312) that enable an operator to manage more than one
aspect of an financial or transactional environment or entity 3330
in a common application environment, such as one that takes
advantage of common data storage in the data storage layer 3310,
common data collection or monitoring in the monitoring systems
layer 3306 and/or common adaptive intelligence of the adaptive
intelligence layer 3304. Outputs from the applications 3312 in the
platform layer 3302 may be provided to the other data handing
layers 3308. These may include, without limitation, state and
status information for various objects, entities, processes, flows
and the like; object information, such as identity, attribute and
parameter information for various classes of objects of various
data types; event and change information, such as for workflows,
dynamic systems, processes, procedures, protocols, algorithms, and
other flows, including timing information; outcome information,
such as indications of success and failure, indications of process
or milestone completion, indications of correct or incorrect
predictions, indications of correct or incorrect labeling or
classification, and success metrics (including relating to yield,
engagement, return on investment, profitability, efficiency,
timeliness, quality of service, quality of product, customer
satisfaction, and others) among others. Outputs from each
application 3312 can be stored in the data storage layer 3310,
distributed for processing by the data collection layer 3306, and
used by the adaptive intelligence layer 3304. The cross-application
nature of the platform layer 3302 thus facilitates convenient
organization of all of the necessary infrastructure elements for
adding intelligence to any given application, such as by supplying
machine learning on outcomes across applications, providing
enrichment of automation of a given application via machine
learning based on outcomes from other applications (or other
elements of the platform 3300, and allowing application developers
to focus on application-native processes while benefiting from
other capabilities of the platform 3300.
[1035] Referring to FIG. 34, additional details, components,
sub-systems, and other elements of an optional embodiment of the
platform 3300 of FIG. 33 are illustrated. The management
application layer 3302 may, in various optional embodiments,
include a set of applications, systems, solutions, interfaces, or
the like, collectively referred to for convenience as applications
3312, by which an operator or owner of a transactional or financial
entity, or other users, may manage, monitor, control, analyze, or
otherwise interact with one or more elements of the entity 3330,
such as any of the elements noted in connection above in connection
FIG. 33. The set of applications 3312 may include, without
limitation, one or more of any of a wide range of types of
applications, such as an investment application 3402 (such as,
without limitation, for investment in shares, interests,
currencies, commodities, options, futures, derivatives, real
property, trusts, cryptocurrencies, tokens, and other asset
classes); an asset management application 3404 (such as, without
limitation, for managing investment assets, real property,
fixtures, personal property, real estate, equipment, intellectual
property, vehicles, human resources, software, information
technology resources, data processing resources, data storage
resources, power generation and/or storage resources, computational
resources and other assets); a lending application 3410 (such as,
without limitation, for personal lending, commercial lending,
collateralized lending, microlending, peer-to-peer lending,
insurance-related lending, asset-backed lending, secured debt
lending, corporate debt lending, student loans, mortgage lending,
automotive lending, and others); a risk management application 3408
(such as, without limitation, for managing risk or liability with
respect to a product, an asset, a person, a home, a vehicle, an
item of equipment, a component, an information technology system, a
security system, a security event, a cybersecurity system, an item
of property, a health condition, mortality, fire, flood, weather,
disability, malpractice, business interruption, infringement,
advertising injury, slander, libel, violation of privacy or
publicity rights, injury, damage to property, damage to a business,
breach of a contract, and others); a payments application 3433
(such as for enabling various payments within and across
marketplaces, including credit card, debit card, wire transfer,
ACH, checking, currency and other payments); a marketing
application 3412 (such as, without limitation, an application for
marketing a financial or transactional product or service, an
advertising application, a marketplace platform or system for
goods, services or other items, a marketing analytics application,
a customer relationship management application, a search engine
optimization application, a sales management application, an
advertising network application, a behavioral tracking application,
a marketing analytics application, a location-based product or
service targeting application, a collaborative filtering
application, a recommendation engine for a product or service, and
others); a trading application 3428 (such as, without limitation, a
buying application, a selling application, a bidding application,
an auction application, a reverse auction application, a bid/ask
matching application, a securities trading application, a
commodities trading application, an option trading application, a
futures trading application, a derivatives trading application, a
cryptocurrency trading application, a token-trading application, an
analytic application for analyzing financial or transactional
performance, yield, return on investment, or other metrics, a
book-building application, or others); a tax application 3414 (such
as, without limitation, for managing, calculating, reporting,
optimizing, or otherwise handling data, events, workflows, or other
factors relating to a tax, a levy, a tariff, a duty, a credit, a
fee or other government-imposed charge, such as, without
limitation, sales tax, income tax, property tax, municipal fees,
pollution tax, renewal energy credit, pollution abatement credit,
value added tax, import duties, export duties, and others); a fraud
prevention application 3416 (such as, without limitation, one or
more of an identity verification application, a biometric identify
validation application, a transactional pattern-based fraud
detection application, a location-based fraud detection
application, a user behavior-based fraud detection application, a
network address-based fraud detection application, a black list
application, a white list application, a content inspection-based
fraud detection application, or other fraud detection application,
a financial service, application or solution 3409 (referred to
collectively as a "financial service", such as, without limitation,
a financial planning service, a tax planning service, a portfolio
management service, a transaction service, a lending service, a
banking service, a currency conversion service, a currency exchange
service, a remittance service, a money transfer service, a wealth
management service, an estate planning service, an investment
banking service, a commercial banking service, a foreign exchange
service, an insurance service, an investment service, an investment
management service, a hedge fund service, a mutual fund service, a
custody service, a credit card service, a safekeeping service, a
checking service, a debit card service, a lending service, an ATM
service, an ETF service, a wire transfer service, an overdraft
service, a reporting service, a certified checking service, a
notary service, a capital markets service, a brokerage service, a
broker-dealer service, a private banking service, an insurance
service, an insurance brokerage service, an underwriting service,
an annuity service, a life insurance service, a health insurance
service, a retirement insurance service, a property insurance
service, a casualty insurance service, a finance and insurance
service, a reinsurance service, an intermediation service, a trade
clearinghouse service, a private equity service, a venture capital
service, an angel investment service, a family office investment
service, an exchange service, a payments service, a settlement
service, an interbank networking service, a debt resolution
service, or other financial service); a security application,
solution or service 3418 (referred to herein as a security
application, such as, without limitation, any of the fraud
prevention applications 3416 noted above, as well as a physical
security system (such as for an access control system (such as
using biometric access controls, fingerprinting, retinal scanning,
passwords, and other access controls), a safe, a vault, a cage, a
safe room, or the like), a monitoring system (such as using
cameras, motion sensors, infrared sensors and other sensors), a
cyber security system (such as for virus detection and remediation,
intrusion detection and remediation, spam detection and
remediation, phishing detection and remediation, social engineering
detection and remediation, cyber attack detection and remediation,
packet inspection, traffic inspection, DNS attack remediation and
detection, and others) or other security application); an
underwriting application 3420 (such as, without limitation, for
underwriting any insurance offering, any loan, or any other
transaction, including any application for detecting,
characterizing or predicting the likelihood and/or scope of a risk,
including underwriting based on any of the data sources, events or
entities noted throughout this disclosure or the documents
incorporated herein by reference); a blockchain application 3422
(such as, without limitation, a distributed ledger capturing a
series of transactions, such as debits or credits, purchases or
sales, exchanges of in kind consideration, smart contract events,
or the like, a cryptocurrency application, or other
blockchain-based application); a real estate application 3424 (such
as, without limitation, a real estate brokerage application, a real
estate valuation application, a real estate investment trust
application, a real estate mortgage or lending application, a real
estate assessment application, a real estate marketing application,
or other); a regulatory application 3426 (such as, without
limitation, an application for regulating any of the applications,
services, transactions, activities, workflows, events, entities, or
other items noted herein and in the documents incorporated by
reference herein, such as regulation of pricing, marketing,
offering of securities, offering of insurance, undertaking of
broker or dealer activities, use of data (including data privacy
regulations, regulations relating to storage of data and others),
banking, marketing, sales, financial planning, and many others); a
platform-operated marketplace application, solution or service 3327
(referred to in some cases simply as a marketplace application
(which term may also, as context permits include various types of
external marketplaces 3390), such as, without limitation an
e-commerce marketplace, an auction marketplace, a physical goods
marketplace, a virtual goods marketplace, an advertising
marketplace, a reverse-auction marketplace, an advertising network,
a marketplace for attention resources, an energy trading
marketplace, a marketplace for computing resources, a marketplace
for networking resources, a spectrum allocation marketplace, an
Internet advertising marketplace, a television advertising
marketplace, a print advertising marketplace, a radio advertising
marketplace, an in-game advertising marketplace, an in-virtual
reality advertising marketplace, an in-augmented reality
marketplace, a real estate marketplace, a hospitality marketplace,
a travel services marketplace, a financial services marketplace, a
blockchain-based marketplace, a cryptocurrency marketplace, a
token-based marketplace, a loyalty program marketplace, a time
share marketplace, a rideshare marketplace, a mobility marketplace,
a transportation marketplace, a space-sharing marketplace, or other
marketplace); a warranty application 3417 (such as, without
limitation, an application for a warranty or guarantee with respect
to a product, a service, an offering, a solution, a physical
product, software, a level of service, quality of service, a
financial instrument, a debt, an item of collateral, performance of
a service, or other item); an analyst application 3419 (such as,
without limitation, an analytic application with respect to any of
the data types, applications, events, workflows, or entities
mentioned throughout this disclosure or the documents incorporated
by reference herein, such as a big data application, a user
behavior application, a prediction application, a classification
application, a dashboard, a pattern recognition application, an
econometric application, a financial yield application, a return on
investment application, a scenario planning application, a decision
support application, and many others); a pricing application 3421
(such as, without limitation, for pricing of goods, services
(including any mentioned throughout this disclosure and the
documents incorporated by reference herein), applications
(including any mentioned throughout this disclosure and the
documents incorporated by reference herein), software, data
services, insurance, virtual goods, advertising placements, search
engine and keyword placements, and many others; and a smart
contract application, solution, or service (referred to
collectively herein as a smart contract application, such as,
without limitation, any of the smart contract types referred to in
this disclosure or in the documents incorporated herein by
reference, such as a smart contract using a token or cryptocurrency
for consideration, a smart contract that vests a right, an option,
a future, or an interest based on a future condition, a smart
contract for a security, commodity, future, option, derivative, or
the like, a smart contract for current or future resources, a smart
contract that is configured to account for or accommodate a tax,
regulatory or compliance parameter, a smart contract that is
configured to execute an arbitrage transaction, or many others).
Thus, the manage application platform 3302 may host an enable
interaction among a wide range of disparate applications 3312 (such
term including the above-referenced and other financial or
transactional applications, services, solutions, and the like),
such that by virtue of shared microservices, shared data
infrastructure, and shared intelligence, any pair or larger
combination or permutation of such services may be improved
relative to an isolated application of the same type.
[1036] In embodiments, the adaptive intelligent systems layer 3304
may include a set of systems, components, services and other
capabilities that collectively facilitate the coordinated
development and deployment of intelligent systems, such as ones
that can enhance one or more of the applications 3312 at the
application platform layer 3302. These adaptive intelligence
systems 3304 may include an adaptive edge compute management
solution 3430, a robotic process automation system 3442, a set of
protocol adaptors 3491, a packet acceleration system 3434, an edge
intelligence system 3438, an adaptive networking system 3440, a set
of state and event managers 3444, a set of opportunity miners 3446,
a set of artificial intelligence systems 3448 and other
systems.
[1037] In embodiments, the financial monitoring systems layer 3306
and its data collection systems 3318 may include a wide range of
systems for collection of data. This layer may include, without
limitation, real time monitoring systems 3468 (such as onboard
monitoring systems like event and status reporting systems on ATMs,
POS systems, kiosks, vending machines and the like; OBD and
telematics systems on vehicle and equipment; systems providing
diagnostic codes and events via an event bus, communication port,
or other communication system; monitoring infrastructure (such as
cameras, motion sensors, beacons, RFID systems, smart lighting
systems, asset tracking systems, person tracking systems, and
ambient sensing systems located in various environments where
transactions and other events take place), as well as removable and
replaceable monitoring systems, such as portable and mobile data
collectors, RFID and other tag readers, smart phones, tablets and
other mobile device that are capable of data collection and the
like); software interaction observation systems 3450 (such as for
logging and tracking events involved in interactions of users with
software user interfaces, such as mouse movements, touchpad
interactions, mouse clicks, cursor movements, keyboard
interactions, navigation actions, eye movements, finger movements,
gestures, menu selections, and many others, as well as software
interactions that occur as a result of other programs, such as over
APIs, among many others); mobile data collectors 3452 (such as
described extensively herein and in documents incorporated by
reference), visual monitoring systems 3454 (such as using video and
still imaging systems, LIDAR, IR and other systems that allow
visualization of items, people, materials, components, machines,
equipment, personnel, gestures, expressions, positions, locations,
configurations, and other factors or parameters of entities 3330,
as well as inspection systems that monitor processes, activities of
workers and the like); point of interaction systems 3470 (such as
point of sale systems, kiosks, ATMs, vending machines, touch pads,
camera-based interaction tracking systems, smart shopping carts,
user interfaces of online and in-store vending and commerce
systems, tablets, and other systems at the point of sale or other
interaction by a customer or worker involved in shopping and/or a
transaction); physical process observation systems 3458 (such as
for tracking physical activities of customers, physical activities
of transaction parties (such as traders, vendors, merchants,
customers, negotiators, brokers, and the like), physical
interactions of workers with other workers, interactions of workers
with physical entities like machines and equipment, and
interactions of physical entities with other physical entities,
including, without limitation, by use of video and still image
cameras, motion sensing systems (such as including optical sensors,
LIDAR, IR and other sensor sets), robotic motion tracking systems
(such as tracking movements of systems attached to a human or a
physical entity) and many others; machine state monitoring systems
3460 (including onboard monitors and external monitors of
conditions, states, operating parameters, or other measures of the
condition of a machine, such as a client, a server, a cloud
resource, an ATM, a kiosk, a vending machine, a POS system, a
sensor, a camera, a smart shopping cart, a smart shelf, a vehicle,
a robot, or other machine); sensors and cameras 3462 and other IoT
data collection systems 3464 (including onboard sensors, sensors or
other data collectors (including click tracking sensors) in or
about a financial or transactional environment (such as, without
limitation, an office, a back office, a store, a mall, a virtual
store, an online environment, a web site, a bank, or many others),
cameras for monitoring an entire environment, dedicated cameras for
a particular machine, process, worker, or the like, wearable
cameras, portable cameras, cameras disposed on mobile robots,
cameras of portable devices like smart phones and tablets, and many
others, including any of the many sensor types disclosed throughout
this disclosure or in the documents incorporated herein by
reference); indoor location monitoring systems 3472 (including
cameras, IR systems, motion-detection systems, beacons, RFID
readers, smart lighting systems, triangulation systems, RF and
other spectrum detection systems, time-of-flight systems, chemical
noses and other chemical sensor sets, as well as other sensors);
user feedback systems 3474 (including survey systems, touch pads,
voice-based feedback systems, rating system, expression monitoring
systems, affect monitoring systems, gesture monitoring systems, and
others); behavioral monitoring systems 3478 (such as for monitoring
movements, shopping behavior, buying behavior, clicking behavior,
behavior indicating fraud or deception, user interface
interactions, product return behavior, behavior indicative of
interest, attention, boredom or the like, mood-indicating behavior
(such as fidgeting, staying still, moving closer, or changing
posture) and many others); and any of a wide variety of Internet of
Things (IoT) data collectors 3464, such as those described
throughout this disclosure and in the documents incorporated by
reference herein.
[1038] In embodiments, the financial entity-oriented data storage
systems layer 3310 may include a range of systems for storage of
data, such as the accounting data 3358, access data 3362, pricing
data 3364, asset and facility data 3320, worker data 3322, event
data 3324, underwriting data 3360 and claims data 3354. These may
include, without limitation, physical storage systems, virtual
storage systems, local storage systems, distributed storage
systems, databases, memory, network-based storage, network-attached
storage systems (such as using NVME, storage attached networks, and
other network storage systems), and many others. In embodiments,
the storage layer 3310 may store data in one or more knowledge
graphs (such as a directed acyclic graph, a data map, a data
hierarchy, a data cluster including links and nodes, a
self-organizing map, or the like). In embodiments, the data storage
layer 3310 may store data in a digital thread, ledger, or the like,
such as for maintaining a longitudinal record of an entity 3330
over time, including any of the entities described herein. In
embodiments, the data storage layer 3310 may use and enable a
virtual asset tag 3488, which may include a data structure that is
associated with an asset and accessible and managed as if the tag
were physically located on the asset, such as by use of access
controls, so that storage and retrieval of data is optionally
linked to local processes, but also optionally open to remote
retrieval and storage options. In embodiments, the storage layer
3310 may include one or more blockchains 3490, such as ones that
store identity data, transaction data, entity data for the entities
3330, pricing data, ownership transfer data, data for operation by
smart contracts 3431, historical interaction data, and the like,
such as with access control that may be role-based or may be based
on credentials associated with an entity 3330, a service, or one or
more applications 3312.
[1039] Referring to FIG. 35, the adaptive intelligence layer 3304
may include a robotic process automation (RPA) system 3442, which
may include a set of components, processes, services, interfaces
and other elements for development and deployment of automation
capabilities for various financial entities 3330, environments, and
applications 3312. Without limitation, robotic process automation
3442 may be applied to each of the processes that is managed,
controlled, or mediated by each of the set of applications 3312 of
the platform application layer.
[1040] In embodiments, robotic process automation 3442 may take
advantage of the presence of multiple applications 3312 within the
management application platform layer 3302, such that a pair of
applications may share data sources (such as in the data storage
layer 3310) and other inputs (such as from the monitoring layer
3306) that are collected with respect to financial entities 3330,
as well sharing outputs, events, state information and outputs,
which collectively may provide a much richer environment for
process automation, including through use of artificial
intelligence 3448 (including any of the various expert systems,
artificial intelligence systems, neural networks, supervised
learning systems, machine learning systems, deep learning systems,
and other systems described throughout this disclosure and in the
documents incorporated by reference). For example, a real estate
application 3424 may use robotic process automation 3442 for
automation of a real estate inspection process that is normally
performed or supervised by a human (such as by automating a process
involving visual inspection using video or still images from a
camera or other that displays images of an entity 3330, such as
where the robotic process automation 3442 system is trained to
automate the inspection by observing interactions of a set of human
inspectors or supervisors with an interface that is used to
identify, diagnose, measure, parameterize, or otherwise
characterize possible defects or favorable characteristics of a
house, a building, or other real estate property or item. In
embodiments, interactions of the human inspectors or supervisors
may include a labeled data set where labels or tags indicate types
of defects, favorable properties, or other characteristics, such
that a machine learning system can learn, using the training data
set, to identify the same characteristics, which in turn can be
used to automate the inspection process such that defects or
favorable properties are automatically classified and detected in a
set of video or still images, which in turn can be used within the
real estate solution 3424 to flag items that require further
inspection, that should be rejected, that should be disclosed to a
prospective buyer, that should be remediated, or the like. In
embodiments, robotic process automation 3442 may involve
multi-application or cross-application sharing of inputs, data
structures, data sources, events, states, outputs or outcomes. For
example, the real estate application 3442 may receive information
from a marketplace application 3327 that may enrich the robotic
process automation 3442 of the real estate application 3442, such
as information about the current pricing of an item from a
particular vendor that is located at a real estate property (such
as a pool, spa, kitchen appliance, TV or other items), which may
assist in populating the characteristics about the real estate for
purpose of facilitating an inspection process, a valuation process,
a disclosure process, or the like. These and many other examples of
multi-application or cross-application sharing for robotic process
automation 3442 across the applications 3312 are encompassed by the
present disclosure.
[1041] In embodiments, robotic process automation may be applied to
shared or converged processes among the various pairs of the
applications 3312 of the application layer 3302, such as, without
limitation, of a converged process involving a security application
3418 and a lending application 3410, integrated automation of
blockchain-based applications 3422 with marketplace applications
3327, and many others. In embodiments, converged processes may
include shared data structures for multiple applications 3312
(including ones that track the same transactions on a blockchain
but may consume different subsets of available attributes of the
data objects maintained in the blockchain or ones that use a set of
nodes and links in a common knowledge graph). For example, a
transaction indicating a change of ownership of an entity 3330 may
be stored in a blockchain and used by multiple applications 3312,
such as to enable role-based access control, role-based permissions
for remote control, identity-based event reporting, and the like.
In embodiments, converged processes may include shared process
flows across applications 3312, including subsets of larger flows
that are involved in one or more of a set of applications 3312. For
example, an underwriting or inspection flow about an entity 3330
may serve a lending solution 3410, an analytics solution 3419, an
asset management solution 3404, and others.
[1042] In embodiments, robotic process automation 3442 may be
provided for the wide range of financial and transactional
processes mentioned throughout this disclosure and the documents
incorporated herein by reference, including without limitation
energy trading, banking, transportation, storage, energy storage,
maintenance processes, service processes, repair processes, supply
chain processes, inspection processes, purchase and sale processes,
underwriting processes, compliance processes, regulatory processes,
fraud detection processes, fault detection processes, power
utilization optimization processes, and many others. An environment
for development of robotic process automation may include a set of
interfaces for developers in which a developer may configure an
artificial intelligence system 3448 to take inputs from selected
data sources of the data storage layer 3310 and events or other
data from the monitoring systems layer 3306 and supply them, such
as to a neural network, either as inputs for classification or
prediction, or as outcomes. The RPA development environment 3442
may be configured to take outputs and outcomes 3328 from various
applications 3312, again to facilitate automated learning and
improvement of classification, prediction, or the like that is
involved in a step of a process that is intended to be automated.
In embodiments, the development environment, and the resulting
robotic process automation 3442 may involve monitoring a
combination of both software program interaction observations 3450
(e.g., by workers interacting with various software interfaces of
applications 3312 involving entities 3330) and physical process
interaction observations 3458 (e.g., by watching workers
interacting with or using machines, equipment, tools or the like).
In embodiments, observation of software interactions 3450 may
include interactions among software components with other software
components, such as how one application 3312 interacts via APIs
with another application 3312. In embodiments, observation of
physical process interactions 3458 may include observation (such as
by video cameras, motion detectors, or other sensors, as well as
detection of positions, movements, or the like of hardware, such as
robotic hardware) of how human workers interact with financial
entities 3330 (such as locations of workers (including routes taken
through a location, where workers of a given type are located
during a given set of events, processes or the like, how workers
manipulate pieces of equipment or other items using various tools
and physical interfaces, the timing of worker responses with
respect to various events (such as responses to alerts and
warnings), procedures by which workers undertake scheduled
maintenance, updates, repairs and service processes, procedures by
which workers tune or adjust items involved in workflows, and many
others). Physical process observation 3458 may include tracking
positions, angles, forces, velocities, acceleration, pressures,
torque, and the like of a worker as the worker operates on
hardware, such as with a tool. Such observations may be obtained by
any combination of video data, data detected within a machine (such
as of positions of elements of the machine detected and reported by
position detectors), data collected by a wearable device (such as
an exoskeleton that contains position detectors, force detectors,
torque detectors and the like that is configured to detect the
physical characteristics of interactions of a human worker with a
hardware item for purposes of developing a training data set). By
collecting both software interaction observations 3450 and physical
process interaction observations 3458 the RPA system 3442 can more
comprehensively automate processes involving financial entities
3330, such as by using software automation in combination with
physical robots.
[1043] In embodiments, robotic process automation 3442 is
configured to train a set of physical robots that have hardware
elements that facilitate undertaking tasks that are conventionally
performed by humans. These may include robots that walk (including
walking up and down stairs), climb (such as climbing ladders), move
about a facility, attach to items, grip items (such as using
robotic arms, hands, pincers, or the like), lift items, carry
items, remove and replace items, use tools and many others.
[1044] With reference to FIG. 35, in embodiments provided herein is
a transactional, financial and marketplace enablement system. An
example system may include a robotic process automation circuit
structured to interpret information from a plurality of data
sources, and to interface with a plurality of management
applications; wherein the plurality of management applications are
each associated with a separate one of a plurality of financial
entities; and wherein the robotic process automation circuit
further comprises an artificial intelligence circuit structured to
improve a process of at least one of the plurality of management
applications in response to the information from the plurality of
data sources.
[1045] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system may include wherein the artificial
intelligence circuit further comprises at least one circuit
selected from the circuits consisting of: a smart contract services
circuit, a valuation circuit, and an automated agent circuit.
[1046] An example system may include wherein the plurality of
management applications comprise at least two applications selected
from the applications consisting of: an investment application, as
asset management application, a lending application, a risk
management application, a marketing application, a trading
application, a tax application, a fraud application, a financial
service application, a security application, an underwriting
application, a blockchain application, a real estate application, a
regulatory application, a platform marketplace application, a
warranty application, an analytics application, a pricing
application, and a smart contract application.
[1047] An example system may include wherein the plurality of data
sources comprise at least two applications selected from the
applications consisting of: an access data source, an asset and
facility data source, a worker data source, a claims data source,
an accounting data source, an event data source, and an
underwriting data source.
[1048] An example system may include wherein the plurality of
management applications includes a real estate application, and
wherein the robotic process automation circuit is further
structured to automate a real estate inspection process.
[1049] An example system may include wherein the robotic process
automation circuit is further structured to automate the real
estate inspection process by performing at least one operation
selected from the operations consisting of: providing one of a
video inspection command or a camera inspection command; utilizing
data from the plurality of data sources to schedule an inspection
event; and determining inspection criteria in response to a
plurality of inspection data and inspection outcomes, and providing
an inspection command in response to the plurality of inspection
data and inspection outcomes.
[1050] An example system may include wherein the robotic process
automation circuit is further structured to automate the real
estate inspection process in response to at least one of the
plurality of data sources that is not accessible to the real estate
application.
[1051] An example system may include wherein at least one of the
plurality of data sources is not accessible to each of the at least
one of the plurality of management applications having an improved
process by the robotic automation circuit.
[1052] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises a real estate
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: a claims data source, a pricing data source,
an asset and facility data source, a worker data source, and an
event data source.
[1053] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises an asset management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an access data source, a pricing data
source, an accounting data source, a worker data source, and an
event data source.
[1054] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises a lending management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an asset and facility data source, a claims
data source, a worker data source, and an event data source.
[1055] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises a marketing management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an asset and facility data source, a claims
data source, a worker data source, an event data source, and an
underwriting data source.
[1056] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises a trading management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an asset and facility data source, a claims
data source, a worker data source, and an event data source.
[1057] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises an analytics management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an access data source, a claims data source,
a worker data source, and an event data source.
[1058] An example system may include wherein the robotic process
automation circuit is further structured to improve the process at
least one of the plurality of management applications by providing
an output to at least one entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1059] An example system may include wherein the robotic process
automation circuit is further structured to interpret an outcome
from the at least one entity, and wherein the artificial
intelligence circuit is further structured to iteratively improve
the process in response to the outcome from the at least one
entity.
[1060] Referring to FIG. 36, a set of opportunity miners 3446 may
be provided as part of the adaptive intelligence layer 3304, which
may be configured to seek and recommend opportunities to improve
one or more of the elements of the platform 3300, such as via
addition of artificial intelligence 3448, automation (including
robotic process automation 3446), or the like to one or more of the
systems, sub-systems, components, applications or the like of the
platform 100 or with which the platform 100 interacts. In
embodiments, the opportunity miners 3446 may be configured or used
by developers of AI or RPA solutions to find opportunities for
better solutions and to optimize existing solutions. In
embodiments, the opportunity miners 3446 may include a set of
systems that collect information within the platform 100 and
collect information within, about and for a set of environments and
entities 3330, where the collected information has the potential to
help identify and prioritize opportunities for increased automation
and/or intelligence. For example, the opportunity miners 3446 may
include systems that observe clusters of workers by time, by type,
and by location, such as using cameras, wearables, or other
sensors, such as to identify labor-intensive areas and processes in
set of financial environments. These may be presented, such as in a
ranked or prioritized list, or in a visualization (such as a heat
map showing dwell times of customers, workers or other individuals
on a map of an environment or a heat map showing routes traveled by
customers or workers within an environment) to show places with
high labor activity. In embodiments, analytics 3419 may be used to
identify which environments or activities would most benefit from
automation for purposes of labor saving, profit optimization, yield
optimization, increased up time, increased throughput, increased
transaction flow, improved security, improved reliability, or other
factors.
[1061] In embodiments, opportunity miners 3446 may include systems
to characterize the extent of domain-specific or entity-specific
knowledge or expertise required to undertake an action, use a
program, use a machine, or the like, such as observing the
identity, credentials and experience of workers involved in given
processes. This may be of particular benefit in situations where
very experienced workers are involved (such as in complex
transactions that require significant experience (such as
multi-party transactions); in complex back-office processes
involving significant expertise or training (such as risk
management, actuarial and underwriting processes, asset allocation
processes, investment decision processes, or the like); in update,
maintenance, porting, backup, or re-build processes on large or
complex machines; or in fine-tuning of complex processes where
accumulated experience is required for effective work), especially
where the population of those workers may be scarce (such as due to
retirement or a dwindling supply of new workers having the same
credentials). Thus, a set of opportunity miners 3446 may collect
and supply to an analytics solution 3419, such as for prioritizing
the development of automation 3442, data indicating what processes
of or about an entity 3330 are most intensively dependent on
workers that have particular sets of experience or credentials,
such as ones that have experience or credentials that are scarce or
diminishing. The opportunity miners 3446 may, for example,
correlate aggregated data (including trend information) on worker
ages, credentials, experience (including by process type) with data
on the processes in which those workers are involved (such as by
tracking locations of workers by type, by tracking time spent on
processes by worker type, and the like). A set of high value
automation opportunities may be automatically recommended based on
a ranking set, such as one that weights opportunities at least in
part based on the relative dependence of a set of processes on
workers who are scarce or are expected to become scarcer.
[1062] In embodiments, the set of opportunity miners 3446 may use
information relating to the cost of the workers involved in a set
of processes, such as by accessing worker data 3322, including
human resource database information indicating the salaries of
various workers (either as individuals or by type), information
about the rates charged by service workers or other contractors, or
the like. An opportunity miner 3446 may provide such cost
information for correlation with process tracking information, such
as to enable an analytics solution 3419 to identify what processes
are occupying the most time of the most expensive workers. This may
include visualization of such processes, such as by heat maps that
show what locations, routes, or processes are involving the most
expensive time of workers in financial environments or with respect
to entities 3330. The opportunity miners 3446 may supply a ranked
list, weighted list, or other data set indicating to developers
what areas are most likely to benefit from further automation or
artificial intelligence deployment.
[1063] In embodiments, mining an environment for robotic process
automation opportunities may include searching an HR database
and/or other labor-tracking database for areas that involve
labor-intensive processes; searching a system for areas where
credentials of workers indicating potential for automation;
tracking clusters of workers by a wearable to find labor-intensive
machines or processes; tracking clusters of workers by a wearable
by type of worker to find labor-intensive processes, and the
like.
[1064] In embodiments, opportunity mining may include facilities
for solicitation of appropriate training data sets that may be used
to facilitate process automation. For example, certain kinds of
inputs, if available, would provide very high value for automation,
such as video data sets that capture very experienced and/or highly
expert workers performing complex tasks. Opportunity miners 3446
may search for such video data sets as described herein; however,
in the absence of success (or to supplement available data), the
platform may include systems by which a user, such as a developer,
may specify a desired type of data, such as software interaction
data (such as of an expert working with a program to perform a
particular task), video data (such as video showing a set of
experts performing a certain kind of repair, an expert rebuilding a
machine, an expert optimizing a certain kind of complex process, or
the like), physical process observation data (such as video, sensor
data, or the like). The specification may be used to solicit such
data, such as by offering some form of consideration (e.g.,
monetary reward, tokens, cryptocurrency, licenses or rights,
revenue share, or other consideration) to parties that provide data
of the requested type. Rewards may be provided to parties for
supplying pre-existing data and/or for undertaking steps to capture
expert interactions, such as by taking video of a process. The
resulting library of interactions captured in response to
specification, solicitation and rewards may be captured as a data
set in the data storage layer 3310, such as for consumption by
various applications 3312, adaptive intelligence systems 3304, and
other processes and systems. In embodiments, the library may
include videos that are specifically developed as instructional
videos, such as to facilitate developing an automation map that can
follow instructions in the video, such as providing a sequence of
steps according to a procedure or protocol, breaking down the
procedure or protocol into sub-steps that are candidates for
automation, and the like. In embodiments, such videos may be
processed by natural language processing, such as to automatically
develop a sequence of labeled instructions that can be used by a
developer to facilitate a map, a graph, or other model of a process
that assists with development of automation for the process. In
embodiments, a specified set of training data sets may be
configured to operate as inputs to learning. In such cases the
training data may be time-synchronized with other data within the
platform 3300, such as outputs and outcomes from applications 3312,
outputs and outcomes of financial entities 3330, or the like, so
that a given video of a process can be associated with those
outputs and outcomes, thereby enabling feedback on learning that is
sensitive to the outcomes that occurred when a given process that
was captured (such as on video, or through observation of software
interactions or physical process interactions).
[1065] In embodiments, opportunity miners 3446 may include methods,
systems, processes, components, services and other elements for
mining for opportunities for smart contract definition, formation,
configuration and execution. Data collected within the platform
3300, such as any data handled by the data handling layers 3308,
stored by the data storage layer 3310, collected by the monitoring
layer 3306 and collection systems 3318, collected about or from
entities 3330 or obtained from external sources may be used to
recognize beneficial opportunities for application or configuration
of smart contracts. For example, pricing information about an
entity 3330, handled by a pricing application 3421, or otherwise
collected, may be used to recognize situations in which the same
item or items is disparately priced (in a spot market, futures
market, or the like), and the opportunity miner 3446 may provide an
alert indicating an opportunity for smart contract formation, such
as a contract to buy in one environment at a price below a given
threshold and sell in another environment at a price above a given
threshold, or vice versa. In embodiments, robotic process
automation 3442 may be used to automate smart contract creation,
configuration, and/or execution, such as by training on a training
set of data relating to experts who form such contract or based on
feedback on outcomes from past contracts. Smart contract
opportunities may also be recognized based on patterns, such as
where predictions are used to indicate opportunities for options,
futures, derivatives, forward market contracts, and other
forward-looking contracts, such as where a smart contract is
created based on a prediction that a future condition will arise
that creates an opportunity for a favorable exchange, such as an
arbitrage transaction, a hedging transaction, an "in-the-money"
option, a tax-favored transaction, or the like. In embodiments, at
a first step an opportunity miner 3446 seeks a price level for an
item, service, good, or the like in a set of current or future
markets. At a second step the opportunity miner 3446 determines a
favorable condition for a smart contract (such as an arbitrage
opportunity, tax saving opportunity, favorable option, favorable
hedge, or the like). At a next step the opportunity miner 3446 may
initiate a smart contract process in which a smart contract is
pre-configured with a description of an item, a description of a
price or other term or condition, a domain for execution (such as a
set of markets in which the contract will be formed) and a time. At
a next step an automation process may form the smart contract and
execute it within the applicable domains. At a final step the
platform may settle the contract, such as when conditions are met.
In embodiments, the opportunity miners 3446 may be configured to
maintain a set of value translators 3447 that may be developed to
calculate exchange values of different items between and across
disparate domains, such as by translating the value of various
resources (e.g., computational, bandwidth, energy, attention,
currency, tokens, credits (e.g., tax credits, renewable energy
credits, pollution credits), cryptocurrency, goods, licenses (e.g.,
government-issued licenses, such as for spectrum, for the right to
perform services or the like, as well as intellectual property
licenses, software licenses and others), services and other items)
with respect to other such resources, including accounting for any
costs of transacting across domains to convert one resource to the
other in a contract or series of contracts, such as ones executed
via smart contracts. Value translators 3447 may translate between
and among current (e.g., spot market) value, value in defined
futures markets (such as day-ahead energy prices) and predicted
future value outside defined futures markets. In embodiments,
opportunity miners 3446 may operate across pairs or other
combinations of value translators (such as across, two, three,
four, five or more domains) to define a series of transaction
amounts, configurations, domains and timing that will result in
generation of value by virtue of undertaking transactions that
result in favorable translation of value. For example, a
cryptocurrency token may be exchanged for a pollution credit, which
may be used to permit generation of energy, which may be sold for a
price that exceeds the value of the cryptocurrency token by more
than the cost of creating the smart contract and undertaking the
series of exchanges.
[1066] With reference to FIG. 36, in embodiments provided herein is
a transactional, financial and marketplace enablement system. An
example system may include a robotic process automation circuit
structured in interpret information from a plurality of data
sources, and to interface with a plurality of management
applications; wherein the plurality of management applications are
each associated with a separate one of a plurality of financial
entities; and wherein the robotic process automation circuit
further comprises an opportunity miner component structured to
determine a process improvement opportunity for at least one of the
plurality of management applications in response to the information
from the plurality of data sources; and to provide an output to at
least one entity associated with the process improvement
opportunity in response to the determined process improvement
opportunity.
[1067] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system may include wherein the plurality of
management applications comprise at least two applications selected
from the applications consisting of: an investment application, as
asset management application, a lending application, a risk
management application, a marketing application, a trading
application, a tax application, a fraud application, a financial
service application, a security application, an underwriting
application, a blockchain application, a real estate application, a
regulatory application, a platform marketplace application, a
warranty application, an analytics application, a pricing
application, and a smart contract application.
[1068] An example system may include wherein the plurality of data
sources comprise at least two applications selected from the
applications consisting of: an access data source, an asset and
facility data source, a worker data source, a claims data source,
an accounting data source, an event data source, and an
underwriting data source.
[1069] An example system may include wherein the at least one
entity each comprise an entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1070] An example system may include wherein the opportunity miner
component is further structured to determine a plurality of process
improvement opportunities for one of the plurality of management
applications in response to the information from the plurality of
data sources, and to provide one of a prioritized list or a
visualization of the plurality of process improvement opportunities
to the one of the plurality of management applications.
[1071] An example system may include wherein the opportunity miner
component is further structured to determine the process
improvement opportunity in response to at least one parameter
selected from the parameters consisting of: a time saving value, a
cost saving value, and an improved outcome value.
[1072] An example system may include wherein the opportunity miner
component is further structured to determine the process
improvement opportunity in response to a value translation from a
value translation application.
[1073] An example system may include wherein the plurality of
management applications includes a trading application, and wherein
the robotic process automation circuit is further structured to
automate a trading service process.
[1074] An example system may include wherein the robotic process
automation circuit is further structured to automate the trading
service process by performing at least one operation selected from
the operations consisting of: utilizing data from the plurality of
data sources to schedule a trading event; and determining trading
criteria in response to a plurality of asset data and trading
outcomes, and providing a trading command in response to the
plurality of asset data and trading outcomes.
[1075] An example system may include wherein the robotic process
automation circuit is further structured to automate the trading
service process in response to at least one of the plurality of
data sources that is not accessible to the trading application.
[1076] An example system may include wherein the robotic process
automation circuit is further structured to improve the process at
least one of the plurality of management applications by providing
an output to at least one entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1077] An example system may include wherein the robotic process
automation circuit is further structured to interpret an outcome
from the at least one entity, and wherein the opportunity miner
component is further structured to iteratively improve the process
in response to the outcome from the at least one entity.
[1078] An example system may include wherein at least one of the
plurality of data sources is not accessible to each of the at least
one of the plurality of management applications having an improved
process by the robotic automation circuit.
[1079] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises a tax application, and
wherein the at least one of the plurality of data sources comprises
at least one data source selected from the data sources consisting
of: a claims data source, a pricing data source, an asset and
facility data source, a worker data source, and an event data
source.
[1080] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises an asset management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an access data source, a pricing data
source, an accounting data source, a worker data source, and an
event data source.
[1081] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises a lending management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an asset and facility data source, a claims
data source, a worker data source, and an event data source.
[1082] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises a marketing management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an asset and facility data source, a claims
data source, a worker data source, an event data source, and an
underwriting data source.
[1083] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises an investment
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, and an
event data source.
[1084] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic automation circuit comprises an underwriting
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an access data
source, a claims data source, a worker data source, and an event
data source.
[1085] Referring to FIG. 37, additional details of an embodiment of
the platform 3300 are provided, in particular relating to elements
of the adaptive intelligence layer 3304 that facilitate improved
edge intelligence, including the adaptive edge compute management
system 3430 and the edge intelligence system 3438. These elements
provide a set of systems that adaptively manage "edge" computation,
storage and processing, such as by varying storage locations for
data and processing locations (e.g., optimized by AI) between
on-device storage, local systems, in the network and in the cloud.
These elements 3430, 3438 enable facilitation of a dynamic
definition by a user, such as a developer, operator, or host of the
platform 100, of what constitutes the "edge" for purposes of a
given application. For example, for environments where data
connections are slow or unreliable (such as where a facility does
not have good access to cellular networks (such as due to
remoteness of some environments (such as in geographies with poor
cellular network infrastructure), shielding or interference (such
as where density of network-using systems, thick walls, underground
location, or presence of large metal objects (such as vaults)
interferes with networking performance), and/or congestion (such as
where there are many devices seeking access to limited networking
facilities), edge computing capabilities can be defined and
deployed to operate on the local area network of an environment, in
peer-to-peer networks of devices, or on computing capabilities of
local financial entities 3330. Where strong data connections are
available (such as where good back-haul facilities exist), edge
computing capabilities can be disposed in the network, such as for
caching frequently used data at locations that improve input/output
performance, reduce latency, or the like. Thus, adaptive definition
and specification of where edge computing operations is enabled,
under control of a developer or operator, or optionally determined
automatically, such as by an expert system or automation system,
such as based on detected network conditions for an environment,
for an entity 3330, or for a network as a whole. In embodiments,
edge intelligence 3438 enables adaptation of edge computation
(including where computation occurs within various available
networking resources, how networking occurs (such as by protocol
selection), where data storage occurs, and the like) that is
multi-application aware, such as accounting for QoS, latency
requirements, congestion, and cost as understood and prioritized
based on awareness of the requirements, the prioritization, and the
value (including ROI, yield, and cost information, such as costs of
failure) of edge computation capabilities across more than one
application, including any combinations and subsets of the
applications 3312 described herein or in the documents incorporated
herein by reference.
[1086] With reference to FIG. 37, in embodiments provided herein is
a transactional, financial and marketplace enablement system. An
example system may include an adaptive edge computing circuit
structured to interpret information from a plurality of data
sources, and to interface with a plurality of management
applications; wherein the plurality of management applications are
each associated with a separate one of a plurality of financial
entities; and wherein the adaptive edge computing circuit further
comprises an edge intelligence component structured to determine an
edge intelligence process improvement for at least one of the
plurality of management applications in response to the information
from the plurality of data sources.
[1087] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system may include wherein the plurality of
management applications comprise at least two applications selected
from the applications consisting of: an investment application, as
asset management application, a lending application, a risk
management application, a marketing application, a trading
application, a tax application, a fraud application, a financial
service application, a security application, an underwriting
application, a blockchain application, a real estate application, a
regulatory application, a platform marketplace application, a
warranty application, an analytics application, a pricing
application, and a smart contract application.
[1088] An example system may include wherein the plurality of data
sources comprise at least two applications selected from the
applications consisting of: an access data source, an asset and
facility data source, a worker data source, a claims data source,
an accounting data source, an event data source, and an
underwriting data source.
[1089] An example system may include wherein the at least one
entity each comprise an entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1090] An example system may include wherein the edge intelligence
component is further structured to determine a plurality of process
improvement opportunities for one of the plurality of management
applications in response to the information from the plurality of
data sources, and to provide one of a prioritized list or a
visualization of the plurality of process improvement opportunities
to the one of the plurality of management applications.
[1091] An example system may include wherein the edge intelligence
component is further structured to determine a process improvement
opportunity in response to at least one parameter selected from the
parameters consisting of: a time saving value, a cost saving value,
and an improved outcome value.
[1092] An example system may include wherein the plurality of
management applications includes a security application, and
wherein the adaptive edge computing circuit is further structured
to automate a security service process.
[1093] An example system may include wherein the adaptive edge
computing circuit is further structured to automate the security
service process by performing at least one operation selected from
the operations consisting of: utilizing data from the plurality of
data sources to schedule a security event; and determining security
criteria in response to a plurality of asset data and security
outcomes, and providing a security command in response to the
plurality of asset data and security outcomes.
[1094] An example system may include wherein the adaptive edge
computing circuit is further structured to automate the security
service process in response to at least one of the plurality of
data sources that is not accessible to the security
application.
[1095] An example system may include wherein the adaptive edge
computing circuit is further structured to improve the process at
least one of the plurality of management applications by providing
an output to at least one entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1096] An example system may include wherein the adaptive edge
computing circuit is further structured to interpret an outcome
from the at least one entity, and wherein the edge intelligence
component is further structured to iteratively improve the process
in response to the outcome from the at least one entity.
[1097] An example system may include wherein at least one of the
plurality of data sources is not accessible to each of the at least
one of the plurality of management applications having an improved
process by the adaptive edge computing circuit.
[1098] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive edge computing circuit comprises a risk
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: a claims data source, a pricing data source,
an asset and facility data source, a worker data source, and an
event data source.
[1099] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive edge computing circuit comprises an asset
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an access data
source, a pricing data source, an accounting data source, a worker
data source, and an event data source.
[1100] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive edge computing circuit comprises a security
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, and an
event data source.
[1101] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive edge computing circuit comprises a platform
marketplace application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, an event
data source, and an underwriting data source.
[1102] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive edge computing circuit comprises a platform
marketplace application, and wherein the adaptive edge computing
circuit is further structured to operate an interface to interpret
an edge definition, and wherein an edge intelligence component is
further structured to determine the edge intelligence process
improvement in response to the edge definition.
[1103] An example system may include wherein the edge definition
comprises an identification of at least one of the following
parameters: a slow data connection, an unreliable data connection,
a network interference description, a network caching description,
a quality of service requirement, or a latency requirement.
[1104] Referring to FIG. 38, additional details, components,
sub-systems, and other elements of an optional embodiment of the
storage layer 3310 of the platform 3300 are illustrated, relating
in particular to embodiments that may include a geofenced virtual
asset tag 3488, such as for one or more assets within the asset and
facility data 3320 described throughout this disclosure and the
document incorporated by reference herein. In embodiments, the
virtual asset tag is a data structure that contains data about an
entity 3330, such as an asset (which may be physical or virtual),
machine, item of equipment, item of inventory, manufactured
article, certificate (such as a stock certificate), deed,
component, tool, device, or worker (among others), where the data
is intended to be tagged to the asset, such as where the data
relates uniquely to the particular asset (e.g., to a unique
identifier for the individual asset) and is linked to proximity or
location of the asset (such as being geofenced to an area or
location of or near the asset, or being associated with a
geo-located digital storage location or defined domain for a
digital asset). The virtual asset tag is thus functionally
equivalent to a physical asset tag, such as an RFID tag, in that it
provides a local reader or similar device to access the data
structure (as a reader would access an RFID tag), and in
embodiments access control is managed as if the tag were physical
located on an asset; for example, certain data may be encrypted
with keys that only permit it to be read, written to, modified, or
the like by an operator who is verified to be in the proximity of a
tagged financial entity 3330, thereby allowing partitioning of
local-only data processing from remote data processing. In
embodiments, the virtual asset tag may be configured to recognize
the presence of an RF reader or other reader (such as by
recognition of an interrogation signal) and communicate to the
reader, such as with help of protocol adaptors, such as over an RF
communication link with the reader, notwithstanding the absence of
a conventional RFID tag. This may occur by communications from IoT
devices, telematics systems, and by other devices residing on a
local area network. In embodiments, a set of IoT devices in a
marketplace or financial or transactional environment can act as
distributed blockchain nodes, such as for storage of virtual asset
tag data, for tracking of transactions, and for validation (such as
by various consensus protocols) of enchained data, including
transaction history for maintenance, repair and service. In
embodiments, the IoT devices in a geofence can collectively
validate location and identity of a fixed asset that is tagged by a
virtual asset tag, such as where peers or neighbors validate other
peers or neighbors as being in a given location, thereby validating
the unique identity and location of the asset. Validation can use
voting protocols, consensus protocols, or the like. In embodiments,
identity of the financial entities that are tagged can be
maintained in a blockchain. In embodiments, an asset tag may
include information that is related to a digital thread 3484, such
as historical information about an asset, its components, its
history, and the like.
[1105] With reference to FIG. 38, In embodiments provided herein is
a transactional, financial and marketplace enablement system. An
example system may include an adaptive intelligence circuit
structured in interpret information from a plurality of data
sources, and to interface with a plurality of management
applications, wherein the adaptive intelligence circuit comprises a
protocol adapter component; wherein the plurality of management
applications are each associated with a separate one of a plurality
of financial entities; and wherein the adaptive intelligence
circuit further comprises an artificial intelligence component
structured to determine an artificial intelligence process
improvement for at least one of the plurality of management
applications in response to the information from the plurality of
data sources.
[1106] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system may include wherein at least one of
the plurality of data sources is a mobile data collector.
[1107] An example system may include wherein the adaptive
intelligence circuit further comprises a protocol adapter component
structured to determine a communication protocol facilitating
communication between an entity accessing the at least one of the
plurality of management applications having an improved
process.
[1108] An example system may include wherein the entity accessing
the at least one of the plurality of management applications
comprises an operator related to the at least one of the plurality
of management applications, and wherein the protocol adapter
component is further structured to determine the communication
protocol as a protocol enabling encrypted communications in
response to a determination from the mobile data collector that the
operator is in a proximity of a tagged financial entity.
[1109] An example system may include wherein the mobile data
collector collects data from at least one geofenced virtual asset
tag.
[1110] An example system may include wherein the adaptive
intelligence circuit further comprises a protocol adapter component
structured to determine a communication protocol facilitating
communication between an entity accessing the at least one of the
plurality of management applications having an improved
process.
[1111] An example system may include wherein the entity accessing
the at least one of the plurality of management applications
comprises an operator related to the at least one of the plurality
of management applications, and wherein the protocol adapter
component is further structured to determine the communication
protocol as a protocol enabling encrypted communications in
response to a determination from the at least one geofenced virtual
asset tag that the operator is in a proximity of a tagged financial
entity.
[1112] An example system may include wherein at least one of the
plurality of data sources is an Internet of Things data
collector.
[1113] An example system may include wherein the adaptive
intelligence circuit further comprises a protocol adapter component
structured to determine a communication protocol facilitating
communication between an entity accessing the at least one of the
plurality of management applications having an improved
process.
[1114] An example system may include wherein the entity accessing
the at least one of the plurality of management applications
comprises an operator related to the at least one of the plurality
of management applications, and wherein the protocol adapter
component is further structured to determine the communication
protocol as a protocol enabling encrypted communications in
response to a determination from the Internet of Things data
collector that the operator is in a proximity of a tagged financial
entity.
[1115] An example system may include wherein at least one of the
plurality of data sources is a blockchain circuit, and wherein the
adaptive intelligence circuit interprets the information from the
blockchain circuit utilizing the adaptive intelligence circuit.
[1116] An example system may include wherein the plurality of
management applications comprise at least two applications selected
from the applications consisting of: an investment application, as
asset management application, a lending application, a risk
management application, a marketing application, a trading
application, a tax application, a fraud application, a financial
service application, a security application, an underwriting
application, a blockchain application, a real estate application, a
regulatory application, a platform marketplace application, a
warranty application, an analytics application, a pricing
application, and a smart contract application.
[1117] An example system may include wherein the plurality of data
sources comprise at least two applications selected from the
applications consisting of: an access data source, an asset and
facility data source, a worker data source, a claims data source,
an accounting data source, an event data source, and an
underwriting data source.
[1118] An example system may include wherein the at least one
entity each comprise an entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1119] An example system may include wherein the artificial
intelligence component is further structured to determine a
plurality of process improvement opportunities for one of the
plurality of management applications in response to the information
from the plurality of data sources, and to provide one of a
prioritized list or a visualization of the plurality of process
improvement opportunities to the one of the plurality of management
applications.
[1120] An example system may include wherein the artificial
intelligence component is further structured to determine a process
improvement opportunity in response to at least one parameter
selected from the parameters consisting of: a time saving value, a
cost saving value, and an improved outcome value.
[1121] An example system may include wherein the plurality of
management applications includes a risk management application, and
wherein the adaptive intelligence circuit is further structured to
automate a risk management process.
[1122] An example system may include wherein the adaptive
intelligence circuit is further structured to automate the risk
management process by performing at least one operation selected
from the operations consisting of: utilizing data from the
plurality of data sources to schedule a risk event; determining
risk criteria in response to a plurality of asset data and risk
outcomes, and providing a risk command in response to the plurality
of asset data and risk management outcomes; and adjusting a
geofencing location to provide at least one of an improved access
for an operator related to at least one of the plurality of
management applications or improve a security of communications to
at least one of the plurality of management applications.
[1123] An example system may include wherein the adaptive
intelligence circuit is further structured to automate the risk
management process in response to at least one of the plurality of
data sources that is not accessible to the risk management
application.
[1124] An example system may include wherein the adaptive
intelligence circuit is further structured to improve the process
of at least one of the plurality of management applications by
providing an output to at least one entity selected from the
entities consisting of: an external marketplace, a banking
facility, an insurance facility, a financial service facility, an
operating facility, a collaborative robotics facility, a worker, a
wearable device, an external process, and a machine.
[1125] An example system may include wherein the adaptive
intelligence circuit is further structured to interpret an outcome
from the at least one entity, and wherein the artificial
intelligence component is further structured to iteratively improve
the process in response to the outcome from the at least one
entity.
[1126] An example system may include wherein at least one of the
plurality of data sources is not accessible to each of the at least
one of the plurality of management applications having an improved
process by the adaptive intelligence circuit.
[1127] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive intelligence circuit comprises a smart contract
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: a claims data source, a pricing data source,
an asset and facility data source, a worker data source, and an
event data source.
[1128] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive intelligence circuit comprises an asset management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an access data source, a pricing data
source, an accounting data source, a worker data source, and an
event data source.
[1129] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive intelligence circuit comprises a security
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, and an
event data source.
[1130] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive intelligence circuit comprises a marketing
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, an event
data source, and an underwriting data source.
[1131] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive intelligence circuit comprises a pricing management
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: an asset and facility data source, a claims
data source, a worker data source, and an event data source.
[1132] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the adaptive intelligence circuit comprises a warranty
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an access data
source, a claims data source, a worker data source, and an event
data source.
[1133] Referring to FIG. 39, in embodiments, a unified RPA system
3442, such as for developing and deploying one or more automation
capabilities may include or enable capabilities for robot
operational analytics 3902, such as for analyzing operational
actions of a set of robots, including with respect to location,
mobility and routing of mobile robots, as well as with respect to
motions of robot components, such as where robotic components are
used within a wide range of protocols or procedures, such as
banking processes, underwriting processes, insurance processes,
risk assessment processes, risk mitigation processes, inspection
processes, exchange processes, sale processes, purchase processes,
delivery processes, warehousing processes, assembly processes,
transport processes, maintenance and repair processes, data
collection processes, and many others.
[1134] In embodiments, the RPA system 3442 may include or enable
capabilities for machine learning on unstructured data 3909, such
as learning on a training set of human labels, tags, or other
activities that allow characterization of the unstructured data,
extraction of content from unstructured data, generation of
diagnostic codes or similar summaries from content of unstructured
data, or the like. For example, the RPA system 3442 may include
sub-systems or capabilities for processing PDFs (such as technical
data sheets, functional specifications, repair instructions, user
manuals and other documentation about financial entities 3330, such
as machines and systems), for processing human-entered notes (such
as notes involved in diagnosis of problems, notes involved in
prescribing or recommending actions, notes involved in
characterizing operational activities, notes involved in
maintenance and repair operations, and many others), for processing
information unstructured content contained on websites, social
media feeds and the like (such as information about products or
systems in an financial environment that can be obtained from
vendor websites), and many others.
[1135] In embodiments, the RPA system 3442 may comprise a unified
platform with a set of RPA capabilities, as well as systems for
monitoring (such as the systems of the monitoring layer 3306 and
data collection systems 3318), systems for raw data processing 3904
(such as by optical character recognition (OCR), natural language
processing (NPL), computer vision processing, sound processing,
sensor processing and the like); systems for workflow
characterization and management 3908; analytics capabilities 3910;
artificial intelligence capabilities 3448; and administrative
systems 3914, such as for policy, governance, provisioning (such as
of services, roles, access controls, and the like) among others.
The RPA system 3442 may include such capabilities as a set of
microservices in a microservices architecture. The RPA system 3442
may have a set of interfaces to other platform layers 3308, as well
as to external systems, for data exchange, such that the RPA system
3442 can be accessed as an RPA platform-as-a-service by external
systems that can benefit from one or more automation
capabilities.
[1136] In embodiments, the RPA system 3442 may include a
quality-of-work characterization capability 3912, such as one that
identifies high quality work as compared to other work. This may
include recognizing human work as different from work performed by
machines, recognizing which human work is likely to be of highest
quality (such as work involving the most experienced or expensive
personnel), recognizing which machine-performed work is likely to
be of the highest quality (such as work that is performed by
machines that have extensively learned on feedback from many
outcomes, as compared to machines that are newly deployed, and
recognizing which work has historically provided favorable outcomes
(such as based on analytics or correlation to past outcomes). A set
of thresholds may be applied, which may be varied under control of
a developer or other user of the RPA system 3442, such as to
indicate by type, by quality-level, or the like, which data sets
indicating past work will be used for training within machine
learning systems that facilitate automation.
[1137] With reference to FIG. 39, in embodiments provided herein is
a transactional, financial and marketplace enablement system. An
example system may include an robotic process automation circuit
structured in interpret information from a plurality of data
sources, and to interface with a plurality of management
applications; wherein the plurality of management applications are
each associated with a separate one of a plurality of financial
entities; and wherein the robotic process automation circuit
further comprises a robot operational analytics component
structured to determine a robot operational process improvement for
at least one of the plurality of management applications in
response to the information from the plurality of data sources.
[1138] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system may further include an
administrative system circuit structured to adapt the robot
operational process improvement through at least one of governance
of robotic operations, provisioning robotic operations, or robotic
operations policies.
[1139] An example system may include wherein the robot operational
process improvement comprises a robotic workflow characterization
and improvement.
[1140] An example system may further include an opportunity mining
circuit structured to adapt the operational process improvement to
one of the plurality of management applications.
[1141] An example system may include wherein the robot operational
process improvement comprises a robotic quality of work
characterization and improvement.
[1142] An example system may include wherein the robot operational
analytics component comprises a robotics machine learning component
for processing the information from a plurality of data sources to
determine the robot operational process improvement.
[1143] An example system may include wherein the robot operational
analytics component comprises a raw data processing component for
processing the information from a plurality of data sources to
determine the robot operational process improvement.
[1144] An example system may include wherein the plurality of
management applications comprise at least two applications selected
from the applications consisting of: an investment application, as
asset management application, a lending application, a risk
management application, a marketing application, a trading
application, a tax application, a fraud application, a financial
service application, a security application, an underwriting
application, a blockchain application, a real estate application, a
regulatory application, a platform marketplace application, a
warranty application, an analytics application, a pricing
application, and a smart contract application.
[1145] An example system may include wherein the plurality of data
sources comprise at least two applications selected from the
applications consisting of: an access data source, an asset and
facility data source, a worker data source, a claims data source,
an accounting data source, an event data source, and an
underwriting data source.
[1146] An example system may include wherein the at least one
entity each comprise an entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1147] An example system may include wherein the robot operational
analytics component is further structured to determine a plurality
of process improvement opportunities for one of the plurality of
management applications in response to the information from the
plurality of data sources, and to provide one of a prioritized list
or a visualization of the plurality of process improvement
opportunities to the one of the plurality of management
applications.
[1148] An example system may include wherein the robot operational
analytics component is further structured to determine a process
improvement opportunity in response to at least one parameter
selected from the parameters consisting of: a time saving value, a
cost saving value, and an improved outcome value.
[1149] An example system may include wherein the plurality of
management applications includes a regulatory management
application, and wherein the robotic process automation circuit is
further structured to automate a regulatory management process.
[1150] An example system may include wherein the robotic process
automation circuit is further structured to automate the regulatory
management process by performing at least one operation selected
from the operations consisting of: utilizing data from the
plurality of data sources to schedule a regulatory event; and
determining regulatory criteria in response to a plurality of asset
data and regulatory outcomes, and providing a regulatory command in
response to the plurality of asset data and regulatory management
outcomes.
[1151] An example system may include wherein the robotic process
automation circuit is further structured to automate the regulatory
management process in response to at least one of the plurality of
data sources that is not accessible to the regulatory management
application.
[1152] An example system may include wherein the robotic process
automation circuit is further structured to improve the process at
least one of the plurality of management applications by providing
an output to at least one entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1153] An example system may include wherein the robotic process
automation circuit is further structured to interpret an outcome
from the at least one entity, and wherein the robot operational
analytics component is further structured to iteratively improve
the process in response to the outcome from the at least one
entity.
[1154] An example system may include wherein at least one of the
plurality of data sources is not accessible to each of the at least
one of the plurality of management applications having an improved
process by the robotic process automation circuit.
[1155] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises an investment
application, and wherein the at least one of the plurality of data
sources comprises at least one data source selected from the data
sources consisting of: a claims data source, a pricing data source,
an asset and facility data source, a worker data source, and an
event data source.
[1156] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises an asset
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an access data
source, a pricing data source, an accounting data source, a worker
data source, and an event data source.
[1157] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises a security
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, and an
event data source.
[1158] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises a marketing
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, an event
data source, and an underwriting data source.
[1159] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises a pricing
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, and an
event data source.
[1160] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises a warranty
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an access data
source, a claims data source, a worker data source, and an event
data source.
[1161] Referring to FIG. 40, in embodiments, various systems,
methods, processes, services, components and other elements for
enabling a blockchain and smart contract platform for a forward
market 4000 for access rights to events. Within a transactional
enablement system such as described in connection with various
embodiments of the platform 3300, a blockchain application 3422 and
associated smart contract 3431 may be used to enable a forward
market 4002 for access rights to events, such as where one or more
event tickets, seat licenses, access rights, rights of entry,
passes (e.g., backstage passes) or other items representing,
comprising or embodying an access token for the right to attend,
enter, view, consume, or otherwise participate in an event (which
may be a live event, a recorded event, an event at a physical
venue, a digital content event, or other event to which access is
controlled)(all of which are encompassed by the term access token
4008 as used herein, except where context indicates otherwise) is
securely stored on a blockchain that is configured by a blockchain
application 3422, such as one in which the blockchain 3422
comprises a ledger of transactions in access tokens 4008 (such term
comprising tickets and other evidence of the right to access the
event), such as with indications of ownership (including identity
information, event information, token information, information
about terms and conditions, and the like) and a record of transfer
of ownership (including terms, condition and policies regarding
transferability). In embodiments, such a blockchain-based access
token may be traded in a marketplace application 3327, such as one
configured to operate with or for a spot market or forward market
4002. In embodiments, the forward market 4002 operated within or by
the platform may be a contingent forward market, such as one where
a future right vests, is triggered, or emerges based on the
occurrence of an event, satisfaction of a condition, or the like,
such as enabled by a smart contract 3431 that operates on one or
more data structures in or associated with a platform-operated
marketplace 3327 or an external marketplace 3390 to execute or
apply a rule, term, condition or the like, optionally resulting in
a transaction that is recorded in the blockchain (such as on a
distributed ledger on the blockchain), which may, in turn, initiate
other processes and result in other smart contract operations. In
such embodiments, a condition triggering an event may include an
event promotor or other party scheduling an event having a defined
set of parameters, an event arising having such parameters, or the
like, and the blockchain-based access token 4008 may be configured
(optionally in conjunction with a smart contract 3431 and with one
or more monitoring systems 3306) to recognize the presence or
existence, such as in an external marketplace 3390 of an event, or
an access token to an event, that satisfies the defined set of
parameters and to initiate an operation with respect to the access
token, such as reporting the existence of availability of the
access token, transferring access to the access token, transferring
ownership, setting a price, or the like. In embodiments, monitoring
systems 3306 may monitor external marketplaces 3390 for relevant
events, tokens and the like, as well as for information indicating
the emergence of conditions that satisfy one or more conditions
that result in triggering, vesting, or emergence of a condition
that impacts an access token or event. As an illustrative example,
a sporting event access token 4008 to a playoff game may be
configured to vest upon the presence of a specific team in a
specific game (e.g., the Super Bowl), at which point the right to a
ticket to a specific seat may be automatically allocated on a
distributed ledger, enabled by a blockchain, to the individual
listed on the ledger as having the right to the ticket for that
team. Thus, a distributed ledger or other blockchains 3422 may
securely maintain multiple prospective owners for an event token
4008 for the same event, provided access rights can be divided such
that they are mutually exclusive but can be designated to a
specific owner upon the emergence of a condition (e.g., a
particular seat at a game, concert, or the like) and allocate
ownership to a specific owner based on upon the emergence of a
condition that determines which prospective owner has the right to
become the actual owner (e.g., that owner's team makes it to the
game). In the example of a sports league, the blockchain can thus
maintain as many owners as there are mutually exclusive conditions
for a seat (e.g., by allocating seats across all teams in a
conference for the Super Bowl, or all teams in a division for a
college football conference final). The defined set of parameters
may include location (where an as-yet-unscheduled event takes
place), participants (teams, individuals and many others), prices
(such as the access token is priced below a defined threshold),
timing (such as a span of hours, days, months, years, or other
periods), type of event (sports, concerts, comedy performances,
theatrical performances, political events, and many others) and
others. In embodiments, one or more monitoring systems 3306 or
other data collection systems may be configured to monitor one or
more external marketplaces 3390 or platform-operated marketplaces
(such as on e-commerce websites and applications, auction sites and
applications, social media sites and applications, exchange sites
and applications, ticketing sites and applications, travel sites
and applications, hospitality sites and applications, concert
promotional sites and applications, or other sites or applications)
or other entities for indicators of available events, for
prospective conditions that can be used to define potentially
divisible or mutually exclusive access right conditions (such as
for identifying events that can be configured on a multi-party
distributed ledger with conditional access distributed across
different prospective owners, optionally conducted via one or more
opportunity miners 3446) and for actual conditions that may trigger
distribution of rights to a specific owner based on the conditions.
Thus, the blockchain may be used to make a contingent market in any
form of event or access token by securely storing access rights on
a distributed ledger, and the contingent market may be automated by
configuring data collection and a set of business rules that
operate upon collected data to determine when ownership rights
should be vested, transferred, or the like. Post-vesting of a
contingency (or set of contingencies), the access token may
continue to be traded, with the blockchain providing a secure
method of validating access. Security may be provided by encryption
of the chain as with cryptocurrency tokens (and a cryptocurrency
token may itself comprise a forward-market cryptocurrency token for
event access), with proof of work, proof of stake, or other methods
for validation in the case of disputes.
[1162] In embodiments, the platform 400 may include or interact
with various applications, services, solutions or the like, such as
those described in connection with the platform 3300, such as
pricing applications 3421 (such as for setting and monitoring
pricing for contingent access rights, underlying access rights,
tokens, fees and the like), analytics applications 3419 (such as
for monitoring, reporting, predicting, and otherwise analyzing all
aspects of the platform 4000, such as to optimize offerings,
timing, pricing, or the like, to recognize and predict patterns, to
establish rules and contingencies, to establish models or
understanding for use by humans or by machine learning system, and
for many other purposes), trading applications 3428 (such as for
trading or exchanging contingent access rights or underlying access
rights or tokens), security applications 3418, or the like.
[1163] With reference to FIG. 40, in embodiments provided herein is
a transactional, financial and marketplace enablement system. An
example system may include an robotic process automation circuit
structured in interpret information from a plurality of data
sources, and to interface with a plurality of management
applications; wherein the plurality of management applications are
each associated with a separate one of a plurality of financial
entities; and wherein the robotic process automation circuit
further comprises an opportunity mining component structured to
determine a robot operational process improvement for at least one
of the plurality of management applications in response to the
information from the plurality of data sources.
[1164] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system may further include a data
collection circuit structured to collect and record physical
process observation data, wherein the physical process observation
data is one of the plurality of data sources.
[1165] An example system may further include a data collection
circuit structured to collect and record software interaction
observation data, wherein the software interaction observation data
is one of the plurality of data sources.
[1166] An example system may include wherein the plurality of
management applications comprise at least two applications selected
from the applications consisting of: a forward market application,
an event access tokens application, a security application, a
blockchain application, a platform marketplace application, an
analytics application, a pricing application, and a smart contract
application.
[1167] An example system may include wherein the plurality of data
sources comprise at least two applications selected from the
applications consisting of: an access data source, an asset and
facility data source, a worker data source, a claims data source,
an accounting data source, an event data source, and an
underwriting data source.
[1168] An example system may include wherein the at least one
entity each comprise an entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1169] An example system may include wherein the opportunity mining
component is further structured to determine a plurality of process
improvement opportunities for one of the plurality of management
applications in response to the information from the plurality of
data sources, and to provide one of a prioritized list or a
visualization of the plurality of process improvement opportunities
to the one of the plurality of management applications.
[1170] An example system may include wherein the opportunity mining
component is further structured to determine a process improvement
opportunity in response to at least one parameter selected from the
parameters consisting of: a time saving value, a cost saving value,
and an improved outcome value.
[1171] An example system may include wherein the plurality of
management applications includes a trading management application,
and wherein the robotic process automation circuit is further
structured to automate a trading management process.
[1172] An example system may include wherein the robotic process
automation circuit is further structured to automate the trading
management process by performing at least one operation selected
from the operations consisting of: utilizing data from the
plurality of data sources to schedule a trading event; and
determining trading criteria in response to a plurality of asset
data and trading outcomes, and providing a trading command in
response to the plurality of asset data and trading management
outcomes.
[1173] An example system may include wherein the robotic process
automation circuit is further structured to automate the trading
management process in response to at least one of the plurality of
data sources that is not accessible to the trading management
application.
[1174] An example system may include wherein the robotic process
automation circuit is further structured to improve the process at
least one of the plurality of management applications by providing
an output to at least one entity selected from the entities
consisting of: an external marketplace, a banking facility, an
insurance facility, a financial service facility, an operating
facility, a collaborative robotics facility, a worker, a wearable
device, an external process, and a machine.
[1175] An example system may include wherein the robotic process
automation circuit is further structured to interpret an outcome
from the at least one entity, and wherein the opportunity mining
component is further structured to iteratively improve the process
in response to the outcome from the at least one entity.
[1176] An example system may include wherein at least one of the
plurality of data sources is not accessible to each of the at least
one of the plurality of management applications having an improved
process by the robotic process automation circuit.
[1177] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises a forward
market application, and wherein the at least one of the plurality
of data sources comprises at least one data source selected from
the data sources consisting of: a claims data source, a pricing
data source, an asset and facility data source, a worker data
source, and an event data source.
[1178] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises an event access
tokens management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an access data
source, a pricing data source, an accounting data source, a worker
data source, and an event data source.
[1179] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises a security
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, and an
event data source.
[1180] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises a blockchain
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, an event
data source, and an underwriting data source.
[1181] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises a pricing
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an asset and facility
data source, a claims data source, a worker data source, and an
event data source.
[1182] An example system may include wherein the at least one of
the plurality of management applications having an improved process
by the robotic process automation circuit comprises an analytics
management application, and wherein the at least one of the
plurality of data sources comprises at least one data source
selected from the data sources consisting of: an access data
source, a claims data source, a worker data source, and an event
data source.
[1183] Referring to FIG. 41, a platform-operated marketplace 3327
for a forward market to access rights to one or more events may be
configured, such as in a dashboard 4118 or other user interface for
an operator of the platform-operated marketplace 3327, using the
various enabling capabilities of the data handling platform 3300
described throughout this disclosure. The operator may use the user
interface or dashboard 4118 to undertake a series of steps to
perform or undertake an algorithm to create a contingent forward
market event access right token as described in connection with
FIG. 40. In embodiments, one or more of the steps of the algorithm
to create a contingent forward market event access right token
within the dashboard 4118 may include identifying one or more
access rights for one or more events at a component 4102 to
identify access rights, such as by monitoring one or more
platform-operated marketplaces 3327 or external marketplaces 3390
for messages, announcements, or other data indicative of the event
or access right. The dashboard 4118 may be configured with
interface elements (including application programming elements)
that allow the event to be imported into the platform marketplace
3327, such as by linking to the environment where the access right
is offered or maintained, which may include using APIs for backend
ticketing systems and the like. In the dashboard 4118, at a
component 4104, one or more conditions (of the type described
herein) for the access right may be configured (e.g., by
interfacing with a user), such as by defining a set of mutually
exclusive conditions that, upon triggering, allocate the access
right to different individuals or entities. The user interface of
the dashboard 4118 may include a set of drop-down menus, tables,
forms, or the like with default, templated, recommended, or
pre-configured conditions, such as ones that are appropriate for
various types of access rights. For example, access rights to a
playoff game for a sporting event can be preconfigured to set an
access condition as the presence of a specific team in the playoff
game, where the team is a member of the set of teams that could be
in the game, and access rights are allocated to a given seat across
mutually exclusive possible teams that could make it to the game
(e.g., the teams in one conference for the Super Bowl). As another
example, access rights to an as-yet-unplanned entertainment event
could be preconfigured to set conditions such as a venue, a span of
dates and a selected entertainer or group. Once the conditions and
other parameters of the access rights are configured, at a
component 4108 a blockchain may be configured to maintain, such as
via a ledger, the data required to provision, allocate, and
exchange ownership of the contingent access rights (and optionally
the underlying access tokens to which the contingent access rights
relate). For example, a ticket to a game may be stored as a
cryptographically secure token on the ledger, and another token may
be created and stored on the blockchain for each contingent access
right that could result in the ownership of the ticket. The
blockchain may be configured to store tokens, identity information,
transaction information (such as for exchanges of contingent rights
and/or underlying tokens) and other data. At a component 4110 a
smart contract 3431 may be configured to embody the conditions that
were configured at the component 4104, and to operate on the
blockchain that was created at the component 4108 as well as to
operate on other data, such as data indicating facts, conditions,
events, or the like in the platform-operated marketplace 3327
and/or an external marketplace 3390. The smart contract may be
configured at a component 4110 to apply one or more rules, execute
one or more conditional operations, or the like upon data that may
include event data 3324, access data 3362, pricing data 3364 or
other data about or relevant to access rights. Once configuration
of one or more blockchains and one or more smart contracts is
complete, at a component 4112 the blockchain and smart contract may
be deployed in the platform-operated marketplace, such as for
interaction by one or more consumers or other users, who may, such
as in a marketplace interface, such as a website, application, or
the like, enter into the smart contract, such as by purchasing a
contingent right to a future event, at which point the platform,
such as using the adaptive intelligent systems 3304 or other
capabilities, may store relevant data, such as pricing data and
identity data for the party or parties entering the smart contract
on the blockchain or otherwise on the platform 3300. At a component
4114, once the smart contract is executed, the component 4114 may
monitor, such as by the monitoring systems layer 3306, the
platform-operated marketplace 3327 and/or one or more external
marketplaces 3390 for event data 3324, access data 3362, pricing
data 3364 or other data, such as events, that may satisfy one or
more conditions or trigger application of one or more rules of the
smart contract. For example, results of games or announcements of
future entertainment events may be monitored, and smart contract
conditions may be satisfied. At a component 4116, upon satisfaction
of conditions, smart contracts may be settled, executed, or the
like, resulting updates or other operations on the blockchain, such
as by transferring ownership of underlying access tokens and/or
contingent access tokens. Thus, via operation of the
above-referenced components, an operator of the platform-operated
marketplace 3327 may discover, configure, deploy and have executed
a set of smart contracts that offer and deliver contingent access
to future events that are cryptographically secured and transferred
on a blockchain to consumers or others. In embodiments, the
adaptive intelligent systems layer 3304 may be used to monitor the
steps of the algorithm described above, and one or more artificial
intelligence systems may be used to automated, such as by robotic
process automation, the entire process or one or more sub-steps or
sub-algorithms. This may occur as described above, such as by
having an artificial intelligence system learn on a training set of
data resulting from observations, such as monitoring software
interactions, of human users as they undertake the above-referenced
steps. Once trained, the adaptive intelligence layer 3304 may thus
enable the platform 3300 to provide a fully automated platform for
discovery and delivery of contingent access rights to future
events.
[1184] Referencing FIG. 42, in embodiments, a platform is provided
herein, with systems, methods, processes, services, components and
other elements for enabling a blockchain and smart contract
platform for forward market demand aggregation 4200. In this case,
a demand aggregation blockchain and smart contract platform 4200,
having various features and enabled by capabilities similar to
those described in connection with the platform 3300 and the
platform 4000 as described above may be based on a set of
contingencies 4204 that influence or represent future demand for an
offering 4202, which may comprise a set of products, services, or
the like (which may include physical goods, virtual goods,
software, physical services, software, access rights, entertainment
content, or many other items). A blockchain 3422, such as enabling
distributed ledger, may record indicators of interest from a set of
parties with respect to the product, service, or the like, such as
ones that define parameters under which the party is willing to
commit to purchase the product or service. Interest may be
expressed or committed in a demand aggregation interface 4322,
which may be included in or associated with one or more sites,
applications, communications systems, or the like, which may be
independently operated or may comprise aspects of a
platform-operated marketplace 3327 or an external marketplace 3390.
Commitments may be taken and administered via a smart contract 3431
or other transaction mechanisms. These commitments may include
various parameters 4208, such as parameters of price, technical
specification (e.g., shoe size, dress size, or the like for
clothing, or performance characteristics for information
technology, such as bandwidth, storage capacity, pixel density, or
the like), timing, and many others for one or more desired
offerings 4202. The blockchain 3422 may thus be used to aggregate
future demand in a forward market 4002 with respect to a variety of
products and services and may be processed by manufacturers,
distributors, retailers and others to help plan for the demand,
such as for assistance (optionally in an analytics system 3419 with
pricing, inventory management, supply chain management, smart
manufacturing, just-in-time manufacturing, product design and many
other activities). The offering 4202, whether a product, service,
or other item, need not exist at the time a set of parameters 4208
are configured; for example, an individual can indicate a
willingness to pay up to $1000 for a 65 inch, 32K quantum dot
television display on or before Jan. 1, 2022. In embodiments, a
vendor can offer a range of potential configurations and conditions
with respect to which consumers can indicate interest, and
optionally commit to purchase within defined conditions. In
embodiments, consumers may present desired items and
configurations. In embodiments, an artificial intelligence system,
which may be a rule-based system, such as enabled by an adaptive
intelligence system 3304, may process a set of potential
configurations having different parameters 4208 for a subset of
configurations that are consistent with each other (e.g., all have
4K or greater capability and all are priced below $500), and the
subset of configurations may be used to aggregate committed future
demand for the offering that satisfies a sufficiently large subset
at a profitable price. In embodiments, the adaptive intelligent
systems 3204 may use a fuzzy logic system, a self-organizing map,
or the like to group potential configurations, such that a human
expert may determine a configuration that is near enough to ones
that have been identified, such that it can be presented as a new
alternative. In embodiments, an artificial intelligence system 3448
may be trained to learn to determine and present new configurations
for offerings 4202 based on a training data set created by human
experts.
[1185] In embodiments, a platform 4200 is provided herein, with
systems, methods, processes, services, components and other
elements for enabling a blockchain and smart contract platform for
forward market rights for accommodations. An accommodation offering
4210 may comprise a combination of products, services, and access
rights that may be handled as with other offerings, including
aggregation demand for the offering 4210 in a forward market 4002.
In embodiments, the forward market capabilities noted above may
include access tokens 4008 for accommodations, as well as future
accommodations, such as hotel rooms, shared spaces offered by
individuals (e.g., Airbnb.TM. spaces), bed-and-breakfasts,
workspaces, conference rooms, convention spaces, fitness
accommodations, health and wellness accommodations, dining
accommodations, and many others. Accommodations offerings 4210 may
be linked to other access tokens 4008, such as in packages; for
example, a hotel room in a city within walking distance of a
sporting event may be linked by or on the same blockchain or linked
blockchains (e.g., by linking ownership or access rights to both on
the same ledger), so that when a condition is met (e.g., a fan's
team makes it to the Super Bowl), vesting of ownership of the
access token to the event also automatically establishes (and
optionally automatically initiates, such as via an application
programming interface of the platform) the right to the
accommodation (such as by booking a hotel room and dining
reservations). Thus, the forward market for the event may enable a
convenient, secure forward market, enabled by automatic processing
on the blockchain for packages of event access tokens,
accommodations, and other elements. In embodiments, accommodations
may be provided with configured forward market parameters 4208
(including conditional parameters) apart from access tokens 4008 to
events, such as where a hotel room or other accommodation is booked
in advance upon meeting a certain condition (such as one relating
to a price within a given time window). For example, an
accommodation offering 4210 at a four-star hotel during a music
festival could be pre-configured to be booked if and when the
accommodation (e.g., a room with a king bed and a city view)
becomes available within a given time window. Thus, demand for
accommodations can be aggregated in advance and conveniently
fulfilled by automatic recognition (such as by monitoring systems
3306) of conditions that satisfy pre-configured commitments
represented on a blockchain (e.g., distributed ledger) and
automatic initiation (optionally including by smart contract
execution) of settlement or fulfillment of the demand (such as by
automated booking of a room or other accommodations).
[1186] In embodiments, a platform is provided herein, with systems,
methods, processes, services, components and other elements for
enabling a blockchain and smart contract platform for forward
market rights to transportation. As with accommodations,
transportation offerings 4212 may be aggregated and fulfilled, with
a wide range of pre-defined contingencies, using the platform 4200.
As with accommodations offerings 4210, travel offerings 4212 can be
linked to other access tokens 4008 (such as event tickets,
accommodations, services and the like), such as where a flight is
automatically booked at or below a predefined price threshold if
and when the fan's team makes it to the Super Bowl, among many
other examples. Travel offerings 4212 can also be offered
separately (such as where travel is automatically booked based on a
commitment, in a distributed ledger, to buy a ticket if it is
offered within a given time window at a given price). As with other
goods and services, aggregation on the blockchain 3422, such as a
distributed ledger, can be used for demand planning, for
determining what resources are deployed to what routes or types of
travel, and the like. Transportation offerings 4212 can be
configured, with predefined contingencies 4204 and parameters 4208,
such as with respect to price, mode of transportation (air, bus,
rail, private car, ride share or other), level of service (e.g.,
First Class, business class, or other), mode of payment (e.g., use
of loyalty programs, rewards points, or particular currencies,
including cryptocurrencies), timing (e.g., defined time period or
linked to an event, location (e.g., specified to be where a given
type of event takes place (such as this year's Super Bowl) or a
specific location), route (e.g., direct or multi-stop, from the
destination of the consumer to a specific location or to wherever
an event takes place), and many others.
[1187] In embodiments, the platform 4200 may include or interact
with various applications, services, solutions or the like, such as
those described in connection with the platform 3300, such as
pricing applications 3421 (such as for setting and monitoring
pricing for goods, services, access rights, tokens, fees and other
items), analytics applications 3419 (such as for monitoring,
reporting, predicting, and otherwise analyzing all aspects of the
platform 4000, such as to optimize offerings, timing, pricing, or
the like, to recognize and predict patterns, to establish rules and
contingencies, to establish models or understanding for use by
humans or by machine learning system, and for many other purposes),
trading applications 3428 (such as for trading or exchanging
contingent access rights, futures or options for goods, services,
or other offerings 4202, tokens and other items), security
applications 3418, or the like.
[1188] Referring to FIG. 43, a platform-operated marketplace 3327
for a forward market to future offerings 4202 may be configured,
such as in a dashboard 4318 or other user interface for an operator
of the platform-operated marketplace 3327, using the various
enabling capabilities of the data handling platform 3300 described
throughout this disclosure. The operator may use the user interface
or dashboard 4318 to undertake a series of steps to perform or
undertake an algorithm to create an offering 4210 as described in
connection with FIG. 42. In embodiments, one or more of the steps
of the algorithm to create a contingent future offering 4210 within
the dashboard 4318 may include, at a component 4302, identifying
offering data 4320, which may come from a platform-operated
marketplace 3327 or an external marketplace 3390, such as via a
demand aggregation interface 4322 presented to one or more
consumers within one of them, or may be entered via a user
interface of or at a site or application that is created for demand
aggregation for offerings 4210, such as via solicitation of
consumer interest or consumer commitments (such as commitments
entered into by smart contracts) based on specification of various
possible parameters 4208 and contingencies 4204 for such offerings
4210.
[1189] The dashboard 4318 may be configured with interface elements
(including application programming elements) that allow an offering
to be managed in the platform marketplace 3327, such as by linking
to the set of environments where various components of the offering
4202, such as descriptions of goods and services, prices, access
rights and the like are specified, offered or maintained, which may
include using APIs for backend ticketing systems, e-commerce
systems, ordering systems, fulfillment systems, and the like. In
the dashboard 4318, a component 4304 may configure one or more
parameters 4208 or contingencies 4204 (e.g., via interactions with
a user), such as comprising or describing the conditions (of the
type described herein) for the offering, such as by defining a set
of conditions that trigger the commitment by a consumer to partake
of the offering 4202, that trigger the right to an allocation of
the offering, or the like. The user interface of the dashboard 4318
may include a set of drop down menus, tables, forms, or the like
with default, templated, recommended, or pre-configured conditions,
parameters 4208, contingencies 4204 and the like, such as ones that
are appropriate for various types of offerings 4202. For example,
access rights to a new line of shoes can be preconfigured to set an
offering condition as the offering of a shoe by a certain designer
of a certain style and color and may be preconfigured to accept a
commitment to buy the shoe if the access is provided below a
certain price during a certain time period. As another example,
demand for an as-yet-unplanned entertainment event can be
preconfigured to set conditions such as a venue, a span of dates
and a selected entertainer or group. Once the conditions and other
parameters of the offering 4202 are configured, a component 4308
may configure a blockchain to maintain, such as via a ledger, the
data required to provision, allocate, and exchange ownership of
items comprising the offering (and optionally underlying access
tokens, virtual goods, digital content items, or the like that are
included in or associated with the offering). For example, a
virtual good for a video may be stored as a cryptographically
secure token on the ledger, and another token may be created and
stored on the blockchain for each contingent access right that
could result in the ownership of the virtual good or each smart
contract to purchase the virtual good if and when it becomes
available under defined conditions. The blockchain may be
configured to store tokens, identity information, transaction
information (such as for exchanges of contingent rights and/or
underlying tokens), virtual goods, license keys, digital content,
entertainment content, and other data. A component 4310 may
configure a smart contract 3431 to embody the conditions that were
configured at the component 4304 and to operate on the blockchain
that was created at the component 4308 as well as to operate on
other data, such as data indicating facts, conditions, events, or
the like in the platform-operated marketplace 3327 and/or an
external marketplace 3390. The smart contract may be configured at
the step 4310 to apply one or more rules, execute one or more
conditional operations, or the like upon data that may include
offering data 4320, event data 3324, access data 3362, pricing data
3364 or other data about or relevant to a set of offerings 4202.
Once configuration of one or more blockchains and one or more smart
contracts is complete, at a component 4312 the blockchain and smart
contract may be deployed in the platform-operated marketplace 3327,
such as for interaction by one or more consumers or other users,
who may, such as in a marketplace interface or a demand aggregation
interface 4322, such as a website, application, or the like, enter
into the smart contract, such as by executing an indication of a
commitment to purchase, attend, or otherwise consume the future
offering 4202, at which point the platform, such as using the
adaptive intelligent systems 3304 or other capabilities, may store
relevant data, such as pricing data and identity data for the party
or parties entering the smart contract on the blockchain or
otherwise on the platform 3300. At a component 4314, once the smart
contract is executed, the platform may monitor, such as by the
monitoring systems layer 3306, the platform-operated marketplace
3327 and/or one or more external marketplaces 3390 for offering
data 4320, event data 3324, access data 3362, pricing data 3364 or
other data, such as events, that may satisfy one or more conditions
or trigger application of one or more rules of the smart contract.
For example, announcements of offerings may be monitored, such as
on e-commerce sites, auction sites, or the like, and smart contract
conditions may be satisfied by one or more of the offerings
4202.
[1190] At a component 4316, upon satisfaction of conditions, smart
contracts may be settled, executed, or the like, resulting updates
or other operations on the blockchain, such as by transferring
ownership of goods, services, underlying access tokens and/or
contingent access tokens and transferring required consideration
(such as obtained by a payments system). Thus, via the
above-referenced steps, an operator of the platform-operated
marketplace 3327 may discover, configure, deploy and have executed
a set of smart contracts that aggregate demand for, and offer and
deliver contingent access to, offerings 4202 that are
cryptographically secured and transferred on a blockchain to
consumers or others. In embodiments, the adaptive intelligent
systems layer 3304 may be used to monitor the steps of the
algorithm described above, and one or more artificial intelligence
systems may be used to automated, such as by robotic process
automation, the entire process or one or more sub-steps or
sub-algorithms. This may occur as described above, such as by
having an artificial intelligence system learn on a training set of
data resulting from observations, such as monitoring software
interactions, of human users as they undertake the above-referenced
steps. Once trained, the adaptive intelligence layer 3304 may thus
enable the platform 3300 to provide a fully automated platform for
discovery and delivery of offerings, as well as demand aggregation
for such offerings 4202 and automated handling of access to and
ownership of such offerings 4202.
[1191] Referring to FIG. 44, in embodiments, a platform is provided
herein, with systems, methods, processes, services, components and
other elements for enabling a blockchain and smart contract
platform 4400 for crowdsourcing for innovation. In such
embodiments, a party seeking a set of innovations 4402, such as
inventions, works of authorship, innovations, technology solutions
to a set of problems, satisfaction of a technical specification, or
other advancement may configure, such as on a blockchain 3422
(optionally comprising a distributed ledger), a set of conditions
4410, capable of being expressed in a smart contract 3431, that are
required to satisfy the requirement. A reward 4412 may be
configured for generating an innovation 4402 of a given set of
capabilities or satisfying a given set of parameters 4408 by a
given date (e.g., a technical specification for a 5G foldable phone
that can be produced for less than $100 per unit before the end of
2019). Satisfaction of the conditions 4410 may be measured by a
monitoring system 3306, by one or more experts, or by a trained
artificial intelligence system 3448 (such as one trained to
evaluate responses based on a training set created by experts). In
embodiments, the platform 4400 may include a dashboard 4414 for
configuration of the specification, requirements or other
conditions 4410, the reward 4412, timing and other parameters 4408
(such as any required qualifications, formats, geographical
requirements, certifications, credentials, or the like that may be
required of a submission or a submitter), and the platform 4400 may
automatically configure a blockchain 3422 to store the parameters
4408 and a smart contract 3431 to operate, such as in coordination
with a website, application, or other marketplace environments, to
offer the reward 4412, receive and record submissions 4418 (such as
on the blockchain 3422), allocate rewards 4412, and the like, with
events, transactions, and activities being recorded in blockchain,
optionally using a distributed ledger. In embodiments, rewards 4412
may be configured to be allocated across multiple submissions, such
as where an innovation requires solution of multiple problems, such
that submissions 4418 may be evaluated for satisfaction of some
conditions and rewards may be allocated among contributing
submissions 4418 when and if a complete solution (comprising
aggregation of multiple submissions 4418) is achieved, unlocking
the reward, at which point the contributing submissions 4418
recorded on the distributed ledger may be allocated appropriate
portions of the reward. Submissions may include software, technical
data, know how, algorithms, firmware, hardware, mechanical
drawings, prototypes, proof-of-concept devices, systems, and many
other forms, which may be identified, described, or otherwise
documented on the blockchain 3422 (e.g., distributed ledger), such
as by one or more links to one or more resources (which may be
secured by cryptographic or other techniques). Submissions may thus
be described and evaluated for purposes of allocation of rewards
4412 (such as by one or more independent experts, by artificial
intelligence systems (which may be trained by experts) or the
like), then locked, such as by encryption, secure storage, or the
like, unless and until a reward is distributed via the distributed
ledger. Thus, the platform provides a secure system for exchange of
information related to innovation that is provided for rewards,
such as in crowdsourcing or other innovation programs. An
artificial intelligence system 3448 may be trained, such as by a
training set of data using interactions of experts with submissions
4418, to automatically evaluate submissions 4418, for either
automatic allocation of rewards or to pre-populate evaluation for
confirmation by human experts. In embodiments, an artificial
intelligence system 3448 may be trained, such as by a training set
of data reflecting expert interactions with the dashboard 4414,
optionally coupled with outcome information, such as from analytics
system 3419, to create rewards 4412, set conditions 4410, specify
innovations 4402, and set other parameters 4408, thereby providing
a fully automated or semi-automated capability for one or more of
those capabilities.
[1192] Referring to FIG. 45, a platform-operated marketplace 3327
for crowdsourcing innovation 4400 may be configured, such as in a
crowdsourcing dashboard 4414 or other user interface for an
operator of the platform-operated marketplace 3327, using the
various enabling capabilities of the data handling platform 3300
described throughout this disclosure. The operator may use the user
interface 4522 or crowdsourcing dashboard 4414 to undertake a
series of steps to perform or undertake an algorithm to create
crowdsourcing offer as described in connection with FIG. 44. In
embodiments, one or more of the components depicted are configured
to create a reward 4412 within the dashboard 4414 may include, at a
component 4502, identifying potential offers, such as what
innovations 4402 are of interest (such as may be indicated by
indications of demand in a platform operated marketplace 3327 or an
external marketplace 3390, or by indications by stakeholders for an
enterprise through various communication channels.
[1193] The dashboard 4414 may be configured with a crowdsourcing
interface 4522, such as with elements (including application
programming elements) that allow a crowdsourcing offering to be
managed in the platform marketplace 3327 and/or in one or more
external marketplaces 3390. In the dashboard 4414, at a component
4504 the user may configure one or more parameters 4408 or
conditions 4410, such as comprising or describing the conditions
(of the type described herein) for the crowdsourcing offer, such as
by defining a set of conditions 4410 that trigger the reward 4412
and determine allocation of the reward 4412 to a set of submitters.
The user interface of the dashboard 4414 may include a set of
drop-down menus, tables, forms, or the like with default,
templated, recommended, or pre-configured conditions, parameters
4408, conditions 4410 and the like, such as ones that are
appropriate for various types of crowdsourcing offers. Once the
conditions and other parameters of the offer are configured, at a
component 4508 a smart contract 3431 and blockchain 3422 may be
configured to maintain, such as via a ledger, the data required to
provision, allocate, and exchange data related to the offer. The
blockchain may be configured to store tokens, identity information,
transaction information (such as for exchanges of information),
technical descriptions, virtual goods, license keys, digital
content, entertainment content, and other data, content or
information that may be relevant to a submission 4418 or a reward
4412. At a component 4510 a smart contract 3431 may be configured
to embody the conditions that were configured at the step 4504 and
to operate on the blockchain that was created at the component 4508
as well as to operate on other data, such as data indicating facts,
conditions, events, or the like in the platform-operated
marketplace 3327 and/or an external marketplace 3390, such as ones
related to submission data 4418. The smart contract 3431 may be
responsive to the component 4510 to apply one or more rules,
execute one or more conditional operations, or the like upon data,
such as submission data 4418 and data indicating satisfaction of
parameters or conditions, as well as identity data, transactional
data, timing data, and other data. Once configuration of one or
more blockchains and one or more smart contracts is complete, at a
component 4512 the blockchain and smart contract may be deployed in
the platform-operated marketplace 3327, external marketplace 3390
or other environment, such as for interaction by one or more
submitters or other users, who may, such as in a crowdsourcing
interface 4512, such as a website, application, or the like, enter
into the smart contract, such as by submitting a submission 4418
and requesting the reward 4412, at which point the platform, such
as using the adaptive intelligent systems 3304 or other
capabilities, may store relevant data, such as submission data
4418, identity data for the party or parties entering the smart
contract on the blockchain or otherwise on the platform 3300. At a
component 4514, once the smart contract is executed, the platform
may monitor, such as by the monitoring systems layer 3306, the
platform-operated marketplace 3327 and/or one or more external
marketplaces 3390 for submission data 4418, event data 3324, or
other data that may satisfy or indicate satisfaction of one or more
conditions 4410 or trigger application of one or more rules of the
smart contract 3431, such as to trigger a reward 4412.
[1194] At a component 4516, upon satisfaction of conditions, smart
contracts may be settled, executed, or the like, resulting updates
or other operations on the blockchain 3422, such as by transferring
consideration (such as via a payments system) and transferring
access to submissions 4418. Thus, via the above-referenced steps,
an operator of the platform-operated marketplace 3327 may discover,
configure, deploy and have executed a set of smart contracts that
crowdsource innovations that are cryptographically secured and
transferred on a blockchain from innovators to parties seeking
innovation. In embodiments, the adaptive intelligent systems layer
3304 may be used to monitor the steps of the algorithm described
above, and one or more artificial intelligence systems may be used
to automate, such as by robotic process automation, the entire
process or one or more sub-steps or sub-algorithms. This may occur
as described above, such as by having an artificial intelligence
system learn on a training set of data resulting from observations,
such as monitoring software interactions of human users as they
undertake the above-referenced steps. Once trained, the adaptive
intelligence layer 3304 may thus enable the platform 3300 to
provide a fully automated platform for crowdsourcing of
innovation.
[1195] Referring to FIG. 46, in embodiments, a platform is provided
herein, with systems, methods, processes, services, components and
other elements for enabling a blockchain and smart contract
platform 4600 for crowdsourcing for evidence. As with other
embodiments described above in connection with sourcing innovation,
product demand, or the like, a blockchain 3422, such as optionally
embodying a distributed ledger, may be configured with a set of
smart contracts 3431 to administer a reward 4612 for the submission
of evidence 4618, such as evidence of infringement, evidence of
prior art, evidence of publication, evidence of use, evidence of
commercial sales, evidence of fraud, evidence of false statements,
evidence of trespassing, evidence of negligence, evidence of
misrepresentation, evidence of slander or libel, evidence of
undertaking illegal activities, evidence of undertaking risky
activities, evidence of omissions, evidence of breach of contract,
evidence of torts, evidence of criminal conduct, evidence of
regulatory violations, evidence of non-compliance with policies or
procedures, evidence of the location of an individual (optionally
including known or preferred locations), evidence of a social
network or other relationship of an individual, evidence of a
business connection of an individual or business, evidence of an
asset of an individual or business, evidence of defects, evidence
of harm, evidence of counterfeiting, evidence of identity (such as
DNA, fingerprinting, video, photography or the like), evidence of
damage, evidence of confusion (such as in cases of trademark
infringement) or other evidence that may be relevant to a civil or
criminal legal proceeding, a contract enforcement or negotiation,
an arbitration or mediation, a hearing, or other proceeding. In
embodiments, a blockchain 3422, such as optionally distributed in a
distributed ledger, may be used to configure a request for evidence
4618 (which may be a formal legal request, such as a subpoena, or
an alternative form of request, such as in a fact-gathering
situation), along with terms and conditions 4610 related to the
evidence, such as a reward 4612 for submission of the evidence
4618, a set of terms and conditions 4610 related to the use of the
evidence 4618 (such as whether it may only be released under
subpoena, whether the submitting party has a right to anonymity,
the nature of proceedings in which the evidence can be used, the
permitted conditions for use of the evidence 4618, and the like),
and various parameters 4608, such as timing parameters, the nature
of the evidence required (such as scientifically validated evidence
like DNA or fingerprints, video footage, photographs, witness
testimony, or the like), and other parameters 4608.
[1196] The platform 4600 may include a crowdsourcing interface
4620, which may be included in or provided in coordination with a
website, application, dashboard, communications system (such as for
sending emails, texts, voice messages, advertisements, broadcast
messages, or other messages), by which a message may be presented
in the interface 4620 or sent to relevant individuals (whether
targeted, such as in the case of a subpoena, or broadcast, such as
to individuals in a given location, company, organization, or the
like) with an appropriate link to the smart contract 3431 and
associated blockchain 3422, such that a reply message submitting
evidence 4618, with relevant attachments, links, or other
information, can be automatically associated (such as via an API or
data integration system) with the blockchain 3422, such that the
blockchain 3422, and any optionally associated distributed ledger,
maintains a secure, definitive record of evidence 4618 submitted in
response to the request. Where a reward 4612 is offered, the
blockchain 3422 and/or smart contract 3431 may be used to record
time of submission, the nature of the submission, and the party
submitting, such that at such time as a submission satisfies the
conditions for a reward 4612 (such as, for example, upon
apprehension of a subject in a criminal case or invalidation of a
patent upon use of submitted prior art, among many other examples),
the blockchain 3422 and any distributed ledger stored thereby can
be used to identify the submitter and, by execution of the smart
contract 3431, convey the reward 4612 (which may take any of the
forms of consideration noted throughout this disclosure. In
embodiments, the blockchain 3422 and any associated ledger may
include identifying information for submissions of evidence 4618
without containing actual evidence 4618, such that information may
be maintained secret (such as being encrypted or being stored
separately with only identifying information), subject to
satisfying or verifying conditions for access (such as a legal
subpoena, a warrant, or other identification or verification of a
person who has legitimate access rights, such as by an identity or
security application 3418). Rewards 4612 may be provided based on
outcomes of cases or situations to which evidence 4618 relates,
based on a set of rules (which may be automatically applied in some
cases, such as using a smart contract 3431 in concert with an
automation system, a rule processing system, an artificial
intelligence system 3448 or other expert system, which in
embodiments may comprise one that is trained on a training data set
created with human experts. For example, a machine vision system
may be used to evaluate evidence of counterfeiting based on images
of items, and parties submitting evidence of counterfeiting may be
rewarded, such as via tokens or other consideration, via
distribution of rewards 4612 through the smart contract 3431,
blockchain 3422 and any distributed ledger. Thus, the platform 4600
may be used for a wide variety of fact-gathering and
evidence-gathering purposes, to facilitate compliance, to deter
improper behavior, to reduce uncertainty, to reduce asymmetries of
information, or the like.
[1197] Referring to FIG. 47, a platform-operated marketplace
crowdsourcing evidence 4608 may be configured, such as in a
crowdsourcing interface 4620 or other user interface for an
operator of the platform-operated marketplace 4600, using the
various enabling capabilities of the data handling platform 3300
described throughout this disclosure. The operator may use the user
interface 4620 or crowdsourcing dashboard 4614 to undertake a
series of steps to perform or undertake an algorithm to create a
crowdsourcing request for evidence 4618 as described in connection
with FIG. 46. In embodiments, one or more interactions with the
components to create a reward 4612 within the dashboard 4614 may
include, at a component 4702, identifying potential rewards 4612,
such as what evidence 4618 is likely to be of value in a given
situation (such as may be indicated through various communication
channels by stakeholders or representatives of an entity, such as
an individual or enterprise, such as attorneys, agents,
investigators, parties, auditors, detectives, underwriters,
inspectors, and many others).
[1198] The dashboard 4614 may be configured with a crowdsourcing
interface 4620, such as with elements (including application
programming elements, data integration elements, messaging
elements, and the like) that allow a crowdsourcing request to be
managed in the platform marketplace 4600 and/or in one or more
external marketplaces 3390. In the dashboard 4614, at a component
4704 the user may configure one or more parameters 4608 or
conditions 4610, such as comprising or describing the conditions
(of the type described herein) for the crowdsourcing request, such
as by defining a set of conditions 4610 that trigger the reward
4612 and determine allocation of the reward 4612 to a set of
submitters of evidence 4618. The user interface of the dashboard
4614, which may include or be associated with the crowdsourcing
interface 4620, may include a set of drop down menus, tables,
forms, or the like with default, templated, recommended, or
pre-configured conditions, parameters 4608, conditions 4610 and the
like, such as ones that are appropriate for various types of
crowdsourcing requests. Once the conditions and other parameters of
the request are configured, at a component 4708 a smart contract
3431 and blockchain 3422 may be configured to maintain, such as via
a ledger, the data required to provision, allocate, and exchange
data related to the request and to submissions of evidence 4618.
The smart contract 3431 and blockchain 3422 may be configured to
identity information, transaction information (such as for
exchanges of information), technical information, other evidence
data 4618 of the type described in connection with FIG. 46,
including any data, testimony, photo or video content or other
information that may be relevant to a submission of evidence 4618
or the conditions 4610 for a reward 4612. At a component 4710 a
smart contract 3431 may be configured to embody the conditions 4610
that were configured at the component 4704 and to operate on the
blockchain 3422 that was created at the component 4708, as well as
to operate on other data, such as data indicating facts,
conditions, events, or the like in the platform-operated
marketplace 4600 and/or an external marketplace 3390 or other
information site or resource, such as ones related to submission
data 4618, such as sites indicating outcomes of legal cases or
portions of cases, sites reporting on investigations, and the like.
The smart contract 3431 may be responsive to apply one or more
rules configured at component 4710, to execute one or more
conditional operations, or the like upon data, such as evidence
data 4618 and data indicating satisfaction of parameters 4608 or
conditions 4610, as well as identity data, transactional data,
timing data, and other data. Once configuration of one or more
blockchains 3422 and one or more smart contracts 3431 is complete,
at a component 4712 the blockchain 3422 and smart contract 3431 may
be deployed in the platform-operated marketplace 4600, external
marketplace 3390 or other site or environment, such as for
interaction by one or more submitters or other users, who may, such
as in a crowdsourcing interface 4620, such as a website,
application, or the like, enter into the smart contract 3431, such
as by submitting a submission of evidence 4618 and requesting the
reward 4612, at which point the platform 4600, such as using the
adaptive intelligent systems 3304 or other capabilities, may store
relevant data, such as submission data 4618, identity data for the
party or parties entering the smart contract 3431 on the blockchain
3422 or otherwise on the platform 4600. At a component 4714, once
the smart contract 3431 is executed, the platform 4600 may monitor,
such as by the monitoring systems layer 3306, the platform-operated
marketplace 3327 and/or one or more external marketplaces 3390 or
other sites for submission data 4618, event data 3324, or other
data that may satisfy or indicate satisfaction of one or more
conditions 4610 or trigger application of one or more rules of the
smart contract 3431, such as to trigger a reward 4612.
[1199] At a component 4716, upon satisfaction of conditions 4610,
smart contracts 3431 may be settled, executed, or the like,
resulting updates or other operations on the blockchain 3422, such
as by transferring consideration (such as via a payments system)
and transferring access to evidence 4618. Thus, via the
above-referenced steps, an operator of the platform-operated
marketplace 3327 may discover, configure, deploy and have executed
a set of smart contracts 3431 that crowdsource evidence and that
are cryptographically secured and transferred on a blockchain 3422
from evidence gatherers to parties seeking evidence. In
embodiments, the adaptive intelligent systems layer 3304 may be
used to monitor the steps of the algorithm described above, and one
or more artificial intelligence systems may be used to automate,
such as by robotic process automation 3442, the entire process or
one or more sub-steps or sub-algorithms. This may occur as
described above, such as by having an artificial intelligence
system 3448 learn on a training set of data resulting from
observations, such as monitoring software interactions of human
users as they undertake the above-referenced steps. Once trained,
the adaptive intelligence layer 3304 may thus enable the platform
3300 to provide a fully automated platform for crowdsourcing of
evidence.
[1200] In embodiments, evidence may relate to fact-gathering or
data-gathering for a variety of applications and solutions that may
be supported by a marketplace platform 3300, including the evidence
crowdsourcing platform 4600, such as for underwriting 3420 (e.g.,
of insurance policies, loans, warranties, guarantees, and other
items), including actuarial processes; risk management solutions
3408 (such as managing a wide variety of risks noted throughout
this disclosure); tax solutions (such as relating to evidence
supporting deductions and tax credits, among others); lending
solutions 3410 (such as evidence of the ownership and or value of
collateral, evidence of the veracity of representations, and the
like); regulatory solutions 3426 (such as with respect to
compliance with a wide range of regulations that may govern
entities 3330 and processes, behaviors or activities of or by
entities 3330); and fraud prevention solutions 3416 (such as to
detect fraud, misrepresentation, improper behavior, libel, slander,
and the like).
[1201] Evidence gathering may include evidence gathering with
respect to entities 3330 and their identities, assertions, claims,
actions or behaviors, among many other factors and may be
accomplished by crowdsourcing in the crowdsourcing platform 4600 or
by data collection systems 3318 and monitoring systems 3306,
optionally with automation via process automation 3442 and adaptive
intelligence, such as using an artificial intelligence system
3448.
[1202] In embodiments, the evidence gathering platform, whether a
crowdsourcing platform 4600 or a more general data collection
platform 3300 that may or may not encompass crowdsourcing, is
provided herein, with systems, methods, processes, services,
components and other elements for enabling a blockchain and smart
contract platform for aggregating identity and behavior information
for insurance underwriting 3420. In embodiments, a blockchain, with
an optional distributed ledger may be used to record a set of
events, transactions, activities, identities, facts, and other
information associated with an underwriting process 3420, such as
identities of applicants for insurance, identities of parties that
may be willing to offer insurance, information regarding risks that
may be insured (of any type, such as property, life, travel,
infringement, health, home, commercial liability, product
liability, auto, fire, flood, casualty, retirement, unemployment
and many others traditionally insured by insurance policies, in
addition to a host of other types of risks that are not
traditionally insured), information regarding coverage, exclusions,
and the like, information regarding terms and conditions, such as
pricing, deductible amounts, interest rates (such as for whole life
insurance) and other information. The blockchain 3422 and an
associated smart contract 3431 may, in coordination with or via a
website, application, communications system, message system,
marketplace, or the like, be used to offer insurance and to record
information submitted by applicants, so that an insurance
application has a secure, canonical record of submitted
information, with access control capabilities that permit only
authorized parties, roles and services to access submitted
information (such as governed by policies, regulations, and terms
and conditions of access). The blockchain 3422 may be used in
underwriting 3420, such as by recording information (including
evidence as noted in connection with evidence gathering above) that
is relevant to pricing, underwriting, coverage, and the like, such
as collected by underwriters, submitted by applicants, collected by
artificial intelligence systems 3448, or submitted by others (such
as in the case of crowdsourcing platform 4600). In embodiments, the
blockchain 3422, smart contract 3431 and any distributed ledger may
be used to facilitate offering and underwriting of microinsurance,
such as for defined risks related to defined activities for defined
time periods that are narrower than for typical insurance
policies). For example, insurance related to adverse weather events
may be obtained for the day of a wedding. The blockchain 3422 may
facilitate allocation of risk and coordination of underwriting
activities for a group of parties, such as where a group of parties
agree to take some fraction of the risk, as recorded in the ledger.
For example, the ledger may allow a party to take any fraction of
the risk, thereby accumulating partial insurance unless and until a
risk is fully covered as the rest of accumulation and aggregation
of multiple parties agreeing, as recorded on the ledger, to insure
an activity, a risk, or the like. The ledger may be used to
allocate payments upon occurrence of the covered risk event. In
embodiments, an artificial intelligence system 3448 may be used to
collect and analyze underwriting data, such as one that is trained
by human expert underwriters. In embodiments, an automated system
3442, such one using artificial intelligence 3448, such as one
trained to recognized and validate events, can be used to determine
that an event has happened (e.g., a roof has collapsed, a car has
been damage, or the like), such as from videos, images, sensors,
IoT devices, witness submissions (such as over social networks), or
the like, such that an operation on the distributed ledger may be
initiated to pay out the insured amount, including initiating
appropriate debits and credits that reflect transfer of funds from
the underwriting/insuring parties to the insured. Thus, a
blockchain-based ledger may simplify and automate much of the
insurance process by reliably validating identities, maintaining
confidentiality of information as needed, automatically
accumulating evidence needed for pricing and underwriting,
automatically processing information indicating occurrence of
insured events, and automatically settling and fulfilling contracts
upon occurrence of validated events.
Lending Platform
[1203] Referring to FIG. 48, an embodiment of a financial,
transactional and marketplace enablement system 3300 is illustrated
wherein a lending enablement system 4800 is enabled and wherein a
platform-oriented marketplace 3327 may comprise a lending platform
3410. The lending enablement system 4800 may include a set of
systems, applications, processes, modules, services, layers,
devices, components, machines, products, sub-systems, interfaces,
connections, and other elements (collectively referred in the
alternative, except where context indicates otherwise, as the
"platform," the "lending platform," the "system," and the like)
working in coordination (such as by data integration and
organization in a services oriented architecture) to enable
intelligent management of a set of entities 3330 that may occur,
operate, transact or the like within, or own, operate, support or
enable, one or more applications, services, solutions, programs or
the like of the lending platform 3410 or external marketplaces 3390
that involve lending transactions or lending-related entities, or
that may otherwise be part of, integrated with, linked to, or
operated on by the platform 3300 and system 4800. References to a
set of services herein should be understood, except where context
indicates otherwise, these and other various systems, applications,
processes, modules, services, layers, devices, components,
machines, products, sub-systems, interfaces, connections, and other
types of elements. A set may include multiple members or a single
member. As with other embodiments of the system 3300, the system
4800 may have various data handling layers, with components,
modules, systems, services, components, functions and other
elements described in connection with other embodiments described
throughout this disclosure and the documents incorporated herein by
reference. This may include various adaptive intelligent systems
3304, monitoring systems 3306, data collection systems 3318, and
data storage systems 3310, as well as a set of interfaces 3316 of,
to, and/or among each of those systems and/or the various other
elements of the platform 3300 and system 4800. In embodiments, the
interfaces 3316 may include application programming interfaces
4812; data integration technologies for extracting, transforming,
cleansing, normalizing, deduplicating, loading and the like as data
is moved among various services using various protocols and formats
(collectively referred to as ETL systems 4814); and various ports,
portals, connectors, gateways, wired connections, sockets, virtual
private networks, containers, secure channels and other connections
configured among elements on a one-to-one, one-to-many, or
many-to-one basis, such as in unicast, broadcast and multi-cast
transmission (collectively referred to as ports 4818). Interfaces
3316 may include, be enabled by, integrate with, or interface with
a real time operating system (RTOS) 4810, such as the FreeRTOS.TM.
operating system, that has a deterministic execution pattern in
which a user may define an execution pattern, such as based on
assignment of a priority to each thread of execution. An instance
of the RTOS 4810 may be embedded, such as on a microcontroller of
an Internet of Things device, such as one used to monitor various
entities 3330. The RTOS 4810 may provide real-time scheduling (such
as scheduling of data transmissions to monitoring systems 3306 and
data collection systems 3318, scheduling of inter-task
communication among various service elements, and other timing and
synchronization elements). In embodiments, the interfaces 3316 may
use or include a set of libraries that enable secure connection
between small, low-power edge devices, such as Internet of Things
devices used to monitor entities 3330, and various cloud-deployed
services of the platform 3300 and system 4800, as well as a set of
edge devices and the systems that enable them, such as ones running
local data processing and computing systems such as AWS IoT
Greengrass.TM. and/or AWS Lambda.TM. functions, such as to allow
local calculation, configuration of data communication, execution
of machine learning models (such as for prediction or
classification), synchronization of devices or device data, and
communication among devices and services. This may include use of
local device resources such as serial ports, GPUs, sensors and
cameras. In embodiments, data may be encrypted for secure
end-to-end communication.
[1204] In the context of a lending enablement system 4800 and set
of lending solutions 3410, entities 3330 may include any of the
wide variety of assets, systems, devices, machines, facilities,
individuals or other entities mentioned throughout this disclosure
or in the documents incorporated herein by reference, such as,
without limitation: machines 3352 and their components (e.g.,
machines that are the subject of a loan or collateral for a loan,
such as various vehicles and equipment, as well as machines used to
conduct lending transactions, such as automated teller machines,
point of sale machines, vending machines, kiosks,
smart-card-enabled machines, and many others, including ones used
to enable microloans, payday loans and others); financial and
transactional processes 3350 (such as lending processes, inspection
processes, collateral tracking processes, valuation processes,
credit checking processes, creditworthiness processes, syndication
processes, interest rate-setting processes, software processes
(including applications, programs, services, and others),
production processes, collection processes, banking processes
(e.g., lending processes, underwriting processes, investing
processes, and many others), financial service processes,
diagnostic processes, security processes, safety processes,
assessment processes, payment processes, valuation processes,
issuance processes, factoring processes, consolidation processes,
syndication processes, collection processes, foreclosure processes,
title transfer processes, title verification processes, collateral
monitoring processes, and many others); wearable and portable
devices 3348 (such as mobile phones, tablets, dedicated portable
devices for financial applications, data collectors (including
mobile data collectors), sensor-based devices, watches, glasses,
hearables, head-worn devices, clothing-integrated devices, arm
bands, bracelets, neck-worn devices, AR/VR devices, headphones, and
many others); workers 3344 (such as banking workers, loan officers,
financial service personnel, managers, inspectors, brokers (e.g.,
mortgage brokers), attorneys, underwriters, regulators, assessors,
appraisers, process supervisors, security personnel, safety
personnel and many others); robotic systems 3342 (e.g., physical
robots, collaborative robots (e.g., "cobots"), software bots and
others); and facilities 3338 (such as banking facilities, inventory
warehousing facilities, factories, homes, buildings, storage
facilities (such as for loan-related collateral, property that is
the subject of a loan, inventory (such as related to loans on
inventory), personal property, components, packaging materials,
goods, products, machinery, equipment, and other items), banking
facilities (such as for commercial banking, investing, consumer
banking, lending and many other banking activities) and others. In
embodiments, entities 3330 may include external marketplaces 3390,
such as financial, commodities, e-commerce, advertising, and other
marketplaces 3390 (including current and futures markets), such as
ones within which transactions occur in various goods and services,
such that monitoring of the marketplaces 3390 and entities 3330
within them may provide lending-relevant information, such as with
respect to the price or value of items, the liquidity of items, the
characteristics of items, the rate of depreciation of items, or the
like. For example, for various entities that may comprise
collateral 4802 or assets for asset-backed lending, a monitoring
system 3306 may monitor not only the collateral 4802 or assets,
such as by cameras, sensors, or other monitoring systems 3306, but
may also collect data, such as via data collection systems 3318 of
various types, with respect to the value, price, or other condition
of the collateral 4802 or assets, such as by determining market
conditions for collateral 4802 or assets that are in similar
condition, of similar age, having similar specifications, having
similar location, or the like. In embodiments, an adaptive
intelligent system 3304 may include a clustering system 4804, such
as one that groups or clusters entities 3330, including collateral
4802, parties, assets, or the like by similarity of attributes,
such as a k-means clustering system, self-organizing map system, or
other system as described herein and in the documents incorporated
herein by reference. The clustering system may organize collections
of collateral, collections of assets, collections of parties, and
collections of loans, for example, such that they may be monitored
and analyzed based on common attributes, such as to enable
performance of a subset of transactions to be used to predict
performance of others, which in turn may be used for underwriting
3420, pricing 3421, fraud detection 3416, or other applications,
including any of the services, solutions, or applications described
in connection with FIG. 48 and FIG. 49 or elsewhere throughout this
disclosure or the documents incorporated herein by reference. In
embodiments, condition information about collateral 4802 or assets
is continuously monitored by a monitoring system 3306, such as a
set of sensors on the collateral 4802 or assets, a set of sensors
or cameras in the environment of the collateral 4802 or assets, or
the like, and market information is collected in real time by a
data collection system 3318, such that the condition and market
information may be time-aligned and used as a basis for real time
estimation of the value of the collateral or assets and forward
prediction of the future value of the collateral or assets. Present
and predicted value for the collateral 4802 or assets may be based
on a model, which may be accessed and used, such as in a smart
contract 3431, to enable automated, or machine-assisted lending on
the collateral or assets, such as the underwriting or offering of a
microloan on the collateral 4802 or assets. Aggregation of data for
a set of collateral 4802 or set of assets, such as a collection or
fleet of collateral 4802 or fleet of assets owned by an entity 3330
may allow real time portfolio valuation and larger scale lending,
including via smart contracts 3431 that automatically adjust
interest rates and other terms and conditions based on the
individual or aggregated value of collateral 4802 or assets based
on real time condition monitoring and real-time market data
collection and integration. Transactions, party information,
transfers of title, changes in terms and conditions, and other
information may be stored in a blockchain 3422, including loan
transactions and information (such as condition information for
collateral 4802 or assets and marketplace data) about the
collateral 4802 or assets. The smart contract 3431 may be
configured to require a party to confirm condition information
and/or market value information, such as by representations and
warranties that are supported or verified by the monitoring systems
3306 (which may flag fraud in a fraud detection system 3416). A
lending model 4808 may be used to value collateral 4802 or assets,
to determine eligibility for lending based on the condition and/or
value of collateral 4802 or assets, to set pricing (e.g., interest
rates), to adjust terms and conditions, and the like. The lending
model 4808 may be created by a set of experts, such as using
analytics 3419 on past lending transactions. The lending model 4808
may be populated by data from monitoring systems 3306 and data
collection systems 3318, may pull data from storage systems 3310,
and the like. The lending model 4808 may be used to configure
parameters of a smart contract 3431, such that smart contract terms
and conditions automatically adjust based on adjustments in the
lending model 4808. The lending model 4808 may be configured to be
improved by artificial intelligence 3448, such as by training it on
a set of outcomes, such as outcomes from lending transactions
(e.g., payment outcomes, default outcomes, performance outcomes,
and the like), outcomes on collateral 4802 or assets (such as
prices or value patterns of collateral or assets over time),
outcomes on entities (such as defaults, foreclosures, performance
results, on time payments, late payments, bankruptcies, and the
like), and others. Training may be used to adjust and improve model
parameters and performance, including for classification of
collateral or assets (such as automatic classification of type
and/or condition, such as using vision-based classification from
camera-based monitoring systems 3306), prediction of value of
collateral 4802 or assets, prediction of defaults, prediction of
performance, and the like. In embodiments, configuration or
handling of smart contracts 3431 for lending on collateral 4802 or
assets may be learned and automated in a robotic process automation
(RPA) system 3442, such as by training the RPA system 3442 to
create smart contracts 3431, configure parameters of smart
contracts 3431, confirm title to collateral 4802 or assets, set
terms and conditions of smart contracts 3431, initiate security
interests on collateral 4802 for smart contracts, monitor status or
performance of smart contracts 3431, terminate or initiate
termination for default of smart contracts 3431, close smart
contracts 3431, foreclose on collateral 4802 or assets, transfer
title, or the like, such as by using monitoring systems 3306 to
monitor expert entities 3330, such as human managers, as they
undertake a training set of similar tasks and actions in the
creation, configuration, title confirmation, initiation of security
interests, monitoring, termination, closing, foreclosing, and the
like for a training set of smart contracts 3431. Once an RPA system
3442 is trained, it may efficiently create the ability to provide
lending at scale across a wide range of entities and assets that
may serve as collateral 4802, that may provide guarantees or
security, or the like, thereby making loans more readily available
for a wider range of situations, entities 3330, and collateral
4802. The RPA system 3442 may itself be improved by artificial
intelligence 3448, such as by continuously adjusting model
parameters, weights, configurations, or the like based on outcomes,
such as loan performance outcomes, collateral valuation outcomes,
default outcomes, closing rate outcomes, interest rate outcomes,
yield outcomes, return-on-investment outcomes, or others. Smart
contracts 3431 may include or be used for direct lending,
syndicated lending, and secondary lending contracts, individual
loans or aggregated tranches of loans, and the like.
[1205] In embodiments, the lending solution 3410 of the management
application platform layer 3302 may, in various optional
embodiments, include, integrate with, or interact with (such as
within other embodiments of the platform 3300) a set of
applications 3312, such as ones by which a lender, a borrower, a
guarantor, an operator or owner of a transactional or financial
entity, or other user, may manage, monitor, control, analyze, or
otherwise interact with one or more elements related to a loan,
such as an entity 3330 that is a party to a loan, the subject of a
loan, the collateral for a loan, or otherwise relevant to the loan.
This may include any of the elements noted above in connection with
FIG. 33. The set of applications 3312 may include a lending
application 3410 (such as, without limitation, for personal
lending, commercial lending, collateralized lending, microlending,
peer-to-peer lending, insurance-related lending, asset-backed
lending, secured debt lending, corporate debt lending, student
loans, subsidized loans, mortgage lending, municipal lending,
sovereign debt, automotive lending, pay day loans, loans against
receivables, factoring transactions, loans against guaranteed or
assured payments (such as tax refunds, annuities, and the like),
and many others). The lending solution 3410 may include, integrate
with, or link with one or more of any of a wide range of other
types of applications that may be relevant to lending, such as an
investment application 3402 (such as, without limitation, for
investment in tranches of loans, corporate debt, bonds, syndicated
loans, municipal debt, sovereign debt, or other types of
debt-related securities); an asset management application 3404
(such as, without limitation, for managing assets that may be the
subject of a loan, the collateral for a loan, assets that back a
loan, the collateral for a loan guarantee, or evidence of
creditworthiness, assets related to a bond, investment assets, real
property, fixtures, personal property, real estate, equipment,
intellectual property, vehicles, and other assets); a risk
management application 3408 (such as, without limitation, for
managing risk or liability with respect to subject of a loan, a
party to a loan, or an activity relevant to the performance of a
loan, such as a product, an asset, a person, a home, a vehicle, an
item of equipment, a component, an information technology system, a
security system, a security event, a cybersecurity system, an item
of property, a health condition, mortality, fire, flood, weather,
disability, business interruption, injury, damage to property,
damage to a business, breach of a contract, and others); a
marketing application 3412 (such as, without limitation, an
application for marketing a loan or a tranche of loans, a customer
relationship management application for lending, a search engine
optimization application for attracting relevant parties, a sales
management application, an advertising network application, a
behavioral tracking application, a marketing analytics application,
a location-based product or service targeting application, a
collaborative filtering application, a recommendation engine for
loan-related product or service, and others); a trading application
3428 (such as, without limitation, an application for trading a
loan, a tranche of loans, a portion of a loan, a loan-related
interest, or the like, such as a buying application, a selling
application, a bidding application, an auction application, a
reverse auction application, a bid/ask matching application, or
others); a tax application 3414 (such as, without limitation, for
managing, calculating, reporting, optimizing, or otherwise handling
data, events, workflows, or other factors relating to a tax-related
impact of a loan); a fraud prevention application 3416 (such as,
without limitation, one or more of an identity verification
application, a biometric identity validation application, a
transactional pattern-based fraud detection application, a
location-based fraud detection application, a user behavior-based
fraud detection application, a network address-based fraud
detection application, a black list application, a white list
application, a content inspection-based fraud detection
application, or other fraud detection application; a security
application, solution or service 3418 (referred to herein as a
security application, such as, without limitation, any of the fraud
prevention applications 3416 noted above, as well as a physical
security system (such as for an access control system (such as
using biometric access controls, fingerprinting, retinal scanning,
passwords, and other access controls), a safe, a vault, a cage, a
safe room, or the like), a monitoring system (such as using
cameras, motion sensors, infrared sensors and other sensors), a
cyber security system (such as for virus detection and remediation,
intrusion detection and remediation, spam detection and
remediation, phishing detection and remediation, social engineering
detection and remediation, cyberattack detection and remediation,
packet inspection, traffic inspection, DNS attack remediation and
detection, and others) or other security application); an
underwriting application 3420 (such as, without limitation, for
underwriting any loan, guarantee, or other loan-related transaction
or obligation, including any application for detecting,
characterizing or predicting the likelihood and/or scope of a risk,
including underwriting based on any of the data sources, events or
entities noted throughout this disclosure or the documents
incorporated herein by reference); a blockchain application 3422
(such as, without limitation, a distributed ledger capturing a
series of transactions, such as debits or credits, purchases or
sales, exchanges of in kind consideration, smart contract events,
or the like, a cryptocurrency application, or other
blockchain-based application); a real estate application 3424 (such
as, without limitation, a real estate brokerage application, a real
estate valuation application, a real estate mortgage or lending
application, a real estate assessment application, or other); a
regulatory application 3426 (such as, without limitation, an
application for regulating the terms and conditions of a loan, such
as the permitted parties, the permitted collateral, the permitted
terms for repayment, the permitted interest rates, the required
disclosures, the required underwriting process, conditions for
syndication, and many others); a marketplace application, solution
or service 3327 (referred to as a marketplace application, such as,
without limitation, a loan syndication marketplace, a
blockchain-based marketplace, a cryptocurrency marketplace, a
token-based marketplace, a marketplace for items used as
collateral, or other marketplace); a warranty or guarantee
application 3417 (such as, without limitation, an application for a
warranty or guarantee with respect to an item that is the subject
of a loan, collateral for a loan, or the like, such as a product, a
service, an offering, a solution, a physical product, software, a
level of service, quality of service, a financial instrument, a
debt, an item of collateral, performance of a service, or other
item); an analyst application 3419 (such as, without limitation, an
analytic application with respect to any of the data types,
applications, events, workflows, or entities mentioned throughout
this disclosure or the documents incorporated by reference herein,
such as a big data application, a user behavior application, a
prediction application, a classification application, a dashboard,
a pattern recognition application, an econometric application, a
financial yield application, a return on investment application, a
scenario planning application, a decision support application, and
many others); a pricing application 3421 (such as, without
limitation, for pricing of interest rates and other terms and
conditions for a loan). Thus, the management application platform
3302 may host and enable interaction among a wide range of
disparate applications 3312 (such term including the
above-referenced and other financial or transactional applications,
services, solutions, and the like), such that by virtue of shared
microservices, shared data infrastructure, and shared intelligence,
any pair or larger combination or permutation of such services may
be improved relative to an isolated application of the same
type.
[1206] In embodiments the data collection systems 3318 and the
monitoring systems 3306 may monitor one or more events related to a
loan, debt, bond, factoring agreement, or other lending
transaction, such as events related to requesting a loan, offering
a loan, accepting a loan, providing underwriting information for a
loan, providing a credit report, deferring a required payment,
setting an interest rate for a loan, deferring a payment
requirement, identifying collateral or assets for a loan,
validating title for collateral or security for a loan, recording a
change in title of property, assessing the value of collateral or
security for a loan, inspecting property that is involved in a
loan, a change in condition of an entity relevant to a loan, a
change in value of an entity that is relevant to a loan, a change
in job status of a borrower, a change in financial rating of a
lender, a change in financial value of an item offered as a
security, providing insurance for a loan, providing evidence of
insurance for property related to a loan, providing evidence of
eligibility for a loan, identifying security for a loan,
underwriting a loan, making a payment on a loan, defaulting on a
loan, calling a loan, closing a loan, setting terms and conditions
for a loan, foreclosing on property subject to a loan, and
modifying terms and conditions for a loan.
[1207] Microservices Lending Platform with Data Collection
Services, Blockchain and Smart Contracts
[1208] In embodiments, provided herein is a platform, consisting of
various services, components, modules, programs, systems, devices,
algorithms, and other elements, for lending. An example platform or
system for lending includes a set of microservices having a set of
application programming interfaces that facilitate connection among
the microservices and to the microservices by programs that are
external to the platform, wherein the microservices include (a) a
multi-modal set of data collection services that collect
information about and monitor entities related to a lending
transaction; (b) a set of blockchain services for maintaining a
secure historical ledger of events related to a loan, the
blockchain services having access control features that govern
access by a set of parties involved in a loan; (c) a set of
application programming interfaces, data integration services, data
processing workflows and user interfaces for handling loan-related
events and loan-related activities; and (d) a set of smart contract
services for specifying terms and conditions of smart contracts
that govern at least one of loan terms and conditions, loan-related
events and loan-related activities.
[1209] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system includes where the entities relevant
to lending include a set of entities among lenders, borrowers,
guarantors, equipment, goods, systems, fixtures, buildings, storage
facilities, and items of collateral.
[1210] An example system includes where collateral items are
monitored and the collateral items are selected from among a
vehicle, a ship, a plane, a building, a home, real estate property,
undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a beverage, a precious metal, an item of jewelry, a
gemstone, an item of intellectual property, an intellectual
property right, a contractual right, an antique, a fixture, an item
of furniture, an item of equipment, a tool, an item of machinery,
and an item of personal property.
[1211] An example system includes where the multi-modal set of data
collection services include services selected from among a set of
Internet of Things systems that monitor the entities, a set of
cameras that monitor the entities, a set of software services that
pull information related to the entities from publicly available
information sites, a set of mobile devices that report on
information related to the entities, a set of wearable devices worn
by human entities, a set of user interfaces by which entities
provide information about the entities and a set of crowdsourcing
services configured to solicit and report information related to
the entities.
[1212] An example system includes where the events related to a
loan are selected from requesting a loan, offering a loan,
accepting a loan, providing underwriting information for a loan,
providing a credit report, deferring a required payment, setting an
interest rate for a loan, deferring a payment requirement,
identifying collateral for a loan, validating title for collateral
or security for a loan, recording a change in title of property,
assessing the value of collateral or security for a loan,
inspecting property that is involved in a loan, a change in
condition of an entity relevant to a loan, a change in value of an
entity that is relevant to a loan, a change in job status of a
borrower, a change in financial rating of a lender, a change in
financial value of an item offered as a security, providing
insurance for a loan, providing evidence of insurance for property
related to a loan, providing evidence of eligibility for a loan,
identifying security for a loan, underwriting a loan, making a
payment on a loan, defaulting on a loan, calling a loan, closing a
loan, setting terms and conditions for a loan, foreclosing on
property subject to a loan, and modifying terms and conditions for
a loan.
[1213] An example system includes where the set of terms and
conditions for the loan that are specified and managed by the set
of smart contract services is selected from among a principal
amount of debt, a balance of debt, a fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of collateral, a
specification of substitutability of collateral, a party, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition,
and a consequence of default.
[1214] An example system includes where a set of parties to the
loan is selected from among a primary lender, a secondary lender, a
lending syndicate, a corporate lender, a government lender, a bank
lender, a secured lender, a bond issuer, a bond purchaser, an
unsecured lender, a guarantor, a provider of security, a borrower,
a debtor, an underwriter, an inspector, an assessor, an auditor, a
valuation professional, a government official, and an
accountant.
[1215] An example system includes where loan-related activities
include activities selected from the set of finding parties
interested in participating in a loan transaction, an application
for a loan, underwriting a loan, forming a legal contract for a
loan, monitoring performance of a loan, making payments on a loan,
restructuring or amending a loan, settling a loan, monitoring
collateral for a loan, forming a syndicate for a loan, foreclosing
on a loan, and closing a loan transaction.
[1216] An example system includes where the loan is of at least one
type selected from among an auto loan, an inventory loan, a capital
equipment loan, a bond for performance, a capital improvement loan,
a building loan, a loan backed by an account receivable, an invoice
finance arrangement, a factoring arrangement, a pay day loan, a
refund anticipation loan, a student loan, a syndicated loan, a
title loan, a home loan, a venture debt loan, a loan of
intellectual property, a loan of a contractual claim, a working
capital loan, a small business loan, a farm loan, a municipal bond,
and a subsidized loan.
[1217] An example system includes where the set of smart contract
services configures at least one smart contract to automatically
undertake a loan-related action based on based on information
collected by the multi-modal set of data collection services.
[1218] An example system includes where the loan-related action is
selected from among offering a loan, accepting a loan, underwriting
a loan, setting an interest rate for a loan, deferring a payment
requirement, modifying an interest rate for a loan, validating
title for collateral, recording a change in title, assessing the
value of collateral, initiating inspection of collateral, calling a
loan, closing a loan, setting terms and conditions for a loan,
providing notices required to be provided to a borrower,
foreclosing on property subject to a loan, and modifying terms and
conditions for a loan.
[1219] An example system includes where the platform or system may
further include an automated agent that processes events relevant
to at least one of the value, the condition and the ownership of
items of collateral and undertakes an action related to a loan to
which the collateral is subject.
[1220] Referring to FIG. 49, additional applications, solutions,
programs, systems, services and the like that may be present in a
lending solution 3410 are depicted, which may be interchangeably
included in the platform 3302 with other elements noted in
connection, with FIG. 48 and elsewhere throughout this disclosure
and the documents incorporated herein by reference. Also depicted
are additional entities 3330, which should be understood to be
interchangeable with the other entities 3330 described in
connection with various embodiments described herein. In addition
to elements already noted above, the lending solution 3410 may
include a set of applications, solutions, programs, systems,
services and the like that include one or more of a social network
analytics solution 4904 that may find and analyze information about
various entities 3330 as depicted in one or more social networks
(such as, without limitation, information about parties, behavior
of parties, conditions of assets, events relating to parties or
assets, conditions of facilities, location of collateral 4802 or
assets, and the like), such as by allowing a user to configure
queries that may be initiated and managed across a set of social
network sites using data collection systems 3318 and monitoring
systems 3306; a loan management solution 4948 (such as for managing
or responding to one or more events related to a loan (such events
including, among others, requests for a loan, offering a loan,
accepting a loan, providing underwriting information for a loan,
providing a credit report, deferring a required payment, setting an
interest rate for a loan, deferring a payment requirement,
identifying collateral for a loan, validating title for collateral
or security for a loan, recording a change in title of property,
assessing the value of collateral or security for a loan,
inspecting property that is involved in a loan, a change in
condition of an entity relevant to a loan, a change in value of an
entity that is relevant to a loan, a change in job status of a
borrower, a change in financial rating of a lender, a change in
financial value of an item offered as a security, providing
insurance for a loan, providing evidence of insurance for property
related to a loan, providing evidence of eligibility for a loan,
identifying security for a loan, underwriting a loan, making a
payment on a loan, defaulting on a loan, calling a loan, closing a
loan, setting terms and conditions for a loan, foreclosing on
property subject to a loan, and modifying terms and conditions for
a loan) for setting terms and conditions for a loan (such as a
principal amount of debt, a balance of debt, a fixed interest rate,
a variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of collateral, a
specification of substitutability of collateral, a party, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition,
and a consequence of default), or managing loan-related activities
(such as, without limitation, finding parties interested in
participating in a loan transaction, handling an application for a
loan, underwriting a loan, forming a legal contract for a loan,
monitoring performance of a loan, making payments on a loan,
restructuring or amending a loan, settling a loan, monitoring
collateral for a loan, forming a syndicate for a loan, foreclosing
on a loan, collecting on a loan, consolidating a set of loans,
analyzing performance of a loan, handling a default of a loan,
transferring title of assets or collateral, and closing a loan
transaction)); a rating solution 6801 (such as for rating an entity
3330 (such as a party 4910, collateral 4802, asset 4918 or the
like), such as involving rating of creditworthiness, financial
health, physical condition, status, value, presence or absence of
defects, quality, or other attribute); a regulatory and/or
compliance solution 3426 (such as for enabling specification,
application and/or monitoring of one or more policies, rules,
regulations, procedures, protocols, processes, or the like, such as
ones that relate to terms and conditions of loan transactions,
steps required in forming lending transactions, steps required in
performing lending transactions, steps required with respect to
security or collateral, steps required for underwriting, steps
required for setting prices, interest rates, or the like, steps
required to provide required legal disclosures and notices (e.g.,
presenting annualized percentage rates) and others); a custodial
solution or set of custodial services 6502 (such as for taking
custody of a set of assets 4918, collateral 4802, or the like
(including cryptocurrencies, currency, securities, stocks, bonds,
agreements evidencing ownership interests, and many other items),
such as on behalf of a party 4910, client, or other entity 3330
that needs assistance in maintaining security of the items, or in
order to provide security, backing, or a guarantee for an
obligation, such as one involved in a lending transaction); a
marketing solution 6702 (such as for enabling a lender to market
availability of a loan to a set of prospective borrowers, to target
a set of borrowers who are appropriate for a type of transaction,
to configure marketing or promotional messages (including placement
and timing of the message), to configure advertisement and
promotional channels for lending transactions, to configure
promotional or loyalty program parameters, and many others); a
brokering solution 4944 (such as for brokering a set of loan
transactions among a set of parties, such as a mortgage loan),
which may allow a user to configure a set of preferences, profiles,
parameters, or the like to find a set of prospective counterparties
to a lending transaction; a bond management solution 4934 such as
for managing, reporting on, syndicating, consolidating, or
otherwise handling a set of bonds (such as municipal bonds,
corporate bonds, performance bonds, and others); a guarantee
monitoring solution 4930, such as for monitoring, classifying,
predicting, or otherwise handling the reliability, quality, status,
health condition, financial condition, physical condition or other
information about a guarantee, a guarantor, a set of collateral
supporting a guarantee, a set of assets backing a guarantee, or the
like; a negotiation solution 4932, such as for assisting,
monitoring, reporting on, facilitating and/or automating
negotiation of a set of terms and conditions for a lending
transaction (such as, without limitation, a principal amount of
debt, a balance of debt, a fixed interest rate, a variable interest
rate, a payment amount, a payment schedule, a balloon payment
schedule, a specification of collateral, a specification of
substitutability of collateral, a party, a guarantee, a guarantor,
a security, a personal guarantee, a lien, a duration, a covenant, a
foreclosure condition, a default condition, and a consequence of
default), which may include a set of user interfaces for
configuration of parameters, profiles, preferences, or the like for
negotiation, such as ones that use or are informed by the lending
model 4808 and ones that use, are informed by, or that are
automated by or with the assistance of a set of artificial
intelligence services and systems 3448, by robotic process
automation 3442, or other adaptive intelligent systems 3304; a
collection solution 4938 for collecting on a loan, which may
optionally use, be informed by, or be automated by or with the
assistance of a set of artificial intelligence services and systems
3448, by robotic process automation 3442, or other adaptive
intelligent systems 3304, such as based on monitoring the status or
condition of various entities 3330 with the monitoring systems 3306
and data collection systems 3318 in order to trigger collection,
such as when one or more covenants has not been met, when
collateral is in poor condition, when financial health of party is
below a threshold, or the like; a consolidation solution 4940 for
consolidating a set of loans, such as using a lending model 4808
that is configured for modeling a consolidated set of loans and
such as using or being automated by one or more adaptive
intelligent systems 3304; a factoring solution 4942, such as for
monitoring, managing, automating or otherwise handling a set of
factoring transactions, such as using a lending model 4808 that is
configured for modeling factoring transactions and such as using or
being automated by one or more adaptive intelligent systems 3304; a
debt restructuring solution 4928, such as for restructuring a set
of loans or debt, such as using a lending model 4808 that is
configured for modeling alternative scenarios for restructuring a
set of loans or debt and such as using or being automated by one or
more adaptive intelligent systems 3304; and/or an interest rate
setting solution 4924, such as for setting or configuring a set of
rules or a model for a set of interest rates for a set of lending
transactions or for automating interest rate setting based on
information collected by data collection systems 3318 or monitoring
systems 3306 (such as information about conditions, status, health,
location, geolocation, storage condition, or other relevant
information about any of the entities 3330), which may set interest
rates or facilitate setting of interest rates for a set of loans,
such as using a lending model 4808 that is configured for modeling
interest rate scenarios for a set of loans and such as using or
being automated by one or more of the adaptive intelligent systems
3304. As with the solutions referenced in connection with FIG. 48,
the various solutions may share the adaptive intelligent systems
3304, the monitoring systems 3306, the data collection systems 3318
and the storage systems 3310, such as by being integrated into the
platform 4800 in a microservices architecture having various
appropriate data integration services, APIs, and interfaces.
[1221] As with the entities 3330 described in connection with FIG.
49, entities 3330 may further include a range of entities that are
involved with loans, debt transactions, bonds, factoring
agreements, and other lending transactions, such as: collateral
4802 and assets 4918 that are used to secure, guarantee, or back a
payment obligation (such as vehicles, ships, planes, buildings,
homes, real estate, undeveloped land, farms, crops, facilities 3338
(such as municipal facilities, factories, warehouses, storage
facilities, treatment facilities, plants, and others), systems, a
set of inventory, commodities, securities, currencies, tokens of
value, tickets, cryptocurrencies, consumables, edibles, beverages,
precious metals, jewelry, gemstones, intellectual property,
intellectual property rights, contractual rights, legal rights,
antiques, fixtures, equipment, furniture, tools, machinery and
personal property); a set of parties 4910 (such as one or more of a
primary lender, a secondary lender, a lending syndicate, a
corporate lender, a government lender, a bank lender, a secured
lender, a bond issuer, a bond purchaser, an unsecured lender, a
guarantor, a provider of security, a borrower, a debtor, an
underwriter, an inspector, an assessor, an auditor, an agent, an
attorney, a valuation professional, a government official, and/or
an accountant); a set of agreements 4920 (such as loans, bonds
4912, lending agreements, corporate debt agreements, subsidized
loan agreements, factoring agreements, consolidation agreements,
syndication agreements, guarantee agreements, underwriting
agreements, and others, which may include a set of terms and
conditions that may be searched, collected, monitored, modified or
otherwise handled by the platform 4800, such as interest rates,
payment schedules, payment amounts, principal amounts,
representations and warranties, indemnities, covenants, and other
terms and conditions); a set of guarantees 4914 (such as provided
by personal guarantors, corporate guarantors, government
guarantors, municipal guarantors and others to secure or back a
payment obligation or other obligation of a lending agreement
4920); a set of performance activities 4922 (such as making
payments of principal and/or interest, maintaining required
insurance, maintaining title, satisfying covenants, maintaining
condition of collateral 4802 or assets 4918, conducting business as
required by an agreement; and many others); and devices 4952 (such
as Internet of Things devices that may be disposed on or in goods,
equipment or other items, such as ones that are collateral 4802 or
assets 4918 used to back a payment obligation or to satisfy a
covenant or other requirement, or that may be disposed on or in
packaging for goods, as well as ones disposed in facilities 3338 or
other environments where entities 3330 may be located). In
embodiments, an agreement 4920 may be for a bond, a factoring
agreement, a syndication agreement, a consolidation agreement, a
settlement agreement, or a loan, such as one or more of an auto
loan, an inventory loan, a capital equipment loan, a bond for
performance, a capital improvement loan, a building loan, a loan
backed by an account receivable, an invoice finance arrangement, a
factoring arrangement, a pay day loan, a refund anticipation loan,
a student loan, a syndicated loan, a title loan, a home loan, a
venture debt loan, a loan of intellectual property, a loan of a
contractual claim, a working capital loan, a small business loan, a
farm loan, a municipal bond, and a subsidized loan.
[1222] As noted elsewhere herein and in documents incorporated by
reference, artificial intelligence (such as any of the techniques
or systems described throughout this disclosure) in connection with
various transactional and marketplace entities 3330 and related
processes and applications may be used to facilitate, among other
things: (a) the optimization, automation and/or control of various
functions, workflows, applications, features, resource utilization
and other factors, (b) recognition or diagnosis of various states,
entities, patterns, events, contexts, behaviors, or other elements;
and/or (c) the forecasting of various states, events, contexts or
other factors. As artificial intelligence improves, a large array
of domain-specific and/or general artificial intelligence systems
have become available and are likely to continue to proliferate. As
developers seek solutions to domain-specific problems, such as ones
relevant to entities 3330 and applications of the platform 100
described throughout this disclosure they face challenges in
selecting artificial intelligence models (such as what set of
neural networks, machine learning systems, expert systems, or the
like to select) and in discovering and selecting what inputs may
enable effective and efficient use of artificial intelligence for a
given problem. As noted above, opportunity miners 153 may assist
with the discovery of opportunities for increased automation and
intelligence; however, once opportunities are discovered, selection
and configuration of an artificial intelligence solution still
presents a significant challenge, one that is likely to continue to
grow as artificial intelligence solutions proliferate.
[1223] One set of solutions to these challenges is an artificial
intelligence store 157 that is configured to enable collection,
organization, recommendation and presentation of relevant sets of
artificial intelligence systems based on one or more attributes of
a domain and/or a domain-related problem. In embodiments, an
artificial intelligence store 157 may include a set of interfaces
to artificial intelligence systems, such as enabling the download
of relevant artificial intelligence applications, establishment of
links or other connections to artificial intelligence systems (such
as links to cloud-deployed artificial intelligence systems via
APIs, ports, connectors, or other interfaces) and the like. The
artificial intelligence store 157 may include descriptive content
with respect to each of a variety of artificial intelligence
systems, such as metadata or other descriptive material indicating
suitability of a system for solving particular types of problems
(e.g., forecasting, NLP, image recognition, pattern recognition,
motion detection, route optimization, or many others) and/or for
operating on domain-specific inputs, data or other entities. In
embodiments, the artificial intelligence store 157 may be organized
by category, such as domain, input types, processing types, output
types, computational requirements and capabilities, cost, energy
usage, and other factors. In embodiments, an interface to the
application store 157 may take input from a developer and/or from
the platform (such as from an opportunity miner 153) that indicates
one or more attributes of a problem that may be addressed through
artificial intelligence and may provide a set of recommendations,
such as via an artificial intelligence attribute search engine, for
a subset of artificial intelligence solutions that may represent
favorable candidates based on the developer's domain-specific
problem.
[1224] In embodiments, a criteria for determining the
recommendation may include level of anticipated human oversight.
This may include, among others, understanding the level and types
of decisions delegated to human workers (such as a decision to
purchase a security, taking a market decision, taking a license on
Intellectual property, financial limits on actions and ordering
(e.g. is the RPA able to order or commit to transactions below a
certain amount, above which a human is involved), the level and
type of anticipated human supervision of robotic process automation
operations, anticipated extent of human supervision and/or
governance of model training and training data set selection. A
further consideration may be the level and type of anticipated
human involvement in the curation of model versions (such as
identifying historical break points where input data should be
discarded); and others.
[1225] In embodiments, criteria for determining the recommendation
may include security considerations such as adversarial training
and complex environments such as network attacks, viruses, and the
like. Additional security considerations may include the security
and management of historic training datasets, including audit
trails. Security considerations may include the model traceability
and accuracy, how will the model or controlling parameters be
updated, who will have authority to update the model, how will the
updates be documented, how will results be correlated with model
updates, how will version control be implemented and documented and
the like. Another security consideration will be documentation of
the results of the AI for audit trails including financial results
and performance results.
[1226] In embodiments, criteria for determining the recommendation
may include the availability of different AI types, models,
algorithms, or systems (including heuristic/model-based AI, neural
networks, and others). Availability may be limited by the
computational environment that the user intends to use such as a
given cloud platform, an on-premises IT system, or in a network
(edge or other networks), and the like and whether a given type,
model, or algorithm will run in the client's environment. In
embodiments, computational factors and configurations may be
criteria. For example, the available processor types for running
the AI solution in the client's environment may be a factor
including: chipsets, modules, device, cloud components, number, and
architecture of processor types (e.g. multi-core processor
availability, GPU availability, CPU availability, FPGA
availability, custom ASIC availability, and the like), and the
like. Additionally, computational factors, which may be expressed
as minimum capability criteria, may include available processing
capacity, both for solution training (for example utilizing a cloud
computing resource) and solution operation deployment
environment/capacity (e.g. IoT, in-vehicle, edge, mesh network,
on-premises IT solution, stand along, or other deployment
environments). Additional criteria may include software and
interface criteria such as software environment such as operating
systems (Linux, Mac, PC, and the like), languages and protocols
used for APIs for access to input data sources for solution
training as well as access to runtime data and data integration and
output.
[1227] In embodiments, criteria may include various network factors
such as available network type, available network bandwidth (input
and output) for both AI solution and AI operation, network uptime,
network redundance, variability of delivery times (sequencing of
data may vary), as well as any of the other networks and network
criteria described herein.
[1228] In embodiments, criteria may include performance or quality
of service factors, either in absolute terms or relative to other
AI and/or non-AI solutions (e.g. conventional models or rule-based
solutions. Criteria may include speed/latency, time to
train/configure and an AI solution, time for the AI solution to
provide result in an operations situation, accuracy, reliability
(e.g. ability to resolve to a result), consistency, absence of
bias, outcome-based measures of quality such as return on
investment (ROI), yield (e.g. output from an AI-governed
operation), profitability, revenue and other economic measures,
performance on safety measures, performance on security measures,
energy consumption (e.g. overall consumption, timing-based
consumption (e.g. ability to shift processing from peak to off-peak
hours), ability to access renewable or low-carbon energy for model
training and/or operation, management of cost of new model training
initiatives (power costs, latency and validation of new models),
and the like.
[1229] In embodiments, criteria may include the ability of the
client to access a given type or model due to license requirements
and limitations, client policies (described elsewhere herein),
regulations (including in the client's jurisdiction, the
jurisdiction of the data source (e.g. European data privacy laws
and Safe Harbor), a jurisdiction governing a particular model,
algorithm, or the like (e.g. export controls on technology),
permissions (e.g. training data or operational data), and the like.
Additionally, the recommendation may be influenced by the type of
problem to be solved and whether there are specialized algorithms
or methods that are optimized for the type of problem (e.g. quantum
annealing based traveling salesperson solver or even classic
heuristic methods that provide for reasonable baseline
results).
[1230] In embodiments, criteria may include conformance or
adherence to governance principles and policies. There may be
policies regarding what input data sources may be used to train the
AI solution. There may be policies regarding what input sources may
be used during operation. For example, input data sources may be
reviewed for potential bias, appropriate representation (either
demographically or of the problem space), scope, and the like.
There may be criteria regarding accreditation or approval of the
solution by a regulatory body, certification organization, internal
IT review, and the like. There may be policies and procedures that
must be in place or implemented with respect to security (e.g.
physical security of the system, cybersecurity, and the like),
safety requirements (e.g. the safety of the user, the safety of
output product, and the like), and the like.
[1231] In embodiments, the criteria for recommending an AI solution
may include criteria regarding data availability such as the
availability of data sources of adequate size, granularity,
quality, reliability, location, time zones, accuracy, or the like
for effective model training. Additional criteria regarding data
availability may include the cost of data for: inputs for the model
training, input for model operation. Additional criteria may
include the availability of data for operation of the AI solution,
and the like. Criteria for AI selection may further include
upstream data processing requirements, master data management
considerations such as dimensional cleanup and data validation, and
the like.
[1232] In embodiments, criteria for solution selection may include
applicability of the model or solution to the given task or
workflow of the "problem" Criteria may include benchmark
performance of a given model relative to other models performing a
known task type (e.g. a convolutional neural network for 2D object
classification, a gated recurrent neural network for tasks that
tend to produce exploding errors, or the like). In embodiments,
selection of a solution may be based on the solution having a
configuration that is similar or analogous to how a biological
brain solves a similar task (e.g. where a sequence of neural
network models are arranged to mimic a sequence or flow which may
include serial elements, parallel elements, feedback loops,
conditional logic junctions, graph-driven elements and other flow
characteristics), such as a flow of modular or quasi-modular
processes, such as ones involved in the brain of a human or other
species, such as for in visual or auditory processing, language
recognition, speech, motion tracking, image recognition, facial
recognition, motion coordination, tactile recognition, spatial
orientation, and the like. Criteria may include application of
class AI heuristic methods to function as guard rails or operations
in less impactful areas.
[1233] In embodiments, criteria may include model deployment
considerations such as requirements for model updates (e.g.
frequency and requirement for retirement of models), management of
historic models and maintaining historical decision engine,
potential for distributed decision making capabilities, model
curation rules (e.g. how long a model or input data are considered
valid for training), and the like.
[1234] Search results or recommendations may, in embodiments, be
based at least in part on collaborative filtering, such as by
asking developers to indicate or select elements of favorable
models, as well as by clustering, such as by using similarity
matrices, k-means clustering, or other clustering techniques that
associate similar developers, similar domain-specific problems,
and/or similar artificial intelligence solutions. The artificial
intelligence store 157 may include e-commerce features, such as
ratings, reviews, links to relevant content, and mechanisms for
provisioning, licensing, delivery and payment (including allocation
of payments to affiliates and or contributors), including ones that
operate using smart contract and/or blockchain features to automate
purchasing, licensing, payment tracking, settlement of
transactions, or other features.
[1235] In embodiments, once a solution has been selected or
recommended, the solution must be configured for the specific
client and problem to be solved. Without limitation, configuration
may include any of the factors mentioned in connection with the
selection of a solution model above. Configuration of a set of
neural network types (e.g., modules) in a flow (with options for
serial elements, parallel elements, feedback loops, conditional
logic junctions, graph-driven flows and the like) that recognizes
the relative strengths and weaknesses of each type of AI solution
(based on any of the selection factors noted above) for the
specific task involved in the flow is critical. In an illustrative
and non-limiting example of a flow, a) identify something by visual
classification (such as with a CNN), b) predict its future state
(such as with a gated RNN), c) optimize the future state (using a
feed forward neural network). Configuration options include
selection of neural network type(s) (including hybrids of different
neural networks and/or other model types in various flows as noted
above); selection of input model type; setting of initial model
weights; setting model size (e.g., number of layers in a deep
neural network); selection of computational deployment environment;
selection of input data sources for training; selection of input
data sources for operation; selection of feedback function/outcome
measures; selection of data integration language(s) for inputs and
outputs; configuration of APIs for model training; configuration of
APIs for model inputs; configuration of APIs for outputs;
configuration of access controls (role-based, user-based,
policy-based and others); configuration of security parameters;
configuration of network protocols; configuration of storage
parameters (type, location, duration); configuration of economic
factors (e.g., pricing for access; cost-allocation; and others);
and others. Additional configuration options may include
configuration of data flows (e.g. flows from multiple security
exchanges into centralized decision engines), configuration of high
availability, fault tolerance environments (e.g. trading systems
are required to fail down to operation state that meets services
levels requirements), price based data acquisition strategies (e.g.
detailed financial data may require additional spending),
combination with heuristic methods, coordination of massively
parallel decision making environments (e.g. distributed vision
systems), and the like. Additional configurations may include
making decision models if there is an area that requires further
consideration (e.g. pushing a decision to the edge to monitor for a
specific event).
[1236] In embodiments, another set of solutions, which may be
deployed alone or in connection with other elements of the
platform, including the artificial intelligence store, may include
a set of functional imaging capabilities, which may comprise
monitoring systems 3306 and data collection systems 3318 and, in
some cases, physical process observation systems 3458 and/or
software interaction observation systems 3450, such as for
monitoring various transactional and marketplace entities 3330.
Functional imaging systems may, in embodiments, provide
considerable insight into the types of artificial intelligence that
are likely to be most effective in solving particular types of
problems most effectively. As noted elsewhere in this disclosure
and in the documents incorporated by reference herein,
computational and networking systems, as they grow in scale,
complexity and interconnections, manifest problems of information
overload, noise, network congestion, energy waste, and many others.
As the Internet of Things grows to hundreds of billions of devices,
and virtually countless potential interconnections, optimization
becomes exceedingly difficult. One source for insight is the human
brain, which faces similar challenges and has evolved, over
millennia, reasonable solutions to a wide range of very difficult
optimization problems. The human brain operates with a massive
neural network organized into interconnected modular systems, each
of which has a degree of adaptation to solve particular problems,
from regulation of biological systems and maintenance of
homeostasis to detection of a wide range of static and dynamic
patterns, to recognition of threats and opportunities, among many
others.
[1237] Setting up a robotic process automation (RPA) system
includes selection of the best AI solution and configuration. There
may be goals to train the RPA system, typically on human
interactions with software and or hardware (e.g., tools) and to use
the system in operation, both of which be enhanced by understanding
what is going on in the human brain as it solves a problem. In a
single neural network solution (using one network to solve a
problem in a single step, like single-step translation), the
process would likely involve setting initial weights for inputs,
selection of input data sources, selection of the type of network
(e.g., convolutional or not, gated or not, deep or not, among
others), the number of layers, and what inputs are provided to it
(and outputs if there are complex outputs). The idea would be to
pick inputs and weights that are the ones the human brain tends to
use to solve the same problem. For hybrids of multiple AI
modules/systems and/or AI combined with more conventional software
systems (like control systems, analytic models, rule-based systems,
conditional logic systems, and others), the value would likely be
the above, plus configuring with awareness of time sequences of
processing, such as reflecting patterns of brain activity as
visual, auditory, tactile and other sensory information is
processed to recognize situation, context, motion, objects, etc.
and then other regions (that behave differently) to do things like
solve a logic puzzle, calculate, follow an algorithm, proliferate
possibilities, and many others. For these, a series of "lego
blocks", each consisting of a different neural network or other AI
type, can be sequenced, set in parallel, linked by conditional
logic, etc. to achieve a solution that automate the process.
[1238] In embodiments, identification of a type of reasoning and/or
a type of processing may be informed by undertaking brain imaging,
such as functional MRI or other magnetic imaging,
electroencephalogram (EEG), or other imaging, such as by
identifying broad brain activity (e.g., wave bands of activity,
such as delta, theta, alpha and gamma waves), by identifying a set
of brain regions that are activated and/or inactive during the set
interactions of the user that are being used for training of the
intelligent agent (such as neocortex regions, such as Fp1 (involved
in judgment and decision making), F7 (involved in imagination and
mimicry), F3 (involved in analytic deduction), T3 (involved in
speech), C3 (involved in storage of facts), T5 (involved in
mediation and empathy), P3 (involved in tactical navigation), O1
(involved in visual engineering), Fp2 (involved in process
management), F8 (involved in belief systems), F4 (involved in
expert classification), T4 (involved in listening and intuition),
C4 (involved in artistic creativity), T6 (involved in prediction),
P4 (involved in strategic gaming), O2 (involved in abstraction),
and/or combinations of the foregoing) or by other neuroscientific,
psychological, or similar techniques that provide insight into how
humans upon which the intelligent agent is trained are solving
particular types of problems that are involved in workflows for
which intelligent agents are deployed. In embodiments, an
intelligent agent may be configured with a neural network type, or
combination of types, that is selected to replicate or simulate a
processing activity that is similar to the activity of the brain
regions of a human expert that is performing a set of activities
for which the intelligent agent is to be trained. As one example
among many possible, a trader may be shown to use visual processing
region O1 and strategic gaming region P4 of the neocortex when
making successful trades, and a neural network may be configured
with a convolutional neural network to provide effective
replication of visual pattern recognition and a gated recurrent
neural network to replicate strategic gaming. In embodiments, a
library of neural network resources representing combinations of
neural network types that mimic or simulate neocortex activities
may be configured to allow selection and implementation of modules
that replicate the combinations used by human experts to undertake
various activities that are subjects of development of intelligent
agents, such as involving robotic process automation. In
embodiments, various neural network types from the library may be
configured in series and/or in parallel configurations to represent
processing flows, which may be arranged to mimic or replicate flows
of processing in the brain, such as based on spatiotemporal imaging
of the brain when involved in the activity that is the subject of
automation. In embodiments, an intelligent software agent for agent
development may be trained, such as using any of the training
techniques described herein, to select a set of neural network
resource types, to arrange the neural network resource types
according to a processing flow, to configure input data sources for
the set of neural network resources, and/or to automatically deploy
the set of neural network types on available computational
resources to initiate training of the configured set of neural
network resources to perform a desired intelligent agent/automation
workflows. In embodiments, the intelligent software agent used for
agent development operates on an input data set of spatiotemporal
imaging data of a human brain, such as an expert who is performing
the workflows that is the subject of development of a further, and
uses the spatiotemporal imaging data to automatically select and
configure the selection and arrangement of the set of neural
network types to initiate learning. Thus, a system for developing
an intelligent agent may be configured for (optionally automatic)
selection of neural network types and/or arrangements based on
spatiotemporal neocortical activity patterns of human users
involved in workflows for which the agent is trained. Once
developed, the resulting intelligent agent/process automation
system may be trained as described throughout this disclosure.
[1239] In embodiments, a system for developing an intelligent agent
(including the aforementioned agent for development of intelligent
agents) may use information from brain imaging of human users to
infer (optionally automatically) what data sources should be
selected as inputs for an intelligent agent. For example, for
processes where neocortex region O1 is highly active (involving
visual processing), visual inputs (such as available information
from cameras, or visual representations of information like price
patterns, among many others) may be selected as favorable data
sources. Similarly, for processes involving region C3 (involving
storage and retrieval of facts), data sources providing reliable
factual information (such as blockchain-based distributed ledgers)
may be selected. Thus, a system for developing an intelligent agent
may be configured for (optionally automatic) selection of input
data types and sources based on spatiotemporal neocortical activity
patterns of human users involved in workflows for which the agent
is trained.
[1240] Functional imaging, such as functional magnetic resonance
imaging (fMRI), electroencephalogram (EEG), computed tomography
(CT) and other brain imaging systems have improved to the point
that patterns of brain activity can be recognized in real time and
temporally associated with other information, such behaviors,
stimulus information, environmental condition data, gestures, eye
movements, and other information, such that via functional imaging,
either alone or in combination with other information collected by
monitoring systems 3306, the platform may determine and classify
what brain modules, operations, systems, and/or functions are
employed during the undertaking of a set of tasks or activities,
such as ones involving software interaction observation systems
3345, physical process observations 3340, or a combination thereof.
This classification may assist in selection and/or configuration of
a set of artificial intelligence solutions, such as from an
artificial intelligence store, that includes a similar set of
capabilities and/or functions to the set of modules and functions
of the human brain when undertaking an activity, such as for the
initial configuration of a robotic process automation (RPA) system
3442 that automates a task performed by an expert human.
[1241] In embodiments, a system may receive and/or monitor a set of
inputs relating to a user, including image/video feeds, audio
feeds, motion sensors, heartbeat monitor, other relevant
biosensors, and the like. In embodiments, the system may also
receive input relating to actions taken by the monitored user, such
as input to a computing device or actions taken with respect to a
physical environment in which the user is working. In embodiments,
all the collected data is time stamped, so that, for example, a
video feed may capture a series of images of a user while the user
is performing a task and may concurrently capture the eye movements
of the user (e.g. eye gaze tracking) to determine what the user is
focusing on (e.g., what is the user looking at on a screen). During
this time the system may also track the user's heart rate or other
biological sensor measurements to determine whether the user is
engaged in a task that requires intense concentration or less
focused concentration. The system may also track the actions taken
and may further determine the amount of time taken between actions.
An RPA solution can then distribute processing, such as to a
heavier, more computationally intensive activity to an AI solution
on a cloud platform (like a deep neural network with many layers)
and placing less computationally intensive tasks, such as ones
where a human makes very quick decisions on minimal input data, on
an edge or IoT device platform using a much more compact model,
such as a TinyML.TM. model.
[1242] In embodiments, the system may determine the relative amount
of time taken between actions, such that long periods of inaction
may indicate that the user is involved in work that requires lots
of thought, while short periods of inaction may indicate that the
user is engaged in work that requires less thought and more action.
The system may also monitor an audio feed and/or state of the
computing device that a user is working on when the period of
inaction occurs, which may be indicative of a user being distracted
rather than focusing. Assuming that the user is actively working
and not exhibiting distraction, then the system can generate a
feature vector relating to the work being performed by the user
that indicates the time-stamped data entries, which can be then fed
into a machine-learned model. In embodiments, the machine-learned
model may determine a brain region (or multiple brain regions) from
the set of brain regions that were likely engaged during the work
period. In embodiments, the machine-learned model may be trained
using a training data set that includes labeled training vectors,
where the label of each training vector indicates the brain region
(or regions) that were being engaged by a subject when the training
vector was generated. For example, each training vector may be
labeled with one or more of: Fp1 (involved in judgment and decision
making), F7 (involved in imagination and mimicry), F3 (involved in
analytic deduction), T3 (involved in speech), C3 (involved in
storage of facts), T5 (involved in mediation and empathy), P3
(involved in tactical navigation), 01 (involved in visual
engineering), Fp2 (involved in process management), F8 (involved in
belief systems), F4 (involved in expert classification), T4
(involved in listening and intuition), C4 (involved in artistic
creativity), T6 (involved in prediction), P4 (involved in strategic
gaming), O2 (involved in abstraction)). In some embodiments, the
training vector may indicate additional data, such as the type of
task being performed, whether the subject was successful in
completing the task, or other suitable information.
[1243] In embodiments, these machine-learned models may be trained
on different types of work tasks, such as negotiating, drafting,
data entry, responding to emails, analyzing data, reviewing
documents, or the like. Furthermore, in some embodiments, such
machine-learned models may be trained by one party but leveraged by
other parties. In these embodiments, the machine-learned models
(and/or the training data vectors) may be bought and sold via a
marketplace. Such machine-learned models may be used in a broader
RPA system, such that the output of the models may be used as a
specific signal in an RPA learning process.
[1244] In general, using data from organizations for predicting
positioning of organization in market and adjusting processes
within organization accordingly. In example embodiments, robotic
imaging may be used to capture data of users (e.g., employees or
workers) within the organization as they complete various tasks and
processes while also correlating this information with completion
of these tasks/processes. Obtaining various analytics regarding
success of completion of tasks (e.g., efficiency). Then, using data
obtained from tracking/monitoring users to determine what factors
indicate some users as being more successful than other users in
completion of tasks (e.g., based on physical movements of users in
doing tasks correctly, brain regions activated, physical strength
of users, etc.). This may be based on scanning/monitoring of users
as they complete tasks. In some example embodiments, using system
to segregate data relating to users with successful task
completions versus data relating to users with less successful
completions. The system may analyze biological data of workers to
determine what makes one worker more successful than other workers.
In some example embodiments, this analysis may also be combined
with data from machines to determine whether workers are using
machines accurately/efficiently. This biological data from workers
may also be used to determine whether more workers may be needed to
improve efficiency. Using historical data and results from process
competitions to look at what improvements should be made whether by
training, selecting workers who are better are some tasks vs.
others, etc. The resulting analytics on outcomes, and contributions
to outcomes, may be used, for example, as a feedback function for
weighting the value of particular capabilities for design of an AI
solution that is intended to perform the same or similar tasks. In
some example embodiments, various data and analysis as described
above may be used with respect to determining whether improvements
made based on the analysis also improves the market positioning of
the organization.
[1245] An operator skilled in a task may develop strong memory
connections to muscle functions--muscle memory--which translates
into easily accomplished actions that, without this connection,
would be difficult or at least require repeated attempts, slower
operation, and the like. A system that can distinguish between
actions accomplished using muscle memory and others may better
identify which actions are worth following/repeating/learning.
[1246] Understanding the mechanisms of muscle memory--e.g.,
understanding the pathways from cognition (visual, auditory, etc.)
inputs to develop muscle memory may be a basis for understanding
how to automate human actions. This may involve repetition type
actions, association of one type of action with another type of
action based on similarities, such as body positioning, expected
result (dropping the hammer in the holster, etc.).
[1247] Additional value might be in understanding how two
individuals can develop a form of muscle memory that allows them to
"get into a rhythm", such as when exchanging physical items. What
cues are they exchanging, visually recognizable actions (placement
of hand/orientation) and how are those interpreted.
[1248] In embodiments, an imaging system may analyze brain images
of multiple members of a team for a set of tasks or workflows that
involve different types of expertise. Team performance can be
tracked, and AI solutions may be configured to replicate the types
of neural processing that are undertaken by different team members,
such as motion tracking and coordination by one team member and
executive decision making by another.
[1249] In embodiments, an imaging system may analyze brain images
of multiple members of a mock trial or negotiation practice
sessions for a set of verbal exchanges regarding an argument,
point-count-point, and the like for negotiations, and the like. In
addition to brain images, audio capture and bio-indicators of
response to exchanges could also be harvested to increase the range
of multi-dimensional data useful for learning how to automate human
actions associated with successful negotiation and the like.
[1250] Given the level of abstraction humans use to trigger
actions, e.g., recognizing an alarm tone or recognizing an action
from a fellow worker, we can get less abstract in machine-machine
communication, e.g., the input that triggered the alarm tone can
trigger a direct machine-machine communication or, if the fellow
worker is now a machine, they can indicate their positioning in
their routine indicating they're ready to hand-off their work. This
is similar to how less intelligent robots have been automated, even
with simple macros where the "intelligence" is wrung out of the
process to make it more robust, and there are strategies and
methods for this that could be applied to these biologic-type
inputs which are a level of abstraction beyond what is needed. This
down-shift in complexity can, itself, be trained into the system as
they recognize what myriad of "soft" triggers (e.g. image
recognition) can be turned into "hard" triggers.)
[1251] Using systems like Fp1 (involved in judgment and decision
making), P3 (involved in tactical navigation), O1 (involved in
visual engineering), Fp2 (involved in process management), F8
(involved in belief systems), and T4 (involved in listening and
intuition), the training vectors may indicate, in some embodiments,
a system of mixed audio and visual concepts. The system may use an
expert system to monitor a set of inputs and reconfigure those
inputs to monitor an asset including image feeds at various
electromagnetic frequencies (such as visual light, thermal, UV, and
the like), and audio feeds from those frequencies to determine use,
sounds of use, and possible sounds of concerns. When examples
include fixed assets (those that cannot move), ambient measurement
of the environment may be measured along with signatures of use or
non-use of the product such as lack of motion, thermal imprints, or
lack thereof. The changing environment in the room, the contact
with asset by user or other fixtures, can cause reconfiguration of
the sensors looking to appreciate the space. When fixed in a room,
such systems may determine that ambient conditions could be
detrimental to the asset such as strong outside lighting (too rich
of UV content) relative to more appropriate lighting. Also included
is sensing the motion of use. In more moveable assets, detection
and parsing of benign motion rather than motion that may have a
higher propensity to age or damage an asset can be recorded and
characterized as an aggregated feed.
[1252] Risk Management--Combination of F3 (analytic deduction) and
Fp1 (judgment and decision making)--Analytics and decision making
in the human brain are informed by experience and knowledge, which
may be partial, limited, negative, positive, factual, emotional,
etc. AI can possibly recognize a situation (sensors, image
recognition, proximity, text and conversation analysis, etc.), and
apply better risk management in decision making using stored
fact-based outcomes for similar situations. This could be applied
to enable consumers to make better purchasing and financial
decisions. In other applications, it could be applied to emergency
response, policing actions, etc.
[1253] In embodiments, an AI solution may be configured as a
companion risk manager for a main operational AI solution, such as
sharing common inputs and resources, but focused on identifying
risks, externalities, and other factors that are not required for
the core process automation, but may improve governance, safety,
emergency response, and other aspects.
[1254] In embodiments, an AI solution may be configured as a
companion risk manager for a main operational AI solution, such as
sharing common inputs and resources, but focused on identifying
risks, externalities, and other factors that are not required for
the core process automation, but may improve governance, safety,
emergency response, and other aspects.
[1255] Thus, the platform may include a system that takes input
from a functional imaging system to configure, optionally
automatically based on matching of attributes between one or more
biological systems, such as brain systems, and one or more
artificial intelligence systems, a set of artificial intelligence
capabilities for a robotic process automation system. Selection and
configuration may further comprise selection of inputs to robotic
process automation and/or artificial intelligence that are
configured at least in part based on functional imaging of the
brain while workers undertake tasks, such as selection of visual
inputs (such as images from cameras) where vision systems of the
brain are highly activated, selection of acoustic inputs where
auditory systems of the brain are highly activated, selection of
chemical inputs (such as chemical sensors) where olfactory systems
of the brain are highly activated, or the like. Thus, a
biologically aware robotic process automation system may be
improved by having initial configuration, or iterative improvement,
be guided, either automatically or under developer control, by
imaging-derived information collected as workers perform expert
tasks that may benefit from automation.
[1256] Functional imaging may provide insight into which tasks
involve serial processing versus parallel processing, providing
insight into the type of AI solution that may be best suited to a
similar tasks (e.g. is it best to receive language and visual
data/inputs at once (in parallel) or sequentially). Is there an
order in which a user takes in data that might suggest an optimal
ordering for performance? Analysis of functional images may enable
identification of which computations tasks are most quickly
processed through visual inputs versus textual (language
processing) and may enable improved matching of task to best
input/stimulus.
[1257] Functional imaging may enable determining efficiencies
resulting from the pairing or multiple combinations of stimuli
(e.g., is a task/command most efficiently communicated by providing
multiple, diverse inputs at once, and/or is it best to omit certain
stimuli from inputs/commands.
[1258] Functional imaging may enable ranking tasks or events to
perform/solve based on the probabilistic improvement in the
performance of a subsequent task (where task could be a computation
or an actual action performed by a device based on a data/stimulus
input).
[1259] Functional imaging may enable measuring negative impacts on
performance/computation based on "noise," where noise may be
unneeded data, irrelevant data, or overwhelming data sizes--similar
to determining "negative stimuli" (in the human context this could
be ambient noise in distinguishing a human voice within a cascade
of auditory inputs, or ambient lighting in image recognition, or
movement in counting objects in a region and so forth.
[1260] As one example among many possible, a marketplace host may
be shown to use prediction region T6 and judgment and decision
making region Fp1 when configuring a new marketplace, such as to
predict favorable marketplace configuration parameters (such as to
optimize marketplace efficiency profitability, and/or fairness) and
to generate decisions related to marketplace parameters, and a
neural network may be configured with a neural network to provide
effective replication of prediction and a neural network to
replicate decision making. The marketplace configuration parameters
may include, but are not limited to, assets, asset types,
description of assets, method for verification of ownership, method
for delivery of traded goods, estimated size of marketplace,
methods for advertising the marketplace, methods for controlling
the marketplace, regulatory constraints, data sources, insider
trading detection techniques, liquidity requirements, access
requirements (such as whether to implement dealer-to-dealer
trading, dealer-to-customer trading, or customer-to-customer
trading), anonymity (such as determining whether counterparty
identities are disclosed), continuity of order handling (e.g.,
continuous or periodic order handling), interaction (e.g.,
bilateral or multilateral), price discovery, pricing drivers (e.g.,
order-driven pricing or quote-driven pricing), price formation
(e.g., centralized price formation or fragmented price formation),
custodial requirements, types of orders allowed (such as limit
orders, stop orders, market orders, and off-market orders),
supported market types (such as dealer markets, auction markets,
absolute auction markets, minimum bid auction markets, reverse
auction markets, sealed bid auction markets, Dutch auction markets,
multi-step auction markets (e.g., two-step, three-step, n-step,
etc.), forward markets, futures markets, secondary markets,
derivatives markets, contingent markets, markets for aggregates
(e.g., mutual funds), and the like), trading rules (e.g., tick
size, trading halts, open/close hours, escrow requirements,
liquidity requirements, geographic rules, jurisdictional rules,
rules on publicity, insider trading prohibitions, conflict of
interest rules, timing rules (e.g., involving spot-market trading,
futures trading and the like) and many others), asset listing
requirements (e.g., financial reporting requirements, auditing
requirements, minimum capital requirements), deposit minimums,
trading minimums, verification rules, commission rules, fee rules,
marketplace lifetime rules (e.g., short-term marketplace with
timing constraints vs. long-term marketplace), and transparency
(e.g., the amount and extent of information disseminated).
[1261] An RPA system may use AI systems related to biological brain
functions F3 (involved in analytic deduction) and O1 (involved in
visual engineering) in conjunction with one another to perform
tasks related to visual calculus. The tasks related to visual
calculus may include, for example, processing image sensor data via
the O1 visual engineering system to determine what the RPA system
"sees," and how to interpret, classify, identify, etc. what is
"seen." Then, the F3 analytic deduction system may perform 1)
deductions to determine what has led to the current state of what
is "seen," and 2) prediction to determine a future state of what is
"seen" based on the current state of visual data. The RPA system
may use the T6 prediction function to assist in performing such
predictions. The deductions may be useful in determining a cause of
an issue, inefficiency, or problem in a system being analyzed. The
predictions may be useful in determining solutions to problems
and/or potential efficiency improvements. The AI system using F3,
01, and/or T6 may then also be used to choose a machine learned
model suitable for performing the problem solving and/or efficiency
improvement. For example, in a manufacturing environment, the RPA
system and AI system may intake data from a plurality of visual IoT
sensors, the visual data being from one or more sites on the
manufacturing floor. The O1 visual engineering system may determine
and/or classify what the visual data is seeing, such as one or more
machines, products, assembly lines, etc. The F3 analytic deduction
system may determine whether one or more of the machines, products,
assembly lines, etc. are indicative of issues or inefficiencies.
The T6 system may then make predictions and forward the predictions
to a suitable machine learned model for determining solutions to
problems and/or improvements to efficiencies.
[1262] Referring to FIG. 50, in embodiments, devices 4952 may be
connected devices that connect (such as through any of the wide
range of interfaces 3316) to a set of Internet of Things (IoT) data
collection services 4908, which may be part of or integrated with
the data collection systems 3318 and monitoring systems 3306 of the
platform 4800. The interfaces 3316 may include network interfaces,
APIs, SDKs, ports, brokers, connectors, gateways, cellular network
facilities, data integration interfaces, data migration systems,
cloud computing interfaces (including ones that include
computational capabilities, such as AWS IoT Greengrass.TM.
Amazon.TM. Lambda.TM. and similar systems), and others. For
example, the IoT data collection services 4908 may be configured to
take data from a set of edge data collection devices in the
Internet of Things, such as low-power sensor devices (e.g., for
sensing movement of entities, for sensing, temperatures, pressures
or other attributes about entities 3330 or their environments, or
the like), cameras that capture still or video images of entities
3330, more fully enabled edge devices (such as Raspberry Pi.TM. or
other computing devices, Unix.TM. devices, and devices running
embedded systems, such as including microcontrollers, FPGAs, ASICs
and the like), and many others. The IoT data collection services
4908 may, in embodiments, collect data about collateral 4802 or
assets 4918, such as, for example, regarding the location,
condition (health, physical, or otherwise), quality, security,
possession, or the like. For example, an item of personal property,
such as a gemstone, vehicle, item of artwork, or the like, may be
monitored by a motion sensor and/or a camera having a known
location (or having a location confirmed by GPS or other location
system), to ensure that it remains in a safe, designated location.
The camera can provide evidence that the item remains in undamaged
condition and in the possession of a party 4910, such as to
indicate that it remains appropriate and adequate collateral 4802
for a loan. In embodiments, this may include items of collateral
for microloans, such as clothing, collectibles, and other
items.
[1263] In embodiments the lending platform 4800 has a set of
data-integrated microservices including data collection services
3318, monitoring services 3306, blockchain services 3422, and smart
contract services 3431 for handling lending entities and
transactions. The smart contract services 3431 may take data from
the data collection services 3318 and monitoring services 3306
(such as from IoT devices) and automatically execute a set of rules
or conditions that embody the smart contract based on the collected
data. For example, upon recognition that collateral 4802 for a loan
has been damaged (such as evidenced by a camera or sensor), the
smart contract services 3431 may automatically initiate a demand
for payment of a loan, automatically initiate a foreclosure
process, automatically initiate an action to claim substitute or
backup collateral, automatically initiate an inspection process,
automatically change a payment or interest rate term that is based
on the collateral (such as setting an interest rate at a level for
an unsecured loan, rather than a secured loan), or the like. Smart
contract events may be recorded on a blockchain by the blockchain
services 3422, such as in a distributed ledger. Automated
monitoring of collateral 4802 and assets 4918 and handling of loans
via smart contract services 3431 may facilitate lending to a much
wider range of parties 4910 and undertaking of loans based on a
much wider range of collateral 4802 and assets 4918 than for
conventional loans, as lenders may have greater certainty as to the
condition of collateral. Monitoring systems 3306 and data
collection systems 3318 may also monitor and collect data from
external marketplaces 3390 or for marketplaces operated with the
platform 4800 to maintain awareness of the value of collateral 4802
and assets 4918, such as to ensure that items remain of adequate
value and liquidity to assure repayment of a loan. For example,
public e-commerce auction sites like eBay.TM. can be monitored to
confirm that personal property items are of a type and condition
likely to be disposed of easily by a lender in a liquid public
market, so that the lender is sure to receive payment if the
borrower defaults. This may allow loans to be made and administered
on a wide range of personal property that is normally difficult to
use as collateral. In embodiments, an automated foreclosure process
may be initiated by a smart contract, which may, upon occurrence of
a condition of default that permits foreclosure (such as uncured
failure to make payments) include a process for automatically
initiating placement of an item of collateral on a public auction
site (such as eBay.TM. or an auction site appropriate for a
particular type of property), automatically securing collateral
(such as by locking a connected device, such as a smart lock, smart
container, or the like that contains or secures collateral),
automatically configuring a set of instructions to a carrier,
freight forwarder, or the like for shipping collateral,
automatically configuring a set of instructions for a drone, a
robot, or the like for transporting collateral, or the like. In
embodiments, a system is provided for facilitating foreclosure on
collateral. An example system for facilitating foreclosure on
collateral may include a set of data collection and monitoring
services for monitoring at least one condition of a lending
agreement; and a set of smart contract services establishing terms
and conditions of the lending agreement that include terms and
conditions for foreclosure on at least one item that provides
collateral securing a repayment obligation of the lending
agreement, wherein upon detection of a default based on data
collected by the data collection and monitoring services, the set
of smart contract services automatically initiates a foreclosure
process on the collateral. Certain further aspects of an example
system are described following, any one or more of which may be
present in certain embodiments. An example system includes where
the set of smart contract services initiates a signal to at least
one of a smart lock and a smart container to lock the collateral.
An example system includes where the set of smart contract services
configures and initiates a listing of the collateral on a public
auction site. An example system includes where the set of smart
contract services configures and delivers a set of transport
instructions for the collateral. An example system includes where
the set of smart contract services configures a set of instructions
for a drone to transport the collateral. An example system includes
where the set of smart contract services configures a set of
instructions for a robot to transport the collateral. An example
system includes where the set of smart contract services initiates
a process for automatically substituting a set of substitute
collateral. An example system includes where the set of smart
contract services initiates a message to a borrower initiating a
negotiation regarding the foreclosure. An example system includes
where the negotiation is managed by a robotic process automation
system that is trained on a training set of foreclosure
negotiations. An example system includes where the negotiation
relates to modification of at least one of the interest rate, the
payment terms, and the collateral for the lending transaction.
[1264] Referring to FIG. 51, in embodiments the lending platform
4800 is provided having an Internet of Things data collection
platform 4908 (with various IoT and edge devices as described
throughout this disclosure) for monitoring at least one of a set of
assets 4918 and a set of collateral 4802 for a loan, a bond, or a
debt transaction. The platform 4800 may include a guarantee and/or
security monitoring solution 4930 for monitoring assets 4918 and/or
collateral 4802 based on the data collected by the IoT data
collection platform 4908, such as where the guarantee and/or
security monitoring solution 4930 uses various adaptive intelligent
systems 3304, such as ones that may use model (which may be
adjusted, reinforced, trained, or the like, such as using
artificial intelligence 3448) that determine the condition or value
of items based on images, sensor data, location data, or other data
of the type collected by the IoT data collection platform 4908.
Monitoring may include monitoring of location of collateral 4802 or
assets 4918, behavior of parties 4910, financial condition of
parties 4910, or the like. The guarantee and/or security monitoring
solution 4930 may include a set of interfaces by which a user may
configure parameters for monitoring, such as rules or thresholds
regarding conditions, behaviors, attributes, financial values,
locations, or the like, in order to obtain alerts regarding
collateral 4802 or assets 4918. For example, a user may set a rule
that collateral must remain in a given jurisdiction, a threshold
value of the collateral as a percentage of a loan balance, a
minimum status condition (e.g., freedom from damage or defects), or
the like. Configured parameters may be used to provide alerts to
personnel responsible for monitoring loan compliance and/or used or
embodied into one or more smart contract contracts that may take
input from the interface of the guarantee and/or security
monitoring solution 4930 to configure conditions for foreclosure,
conditions for changing interest rates, conditions for accelerating
payments, or the like. The platform 4800 may have a loan management
solution 4948 that allows a loan manager to access information from
the IoT data collection system 4908 and/or the guarantee and/or
security monitoring solution 4930, such that a user may manage
various actions with respect to a loan (of the many types describe
herein, such as setting interest rates, foreclosing, sending
notices, and the like) based on the condition of collateral 4802 or
assets 4918, based on events involving entities 3330, based on
behaviors, based on loan-related actions (such as payments) and
other factors. The loan management solution 4948 may include a set
of interfaces, workflows, models (including adaptive intelligent
systems 3304) that are configured for a particular type of loan (of
the many types described herein) and that allow a user to configure
parameters, set rules, set thresholds, design workflows, configure
smart contract services, configure blockchain services, and the
like in order to facilitate automated or assisted management of a
loan, such as enabling automated handing of loan actions by a smart
contract in response to collected data from the IoT data collection
system 4908 or enabling generation of a set of recommended actions
for a human user based on that data.
[1265] In embodiments, a lending platform is provided having a
smart contract and distributed ledger platform for managing at
least one of ownership of a set of collateral and a set of events
related to a set of collateral. A set of smart contract services
3431 may, for example, transfer ownership of the collateral 4802 or
other assets 4918 upon recognition of an event of failure to make
payment or other default, occurrence of a foreclosure condition
(such as failure to satisfy with a covenant or failure to comply
with an obligation), or the like, where the ownership transfer and
related events are recorded by the set of blockchain services 3422
in a distributed ledger, such as one that provides a secure record
of title to the assets 4918 or collateral 4802. As an example, a
covenant of a loan embodied in a smart contract may require that
collateral 4802 have a value that exceeds a minimum fraction (or
multiple) of the remaining balance of a loan. Based on data
collected about the value of collateral (such as by monitoring one
or more external marketplaces 3390 or marketplaces of the platform
4800), a smart contract may calculate whether the covenant is
satisfied and record the outcome on a blockchain. If the covenant
is not satisfied, such as if market factors indicate that the type
of collateral has diminished, while the loan balance remains high,
the smart contract may initiate a foreclosure, including recording
an ownership transfer on a distributed ledger via the blockchain
services 3422. A smart contract may also process events related to
an entity 3330 such as a party 4910. For example, a covenant of a
loan may require the party to maintain a level of debt below a
threshold or ratio, to maintain a level of income, to maintain a
level of profit, or the like. The monitoring systems 3306 or data
collection systems 3318 may provide data used by the smart contract
services 3431 to determine covenant compliance and to enable
automated action, including recording events like foreclosure and
ownership transfers on a distributed ledger. In another example, a
covenant may relate to a behavior of a party 4910 or a legal status
of a party 4910, such as requiring the party to refrain from taking
a particular action with respect to an item of property. For
example, a covenant may require a party to comply with zoning
regulations that prohibit certain usage of real property. IoT data
collection systems 4908 may be used to monitor the party 4910, the
property, or other items to confirm compliance with the covenant or
to trigger alerts or automated actions in cases of
non-compliance.
[1266] Referring to FIG. 52, in embodiments a lending platform is
provided having a crowdsourcing system for obtaining information
about at least one of a state of a set of collateral for a loan and
a state of an entity relevant to a guarantee for a loan. Thus, in
embodiments, a platform is provided herein, with systems, methods,
processes, services, components and other elements for enabling a
blockchain and smart contract platform 5200 for crowdsourcing
information relevant to lending. As with other embodiments
described above in connection with sourcing innovation, product
demand, or the like, a blockchain 3422, such as optionally
embodying a distributed ledger, may be configured with a set of
smart contracts 3431 to administer a reward 5212 for the submission
of loan information 4418 such as evidence of ownership of property,
evidence of title, information about ownership of collateral,
information about condition of collateral, information about the
location of collateral, information about a party's identity,
information about a party's creditworthiness, information about a
party's activities or behavior, information about a party's
business practices, information about the status of performance of
a contract, information about accounts receivable, information
about accounts payable, information about the value of collateral,
and many other types of information. In embodiments, a blockchain
3422, such as optionally distributed in a distributed ledger, may
be used to configure a request for information 17862 along with
terms and conditions 5210 related to the information, such as a
reward 5212 for submission of the information 4418, a set of terms
and conditions 5210 related to the use of the information 17862),
and various parameters 5208, such as timing parameters, the nature
of the information required (such as independently validated
information like title records, video footage, photographs,
witnessed statements, or the like), and other parameters 5208.
[1267] The platform 5200 may include a crowdsourcing interface
5220, which may be included in or provided in coordination with a
website, application, dashboard, communications system (such as for
sending emails, texts, voice messages, advertisements, broadcast
messages, or other messages), by which a message may be presented
in the interface 5220 or sent to relevant individuals (whether
targeted, such as in the case of a request to a particular
individual, or broadcast, such as to individuals in a given
location, company, organization, or the like) with an appropriate
link to the smart contract 3431 and associated blockchain 3422,
such that a reply message submitting information 4418, with
relevant attachments, links, or other information, can be
automatically associated (such as via an API or data integration
system) with the blockchain 3422, such that the blockchain 3422,
and any optionally associated distributed ledger, maintains a
secure, definitive record of information 17862 submitted in
response to the request. Where a reward 5212 is offered, the
blockchain 3422 and/or smart contract 3431 may be used to record
time of submission, the nature of the submission, and the party
submitting, such that at such time as a submission satisfies the
conditions for a reward 5212 (such as, for example, upon completion
of a loan transaction in which the information 17862 was useful),
the blockchain 3422 and any distributed ledger stored thereby can
be used to identify the submitter and, by execution of the smart
contract 3431, convey the reward 5212 (which may take any of the
forms of consideration noted throughout this disclosure. In
embodiments, the blockchain 3422 and any associated ledger may
include identifying information for submissions of information 4418
without containing actual information 17862, such that information
may be maintained secret (such as being encrypted or being stored
separately with only identifying information), subject to
satisfying or verifying conditions for access (such as
identification or verification of a person who has legitimate
access rights, such as by an identity or security application
3418). Rewards 5212 may be provided based on outcomes of cases or
situations to which information 17862 relates, based on a set of
rules (which may be automatically applied in some cases, such as
using a smart contract 3431 in concert with an automation system, a
rule processing system, an artificial intelligence system 3448 or
other expert system, which in embodiments may comprise one that is
trained on a training data set created with human experts. For
example, a machine vision system may be used to evaluate evidence
of the existence and/or condition of collateral based on images of
items, and parties submitting information about collateral may be
rewarded, such as via tokens or other consideration, via
distribution of rewards 5212 through the smart contract 3431,
blockchain 3422 and any distributed ledger. Thus, the platform 5200
may be used for a wide variety of fact-gathering and
information-gathering purposes, to facilitate validation of
collateral, to validate representations about behavior, to validate
occurrence of conditions of compliance, to validate occurrence of
conditions of default, to deter improper behavior or
misrepresentations, to reduce uncertainty, to reduce asymmetries of
information, or the like.
[1268] In embodiments, information may relate to fact-gathering or
data-gathering for a variety of applications and solutions that may
be supported by a marketplace platform 3300, including the
crowdsourcing platform 5200, such as for underwriting 3420 (e.g.,
of various types of loans, guarantees, and other items), risk
management solutions 3408 (such as managing a wide variety of risks
noted throughout this disclosure, such as risks associated with
individual loans, packages of loans, tranches of loans and the
like); lending solutions 3410 (such as evidence of the ownership
and or value of collateral, evidence of the veracity of
representations, evidence of performance or compliance with loan
covenants, and the like); regulatory solutions 3426 (such as with
respect to compliance with a wide range of regulations that may
govern entities 3330 and processes, behaviors or activities of or
by entities 3330); and fraud prevention solutions 3416 (such as to
detect fraud, misrepresentation, improper behavior, libel, slander,
and the like). For example, a capital loan for a building may
include a covenant regarding the use of the property, such as
permitting certain uses and prohibiting others, permitting a given
occupancy, or the like, and the crowdsourcing platform 5200 may
solicit and provide consideration for compliance information about
the building (e.g., requesting confirmation from the crowd that a
building is in fact being used for its intended use as permitted by
zone regulations). Crowdsourced information may be combined with
information from monitoring systems 3306. In embodiments, an
adaptive intelligent system 3304 may, for example, continuously
monitor a property, an item of collateral 4802 or other entity 3330
and, upon recognition (such as by an AI system, such as a neural
network classifier) of a suspicious event (e.g., one that may
indicate violation of a loan covenant), the adaptive intelligent
system 3304 may provide a signal to the crowdsourcing system 5220
indicating that a crowdsourcing process should be initiated to
verify the presence or absence of the violation. In embodiments,
this may include classifying the covenant-related condition that
using a machine classifier, providing the classification along with
identifying data about an entity, and automatically configuring,
such as based on a model or set of rules, a crowdsource request
that identifies what information is requested about what entity
3330 and what reward 5212 is provided. In embodiment, rewards 5212
may be configured by experts, rewards 5212 may be based on a set of
rules (such as ones that operate on parameters of the loan, the
terms and conditions of a covenant n a smart contract 3431 (such as
loan value, remaining term, and the like), the value of collateral
4802, or the like), and/or reward 5212 may be set by robotic
process automation 3442, such as where an RPA system 3442 is
trained on a training set of expert activities in setting rewards
in various contexts that collectively show what rewards are
appropriate in given situations. Robotic process automation 3442 of
reward configuration may be continuously improved by artificial
intelligence 3448, such as based on a continuous feedback of
outcomes of crowdsourcing, such as outcomes of success (e.g.,
verification of covenant defaults, yield outcomes, and the
like).
[1269] Information gathering may include information gathering with
respect to entities 3330 and their identities, assertions, claims,
actions or behaviors, among many other factors and may be
accomplished by crowdsourcing in the platform 5200 or by data
collection systems 3318 and monitoring systems 3306, optionally
with automation via process automation 3442 and adaptive
intelligence, such as using an artificial intelligence system
3448.
[1270] Referring to FIG. 53, a platform-operated marketplace
crowdsourcing evidence 5200 may be configured, such as in a
crowdsourcing interface 5220 or other user interface for an
operator of the platform-operated marketplace 5200, using the
various enabling capabilities of the data handling platform 3300
described throughout this disclosure. The operator may use the user
interface or crowdsourcing dashboard 5414 to undertake a series of
steps to perform or undertake an algorithm to create a
crowdsourcing request for information 17862 as described in
connection with FIG. 52. In embodiments, one or more of the steps
of the algorithm to create a reward 5212 within the dashboard 5414
may include, at a component 5302, identifying potential rewards
5312, such as what information 5318 is likely to be of value in a
given situation (such as may be indicated through various
communication channels by stakeholders or representatives of an
entity, such as an individual or enterprise, such as attorneys,
agents, investigators, parties, auditors, detectives, underwriters,
inspectors, and many others).
[1271] The dashboard 5414 may be configured with a crowdsourcing
interface 5220, such as with elements (including application
programming elements, data integration elements, messaging
elements, and the like) that allow a crowdsourcing request to be
managed in the platform marketplace 5200 and/or in one or more
external marketplaces 5204. In the dashboard 5414, at a component
5304 the user may configure one or more parameters 5208 or
conditions 5210, such as comprising or describing the conditions
(of the type described herein) for the crowdsourcing request, such
as by defining a set of conditions 5210 that trigger the reward
5212 and determine allocation of the reward 5212 to a set of
submitters of information 5218. The user interface of the dashboard
5414, which may include or be associated with the crowdsourcing
interface 5220, may include a set of drop down menus, tables,
forms, or the like with default, templated, recommended, or
pre-configured conditions, parameters 5208, conditions 5210 and the
like, such as ones that are appropriate for various types of
crowdsourcing requests. Once the conditions and other parameters of
the request are configured, at a component 5308 a smart contract
3431 and blockchain 3422 may be configured to maintain, such as via
a ledger, the data required to provision, allocate, and exchange
data related to the request and to submissions of information 5218.
The smart contract 3431 and blockchain 3422 may be configured to
identity information, transaction information (such as for
exchanges of information), technical information, other evidence
data 518 of the type described in connection with FIG. 52,
including any data, testimony, photo or video content or other
information that may be relevant to a submission of information
5218 or the conditions 5210 for a reward 5212. At a component 5310
a smart contract 3431 may be configured to embody the conditions
5210 that were configured at the component 5304 and to operate on
the blockchain 3422 that was created at the component 5308, as well
as to operate on other data, such as data indicating facts,
conditions, events, or the like in the platform-operated
marketplace 5200 and/or an external marketplace 5204 or other
information site or resource, such as ones related to submission
data 4418, such as sites indicating outcomes of legal cases or
portions of cases, sites reporting on investigations, and the like.
The smart contract 3431 may be responsive to the configuration from
component 5310 to apply one or more rules, execute one or more
conditional operations, or the like upon data, such as evidence
data 5218 and data indicating satisfaction of parameters 5208 or
conditions 5210, as well as identity data, transactional data,
timing data, and other data. Once configuration of one or more
blockchains 3422 and one or more smart contracts 3431 is complete,
at a component 5312 the blockchain 3422 and smart contract 3431 may
be deployed in the platform-operated marketplace 5200, external
marketplace 5204 or other site or environment, such as for
interaction by one or more submitters or other users, who may, such
as in a crowdsourcing interface 5220, such as a website,
application, or the like, enter into the smart contract 3431, such
as by submitting a submission of information 4418 and requesting
the reward 5212, at which point the platform 5200, such as using
the adaptive intelligent systems 3304 or other capabilities, may
store relevant data, such as submission data 4418, identity data
for the party or parties entering the smart contract 3431 on the
blockchain 3422 or otherwise on the platform 5200. At a component
5314, once the smart contract 3431 is executed, the platform 5200
may monitor, such as by the monitoring systems layer 3306, the
platform-operated marketplace 5200 and/or one or more external
marketplaces 5204 or other sites for submission data 4418, event
data 3324, or other data that may satisfy or indicate satisfaction
of one or more conditions 5210 or trigger application of one or
more rules of the smart contract 3431, such as to trigger a reward
5212.
[1272] At a component 5316, upon satisfaction of conditions 5210,
smart contracts 3431 may be settled, executed, or the like,
resulting updates or other operations on the blockchain 3422, such
as by transferring consideration (such as via a payments system)
and transferring access to information 17862. Thus, via the
above-referenced steps, an operator of the platform-operated
marketplace 5200 may discover, configure, deploy and have executed
a set of smart contracts 3431 that crowdsource information relevant
to a loan (such as information about value or condition of
collateral 4802, compliance with covenants, fraud or
misrepresentation, and the like) and that are cryptographically
secured and transferred on a blockchain 3422 from information
gatherers to parties seeking information. In embodiments, the
adaptive intelligent systems layer 3304 may be used to monitor the
steps of the algorithm described above, and one or more artificial
intelligence systems may be used to automate, such as by robotic
process automation 3442, the entire process or one or more
sub-steps or sub-algorithms. This may occur as described above,
such as by having an artificial intelligence system 3448 learn on a
training set of data resulting from observations, such as
monitoring software interactions of human users as they undertake
the above-referenced steps. Once trained, the adaptive intelligence
layer 3304 may thus enable the platform 3300 to provide a fully
automated platform for crowdsourcing of loan information.
[1273] Referring to FIG. 54, in embodiments a lending platform is
provided having a smart contract system 3431 that automatically
adjusts an interest rate for a loan based on information collected
via at least one of an Internet of Things system, a crowdsourcing
system, a set of social network analytic services and a set of data
collection and monitoring services. The platform 4800 may include
an interest rate automation solution 4924 that may include a set of
interfaces, workflows, and models (which may include, use or be
enabled by various adaptive intelligent systems 3304) and other
components that are configured to enable automation of the setting
of interest rates based on a set of conditions, which may include
smart contract 3431 terms and conditions, marketplace conditions
(of platform marketplaces and/or external marketplaces 3390,
conditions monitored by monitoring systems 3306 and data collection
systems 3318, and the like (such as of entities 3330, including
without limitation parties 4910, collateral 4802 and assets 4918,
among others). For example, a user of the interest rate automation
solution 4924 may set (such as in a user interface) rules,
thresholds, model parameters, and the like that determine, or
recommend, an interest rate for a loan based on the above, such as
based on interest rates available to the lender from secondary
lenders, risk factors of the borrower (including predicted risk
based on one or more predictive models using artificial
intelligence 3448), or the system may automatically recommend or
set such rules, thresholds, parameters and the like (optionally by
learning to do so based on a training set of outcomes over time).
Interest rates may be determined based on marketing factors (such
as competing interest rates offered by other lenders). Interest
rates may be calculated for new loans, for modifications of
existing loans, for refinancing, for foreclosure situations (e.g.,
changing from secured loan rates to unsecured loan rates), and the
like.
[1274] Smart Contract that Automatically Restructures Debt Based on
a Monitored Condition
[1275] Referring to FIG. 55, in embodiments a lending platform is
provided having a smart contract that automatically restructures
debt based on a monitored condition. The platform 4800 may include
a debt restructuring solution 4928 that may include a set of
interfaces, workflows, and models (which may include, use or be
enabled by various adaptive intelligent systems 3304) and other
components that are configured to enable automation of the
restructuring of debt based on a set of conditions, which may
include smart contract 3431 terms and conditions, marketplace
conditions (of platform marketplaces and/or external marketplaces
3390, conditions monitored by monitoring systems 3306 and data
collection systems 3318, and the like (such as of entities 3330,
including without limitation parties 4910, collateral 4802 and
assets 4918, among others). For example, a user of the debt
restructuring solution 4928 may create, configure (such as using
one or more templates or libraries), modify, set or otherwise
handle (such as in a user interface of the debt restructuring
solution 4928) various rules, thresholds, procedures, workflows,
model parameters, and the like that determine, or recommend, a debt
restructuring action for a loan based on one or more events,
conditions, states, actions, or the like, where restructuring may
be based on various factors, such as prevailing market interest
rates, interest rates available to the lender from secondary
lenders, risk factors of the borrower (including predicted risk
based on one or more predictive models using artificial
intelligence 3448), status of other debt (such as new debt of a
borrower, elimination of debt of a borrower, or the like),
condition of collateral 4802 or assets 4918 used to secure or back
a loan, state of a business or business operation (e.g.,
receivables, payables, or the like), and many others. Restructuring
may include changes in interest rate, changes in priority of
secured parties, changes in collateral 4802 or assets 4918 used to
back or secure debt, changes in parties, changes in guarantors,
changes in payment schedule, changes in principal balance (e.g.,
including forgiveness or acceleration of payments), and others. In
embodiments, the debt restructuring solution 4928 may automatically
recommend or set such rules, thresholds, actions, parameters and
the like (optionally by learning to do so based on a training set
of outcomes over time), resulting in a recommended restructuring
plan, which may specify a series of actions required to accomplish
a recommended restructuring, which may be automated and may involve
conditional execution of steps based on monitored conditions and/or
smart contract terms, which may be created, configured, and/or
accounted for by the debt restructuring plan.
[1276] Restructuring plans may be determined and executed based at
least one part on market factors (such as competing interest rates
offered by other lenders, values of collateral, and the like) as
well as regulatory and/or compliance factors. Restructuring plans
may be generated and/or executed for modifications of existing
loans, for refinancing, for foreclosure situations (e.g., changing
from secured loan rates to unsecured loan rates), for bankruptcy or
insolvency situations, for situations involving market changes
(e.g., changes in prevailing interest rates) and others. In
embodiments, adaptive intelligent systems 3304, including
artificial intelligence 3448 may be trained on a training set of
restructuring activities by experts and/or on outcomes of
restructuring actions to generate a set of predictions,
classifications, control instructions, plans, models, or the like
for automated creation, management and/or execution of one or more
aspects of a restructuring plan. In embodiments, provided herein is
a smart contract system for modifying a loan, the system having a
set of computational services. An example platform or system
includes (a) a set of data collection and monitoring services for
monitoring a set of entities involved in a loan; and (b) a set of
smart contract services for managing a smart lending contract,
wherein the set of smart contract services processes information
from the set of data collection and monitoring services and
automatically restructures debt based on a monitored condition.
Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments.
[1277] Referring to FIG. 56, in embodiments a lending platform 4800
is provided having a social network monitoring system 4904 for
validating the reliability of a guarantee for a loan. The platform
4800 may include a guarantee and/or security monitoring solution
4930 that may include a set of interfaces, workflows, and models
(which may include, use or be enabled by various adaptive
intelligent systems 3304) and other components that are configured
to enable monitoring of a guarantee and/or security for a lending
transaction based on a set of conditions, which may include smart
contract 3431 terms and conditions, marketplace conditions (of
platform marketplaces and/or external marketplaces 3390, conditions
monitored by monitoring systems 3306 and data collection systems
3318, and the like (such as of entities 3330, including without
limitation parties 4910, collateral 4802 and assets 4918, among
others). For example, a user of the guarantee and/or security
monitoring solution 4930 may set (such as in a user interface)
rules, thresholds, model parameters, and the like that determine,
or recommend, a monitoring plan for lending transaction such as
based on risk factors of the borrower, risk factors of the lender,
market risk factors, and/or risk factors of collateral 4802 or
assets 4918 (including predicted risk based on one or more
predictive models using artificial intelligence 3448), or the
platform 4800 may automatically recommend or set such rules,
thresholds, parameters and the like (optionally by learning to do
so based on a training set of outcomes over time). The guarantee
and/or security monitoring solution 4930 may configure a set of
social network analytics services 4904 and/or other monitoring
systems 3306 and/or data collection systems 3318 to search, parse,
extract, and process data from one or more social networks,
website, or the like, such as ones that may contain information
about collateral 4802 or assets 4918 (e.g., photos that show a
vehicle, boat, or other personal property of a party 4910, photos
of a home or other real property, photos or text that describes
activities of a party 4910 (including ones that indicate financial
risk, physical risk, health risk, or other risk that may be
relevant to the quality of the guarantor and/or the guarantee for a
payment obligation and/or the ability of the borrower to repay a
loan when due). For example a photo showing a borrower driving a
regular passenger vehicle in off-road conditions may be flagged as
indicating that the vehicle cannot be fully relied upon as
collateral for an automobile loan that has a high remaining
balance.
[1278] Social Network Monitoring System for Validating Quality of a
Personal Guarantee for a Loan
[1279] Thus, in embodiments, provided herein is a social network
monitoring system for validating conditions of a guarantee for a
loan. An example platform or system includes (a) a set of social
network data collection and monitoring services by which data is
collected by a set of algorithms that are configured to monitor
social network information about entities involved in a loan; and
(b) an interface to the set of social networking services that
enables configuration of parameters of the social network data
collection and monitoring services to obtain information related to
the condition of guarantee. Certain further aspects of an example
system are described following, any one or more of which may be
present in certain embodiments.
[1280] An example system includes where the set of social network
data collection and monitoring services obtains information about
the financial condition of an entity that is the guarantor for the
loan.
[1281] An example system includes where the financial condition is
determined at least in part based on information contained in a
social network about the entity selected from among a publicly
stated valuation of the entity, a set of property owned by the
entity as indicated by public records, a valuation of a set of
property owned by the entity, a bankruptcy condition of an entity,
a foreclosure status of an entity, a contractual default status of
an entity, a regulatory violation status of an entity, a criminal
status of an entity, an export controls status of an entity, an
embargo status of an entity, a tariff status of an entity, a tax
status of an entity, a credit report of an entity, a credit rating
of an entity, a website rating of an entity, a set of customer
reviews for a product of an entity, a social network rating of an
entity, a set of credentials of an entity, a set of referrals of an
entity, a set of testimonials for an entity, a set of behavior of
an entity, a location of an entity, and a geolocation of an
entity.
[1282] An example system includes where the loan is of at least one
type selected from among an auto loan, an inventory loan, a capital
equipment loan, a bond for performance, a capital improvement loan,
a building loan, a loan backed by an account receivable, an invoice
finance arrangement, a factoring arrangement, a pay day loan, a
refund anticipation loan, a student loan, a syndicated loan, a
title loan, a home loan, a venture debt loan, a loan of
intellectual property, a loan of a contractual claim, a working
capital loan, a small business loan, a farm loan, a municipal bond,
and a subsidized loan.
[1283] An example system includes where the platform or system may
further include an interface of the social network data collection
and monitoring services An example system includes where the data
collection and monitoring service is configured to obtain
information about condition of a set of collateral for the loan,
wherein the set of collateral items is selected from among a
vehicle, a ship, a plane, a building, a home, real estate property,
undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a beverage, a precious metal, an item of jewelry, a
gemstone, an item of intellectual property, an intellectual
property right, a contractual right, an antique, a fixture, an item
of furniture, an item of equipment, a tool, an item of machinery,
and an item of personal property.
[1284] An example system includes where condition of collateral
includes condition attributes selected from the group consisting of
the quality of the collateral, the condition of the collateral, the
status of title to the collateral, the status of possession of the
collateral, the status of a lien on the collateral, a new or used
status of item, a type of item, a category of item, a specification
of an item, a product feature set of an item, a model of item, a
brand of item, a manufacturer of item, a status of item, a context
of item, a state of item, a value of item, a storage location of
item, a geolocation of item, an age of item, a maintenance history
of item, a usage history of item, an accident history of an item, a
fault history of an item, an ownership of an item, an ownership
history of an item, a price of a type of item, a value of a type of
item, an assessment of an item, and a valuation of an item.
[1285] An example system includes where the interface is a
graphical user interface configured to enable a workflow by which a
human user enters parameters to establish the social network data
collection and monitoring request.
[1286] An example system includes where the platform or system may
further include a set of smart contract services that administer a
smart lending contract, wherein the smart contract services process
information from the set of social network data collection and
monitoring services and automatically undertake an action related
to the loan.
[1287] An example system includes where the action is at least one
of a foreclosure action, a lien administration action, an
interest-rate setting action, a default initiation action, a
substitution of collateral, and a calling of the loan.
[1288] An example system includes where the platform or system may
further include a robotic process automation system that is
trained, based on a training set of interactions of human users
with the interface to the set of social network data collection and
monitoring services, to configure a data collection and monitoring
action based on a set of attributes of a loan.
[1289] An example system includes where the attributes of the loan
are obtained from a set of smart contract services that manage the
loan.
[1290] An example system includes where the robotic process
automation system is configured to be iteratively trained and
improved based on a set of outcomes from a set of social network
data collection and monitoring requests.
[1291] An example system includes where training includes training
the robotic process automation system to determine a set of domains
to which the social network data collection and monitoring services
will applied.
[1292] An example system includes where training includes training
the robotic process automation system to configure the content of a
social network data collection and monitoring search.
[1293] IoT Data Collection and Monitoring System for Validating
Quality of a Personal Guarantee for a Loan
[1294] Referring still to FIG. 56, in embodiments a lending
platform is provided having an Internet of Things data collection
and monitoring system for validating reliability of a guarantee for
a loan. The guarantee and/or security monitoring solution 4930 may
include the capability to use data from, and configure collection
activities by, a set of Internet of Things services 4908 (which may
include various IoT devices, edge devices, edge computation and
processing capabilities, and the like as described in connection
with various embodiments), such as ones that monitor various
entities 3330 and their environments involved in lending
transactions.
[1295] In embodiments, provided herein is a monitoring system for
validating conditions of a guarantee for a loan. For example, a set
of algorithms may be configured to initiate data collection by IoT
devices, to manage data collection, and the like such as based on
the conditions referenced above, including conditions that relate
to risk factors of the borrower or lender, market risk factors,
physical risk factors, or the like. For example, an IoT system may
be configured to capture video or images of a home during periods
of bad weather, such as to determine whether the home is at risk of
a flood, wind damage, or the like, in order to confirm whether the
home can be predicted to serve as adequate collateral for a home
loan, a line of credit, or other lending transaction.
[1296] An example platform or system includes (a) a set of Internet
of Things data collection and monitoring services by which data is
collected by a set of algorithms that are configured to monitor
Internet of Things information collected from and about entities
involved in a loan; and (b) an interface to the set of Internet of
Things data collection and monitoring services that enables
configuration of parameters of the social network data collection
and monitoring services to obtain information related to the
condition of guarantee. Certain further aspects of an example
system are described following, any one or more of which may be
present in certain embodiments.
[1297] An example system includes where the set of Internet of
Things data collection and monitoring services obtains information
about the financial condition of an entity that is the guarantor
for the loan.
[1298] An example system includes where the financial condition is
determined at least in part based on information collected by an
Internet of Things device about the entity selected from among a
publicly stated valuation of the entity, a set of property owned by
the entity as indicated by public records, a valuation of a set of
property owned by the entity, a bankruptcy condition of an entity,
a foreclosure status of an entity, a contractual default status of
an entity, a regulatory violation status of an entity, a criminal
status of an entity, an export controls status of an entity, an
embargo status of an entity, a tariff status of an entity, a tax
status of an entity, a credit report of an entity, a credit rating
of an entity, a website rating of an entity, a set of customer
reviews for a product of an entity, a social network rating of an
entity, a set of credentials of an entity, a set of referrals of an
entity, a set of testimonials for an entity, a set of behavior of
an entity, a location of an entity, and a geolocation of an
entity.
[1299] An example system includes where the loan is of at least one
type selected from among an auto loan, an inventory loan, a capital
equipment loan, a bond for performance, a capital improvement loan,
a building loan, a loan backed by an account receivable, an invoice
finance arrangement, a factoring arrangement, a pay day loan, a
refund anticipation loan, a student loan, a syndicated loan, a
title loan, a home loan, a venture debt loan, a loan of
intellectual property, a loan of a contractual claim, a working
capital loan, a small business loan, a farm loan, a municipal bond,
and a subsidized loan.
[1300] An example system includes where the platform or system may
further include an interface of the set of Internet of Things data
collection and monitoring services. An example system includes
where the set of data collection and monitoring services is
configured to obtain information about condition of a set of
collateral for the loan, wherein the set of collateral items is
selected from among a vehicle, a ship, a plane, a building, a home,
real estate property, undeveloped land, a farm, a crop, a municipal
facility, a warehouse, a set of inventory, a commodity, a security,
a currency, a token of value, a ticket, a cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, an item of equipment, a tool, an
item of machinery, and an item of personal property.
[1301] An example system includes where condition of collateral
includes condition attributes selected from the group consisting of
the quality of the collateral, the condition of the collateral, the
status of title to the collateral, the status of possession of the
collateral, the status of a lien on the collateral, a new or used
status of item, a type of item, a category of item, a specification
of an item, a product feature set of an item, a model of item, a
brand of item, a manufacturer of item, a status of item, a context
of item, a state of item, a value of item, a storage location of
item, a geolocation of item, an age of item, a maintenance history
of item, a usage history of item, an accident history of an item, a
fault history of an item, an ownership of an item, an ownership
history of an item, a price of a type of item, a value of a type of
item, an assessment of an item, and a valuation of an item.
[1302] An example system includes where the interface is a
graphical user interface configured to enable a workflow by which a
human user enters parameters to establish an Internet of Things
data collection and monitoring services monitoring action.
[1303] An example system includes where the platform or system may
further include a set of smart contract services that administer a
smart lending contract, wherein the set of smart contract services
process information from the set of Internet of Things data
collection and monitoring services and automatically undertakes an
action related to the loan.
[1304] An example system includes where the action is at least one
of a foreclosure action, a lien administration action, an
interest-rate setting action, a default initiation action, a
substitution of collateral, and a calling of the loan.
[1305] An example system includes where the platform or system may
further include a robotic process automation system that is
trained, based on a training set of interactions of human users
with the interface to the set of Internet of Things data collection
and monitoring services, to configure a data collection and
monitoring action based on a set of attributes of a loan.
[1306] An example system includes where the attributes of the loan
are obtained from a set of smart contract services that manage the
loan.
[1307] An example system includes where the robotic process
automation system is configured to be iteratively trained and
improved based on a set of outcomes from a set of Internet of
Things data collection and monitoring services activities.
[1308] An example system includes where training includes training
the robotic process automation system to determine a set of domains
to which the Internet of Things data collection and monitoring
services will applied.
[1309] An example system includes where training includes training
the robotic process automation system to configure the content of
Internet of Things data collection and monitoring services
activities.
[1310] An RPA Bank Loan Negotiator Trained on a Training Set of
Expert Lender Interactions with Borrowers
[1311] Referring to FIG. 57, in embodiments a lending platform is
provided having a robotic process automation system 3442 for
negotiation of a set of terms and conditions for a loan. The RPA
system 3442 may provide automation for one or more aspects of a
negotiation solution 4932 that enables automated negotiation and/or
provides a recommendation or plan for a negotiation relevant to a
lending transaction. The negotiation solution 4932 and/or RPA
system 3442 for negotiation may include a set of interfaces,
workflows, and models (which may include, use or be enabled by
various adaptive intelligent systems 3304) and other components
that are configured to enable automation of one or more aspects of
a negotiation of one or more terms and conditions of a lending
transaction, such as based on a set of conditions, which may
include smart contract 3431 terms and conditions, marketplace
conditions (of platform marketplaces and/or external marketplaces
3390, conditions monitored by monitoring systems 3306 and data
collection systems 3318, and the like (such as of entities 3330,
including without limitation parties 4910, collateral 4802 and
assets 4918, among others). For example, a user of the negotiation
solution 4932 may create, configure (such as using one or more
templates or libraries), modify, set or otherwise handle (such as
in a user interface of the negotiation solution 4932 and/or RPA
system 3442) various rules, thresholds, conditional procedures,
workflows, model parameters, and the like that determine, or
recommend, a negotiation action or plan for a lending transaction
negotiation based on one or more events, conditions, states,
actions, or the like, where the negotiation plan may be based on
various factors, such as prevailing market interest rates, interest
rates available to the lender from secondary lenders, risk factors
of the borrower, the lender, one or more guarantors, market risk
factors and the like (including predicted risk based on one or more
predictive models using artificial intelligence 3448), status of
debt, condition of collateral 4802 or assets 4918 used to secure or
back a loan, state of a business or business operation (e.g.,
receivables, payables, or the like), conditions of parties 4910
(such as net worth, wealth, debt, location, and other conditions),
behaviors of parties (such as behaviors indicating preferences,
behaviors indicating negotiation styles), and many others.
Negotiation may include negotiation of lending transaction terms
and conditions, debt restructuring, foreclosure activities, setting
interest rates, changes in interest rate, changes in priority of
secured parties, changes in collateral 4802 or assets 4918 used to
back or secure debt, changes in parties, changes in guarantors,
changes in payment schedule, changes in principal balance (e.g.,
including forgiveness or acceleration of payments), and many other
transactions or terms and conditions. In embodiments, the
negotiation solution 4932 may automatically recommend or set rules,
thresholds, actions, parameters and the like (optionally by
learning to do so based on a training set of outcomes over time),
resulting in a recommended negotiation plan, which may specify a
series of actions required to accomplish a recommended or desired
outcome of negotiation (such as within a range of acceptable
outcomes), which may be automated and may involve conditional
execution of steps based on monitored conditions and/or smart
contract terms, which may be created, configured, and/or accounted
for by the negotiation plan. Negotiation plans may be determined
and executed based at least one part on market factors (such as
competing interest rates offered by other lenders, values of
collateral, and the like) as well as regulatory and/or compliance
factors. Negotiation plans may be generated and/or executed for
creation of new loans, for creation of guarantees and security, for
secondary loans, for modifications of existing loans, for
refinancing, for foreclosure situations (e.g., changing from
secured loan rates to unsecured loan rates), for bankruptcy or
insolvency situations, for situations involving market changes
(e.g., changes in prevailing interest rates) and others. In
embodiments, adaptive intelligent systems 3304, including
artificial intelligence 3448 may be trained on a training set of
negotiation activities by experts and/or on outcomes of negotiation
actions to generate a set of predictions, classifications, control
instructions, plans, models, or the like for automated creation,
management and/or execution of one or more aspects of a negotiation
plan.
[1312] In embodiments, provided herein is a robotic process
automation system for negotiating a loan. An example platform or
system includes (a) a set of data collection and monitoring
services for collecting a training set of interactions among
entities for a set of loan transactions; (b) an artificial
intelligence system that is trained on the training set of
interactions to classify a set of loan negotiation actions; and (c)
a robotic process automation system that is trained on a set of
loan transaction interactions and a set of loan transaction
outcomes to negotiate the terms and conditions of a loan on behalf
of a party to a loan. Certain further aspects of an example system
are described following, any one or more of which may be present in
certain embodiments.
[1313] An example system includes where the set of data collection
and monitoring services includes services selected from among a set
of Internet of Things systems that monitor the entities, a set of
cameras that monitor the entities, a set of software services that
pull information related to the entities from publicly available
information sites, a set of mobile devices that report on
information related to the entities, a set of wearable devices worn
by human entities, a set of user interfaces by which entities
provide information about the entities and a set of crowdsourcing
services configured to solicit and report information related to
the entities.
[1314] An example system includes where the entities are a set of
parties to a loan transaction.
[1315] An example system includes where the set of parties is
selected from among a primary lender, a secondary lender, a lending
syndicate, a corporate lender, a government lender, a bank lender,
a secured lender, bond issuer, a bond purchaser, an unsecured
lender, a guarantor, a provider of security, a borrower, a debtor,
an underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, and an accountant.
[1316] An example system includes where the artificial intelligence
system includes at least one of a machine learning system, a
model-based system, a rule-based system, a deep learning system, a
hybrid system, a neural network, a convolutional neural network, a
feed forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system,
and a simulation system.
[1317] An example system includes where the robotic process
automation is trained on a set of interactions of parties with a
set of user interfaces involved in a set of lending processes.
[1318] An example system includes where upon completion of
negotiation a smart contract for a loan is automatically configured
by a set of smart contract services based on the outcome of the
negotiation.
[1319] An example system includes where at least one of an outcome
and a negotiating event of the negotiation is recorded in a
distributed ledger associated with the loan.
[1320] An example system includes where the loan is of a type
selected from among an auto loan, an inventory loan, a capital
equipment loan, a bond for performance, a capital improvement loan,
a building loan, a loan backed by an account receivable, an invoice
finance arrangement, a factoring arrangement, a pay day loan, a
refund anticipation loan, a student loan, a syndicated loan, a
title loan, a home loan, a venture debt loan, a loan of
intellectual property, a loan of a contractual claim, a working
capital loan, a small business loan, a farm loan, a municipal bond,
and a subsidized loan.
[1321] An example system includes where the artificial intelligence
system includes at least one of a machine learning system, a
model-based system, a rule-based system, a deep learning system, a
hybrid system, a neural network, a convolutional neural network, a
feed forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system,
and a simulation system.
[1322] An RPA Bank Loan Refinancing Negotiator Trained on a
Training Set of Expert Lender Re-Financing Interactions with
Borrowers
[1323] In embodiments, provided herein is a robotic process
automation system for negotiating refinancing of a loan. An example
platform or system includes (a) a set of data collection and
monitoring services for collecting a training set of interactions
between entities for a set of loan refinancing activities; an
artificial intelligence system that is trained on the training set
of interactions to classify a set of loan refinancing actions; and
(c) a robotic process automation system that is trained on a set of
loan refinancing interactions and a set of loan refinancing
outcomes to undertake a loan refinancing activity on behalf of a
party to a loan. Certain further aspects of an example system are
described following, any one or more of which may be present in
certain embodiments.
[1324] An example system includes where the loan refinancing
activity includes initiating an offer to refinance, initiating a
request to refinance, configuring a refinancing interest rate,
configuring a refinancing payment schedule, configuring a
refinancing balance, configuring collateral for a refinancing,
managing use of proceeds of a refinancing, removing or placing a
lien associated with a refinancing, verifying title for a
refinancing, managing an inspection process, populating an
application, negotiating terms and conditions for a refinancing and
closing a refinancing.
[1325] An example system includes where the set of data collection
and monitoring services includes services selected from among a set
of Internet of Things systems that monitor the entities, a set of
cameras that monitor the entities, a set of software services that
pull information related to the entities from publicly available
information sites, a set of mobile devices that report on
information related to the entities, a set of wearable devices worn
by human entities, a set of user interfaces by which entities
provide information about the entities and a set of crowdsourcing
services configured to solicit and report information related to
the entities.
[1326] An example system includes where the entities are a set of
parties to a loan transaction.
[1327] An example system includes where the set of parties is
selected from among a primary lender, a secondary lender, a lending
syndicate, a corporate lender, a government lender, a bank lender,
a secured lender, bond issuer, a bond purchaser, an unsecured
lender, a guarantor, a provider of security, a borrower, a debtor,
an underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, and an accountant.
[1328] An example system includes where the artificial intelligence
system includes at least one of a machine learning system, a
model-based system, a rule-based system, a deep learning system, a
hybrid system, a neural network, a convolutional neural network, a
feed forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system,
and a simulation system.
[1329] An example system includes where the robotic process
automation is trained on a set of interactions of parties with a
set of user interfaces involved in a set of lending processes.
[1330] An example system includes where upon completion of a
refinancing process a smart contract for a refinance loan is
automatically configured by a set of smart contract services based
on the outcome of the refinancing activity.
[1331] An example system includes where at least one of an outcome
and an event of the refinancing is recorded in a distributed ledger
associated with the refinancing loan.
[1332] An example system includes where the loan is of a type
selected from among an auto loan, an inventory loan, a capital
equipment loan, a bond for performance, a capital improvement loan,
a building loan, a loan backed by an account receivable, an invoice
finance arrangement, a factoring arrangement, a pay day loan, a
refund anticipation loan, a student loan, a syndicated loan, a
title loan, a home loan, a venture debt loan, a loan of
intellectual property, a loan of a contractual claim, a working
capital loan, a small business loan, a farm loan, a municipal bond,
and a subsidized loan.
[1333] An example system includes where the artificial intelligence
system includes at least one of a machine learning system, a
model-based system, a rule-based system, a deep learning system, a
hybrid system, a neural network, a convolutional neural network, a
feed forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system,
and a simulation system.
[1334] An RPA Bank Loan Collector Trained on a Training Set of
Expert Collection Interactions with Borrowers
[1335] Referring to FIG. 58, in embodiments a lending platform is
provided having a robotic process automation system for loan
collection. The RPA system 3442 may provide automation for one or
more aspects of a collection solution 4938 that enables automated
collection and/or provides a recommendation or plan for a
collection activity relevant to a lending transaction. The
collection solution 4938 and/or RPA system 3442 for collection may
include a set of interfaces, workflows, and models (which may
include, use or be enabled by various adaptive intelligent systems
3304) and other components that are configured to enable automation
of one or more aspects of a collection action of one or more terms
and conditions of a collection process for a lending transaction,
such as based on a set of conditions, which may include smart
contract 3431 terms and conditions, marketplace conditions (of
platform marketplaces and/or external marketplaces 3390, conditions
monitored by monitoring systems 3306 and data collection systems
3318, and the like (such as of entities 3330, including without
limitation parties 4910, collateral 4802 and assets 4918, among
others). For example, a user of the collection solution 4938 may
create, configure (such as using one or more templates or
libraries), modify, set or otherwise handle (such as in a user
interface of the collection solution 4938 and/or RPA system 3442)
various rules, thresholds, conditional procedures, workflows, model
parameters, and the like that determine, or recommend, a collection
action or plan for a lending transaction or loan monitoring
solution based on one or more events, conditions, states, actions,
or the like, where the collection plan may be based on various
factors, such as the status of payments, the status of the
borrower, the status of collateral 4802 or assets 4918, risk
factors of the borrower, the lender, one or more guarantors, market
risk factors and the like (including predicted risk based on one or
more predictive models using artificial intelligence 3448), status
of debt, condition of collateral 4802 or assets 4918 used to secure
or back a loan, state of a business or business operation (e.g.,
receivables, payables, or the like), conditions of parties 4910
(such as net worth, wealth, debt, location, and other conditions),
behaviors of parties (such as behaviors indicating preferences,
behaviors indicating how borrowers respond to communication styles,
communication cadence, and the like), and many others. Collection
may include collection with respect to loans, communications to
encourage payments, and the like. In embodiments, the collection
solution 4938 may automatically recommend or set rules, thresholds,
actions, parameters and the like (optionally by learning to do so
based on a training set of outcomes over time), resulting in a
recommended collection plan, which may specify a series of actions
required to accomplish a recommended or desired outcome of
collection (such as within a range of acceptable outcomes), which
may be automated and may involve conditional execution of steps
based on monitored conditions and/or smart contract terms, which
may be created, configured, and/or accounted for by the collection
plan. Collection plans may be determined and executed based at
least one part on market factors (such as competing interest rates
offered by other lenders, values of collateral, and the like) as
well as regulatory and/or compliance factors. Collection plans may
be generated and/or executed for creation of new loans, for
secondary loans, for modifications of existing loans, for
refinancing, for foreclosure situations (e.g., changing from
secured loan rates to unsecured loan rates), for bankruptcy or
insolvency situations, for situations involving market changes
(e.g., changes in prevailing interest rates) and others. In
embodiments, adaptive intelligent systems 3304, including
artificial intelligence 3448 may be trained on a training set of
collection activities by experts and/or on outcomes of collection
actions to generate a set of predictions, classifications, control
instructions, plans, models, or the like for automated creation,
management and/or execution of one or more aspects of a collection
plan.
[1336] In embodiments, provided herein is a robotic process
automation system for handling collection of a loan. An example
platform or system includes (a) a set of data collection and
monitoring services for collecting a training set of interactions
among entities for a set of loan transactions that involve
collection of a set of payments for a set of loans; (b) an
artificial intelligence system that is trained on the training set
of interactions to classify a set of loan collection actions; and
(c) a robotic process automation system that is trained on a set of
loan transaction interactions and a set of loan collection outcomes
to undertake a loan collection action on behalf of a party to a
loan. Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments.
[1337] An example system includes where the loan collection action
undertaken by the robotic process automation system is selected
from among initiation of a collection process, referral of a loan
to an agent for collection, configuration of a collection
communication, scheduling of a collection communication,
configuration of content for a collection communication,
configuration of an offer to settle a loan, termination of a
collection action, deferral of a collection action, configuration
of an offer for an alternative payment schedule, initiation of a
litigation, initiation of a foreclosure, initiation of a bankruptcy
process, a repossession process, and placement of a lien on
collateral.
[1338] An RPA Bank Loan Consolidator Trained on a Training Set of
Expert Consolidation Interactions with Other Lenders
[1339] Referring to FIG. 59, in embodiments a lending platform is
provided having a robotic process automation system for
consolidating a set of loans. The RPA system 3442 may provide
automation for one or more aspects of a consolidation solution 4940
that enables automated consolidation and/or provides a
recommendation or plan for a consolidation activity relevant to a
lending transaction. The consolidation solution 4940 and/or RPA
system 3442 for consolidation may include a set of interfaces,
workflows, and models (which may include, use or be enabled by
various adaptive intelligent systems 3304) and other components
that are configured to enable automation of one or more aspects of
a consolidation action or a consolidation process for a lending
transaction, such as based on a set of conditions, which may
include smart contract 3431 terms and conditions, marketplace
conditions (of platform marketplaces and/or external marketplaces
3390, conditions monitored by monitoring systems 3306 and data
collection systems 3318, and the like (such as of entities 3330,
including without limitation parties 4910, collateral 4802 and
assets 4918, among others). For example, a user of the
consolidation solution 4940 may create, configure (such as using
one or more templates or libraries), modify, set or otherwise
handle (such as in a user interface of the consolidation solution
4940 and/or RPA system 3442) various rules, thresholds, conditional
procedures, workflows, model parameters, and the like that
determine, or recommend, a consolidation action or plan for a
lending transaction or a set of loans based on one or more events,
conditions, states, actions, or the like, where the consolidation
plan may be based on various factors, such as the status of
payments, interest rates of the set of loans, prevailing interest
rates in a platform marketplace or external marketplace, the status
of the borrowers of a set of loans, the status of collateral 4802
or assets 4918, risk factors of the borrower, the lender, one or
more guarantors, market risk factors and the like (including
predicted risk based on one or more predictive models using
artificial intelligence 3448), status of debt, condition of
collateral 4802 or assets 4918 used to secure or back a set of
loans, the state of a business or business operation (e.g.,
receivables, payables, or the like), conditions of parties 4910
(such as net worth, wealth, debt, location, and other conditions),
behaviors of parties (such as behaviors indicating preferences,
behaviors indicating debt preferences), and many others.
Consolidation may include consolidation with respect to terms and
conditions of sets of loans, selection of appropriate loans,
configuration of payment terms for consolidated loans,
configuration of payoff plans for pre-existing loans,
communications to encourage consolidation, and the like. In
embodiments, the consolidation solution 4940 may automatically
recommend or set rules, thresholds, actions, parameters and the
like (optionally by learning to do so based on a training set of
outcomes over time), resulting in a recommended consolidation plan,
which may specify a series of actions required to accomplish a
recommended or desired outcome of consolidation (such as within a
range of acceptable outcomes), which may be automated and may
involve conditional execution of steps based on monitored
conditions and/or smart contract terms, which may be created,
configured, and/or accounted for by the consolidation plan.
Consolidation plans may be determined and executed based at least
one part on market factors (such as competing interest rates
offered by other lenders, values of collateral, and the like) as
well as regulatory and/or compliance factors. Consolidation plans
may be generated and/or executed for creation of new consolidated
loans, for secondary loans related to consolidated loans, for
modifications of existing loans related to consolidation, for
refinancing terms of a consolidated loan, for foreclosure
situations (e.g., changing from secured loan rates to unsecured
loan rates), for bankruptcy or insolvency situations, for
situations involving market changes (e.g., changes in prevailing
interest rates) and others. In embodiments, adaptive intelligent
systems 3304, including artificial intelligence 3448 may be trained
on a training set of consolidation activities by experts and/or on
outcomes of consolidation actions to generate a set of predictions,
classifications, control instructions, plans, models, or the like
for automated creation, management and/or execution of one or more
aspects of a consolidation plan.
[1340] In embodiments, provided herein is a robotic process
automation system for consolidating a set of loans. An example
platform or system includes (a) a set of data collection and
monitoring services for collecting information about a set of loans
and for collecting a training set of interactions between entities
for a set of loan consolidation transactions: (b) an artificial
intelligence system that is trained on the training set of
interactions to classify a set of loans as candidates for
consolidation; and (c) a robotic process automation system that is
trained on a set of loan consolidation interactions to manage
consolidation of at least a subset of the set of loans on behalf of
a party to the consolidation.
[1341] An example system includes where the set of data collection
and monitoring services includes services selected from among a set
of Internet of Things systems that monitor the entities, a set of
cameras that monitor the entities, a set of software services that
pull information related to the entities from publicly available
information sites, a set of mobile devices that report on
information related to the entities, a set of wearable devices worn
by human entities, a set of user interfaces by which entities
provide information about the entities and a set of crowdsourcing
services configured to solicit and report information related to
the entities.
[1342] An RPA Factoring Loan Negotiator Trained on a Training Set
of Expert Factoring Interactions with Borrowers
[1343] Referring to FIG. 60, in embodiments a lending platform is
provided having a robotic process automation system for managing a
factoring transaction. The RPA system 3442 may provide automation
for one or more aspects of a factoring solution 4942 that enables
automated factoring and/or provides a recommendation or plan for a
factoring activity relevant to a lending transaction, such as one
involving factoring of receivables. The factoring solution 4942
and/or RPA system 3442 for factoring may include a set of
interfaces, workflows, and models (which may include, use or be
enabled by various adaptive intelligent systems 3304) and other
components that are configured to enable automation of one or more
aspects of a factoring action of one or more terms and conditions
of a factoring transaction, such as based on a set of conditions,
which may include smart contract 3431 terms and conditions,
marketplace conditions (of platform marketplaces and/or external
marketplaces 3390, conditions monitored by monitoring systems 3306
and data collection systems 3318, and the like (such as of entities
3330, including without limitation parties 4910, collateral 4802
and assets 4918, accounts receivable, and inventory, among others).
For example, a user of the factoring solution 4942 may create,
configure (such as using one or more templates or libraries),
modify, set or otherwise handle (such as in a user interface of the
factoring solution 4942 and/or RPA system 3442) various rules,
thresholds, conditional procedures, workflows, model parameters,
and the like that determine, or recommend, a factoring action or
plan for a factoring transaction or monitoring solution based on
one or more events, conditions, states, actions, or the like, where
the factoring plan may be based on various factors, such as the
status of receivables, the status of work-in-progress, the status
of inventory, the status of delivery and/or shipment, the status of
payments, the status of the borrower, the status of collateral 4802
or assets 4918, risk factors of the borrower, the lender, one or
more guarantors, market risk factors and the like (including
predicted risk based on one or more predictive models using
artificial intelligence 3448), status of debt, condition of
collateral 4802 or assets 4918 used to secure or back a loan, state
of a business or business operation (e.g., receivables, payables,
or the like), conditions of parties 4910 (such as net worth,
wealth, debt, location, and other conditions), behaviors of parties
(such as behaviors indicating preferences, behaviors indicating
negotiation styles, and the like), and many others. Factoring may
include factoring with respect to loans, communications to
encourage payments, and the like. In embodiments, the factoring
solution 4942 may automatically recommend or set rules, thresholds,
actions, parameters and the like (optionally by learning to do so
based on a training set of outcomes over time), resulting in a
recommended factoring plan, which may specify a series of actions
required to accomplish a recommended or desired outcome of
factoring (such as within a range of acceptable outcomes), which
may be automated and may involve conditional execution of steps
based on monitored conditions and/or smart contract terms, which
may be created, configured, and/or accounted for by the factoring
plan. Factoring plans may be determined and executed based at least
one part on market factors (such as competing interest rates or
other terms and conditions offered by other lenders, values of
collateral, values of accounts receivable, interest rates, and the
like) as well as regulatory and/or compliance factors. Factoring
plans may be generated and/or executed for creation of new
factoring arrangements, for modifications of existing factoring
arrangements, and others. In embodiments, adaptive intelligent
systems 3304, including artificial intelligence 3448 may be trained
on a training set of factoring activities by experts and/or on
outcomes of factoring actions to generate a set of predictions,
classifications, control instructions, plans, models, or the like
for automated creation, management and/or execution of one or more
aspects of a factoring plan.
[1344] In embodiments, provided herein is a robotic process
automation system for consolidating a set of loans. An example
platform or system includes (a) a set of data collection and
monitoring services for collecting information about entities
involved in a set of factoring loans and for collecting a training
set of interactions between entities for a set of factoring loan
transactions; (b) an artificial intelligence system that is trained
on the training set of interactions to classify the entities
involved in the set of factoring loans; and (c) a robotic process
automation system that is trained on the set of factoring loan
interactions to manage a factoring loan. Certain further aspects of
an example system are described following, any one or more of which
may be present in certain embodiments.
[1345] An example system includes where the set of data collection
and monitoring services includes services selected from among a set
of Internet of Things systems that monitor the entities, a set of
cameras that monitor the entities, a set of software services that
pull information related to the entities from publicly available
information sites, a set of mobile devices that report on
information related to the entities, a set of wearable devices worn
by human entities, a set of user interfaces by which entities
provide information about the entities and a set of crowdsourcing
services configured to solicit and report information related to
the entities.
[1346] An RPA Mortgage Loan Broker Trained on a Training Set of
Expert Broker Interactions with Borrowers
[1347] Referring to FIG. 61, in embodiments a lending platform is
provided having a robotic process automation system for brokering a
loan. The loan may be, for example, a mortgage loan.
[1348] The RPA system 3442 may provide automation for one or more
aspects of a brokering solution 4944 that enables automated
brokering and/or provides a recommendation or plan for a brokering
activity relevant to a lending transaction, such as for brokering a
set of mortgage loans, home loans, lines of credit, automobile
loans, construction loans, or other loans of any of the types
described herein. The brokering solution 4944 and/or RPA system
3442 for brokering may include a set of interfaces, workflows, and
models (which may include, use or be enabled by various adaptive
intelligent systems 3304) and other components that are configured
to enable automation of one or more aspects of a brokering action
or a brokering process for a lending transaction, such as based on
a set of conditions, which may include smart contract 3431 terms
and conditions, marketplace conditions (of platform marketplaces
and/or external marketplaces 3390, conditions monitored by
monitoring systems 3306 and data collection systems 3318, and the
like (such as of entities 3330, including without limitation
parties 4910, collateral 4802 and assets 4918, among others, as
well as of interest rates, available lenders, available terms and
the like). For example, a user of the brokering solution 4944 may
create, configure (such as using one or more templates or
libraries), modify, set or otherwise handle (such as in a user
interface of the brokering solution 4944 and/or RPA system 3442)
various rules, thresholds, conditional procedures, workflows, model
parameters, and the like that determine, or recommend, a brokering
action or plan for brokering a set of loans of a given type or
types based on one or more events, conditions, states, actions, or
the like, where the brokering plan may be based on various factors,
such as the interest rates of the set of loans available from
various primary and secondary lenders, permitted attributes of
borrowers (e.g., based on income, wealth, location, or the like)
prevailing interest rates in a platform marketplace or external
marketplace, the status of the borrowers of a set of loans, the
status or other attributes of collateral 4802 or assets 4918, risk
factors of the borrower, the lender, one or more guarantors, market
risk factors and the like (including predicted risk based on one or
more predictive models using artificial intelligence 3448), status
of debt, condition of collateral 4802 or assets 4918 available to
secure or back a set of loans, the state of a business or business
operation (e.g., receivables, payables, or the like), conditions of
parties 4910 (such as net worth, wealth, debt, location, and other
conditions), behaviors of parties (such as behaviors indicating
preferences, behaviors indicating debt preferences), and many
others. Brokering may include brokering with respect to terms and
conditions of sets of loans, selection of appropriate loans,
configuration of payment terms for consolidated loans,
configuration of payoff plans for pre-existing loans,
communications to encourage borrowing, and the like. In
embodiments, the brokering solution 4944 may automatically
recommend or set rules, thresholds, actions, parameters and the
like (optionally by learning to do so based on a training set of
outcomes over time), resulting in a recommended brokering plan,
which may specify a series of actions required to accomplish a
recommended or desired outcome of brokering (such as within a range
of acceptable outcomes), which may be automated and may involve
conditional execution of steps based on monitored conditions and/or
smart contract terms, which may be created, configured, and/or
accounted for by the brokering plan. Brokering plans may be
determined and executed based at least one part on market factors
(such as competing interest rates offered by other lenders,
property values, attributes of borrowers, values of collateral, and
the like) as well as regulatory and/or compliance factors.
Brokering plans may be generated and/or executed for creation of
new loans, for secondary loans, for modifications of existing
loans, for refinancing terms, for situations involving market
changes (e.g., changes in prevailing interest rates or property
values) and others. In embodiments, adaptive intelligent systems
3304, including artificial intelligence 3448 may be trained on a
training set of brokering activities by experts and/or on outcomes
of brokering actions to generate a set of predictions,
classifications, control instructions, plans, models, or the like
for automated creation, management and/or execution of one or more
aspects of a brokering plan.
[1349] In embodiments, provided herein is a robotic process
automation system for automating brokering of a mortgage. An
example platform or system includes (a) a set of data collection
and monitoring services for collecting information about entities
involved in a set of mortgage loan activities and for collecting a
training set of interactions between entities for a set of mortgage
loan transactions; (b) an artificial intelligence system that is
trained on the training set of interactions to classify the
entities involved in the set of mortgage loans; and (c) a robotic
process automation system that is trained on at least one of the
set of mortgage loan activities and the set of mortgage loan
interactions to broker a mortgage loan. Certain further aspects of
an example system are described following, any one or more of which
may be present in certain embodiments.
[1350] An example system includes where at least one of the set of
mortgage loan activities and the set of mortgage loan interactions
includes activities among marketing activity, identification of a
set of prospective borrowers, identification of property,
identification of collateral, qualification of borrower, title
search, title verification, property assessment, property
inspection, property valuation, income verification, borrower
demographic analysis, identification of capital providers,
determination of available interest rates, determination of
available payment terms and conditions, analysis of existing
mortgage, comparative analysis of existing and new mortgage terms,
completion of application workflow, population of fields of
application, preparation of mortgage agreement, completion of
schedule to mortgage agreement, negotiation of mortgage terms and
conditions with capital provider, negotiation of mortgage terms and
conditions with borrower, transfer of title, placement of lien and
closing of mortgage agreement.
[1351] An example system includes where the set of data collection
and monitoring services includes services selected from among a set
of Internet of Things systems that monitor the entities, a set of
cameras that monitor the entities, a set of software services that
pull information related to the entities from publicly available
information sites, a set of mobile devices that report on
information related to the entities, a set of wearable devices worn
by human entities, a set of user interfaces by which entities
provide information about the entities and a set of crowdsourcing
services configured to solicit and report information related to
the entities.
[1352] An example system includes where the artificial intelligence
system uses a model that processes attributes of entities involved
in the set of mortgage loans, wherein the attributes are selected
from properties that are subject to mortgages, assets used for
collateral, identity of a party, interest rate, payment balance,
payment terms, payment schedule, type of mortgage, type of
property, financial condition of party, payment status, condition
of property, and value of property.
[1353] An example system includes where managing a mortgage loan
includes managing at least one of a property that is subject to a
mortgage, identification of candidate mortgages from a set of
borrower situations, preparation of a mortgage offer, preparation
of content communicating a mortgage offer, scheduling a mortgage
offer, communicating a mortgage offer, negotiating a modification
of a mortgage offer, preparing a mortgage agreement, executing a
mortgage agreement, modifying collateral for a set of mortgage
loans, handing transfer of a lien, handling an application
workflow, managing an inspection, managing an assessment of a set
of assets to be subject to a mortgage, setting an interest rate,
deferring a payment requirement, setting a payment schedule, and
closing a mortgage agreement. An example system includes where the
entities are a set of parties to a loan transaction. An example
system includes where the set of parties is selected from among a
primary lender, a secondary lender, a lending syndicate, a
corporate lender, a government lender, a bank lender, a secured
lender, bond issuer, a bond purchaser, an unsecured lender, a
guarantor, a provider of security, a borrower, a debtor, an
underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, and an accountant.
[1354] An example system includes where the artificial intelligence
system includes at least one of a machine learning system, a
model-based system, a rule-based system, a deep learning system, a
hybrid system, a neural network, a convolutional neural network, a
feed forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system,
and a simulation system.
[1355] An example system includes where the robotic process
automation is trained on a set of interactions of parties with a
set of user interfaces involved in a set of mortgage-related
activities. An example system includes where upon completion of
negotiation a smart contract for a mortgage loan is automatically
configured by a set of smart contract services based on the outcome
of the negotiation. An example system includes where at least one
of an outcome and a negotiating event of the negotiation is
recorded in a distributed ledger associated with the loan. An
example system includes where the artificial intelligence system
includes at least one of a machine learning system, a model-based
system, a rule-based system, a deep learning system, a hybrid
system, a neural network, a convolutional neural network, a feed
forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system,
and a simulation system.
[1356] Crowdsourcing and Automated Classification System for
Validating Condition of an Issuer for a Bond
[1357] Referring to FIG. 62, in embodiments a lending platform is
provided having a crowdsourcing and automated classification system
for validating condition of an issuer for a bond. The RPA system
3442 may provide automation for one or more aspects of a bond
management solution 4934 that enables automated bond management
and/or provides a recommendation or plan for a bond management
activity relevant to a bond transaction, such as for municipal
bonds, corporate bonds, government bonds, or other bonds that may
be backed by assets, collateral, or commitments of a bond issuer.
The bond management solution 4934 and/or RPA system 3442 for bond
management may include a set of interfaces, workflows, and models
(which may include, use or be enabled by various adaptive
intelligent systems 3304) and other components that are configured
to enable automation of one or more aspects of a bond management
action or a management process for a bond transaction, such as
based on a set of conditions, which may include smart contract 3431
terms and conditions, marketplace conditions (of platform
marketplaces and/or external marketplaces 3390, conditions
monitored by monitoring systems 3306 and data collection systems
3318, and the like (such as of entities 3330, including without
limitation parties 4910, collateral 4802 and assets 4918, among
others, as well as of interest rates, available lenders, available
terms and the like). For example, a user of the bond management
solution 4934 may create, configure (such as using one or more
templates or libraries), modify, set or otherwise handle (such as
in a user interface of the bond management solution 4934 and/or RPA
system 3442) various rules, thresholds, conditional procedures,
workflows, model parameters, and the like that determine, or
recommend, a bond management action or plan for management a set of
bonds of a given type or types based on one or more events,
conditions, states, actions, or the like, where the bond management
plan may be based on various factors, such as the interest rates
available from various primary and secondary lenders or issuers,
permitted attributes of issuers and buyers (e.g., based on income,
wealth, location, or the like) prevailing interest rates in a
platform marketplace or external marketplace, the status of the
issuers of a set of bonds, the status or other attributes of
collateral 4802 or assets 4918, risk factors of the issuer, one or
more guarantors, market risk factors and the like (including
predicted risk based on one or more predictive models using
artificial intelligence 3448), status of debt, condition of
collateral 4802 or assets 4918 available to secure or back a set of
bonds, the state of a business or business operation (e.g.,
receivables, payables, or the like), conditions of parties 4910
(such as net worth, wealth, debt, location, and other conditions),
behaviors of parties (such as behaviors indicating preferences,
behaviors indicating debt preferences), and many others. Bond
management may include management with respect to terms and
conditions of sets of bonds, selection of appropriate bonds,
communications to encourage transactions, and the like. In
embodiments, the bond management solution 4934 may automatically
recommend or set rules, thresholds, actions, parameters and the
like (optionally by learning to do so based on a training set of
outcomes over time), resulting in a recommended bond management
plan, which may specify a series of actions required to accomplish
a recommended or desired outcome of bond management (such as within
a range of acceptable outcomes), which may be automated and may
involve conditional execution of steps based on monitored
conditions and/or smart contract terms, which may be created,
configured, and/or accounted for by the bond management plan. Bond
management plans may be determined and executed based at least one
part on market factors (such as competing interest rates offered by
other issuers, property values, attributes of issuers, values of
collateral or assets, and the like) as well as regulatory and/or
compliance factors. Bond management plans may be generated and/or
executed for creation of new bonds, for secondary loans or
transactions to back bonds, for modifications of existing bonds,
for situations involving market changes (e.g., changes in
prevailing interest rates or property values) and others. In
embodiments, adaptive intelligent systems 3304, including
artificial intelligence 3448 may be trained on a training set of
bond management activities by experts and/or on outcomes of bond
management actions to generate a set of predictions,
classifications, control instructions, plans, models, or the like
for automated creation, management and/or execution of one or more
aspects of a bond management plan.
[1358] Systems that Varies the Interest Rate or Other Terms on a
Subsidized Loan Based on a Parameter Monitored by the IoT
[1359] Referring to FIG. 63, in embodiments a lending platform is
provided having a system that varies the terms and conditions of
loan based on a parameter monitored by the IoT. The loan may be a
subsidized loan. The RPA system 3442 may provide automation for one
or more aspects of a loan management solution 4948 that enables
automated loan management and/or provides a recommendation or plan
for a loan management activity relevant to a loan transaction, such
as for personal loans, corporate loans, subsidized loans, student
loans, or other loans, including ones that may be backed by assets,
collateral, or commitments of a borrower. The loan management
solution 4948 and/or RPA system 3442 for loan management may
include a set of interfaces, workflows, and models (which may
include, use or be enabled by various adaptive intelligent systems
3304) and other components that are configured to enable automation
of one or more aspects of a loan management action or a management
process for a loan transaction, such as based on a set of
conditions, which may include smart contract 3431 terms and
conditions, marketplace conditions (of platform marketplaces and/or
external marketplaces 3390, conditions monitored by monitoring
systems 3306 and data collection systems 3318, and the like (such
as of entities 3330, including without limitation parties 4910,
collateral 4802 and assets 4918, among others, as well as of
interest rates, available lenders, available terms and the like).
For example, a user of the loan management solution 4948 may
create, configure (such as using one or more templates or
libraries), modify, set or otherwise handle (such as in a user
interface of the loan management solution 4948 and/or RPA system
3442) various rules, thresholds, conditional procedures, workflows,
model parameters, and the like that determine, or recommend, a loan
management action or plan for management a set of loans of a given
type or types based on one or more events, conditions, states,
actions, or the like, where the loan management plan may be based
on various factors, such as the interest rates available from
various primary and secondary lenders or issuers, permitted
attributes of borrowers (e.g., based on income, wealth, location,
or the like) prevailing interest rates in a platform marketplace or
external marketplace, the status of the parties of a set of loans,
the status or other attributes of collateral 4802 or assets 4918,
risk factors of the borrower, one or more guarantors, market risk
factors and the like (including predicted risk based on one or more
predictive models using artificial intelligence 3448), status of
debt, condition of collateral 4802 or assets 4918 available to
secure or back a set of loans, the state of a business or business
operation (e.g., receivables, payables, or the like), conditions of
parties 4910 (such as net worth, wealth, debt, location, and other
conditions), behaviors of parties (such as behaviors indicating
preferences, behaviors indicating debt preferences, payment
preferences, or communication preferences), and many others. Loan
management may include management with respect to terms and
conditions of sets of loans, selection of appropriate loans,
communications to encourage transactions, and the like. In
embodiments, the loan management solution 4948 may automatically
recommend or set rules, thresholds, actions, parameters and the
like (optionally by learning to do so based on a training set of
outcomes over time), resulting in a recommended loan management
plan, which may specify a series of actions required to accomplish
a recommended or desired outcome of loan management (such as within
a range of acceptable outcomes), which may be automated and may
involve conditional execution of steps based on monitored
conditions and/or smart contract terms, which may be created,
configured, and/or accounted for by the loan management plan. Loan
management plans may be determined and executed based at least one
part on market factors (such as competing interest rates offered by
other issuers, property values, attributes of issuers, values of
collateral or assets, and the like) as well as regulatory and/or
compliance factors. Loan management plans may be generated and/or
executed for creation of new loans, for secondary loans or
transactions to back loans, for collection, for consolidation, for
foreclosure, for situations of bankruptcy of insolvency, for
modifications of existing loans, for situations involving market
changes (e.g., changes in prevailing interest rates or property
values) and others. In embodiments, adaptive intelligent systems
3304, including artificial intelligence 3448 may be trained on a
training set of loan management activities by experts and/or on
outcomes of loan management actions to generate a set of
predictions, classifications, control instructions, plans, models,
or the like for automated creation, management and/or execution of
one or more aspects of a loan management plan.
[1360] Automated Blockchain Custody Service
[1361] Referring to FIG. 64, in embodiments a lending platform is
provided having an automated blockchain custody service and
solution for managing a set of custodial assets. The RPA system
3442 may provide automation for one or more aspects of a custodial
solution 6502 that enables automated custodial management and/or
provides a recommendation or plan for a custodial activity relevant
to a set of assets, such as ones involved in or backing a lending
transaction or ones for which clients seek custodial for security
or administrative purposes, such as for assets of any of the types
described herein, including cryptocurrencies and other currencies,
stock certificates and other evidence of ownership, securities, and
many others. The custodial solution 6502 and/or RPA system 3442 for
handling custodial activity may include a set of interfaces,
workflows, and models (which may include, use or be enabled by
various adaptive intelligent systems 3304) and other components
that are configured to enable automation of one or more aspects of
a custodial action or a management process for trust or custody of
a set of assets 4918, such as based on a set of conditions, which
may include smart contract 3431 terms and conditions, marketplace
conditions (of platform marketplaces and/or external marketplaces
3390, conditions monitored by monitoring systems 3306 and data
collection systems 3318, and the like (such as of entities 3330,
including without limitation parties 4910, collateral 4802 and
assets 4918, among others, and the like). For example, a user of
the custodial solution 6502 may create, configure (such as using
one or more templates or libraries), modify, set or otherwise
handle (such as in a user interface of the custodial solution 6502
and/or RPA system 3442) various rules, thresholds, conditional
procedures, workflows, model parameters, and the like that
determine, or recommend, a custodial action or plan for management
a set of assets of a given type or types based on one or more
events, conditions, states, actions, status or the like, where the
custodial plan may be based on various factors, such as the storage
options available, the basis for retrieval of assets, the basis for
transfer of ownership of assets, and the like, condition of assets
4918 for which custodial services will be required, behaviors of
parties (such as behaviors indicating preferences), and many
others. Custodial services may include management with respect to
terms and conditions of sets of assets, selection of appropriate
terms and conditions for trust and custody, selection of parameters
for transfer of ownership, selection and provision of storage,
selection and provision of secure infrastructure for data storage,
and others. In embodiments, the custodial solution 48802 may
automatically recommend or set rules, thresholds, actions,
parameters and the like (optionally by learning to do so based on a
training set of outcomes over time), resulting in a recommended
custodial plan, which may specify a series of actions required to
accomplish a recommended or desired outcome of custodial services
(such as within a range of acceptable outcomes), which may be
automated and may involve conditional execution of steps based on
monitored conditions and/or smart contract terms, which may be
created, configured, and/or accounted for by the custodial plan.
Custodial plans may be determined and executed based at least one
part on market factors (such as competing terms and conditions
offered by other custodians, property values, attributes of
clients, values of collateral or assets, costs of physical storage,
costs of data storage, and the like) as well as regulatory and/or
compliance factors. In embodiments, adaptive intelligent systems
3304, including artificial intelligence 3448 may be trained on a
training set of custodial activities by experts and/or on outcomes
of custodial actions to generate a set of predictions,
classifications, control instructions, plans, models, or the like
for automated creation, management and/or execution of one or more
aspects of a custodial plan. In embodiments, actions with respect
to custody of a set of assets may be stored in a blockchain 3422,
such as in a distributed ledger.
[1362] In embodiments, provided herein is a system for handling
trust and custody for a set of assets. An example platform or
system for handling trust and custody for a set of assets may
include (a) a set of asset identification services for identifying
a set of assets for which a financial institution is responsible
for taking custody; and (b) a set of identity management services
by which the financial institution verifies identities and
credentials of a set of entities entitled to take action with
respect to the assets and a set of blockchain services. Wherein at
least one of the set of assets and identifying information for the
set of assets is stored in a blockchain and wherein events related
to the set of assets are recorded in a distributed ledger. Certain
further aspects of an example system are described following, any
one or more of which may be present in certain embodiments.
[1363] An example system includes where the credentials include
owner credentials, agent credentials, beneficiary credentials,
trustee credentials, and custodian credentials.
[1364] In embodiments the events related to the set of assets
include transfer of title, death of an owner, disability of an
owner, bankruptcy of an owner, foreclosure, placement of a lien,
use of assets as collateral, designation of a beneficiary,
undertaking a loan against assets, providing a notice with respect
to assets, inspection of assets, assessment of assets, reporting on
assets for taxation purposes, allocation of ownership of assets,
disposal of assets, sale of assets, purchase of assets, and
designation of an ownership status.
[1365] In embodiments the platform or system further includes a set
of data collection and monitoring services for monitoring at least
one of the set of assets, a set of entities, and a set of events
related to the assets.
[1366] In embodiments the set of entities includes at least one of
an owner, a beneficiary, an agent, a trustee and a custodian.
[1367] In embodiments the platform or system further includes a set
of smart contract services for managing the custody of the set of
assets, wherein at least one event related to the set of assets is
managed automatically by the smart contract based on a set of terms
and conditions embodied in the smart contract and based on
information collected by the set of data collection and monitoring
services.
[1368] In embodiments the events related to the set of assets
include transfer of title, death of an owner, disability of an
owner, bankruptcy of an owner, foreclosure, placement of a lien,
use of assets as collateral, designation of a beneficiary,
undertaking a loan against assets, providing a notice with respect
to assets, inspection of assets, assessment of assets, reporting on
assets for taxation purposes, allocation of ownership of assets,
disposal of assets, sale of assets, purchase of assets, and
designation of an ownership status.
[1369] Referring to FIG. 65, in embodiments a lending platform is
provided having an underwriting system for a loan with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions. The RPA system 3442 may provide
automation for one or more aspects of an underwriting solution 3420
that enables automated underwriting and/or provides a
recommendation or plan for an underwriting activity relevant to a
loan transaction, such as for personal loans, corporate loans,
subsidized loans, student loans, or other loans, including ones
that may be backed by assets, collateral, or commitments of a
borrower. The underwriting solution 3420 and/or RPA system 3442 for
underwriting may include a set of interfaces, workflows, and models
(which may include, use or be enabled by various adaptive
intelligent systems 3304) and other components that are configured
to enable automation of one or more aspects of a underwriting
action or a management process for a loan transaction, such as
based on a set of conditions, which may include smart contract 3431
terms and conditions, marketplace conditions (of platform
marketplaces and/or external marketplaces 3390, conditions
monitored by monitoring systems 3306 and data collection systems
3318, and the like (such as of entities 3330, including without
limitation parties 4910, collateral 4802 and assets 4918, among
others, as well as of interest rates, available lenders, available
terms and the like)). For example, a user of the underwriting
solution 3420 may create, configure (such as using one or more
templates or libraries), modify, set or otherwise handle (such as
in a user interface of the underwriting solution 3420 and/or RPA
system 3442) various rules, thresholds, conditional procedures,
workflows, model parameters, and the like that determine, or
recommend, a underwriting action or plan for management a set of
loans of a given type or types based on one or more events,
conditions, states, actions, or the like, where the underwriting
plan may be based on various factors, such as the interest rates
available from various primary and secondary lenders or issuers,
permitted attributes of borrowers (e.g., based on income, wealth,
location, or the like), prevailing interest rates in a platform
marketplace or external marketplace, the status of the parties of a
set of loans, the status or other attributes of collateral 4802 or
assets 4918, risk factors of the borrower, one or more guarantors,
market risk factors and the like (including predicted risk based on
one or more predictive models using artificial intelligence 3448),
status of debt, condition of collateral 4802 or assets 4918
available to secure or back a set of loans, the state of a business
or business operation (e.g., receivables, payables, or the like),
conditions of parties 4910 (such as net worth, wealth, debt,
location, and other conditions), behaviors of parties (such as
behaviors indicating preferences, behaviors indicating debt
preferences, payment preferences, or communication preferences),
and many others. Underwriting may include management with respect
to terms and conditions of sets of loans, selection of appropriate
loans, communications relevant to underwriting processes, and the
like. In embodiments, the underwriting solution 3420 may
automatically recommend or set rules, thresholds, actions,
parameters and the like (optionally by learning to do so based on a
training set of outcomes over time), resulting in a recommended
underwriting plan, which may specify a series of actions required
to accomplish a recommended or desired outcome of underwriting
(such as within a range of acceptable outcomes), which may be
automated and may involve conditional execution of steps based on
monitored conditions and/or smart contract terms, which may be
created, configured, and/or accounted for by the underwriting plan.
Underwriting plans may be determined and executed based at least
one part on market factors (such as competing interest rates
offered by other issuers, property values, borrower behavior,
demographic trends, payment trends, attributes of issuers, values
of collateral or assets, and the like) as well as regulatory and/or
compliance factors. Underwriting plans may be generated and/or
executed for new loans, for secondary loans or transactions to back
loans, for collection, for consolidation, for foreclosure, for
situations of bankruptcy of insolvency, for modifications of
existing loans, for situations involving market changes (e.g.,
changes in prevailing interest rates or property values), for
foreclosure activities, and others. In embodiments, adaptive
intelligent systems 3304, including artificial intelligence 3448
may be trained on a training set of underwriting activities by
experts and/or on outcomes of underwriting actions to generate a
set of predictions, classifications, control instructions, plans,
models, or the like for automated creation, management and/or
execution of one or more aspects of an underwriting plan. In
embodiments, events and outcomes of underwriting may be recorded in
a blockchain 3422, such as in a distributed ledger, for secure
access and retrieval by authorized users. Adaptive intelligent
systems 3304 may, such as using various artificial intelligence
3448 or expert systems disclosed herein and in the documented
incorporated by reference herein, may improve or automated one or
more aspects of underwriting, such as by training a model, a neural
net, a deep learning system, or the like based on a training set of
expert interactions and/or a training set of outcomes from
underwriting activities.
[1370] Referring to FIG. 66, in embodiments a lending platform is
provided having a loan marketing system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services and smart contract services for marketing a loan to a set
of prospective parties. The system 4800 may enable one or more
aspects of a loan marketing solution 6702 that enables automated
loan marketing and/or provides a recommendation or plan for a loan
marketing activity relevant to a loan transaction, such as for
personal loans, corporate loans, subsidized loans, student loans,
or other loans, including ones that may be backed by assets,
collateral, or commitments of a borrower. The loan marketing
solution 6702 (which in embodiments may include or use an RPA
system 3442 configured for loan marketing) may include a set of
interfaces, workflows, and models (which may include, use or be
enabled by various adaptive intelligent systems 3304) and other
components that are configured to enable automation of one or more
aspects of a loan marketing action or a management process for a
loan transaction, such as based on a set of conditions, which may
include smart contract 3431 terms and conditions (which may be
configured, e.g., for a marketed set of loans), available capital
for lending, regulatory factors, marketplace conditions (of
platform marketplaces and/or external marketplaces 3390, conditions
monitored by monitoring systems 3306 and data collection systems
3318, and the like (such as of entities 3330, including without
limitation parties 4910, collateral 4802 and assets 4918, among
others, as well as of interest rates, available lenders, available
terms and the like)), and others. For example, a user of the loan
marketing solution 6702 may create, configure (such as using one or
more templates or libraries), modify, set or otherwise handle (such
as in a user interface of the loan marketing solution 6702 and/or
RPA system 3442) various rules, thresholds, conditional procedures,
workflows, model parameters, and the like that determine, or
recommend, a loan marketing action or plan for management a set of
loans of a given type or types based on one or more events,
conditions, states, actions, or the like, where the loan marketing
plan may be based on various factors, such as the interest rates
available from various primary and secondary lenders or issuers,
returns on the capital that is made available for loans, permitted
or desired attributes of borrowers (e.g., based on income, wealth,
location, or the like), prevailing interest rates in a platform
marketplace or external marketplace, the status of the parties of a
set of loans, the status or other attributes of collateral 4802 or
assets 4918, risk factors of the borrower, one or more guarantors,
market risk factors and the like (including predicted risk based on
one or more predictive models using artificial intelligence 3448),
status of debt, condition of collateral 4802 or assets 4918
available to secure or back a set of loans, the state of a business
or business operation (e.g., receivables, payables, or the like),
conditions of parties 4910 (such as net worth, wealth, debt,
location, and other conditions), behaviors of parties (such as
behaviors indicating preferences, behaviors indicating debt
preferences, payment preferences, or communication preferences),
and many others. Loan marketing may include management with respect
to terms and conditions of sets of loans, selection of appropriate
loans, communications relevant to loan marketing processes, and the
like. In embodiments, the loan marketing solution 6702 may
automatically recommend or set rules, thresholds, actions,
parameters and the like (optionally by learning to do so based on a
training set of outcomes over time), resulting in a recommended
loan marketing plan, which may specify a series of actions required
to accomplish a recommended or desired outcome of loan marketing
(such as within a range of acceptable outcomes), which may be
automated and may involve conditional execution of steps based on
monitored conditions and/or smart contract terms, which may be
created, configured, and/or accounted for by the loan marketing
plan. Loan marketing plans may be determined and executed based at
least one part on market factors (such as competing interest rates
offered by other issuers, property values, borrower behavior,
demographic trends, payment trends, attributes of issuers, values
of collateral or assets, and the like) as well as regulatory and/or
compliance factors. Loan marketing plans may be generated and/or
executed for new loans, for secondary loans or transactions to back
loans, for collection, for consolidation, for foreclosure
situations (e.g., as an alternative to foreclosure), for situations
of bankruptcy of insolvency, for modifications of existing loans,
for situations involving market changes (e.g., changes in
prevailing interest rates, available capital, or property values),
and others. In embodiments, adaptive intelligent systems 3304,
including artificial intelligence 3448 may be trained on a training
set of loan marketing activities by experts and/or on outcomes of
loan marketing actions to generate a set of predictions,
classifications, control instructions, plans, models, or the like
for automated creation, management and/or execution of one or more
aspects of a loan marketing plan. In embodiments, events and
outcomes of loan marketing may be recorded in a blockchain 3422,
such as in a distributed ledger, for secure access and retrieval by
authorized users. Adaptive intelligent systems 3304 may, such as
using various artificial intelligence 3448 or expert systems
disclosed herein and in the documented incorporated by reference
herein, may improve or automated one or more aspects of entity
rating, such as by training a model, a neural net, a deep learning
system, or the like based on a training set of expert interactions
and/or a training set of outcomes from loan marketing
activities.
[1371] Referring to FIG. 67, in embodiments a lending platform is
provided having a rating system with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for rating a set of loan-related entities. The
system 4800 may enable one or more aspects of an entity rating
solution 6801 that enables automated entity rating and/or provides
a recommendation or plan for an entity rating activity relevant to
a loan transaction, such as for personal loans, corporate loans,
subsidized loans, student loans, or other loans, including ones
that may be backed by assets, collateral, or commitments of a
borrower. The entity rating solution 6801 (which in embodiments may
include or use an RPA system 3442 configured for entity rating) may
include a set of interfaces, workflows, and models (which may
include, use or be enabled by various adaptive intelligent systems
3304) and other components that are configured to enable automation
of one or more aspects of an entity rating action or a rating
process for a loan transaction, such as based on a set of
conditions, attributes, events, or the like, which may include
attributes of entities 3330 (such as value, quality, location, net
worth, price, physical condition, health condition, security,
safety, ownership and the like), smart contract 3431 terms and
conditions (which may be configured or populated, e.g., based on
ratings for a rated set of loans), regulatory factors, marketplace
conditions (of platform marketplaces and/or external marketplaces
3390, conditions monitored by monitoring systems 3306 and data
collection systems 3318, and the like (such as of entities 3330,
including without limitation parties 4910, collateral 4802 and
assets 4918, among others, as well as of interest rates, available
lenders, available terms and the like)), and others. For example, a
user of the entity rating solution 49101 may create, configure
(such as using one or more templates or libraries), modify, set or
otherwise handle (such as in a user interface of the entity rating
solution 6801 and/or RPA system 3442) various rules, thresholds,
conditional procedures, workflows, model parameters, and the like
that determine, or recommend, an entity rating action or plan for
rating a set of loans of a given type or types based on one or more
events, attributes, parameters, characteristics, conditions,
states, actions, or the like, where the entity rating plan may be
based on various factors (e.g., based on income, wealth, location,
or the like or parties 4910, relative to others, or based on
condition of collateral 4802 or assets 4918, or the like),
prevailing conditions of a platform marketplace or external
marketplace, the status of the parties of a set of loans, the
status or other attributes of collateral 4802 or assets 4918, risk
factors of the borrower, one or more guarantors, market risk
factors and the like (including predicted risk based on one or more
predictive models using artificial intelligence 3448), status of
debt, condition of collateral 4802 or assets 4918 available to
secure or back a set of loans, the state of a business or business
operation (e.g., receivables, payables, or the like), conditions of
parties 4910 (such as net worth, wealth, debt, location, and other
conditions), behaviors of parties (such as behaviors indicating
preferences, behaviors indicating debt preferences, payment
preferences, or communication preferences), and many others. Entity
rating may include management with respect to terms and conditions
of sets of loans, selection of appropriate loans, communications
relevant to entity rating processes, and the like. In embodiments,
the entity rating solution 6801 may automatically recommend or set
rules, thresholds, actions, parameters and the like (optionally by
learning to do so based on a training set of outcomes over time),
resulting in a recommended entity rating plan, which may specify a
series of actions required to accomplish a recommended or desired
outcome of entity rating (such as within a range of acceptable
outcomes), which may be automated and may involve conditional
execution of steps based on monitored conditions and/or smart
contract terms, which may be created, configured, and/or accounted
for by the entity rating plan. Entity rating plans may be
determined and executed based at least one part on market factors
(such as competing interest rates offered by other issuers,
property values, borrower behavior, demographic trends, payment
trends, attributes of issuers, values of collateral or assets, and
the like) as well as regulatory and/or compliance factors. Entity
rating plans may be generated and/or executed for new loans, for
secondary loans or transactions to back loans, for collection, for
consolidation, for foreclosure situations (e.g., as an alternative
to foreclosure), for situations of bankruptcy of insolvency, for
modifications of existing loans, for situations involving market
changes (e.g., changes in prevailing interest rates, available
capital, or property values), and others. In embodiments, adaptive
intelligent systems 3304, including artificial intelligence 3448
may be trained on a training set of entity rating activities by
experts and/or on outcomes of entity rating actions to generate a
set of predictions, classifications, control instructions, plans,
models, or the like for automated creation, management and/or
execution of one or more aspects of an entity rating plan. In
embodiments, events and outcomes of entity rating may be recorded
in a blockchain 3422, such as in a distributed ledger, for secure
access and retrieval by authorized users. Adaptive intelligent
systems 3304 may, such as using various artificial intelligence
3448 or expert systems disclosed herein and in the documented
incorporated by reference herein, may improve or automated one or
more aspects of entity rating, such as by training a model, a
neural net, a deep learning system, or the like based on a training
set of expert interactions and/or a training set of outcomes from
entity rating activities.
[1372] Referring to FIG. 68, in embodiments a lending platform is
provided having a regulatory and/or compliance system 3426 with a
set of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for automatically
facilitating compliance with at least one of a law, a regulation
and a policy that applies to a lending transaction. The system 4800
may enable one or more aspects of a regulatory and compliance
solution 3426 that enables automated regulatory and compliance
and/or provides a recommendation or plan for a regulatory and
compliance activity relevant to a loan transaction, such as for
personal loans, corporate loans, subsidized loans, student loans,
or other loans, including ones that may be backed by assets,
collateral, or commitments of a borrower. The regulatory and
compliance solution 3426 (which in embodiments may include or use
an RPA system 3442 configured for automating regulatory and
compliance activities based on a training set of interactions by
experts in regulatory and/or compliance activities) may include a
set of interfaces, workflows, and models (which may include, use or
be enabled by various adaptive intelligent systems 3304) and other
components that are configured to enable automation of one or more
aspects of a regulatory and compliance action or a regulatory
and/or compliance process for a loan transaction, such as based on
a set of policies, regulations, laws, requirements, specifications,
conditions, attributes, events, or the like, which may include
attributes of or applicable to entities 3330 involved in a lending
transaction and/or the terms and conditions of loans (including
smart contract 3431 terms and conditions (which may be configured
or populated, e.g., based on terms and conditions that are
permitted for a given set of loans)), as well as various
marketplace conditions (of platform marketplaces and/or external
marketplaces 3390, conditions monitored by monitoring systems 3306
and data collection systems 3318, and the like (such as of entities
3330, including without limitation parties 4910, collateral 4802
and assets 4918, among others, as well as of interest rates,
available lenders, available terms and the like)), and others. For
example, a user of the regulatory and compliance solution 3426 may
create, configure (such as using one or more templates or
libraries), modify, set or otherwise handle (such as in a user
interface of the regulatory and/or compliance solution 3426 and/or
RPA system 3442) various rules, thresholds, conditional procedures,
workflows, model parameters, and the like that determine, or
recommend, a regulatory and compliance action or plan for governing
a set of loans of a given type or types based on one or more
events, attributes, parameters, characteristics, conditions,
states, actions, or the like, where the regulatory and compliance
plan may be based on various factors (e.g., based on permitted
interest rates, required notices (e.g., regarding annualized
percentage rate reporting), permitted borrowers (e.g., students for
federally subsidized student loans), permitted lenders, permitted
issuers, income (e.g., for low-income loans), wealth (e.g., for
loans that are permitted by policy to be provided only to
adequately capitalized parties), location (e.g., for geographically
governed lending programs, such as for municipal development),
conditions of a platform marketplace or external marketplace (such
as where loans are required to have interest rates that do not
exceed a threshold that is calculated based on prevailing interest
rates), the status of the parties of a set of loans, the status or
other attributes of collateral 4802 or assets 4918, risk factors of
the borrower, one or more guarantors, market risk factors and the
like (including predicted risk based on one or more predictive
models using artificial intelligence 3448), status of debt,
condition of collateral 4802 or assets 4918 available to secure or
back a set of loans, the state of a business or business operation
(e.g., receivables, payables, or the like), conditions of parties
4910 (such as net worth, wealth, debt, location, and other
conditions), behaviors of parties (such as behaviors indicating
preferences, behaviors indicating debt preferences, payment
preferences, or communication preferences), and many others.
Regulatory and compliance may include governance with respect to
terms and conditions of sets of loans, selection of appropriate
loans, notices required to be provided, underwriting policies,
communications relevant to regulatory and compliance processes, and
the like. In embodiments, the regulatory and compliance solution
49101 may automatically recommend or set rules, thresholds,
actions, parameters and the like (optionally by learning to do so
based on a training set of outcomes over time), resulting in a
recommended regulatory and compliance plan, which may specify a
series of actions required to accomplish a recommended or desired
outcome of regulatory and compliance (such as within a range of
acceptable outcomes), which may be automated and may involve
conditional execution of steps based on monitored conditions and/or
smart contract terms, which may be created, configured, and/or
accounted for by the regulatory and compliance plan. Regulatory and
compliance plans may be determined and executed based at least one
part on market factors (such as competing interest rates offered by
other issuers, property values, borrower behavior, demographic
trends, payment trends, attributes of issuers, values of collateral
or assets, and the like) as well as regulatory and/or compliance
factors. Regulatory and compliance plans may be generated and/or
executed for new loans, for secondary loans or transactions to back
loans, for collection, for consolidation, for foreclosure
situations (e.g., as an alternative to foreclosure), for situations
of bankruptcy of insolvency, for modifications of existing loans,
for situations involving market changes (e.g., changes in
prevailing interest rates, available capital, or property values),
and others. In embodiments, adaptive intelligent systems 3304,
including artificial intelligence 3448 may be trained on a training
set of regulatory and compliance activities by experts and/or on
outcomes of regulatory and compliance actions to generate a set of
predictions, classifications, control instructions, plans, models,
or the like for automated creation, management and/or execution of
one or more aspects of a regulatory and compliance plan. In
embodiments, events and outcomes of regulatory and compliance may
be recorded in a blockchain 3422, such as in a distributed ledger,
for secure access and retrieval by authorized users. Adaptive
intelligent systems 3304 may, such as using various artificial
intelligence 3448 or expert systems disclosed herein and in the
documented incorporated by reference herein, may improve or
automate one or more aspects of regulatory and compliance, such as
by training a model, a neural net, a deep learning system, or the
like based on a training set of expert interactions and/or a
training set of outcomes from regulatory and compliance
activities.
[1373] An example lending platform is provided herein having a set
of data-integrated microservices including data collection and
monitoring services, blockchain services, and smart contract
services for handling lending entities and transactions. An example
system includes an Internet of Things and sensor platform for
monitoring at least one of a set of assets and a set of collateral
for a loan, a bond, or a debt transaction. An example system
includes a smart contract and distributed ledger platform for
managing at least one of ownership of a set of collateral and a set
of events related to a set of collateral. An example system
includes a smart contract system that automatically adjusts an
interest rate for a loan based on information collected via at
least one of an Internet of Things system, a crowdsourcing system,
a set of social network analytic services and a set of data
collection and monitoring services. An example system includes a
crowdsourcing system for obtaining information about at least one
of a state of a set of collateral for a loan and a state of an
entity relevant to a guarantee for a loan. An example system
includes a smart contract that automatically adjusts an interest
rate for a loan based on at least one of a regulatory factor and a
market factor for a specific jurisdiction. An example system
includes a smart contract that automatically restructures debt
based on a monitored condition. An example system includes a social
network monitoring system for validating the reliability of a
guarantee for a loan. An example system includes an Internet of
Things data collection and monitoring system for validating
reliability of a guarantee for a loan. An example system includes a
robotic process automation system for negotiation of a set of terms
and conditions for a loan. An example system includes a robotic
process automation system for loan collection. An example system
includes a robotic process automation system for consolidating a
set of loans. An example system includes a robotic process
automation system for managing a factoring loan. An example system
includes a robotic process automation system for brokering a
mortgage loan. An example system includes a crowdsourcing and
automated classification system for validating condition of an
issuer for a bond. An example system includes a social network
monitoring system with artificial intelligence for classifying a
condition about a bond. An example system includes an Internet of
Things data collection and monitoring system with artificial
intelligence for classifying a condition about a bond. An example
system includes a system that varies the terms and conditions of a
subsidized loan based on a parameter monitored by the IoT. An
example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored in a
social network. An example system includes a system that varies the
terms and conditions of a subsidized loan based on a parameter
monitored by crowdsourcing. An example system includes an automated
blockchain custody service for managing a set of custodial assets.
An example system includes an underwriting system for a loan with a
set of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions. An example system includes a loan
marketing system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services and smart contract
services for marketing a loan to a set of prospective parties. An
example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1374] An example lending platform is provided herein having an
Internet of Things and sensor platform for monitoring at least one
of a set of assets and a set of collateral for a loan, a bond, or a
debt transaction. An example system includes a smart contract and
distributed ledger platform for managing at least one of ownership
of a set of collateral and a set of events related to a set of
collateral. An example system includes a smart contract system that
automatically adjusts an interest rate for a loan based on
information collected via at least one of an Internet of Things
system, a crowdsourcing system, a set of social network analytic
services and a set of data collection and monitoring services. An
example system includes a crowdsourcing system for obtaining
information about at least one of a state of a set of collateral
for a loan and a state of an entity relevant to a guarantee for a
loan. An example system includes a smart contract that
automatically adjusts an interest rate for a loan based on at least
one of a regulatory factor and a market factor for a specific
jurisdiction. An example system includes a smart contract that
automatically restructures debt based on a monitored condition. An
example system includes a social network monitoring system for
validating the reliability of a guarantee for a loan. An example
system includes an Internet of Things data collection and
monitoring system for validating reliability of a guarantee for a
loan. An example system includes a robotic process automation
system for one or more of negotiation of a set of terms and
conditions for a loan, loan collection, consolidating a set of
loans, managing a factoring loan, or brokering a mortgage loan. An
example system includes a crowdsourcing and automated
classification system for validating condition of an issuer for a
bond. An example system includes a social network monitoring system
with artificial intelligence for classifying a condition about a
bond. An example system includes an Internet of Things data
collection and monitoring system with artificial intelligence for
classifying a condition about a bond.
[1375] An example system includes a system that varies the terms
and conditions of a subsidized loan based on a parameter monitored
by at least one of the IoT, a social network, or crowdsourcing.
[1376] An example system includes an automated blockchain custody
service for managing a set of custodial assets. An example system
includes an underwriting system for a loan with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions. An example system includes a loan
marketing system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services and smart contract
services for marketing a loan to a set of prospective parties. An
example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1377] An example lending platform is provided herein having a
smart contract and distributed ledger platform for managing at
least one of ownership of a set of collateral and a set of events
related to a set of collateral. An example system includes a smart
contract system that automatically adjusts an interest rate for a
loan based on information collected via at least one of an Internet
of Things system, a crowdsourcing system, a set of social network
analytic services and a set of data collection and monitoring
services. An example system includes a crowdsourcing system for
obtaining information about at least one of a state of a set of
collateral for a loan and a state of an entity relevant to a
guarantee for a loan. An example system includes a smart contract
that automatically adjusts an interest rate for a loan based on at
least one of a regulatory factor and a market factor for a specific
jurisdiction. An example system includes a smart contract that
automatically restructures debt based on a monitored condition. An
example system includes a social network monitoring system for
validating the reliability of a guarantee for a loan.
[1378] An example system includes an Internet of Things data
collection and monitoring system for validating reliability of a
guarantee for a loan. An example system includes a robotic process
automation system for negotiation of a set of terms and conditions
for a loan. An example system includes a robotic process automation
system for loan collection. An example system includes a robotic
process automation system for at least one of consolidating a set
of loans, managing a factoring loan, or brokering a mortgage loan.
An example system includes a crowdsourcing and automated
classification system for validating condition of an issuer for a
bond. An example system includes a social network monitoring system
with artificial intelligence for classifying a condition about a
bond. An example system includes an Internet of Things data
collection and monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes a
system that varies the terms and conditions of a subsidized loan
based on a parameter monitored by at least one of the IoT, a social
network, or crowdsourcing. An example system includes an automated
blockchain custody service for managing a set of custodial assets.
An example system includes an underwriting system for a loan with a
set of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions. An example system includes a loan
marketing system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services and smart contract
services for marketing a loan to a set of prospective parties. An
example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1379] An example lending platform is provided herein having a
smart contract system that automatically adjusts an interest rate
for a loan based on information collected via at least one of an
Internet of Things system, a crowdsourcing system, a set of social
network analytic services and a set of data collection and
monitoring services. An example system includes a crowdsourcing
system for obtaining information about at least one of a state of a
set of collateral for a loan and a state of an entity relevant to a
guarantee for a loan. An example system includes a smart contract
that automatically adjusts an interest rate for a loan based on at
least one of a regulatory factor and a market factor for a specific
jurisdiction. An example system includes a smart contract that
automatically restructures debt based on a monitored condition. An
example system includes a social network monitoring system for
validating the reliability of a guarantee for a loan. An example
system includes an Internet of Things data collection and
monitoring system for validating reliability of a guarantee for a
loan. An example system includes a robotic process automation
system for negotiation of a set of terms and conditions for a loan.
An example system includes a robotic process automation system for
at least one of a loan collection, consolidating a set of loans,
managing a factoring loan, or brokering a mortgage loan. An example
system includes a crowdsourcing and automated classification system
for validating condition of an issuer for a bond. An example system
includes a social network monitoring system with artificial
intelligence for classifying a condition about a bond. An example
system includes an Internet of Things data collection and
monitoring system with artificial intelligence for classifying a
condition about a bond. An example system includes a system that
varies the terms and conditions of a subsidized loan based on a
parameter monitored by at least one of the IoT, a social network,
or crowdsourcing. An example system includes an automated
blockchain custody service for managing a set of custodial assets.
An example system includes an underwriting system for a loan with a
set of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions. An example system includes a loan
marketing system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services and smart contract
services for marketing a loan to a set of prospective parties. An
example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1380] An example lending platform is provided herein having a
crowdsourcing system for obtaining information about at least one
of a state of a set of collateral for a loan and a state of an
entity relevant to a guarantee for a loan. An example system
includes a smart contract that automatically adjusts an interest
rate for a loan based on at least one of a regulatory factor and a
market factor for a specific jurisdiction. An example system
includes a smart contract that automatically restructures debt
based on a monitored condition. An example system includes a social
network monitoring system for validating the reliability of a
guarantee for a loan. An example system includes an Internet of
Things data collection and monitoring system for validating
reliability of a guarantee for a loan. An example system includes a
robotic process automation system for at least one of negotiation
of a set of terms and conditions for a loan, loan collection,
consolidating a set of loans, managing a factoring loan, or
brokering a mortgage loan. An example system includes a
crowdsourcing and automated classification system for validating
condition of an issuer for a bond. An example system includes a
social network monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes an
Internet of Things data collection and monitoring system with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1381] An example lending platform is provided herein having a
smart contract that automatically adjusts an interest rate for a
loan based on at least one of a regulatory factor and a market
factor for a specific jurisdiction. An example system includes a
smart contract that automatically restructures debt based on a
monitored condition. An example system includes a social network
monitoring system for validating the reliability of a guarantee for
a loan. An example system includes an Internet of Things data
collection and monitoring system for validating reliability of a
guarantee for a loan. An example system includes a robotic process
automation system for at least one of negotiation of a set of terms
and conditions for a loan, loan collection, consolidating a set of
loans, managing a factoring loan, or brokering a mortgage loan. An
example system includes a crowdsourcing and automated
classification system for validating condition of an issuer for a
bond. An example system includes a social network monitoring system
with artificial intelligence for classifying a condition about a
bond.
[1382] An example system includes an Internet of Things data
collection and monitoring system, with artificial intelligence for
classifying a condition about a bond.
[1383] An example system includes a system that varies the terms
and conditions of a subsidized loan based on a parameter monitored
by at least one of the IoT, a social network, or crowdsourcing.
[1384] An example system includes an automated blockchain custody
service for managing a set of custodial assets.
[1385] An example system includes an underwriting system for a loan
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for underwriting
lending entities and transactions.
[1386] An example system includes a loan marketing system with a
set of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services and smart contract services for marketing a loan to a set
of prospective parties.
[1387] An example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities.
[1388] An example system includes a compliance system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for automatically
facilitating compliance with at least one of a law, a regulation
and a policy related to a lending transaction.
[1389] An example lending platform is provided herein having a
smart contract that automatically restructures debt based on a
monitored condition. An example system includes a social network
monitoring system for validating the reliability of a guarantee for
a loan. An example system includes an Internet of Things data
collection and monitoring system for validating reliability of a
guarantee for a loan. An example system includes a robotic process
automation system for at least one of negotiation of a set of terms
and conditions for a loan, loan collection, consolidating a set of
loans, managing a factoring loan, brokering a mortgage loan. An
example system includes a crowdsourcing and automated
classification system for validating condition of an issuer for a
bond. An example system includes a social network monitoring system
with artificial intelligence for classifying a condition about a
bond. An example system includes an Internet of Things data
collection and monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes a
system that varies the terms and conditions of a subsidized loan
based on a parameter monitored by at least one of the IoT, a social
network, crowdsourcing. An example system includes an automated
blockchain custody service for managing a set of custodial assets.
An example system includes an underwriting system for a loan with a
set of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions. An example system includes a loan
marketing system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services and smart contract
services for marketing a loan to a set of prospective parties. An
example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1390] An example lending platform is provided herein having a
social network monitoring system for validating the reliability of
a guarantee for a loan. An example system includes an Internet of
Things data collection and monitoring system for validating
reliability of a guarantee for a loan. An example system includes a
robotic process automation system for at least one of negotiation
of a set of terms and conditions for a loan, loan collection,
consolidating a set of loans, managing a factoring loan, or
brokering a mortgage loan. An example system includes a
crowdsourcing and automated classification system for validating
condition of an issuer for a bond. An example system includes a
social network monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes an
Internet of Things data collection and monitoring system with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1391] An example lending platform is provided herein having an
Internet of Things data collection and monitoring system for
validating reliability of a guarantee for a loan. An example system
includes a robotic process automation system for at least one of
negotiation of a set of terms and conditions for a loan, loan
collection, consolidating a set of loans, managing a factoring
loan, or brokering a mortgage loan. An example system includes a
crowdsourcing and automated classification system for validating
condition of an issuer for a bond. An example system includes a
social network monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes an
Internet of Things data collection and monitoring system with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1392] An example lending platform is provided herein having a
robotic process automation system for negotiation of a set of terms
and conditions for a loan. An example system includes a robotic
process automation system for at least one of loan collection,
consolidating a set of loans, managing a factoring loan, or
brokering a mortgage loan. An example system includes a
crowdsourcing and automated classification system for validating
condition of an issuer for a bond. An example system includes a
social network monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes an
Internet of Things data collection and monitoring system with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
loan and having a compliance system with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for automatically facilitating compliance with at
least one of a law, a regulation and a policy related to a lending
transaction.
[1393] An example lending platform is provided herein having a
robotic process automation system for loan collection. An example
system includes a robotic process automation system for at least
one of consolidating a set of loans, managing a factoring loan, or
brokering a mortgage loan. An example system includes a
crowdsourcing and automated classification system for validating
condition of an issuer for a bond. An example system includes a
social network monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes an
Internet of Things data collection and monitoring system with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1394] An example lending platform is provided herein having a
robotic process automation system for consolidating a set of loans.
An example system includes a robotic process automation system for
at least one of managing a factoring loan or brokering a mortgage
loan. An example system includes a crowdsourcing and automated
classification system for validating condition of an issuer for a
bond. An example system includes a social network monitoring system
with artificial intelligence for classifying a condition about a
bond. An example system includes an Internet of Things data
collection and monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes a
system that varies the terms and conditions of a subsidized loan
based on a parameter monitored by at least one of the IoT, a social
network, or crowdsourcing. An example system includes an automated
blockchain custody service for managing a set of custodial assets.
An example system includes an underwriting system for a loan with a
set of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions. An example system includes a loan
marketing system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services and smart contract
services for marketing a loan to a set of prospective parties. An
example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1395] An example lending platform is provided herein having a
robotic process automation system for managing a factoring loan. An
example system includes a robotic process automation system for
brokering a mortgage loan. An example system includes a
crowdsourcing and automated classification system for validating
condition of an issuer for a bond. An example system includes a
social network monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes an
Internet of Things data collection and monitoring system with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1396] An example lending platform is provided herein having a
robotic process automation system for brokering a mortgage loan. An
example system includes a crowdsourcing and automated
classification system for validating condition of an issuer for a
bond. An example system includes a social network monitoring system
with artificial intelligence for classifying a condition about a
bond. An example system includes an Internet of Things data
collection and monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes a
system that varies the terms and conditions of a subsidized loan
based on a parameter monitored by at least one of the IoT, a social
network. An example system includes a system that varies the terms
and conditions of a subsidized loan based on a parameter monitored
by crowdsourcing. An example system includes an automated
blockchain custody service for managing a set of custodial assets.
An example system includes an underwriting system for a loan with a
set of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for underwriting lending
entities and transactions. An example system includes a loan
marketing system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services and smart contract
services for marketing a loan to a set of prospective parties. An
example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1397] An example lending platform is provided herein having a
crowdsourcing and automated classification system for validating
condition of an issuer for a bond. An example system includes a
social network monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes an
Internet of Things data collection and monitoring system, with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1398] An example lending platform is provided herein having a
social network monitoring system with artificial intelligence for
classifying a condition about a bond. An example system includes an
Internet of Things data collection and monitoring system with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1399] An example lending platform is provided herein having an
Internet of Things data collection and monitoring system with
artificial intelligence for classifying a condition about a bond.
An example system includes a system that varies the terms and
conditions of a subsidized loan based on a parameter monitored by
at least one of the IoT, a social network, or crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1400] An example lending platform is provided herein having a
system that varies the terms and conditions of a subsidized loan
based on a parameter monitored by the IoT. An example system
includes a system that varies the terms and conditions of a
subsidized loan based on a parameter monitored at least one of in a
social network or by crowdsourcing. An example system includes an
automated blockchain custody service for managing a set of
custodial assets. An example system includes an underwriting system
for a loan with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
underwriting lending entities and transactions. An example system
includes a loan marketing system with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services and smart
contract services for marketing a loan to a set of prospective
parties. An example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1401] An example lending platform is provided herein having a
system that varies the terms and conditions of a subsidized loan
based on a parameter monitored in a social network. An example
system includes a system that varies the terms and conditions of a
subsidized loan based on a parameter monitored by crowdsourcing. An
example system includes an automated blockchain custody service for
managing a set of custodial assets. An example system includes an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes a
compliance system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for automatically facilitating compliance with at least
one of a law, a regulation and a policy related to a lending
transaction.
[1402] An example lending platform is provided herein having a
system that varies the terms and conditions of a subsidized loan
based on a parameter monitored by crowdsourcing. An example system
includes an automated blockchain custody service for managing a set
of custodial assets. An example system includes an underwriting
system for a loan with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services, and smart contract
services for underwriting lending entities and transactions. An
example system includes a loan marketing system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services and smart contract services for marketing a loan to a set
of prospective parties. An example system includes a rating system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for rating a set
of loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1403] An example lending platform is provided herein having an
automated blockchain custody service for managing a set of
custodial assets. An example system includes an underwriting system
for a loan with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
underwriting lending entities and transactions. An example system
includes a loan marketing system with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services and smart
contract services for marketing a loan to a set of prospective
parties. An example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction.
[1404] An example lending platform is provided herein having an
underwriting system for a loan with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for underwriting lending entities and
transactions. An example system includes a loan marketing system
with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services and smart contract services for marketing a
loan to a set of prospective parties. An example system includes a
rating system with a set of data-integrated microservices including
data collection and monitoring services, blockchain services,
artificial intelligence services, and smart contract services for
rating a set of loan-related entities. An example system includes
having a compliance system with a set of data-integrated
microservices including data collection and monitoring services,
blockchain services, artificial intelligence services, and smart
contract services for automatically facilitating compliance with at
least one of a law, a regulation and a policy related to a lending
transaction.
[1405] An example lending platform is provided herein having a loan
marketing system with a set of data-integrated microservices
including data collection and monitoring services, blockchain
services, artificial intelligence services and smart contract
services for marketing a loan to a set of prospective parties. An
example system includes a rating system with a set of
data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for rating a set of
loan-related entities. An example system includes a compliance
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for
automatically facilitating compliance with at least one of a law, a
regulation and a policy related to a lending transaction. In
embodiments a lending platform is provided herein having a rating
system with a set of data-integrated microservices including data
collection and monitoring services, blockchain services, artificial
intelligence services, and smart contract services for rating a set
of loan-related entities and having a compliance system with a set
of data-integrated microservices including data collection and
monitoring services, blockchain services, artificial intelligence
services, and smart contract services for automatically
facilitating compliance with at least one of a law, a regulation
and a policy related to a lending transaction.
[1406] In embodiments, a database service may be provided herein
that embodies, enables, or is associated with a blockchain, ledger,
such as a distributed ledger, or the like, such as in connection
with any of the embodiments described herein or in the document
incorporated by reference that refer to them. In embodiments, the
database service may comprise a transparent, immutable, and
cryptographically verifiable ledger database service, such as the
Amazon.TM. QLDB.TM. database service. The database service may be
included within one or connected with or more of the layers or
microservices of a system 3300, such as the adaptive intelligent
services layer 3304 or the data storage layer 3308. The service may
be used, for example, in connection with a centralized ledger that
records all changes or transactions and maintains an immutable
record of these changes, such as by tracing an entity through
various environments or processes, tracking the history of debits
and credits in a series of transactions, or validating facts
relevant to an underwriting process, a claim, or a legal or
regulatory proceeding. A ledger may be owned by a single trusted
entity or set of trusted entities and may be shared with any other
entities, such as ones that working together in a coordinated
process, such as a transaction, a production process, a joint
service, or many others. As compared to a relational database, the
database service may provide immutable, cryptographically
verifiable ledger entries, without the need for custom audit tables
or trails. As compared to a blockchain framework, such a database
service may include capabilities to perform queries, create tables,
index data, and the like. The database service may optionally omit
requirements for many blockchain frameworks that slow performance,
such as requirement of consensus before committing transactions, or
the database service may employ optional consensus features. In
embodiments, the database service may comprise transparent,
immutable, and cryptographically verifiable ledger that users can
use to build applications that act as a system of record, where
multiple parties are transacting within a centralized, trusted
entity or set of entities. The database service may complement or
substitute for the building audit functionality into a relational
database or for using conventional distributed ledger capabilities
in a blockchain framework. The database service may use an
immutable transactional log or journal, which may track each
application data change and maintain a comprehensive and verifiable
history of changes. In embodiments, transactions may be configured
to comply with requirements of atomicity, consistency, isolation,
and durability (ACID) to be logged in the log or journal, which is
configured to prevent deletions or modifications. Changes may be
cryptographically chained, such that they are auditable and
verifiable, such as in a history that users can query or analyze,
such as using conventional query types, such as SQL queries. In
embodiments, the database service may be provided in a serverless
form, such that there is no need to provision specific server
capacity or to configure read/write limits. To initiate the
database service, the user can create a ledger, define tables, and
the like, and the database service will automatically scale to
support application demands. In contrast to blockchain-based
ledgers, a database service may omit requirements for a distributed
consensus, so it can execute more transactions in the same
time.
[1407] In embodiments of the present disclosure that refer to a
blockchain or distributed ledger, a managed blockchain service may
be used, such as the Amazon.TM. Managed Blockchain.TM., which may
comprise a facility for convenient creation and management of a
scaled blockchain network. The managed blockchain service may be
provided as part of a layered data services architecture as
described in this disclosure. In situations where users want
immutable and verifiable capability provided by a blockchain or
ledger, they may also seek the ability to allow multiple parties to
transact, execute contracts (such as in smart contract embodiments
described herein), share data, and the like without a trusted
central authority. As setting up conventional blockchain frameworks
requires significant time and technical expertise, where each
participant in a permissioned network has to provision hardware,
install software, create, and manage certificates for access
control, and configure network settings. As a given blockchain
application grows, there is also activity required to scale the
network, monitor resources across blockchain nodes, add or remove
hardware and manage network availability. In embodiments, a managed
blockchain service may provide for management of each of these
requirements and enabling capabilities. This may include supporting
open source blockchain frameworks and enabling selection, setup and
deployment of a selected framework in a dashboard, console, or
other user interface, wherein users may choose their preferred
framework, add network members, and configure member nodes that
will process transaction requests. The managed blockchain service
may then automatically create a blockchain network, such as one
that can span multiple accounts with multiple nodes per member, and
configure software, security, and network settings. The managed
blockchain service may secure and manage network certificates, such
as with a key management service, which may allow customer
management of the keys. In embodiments, the managed blockchain
service may include one or more APIs, such as a voting API, such as
one that allows network members to vote, such as to vote to add or
remove members. As application usage grows for a given application
(such as any of the noted applications described in connection with
the platform 3300), users can add more capacity to the blockchain
network, such as with a simple API call. In embodiments, the
managed blockchain service may be provided with a range of
combinations of compute and memory capacity, such as to give users
the ability to choose the right mix of resources for a given
blockchain-based application.
[1408] Referring to FIG. 69, a system for automated loan management
is depicted. A variety of entities/parties 6938 may have a
connection to a loan 6924 including a borrower 6940, a lender 6942,
3rd parties 6944 such as a neutral 3rd party (e.g. such as an
assessor, or an interested 3rd party (e.g., a regulator, company
employees, and the like). A loan 6924 may be subject to a smart
lending contract 6990 including information such as loan terms and
conditions 6929, loan actions 6930, loan events 6932, lender
priorities 6928. And the like. The smart lending contract 6990 may
be recording in loan entry 6941 in a distributed ledger 6963. The
smart lending contract 6990 may be stored as blockchain data
6934.
[1409] In an illustrative example, controller 6922 may receive
collateral data 6974 such as collateral related events 6908,
collateral attributes 6910, environmental data 6912 about an
environment in which the collateral 6902 is situated, sensor data
6914 where the senor 6904 may be affixed to an item of collateral,
to a case containing an item of collateral or in proximity to an
item of collateral. In embodiments, collateral data may be acquired
by an Internet of Things Circuit 6920, a camera system, a networked
monitoring system, an internet monitoring system, a mobile device
system, a wearable device system, a user interface system, and an
interactive crowdsourcing system.
[1410] The controller 6922 may also monitor and/or receive data
from a social network information 6958 from which a financial
condition 6992 may be inferred such as a rating of a party, a tax
status of a party, a credit report of the party, a credit rating of
a party, a website rating of a party, a set of customer reviews for
a product of a party, a social network rating of a party, a set of
credentials of a party, a set of referrals of a party, a set of
testimonials for a party, a set of behavior of a party, and the
like. The controller 6922 may also receive marketplace information
6948 such as pricing 6950, financial data 6954 such as a publicly
stated valuation of the party, a set of property owned by the party
as indicated by public records, a valuation of a set of property
owned by the party, a bankruptcy condition of the party, a
foreclosure status of the entity, a contractual default status of
the entity, a regulatory violation status of the entity, a criminal
status of the entity, an export controls status of the entity, an
embargo status of the entity, a tariff status of the entity, a tax
status of the entity, a credit report of the entity, a credit
rating of the entity, and the like.
[1411] In embodiments, artificial intelligence systems 6962 may be
part of a controller 6922 or on remote systems. The AI systems 6962
may include a valuation circuit 6964 structured to determine a
value for an item of collateral based on collateral data 6974 and a
valuation model and a value model improvement circuit 6966 to
improve the valuation model on the basis of a first set of received
collateral data 6974 and the outcome of loans for which collateral
associated with that first set of received collateral data acted as
security. The AI systems 6962 may include an automated agent
circuit 6970 that takes action based on collateral events,
loan-events and the like. Actions may include loan-related actions
such as offering the loan, accepting the loan, underwriting the
loan, setting an interest rate for the loan, deferring a payment
requirement, modifying an interest rate for the loan, validating
title for collateral, recording a change in title, assessing a
value of collateral, initiating inspection of collateral, calling
the loan, closing the loan, setting terms and conditions for the
loan, providing notices required to be provided to a borrower,
foreclosing on property subject to the loan, modifying terms and
conditions for the loan, and the like. Actions may include
collateral-related actions such as validating title for the one of
the assigned set of items of collateral, recording a change in
title for the one of the assigned set of items of collateral,
assessing the value of the one of the assigned set of items of
collateral, initiating inspection of the one of the assigned set of
items of collateral, initiating maintenance of the one of the
assigned set of items of collateral, initiating security for the
one of the assigned set of items of collateral, modifying terms and
conditions for the one of the assigned set of items of collateral,
and the like. The AI systems 6962 may include a cluster circuit
6972 to create groups of items of collateral based on a common
attribute. The cluster circuit 6972 may also determine a group of
off-set items of collateral where the off-set items of collateral
share a common attribute with one or more items of collateral. Data
may be gathered on the off-set items of collateral and use it as
representative of the items of collateral. A smart contract circuit
6968 may create a smart lending contract 6990 as described
elsewhere herein.
[1412] Referring to FIG. 70, a controller may include a blockchain
service circuit 7044 structured to interpret a plurality of access
control features 7048 such as corresponding to parties associated
with a loan 7030 and associated with blockchain data 7040. The
system may include a data collection circuit 7012 structured to
interpret entity information 7002, collateral data 7004, and the
like, such as corresponding to entities related to a lending
transaction corresponding to the loan, collateral conditions, and
the like. The system may include a smart contract circuit 7022
structured to specify loan terms and conditions 7024, contracts
7028, and the like, relating to the loan. The system may include a
loan management circuit 7032 structured to interpret loan related
actions 7034 and/or events 7038 in response to the entity
information, the plurality of access control features, and the loan
terms and conditions, where the loan related events are associated
with the loan; implement loan related activities in response to the
entity information, the plurality of access control features, and
the loan terms and conditions, wherein the loan related activities
are associated with the loan; and where each of the blockchain
service circuit, the data collection circuit, the smart contract
circuit, and the loan management circuit further comprise a
corresponding application programming interface (API) component
structured to facilitate communication among the circuits of the
system. For example, a lender 7008 may interface with the
controller through secure access control interface 7052 (e.g.,
through access control instructions 7054) structured to interface
to the controller through a secure access control circuit 7050. The
data collection circuit 7012 may be structured to receive
collateral data 7004 and entity information 7002 such as
information about parties to the loan such as a lender, a borrower,
or a third party, an item of collateral, a machine or property
associated with a party to the loan, a product of a party to the
loan, and the like. Collateral data 7004 may include a type of the
item of collateral, a category of the item of collateral, a value
of the item of collateral, a price of a type of the item of
collateral, a value of a type of the item of collateral, a
specification of the item of collateral, a product feature set of
the item of collateral, a model of the item of collateral, a brand
of the item of collateral, a manufacturer of the item of
collateral, an age of the item of collateral, a liquidity of the
item of collateral, a shelf-life of the item of collateral, a
useful life of the item of collateral, a condition of the item of
collateral, a valuation of the item of collateral, a status of the
item of collateral, a context of the item of collateral, a state of
the item of collateral, a storage location of the item of
collateral, a history of the item of collateral, an ownership of
the item of collateral, a caretaker of the item of collateral, a
security of the item of collateral, a condition of an owner of the
item of collateral, a lien on the item of collateral, a storage
condition of the item of collateral, a maintenance history of the
item of collateral, a usage history of the item of collateral, an
accident history of the item of collateral, a fault history of the
item of collateral, a history of ownership of the item of
collateral, an assessment of the item of collateral, a geolocation
of the item of collateral, a jurisdictional location of the item of
collateral, and the like. The data collection circuit 7012 may
determine a collateral condition based on the received data. The
received data 7002, 7004 and the collateral condition 7010 may be
provided to AI circuits 7042 which may include an automated agent
circuit 7014 (e.g., processing events 7018, 7020), a smart contract
services circuit 7022 and a loan management circuit 7032.
[1413] Referring to FIG. 71, an illustrative and non-limiting
example method for handling a loan 7100 is depicted. The example
method may include interpreting a plurality of access control
features (step 7102); interpreting entity information (step 7104);
specifying loan terms and conditions (step 7108); performing a
contract related events in response to entity information (step
7110); interpreting an event relevant to the loan (step 7112);
performing a loan action in response to the event (step 7114);
providing a user interface (step 7118); creating a smart lending
contract (step 7120); and recording the smart lending contract as
blockchain data (step 7122).
[1414] Referring to FIG. 72, depicts a system 7200 for adaptive
intelligence and robotic process automation capabilities of a
transactional, financial and marketplace enablement. The system
7200 may include a controller 7223 which may include a data
collection circuit 7202 which receives collateral data 7201 and
determines collateral condition 7204. The controller 7223 may
further include a plurality of AI circuits 7254. The plurality of
AI circuits 7254 may include a valuation circuit 7208 which may
include a valuation model improvement circuit 7210 and a cluster
circuit 7212. The plurality of AI circuits 7254 may include a smart
contract services circuit 7214 including smart lending contracts
7216 for loans 7225. The plurality of AI circuits 7254 may include
an automated agent circuit 7218 which takes loan-related actions
7220. The controller 7223 may further include a reporting circuit
7222 and a market value monitoring circuit 7224 which also
determines collateral condition 7204. The controller 7223 may
further include a secure access user interface 7228 which receives
access control instructions 7230 from lenders 7242. The access
control instructions 7230 are provided to a secure access control
circuit 7232 which provides instructions to blockchain service
circuit 7234 which interprets access control features 7238 and
provides access to a lender 7242 or other party. The blockchain
service circuit 7234 all stores the collateral data and a unique
collateral ID as blockchain data 7235.
[1415] Referring to FIG. 73, a method 7300 for automated smart
contract creation and collateral assignment is depicted. The method
7300 may include receiving first and second collateral data
regarding an item of collateral 7302, creating a smart lending
contract 7304, associating the collateral data with a unique
identifier for the item of collateral 7308, and storing the unique
identifier and the collateral in a blockchain structure 7310. The
method may further include interpreting a condition of the
collateral based on the collateral data 7312, identifying a
collateral event 7314, reporting a collateral event 7318, and
performing an action in response to the collateral 7320. The method
7300 may further include identifying a group of off-set items of
collateral 7322, accessing marketplace information relevant to the
off-set items of collateral or the item of collateral 7314, and
modifying a term or condition of the loan based on the marketplace
information 7328. The method 7300 may further include receiving
access control instructions 7330, interpreting a plurality of
access control features 7332, and providing access to the
collateral date 7334.
[1416] Referring to FIG. 74, an illustrative and non-limiting
example system for handling a loan 7400 is depicted. The example
system may include a controller 7401. The controller 7401 may
include a data collection circuit 7412, a valuation circuit 7444, a
user interface 7454 (e.g., for interface with a user 7406), a
blockchain service circuit 7458, and several artificial
intelligence circuits 7442 including a smart contract services
circuit 7422, a loan management circuit 7492, a clustering circuit
7432, an automated agent circuit 7414 (e.g., for processing loan
related events 7439 and loan actions 7438).
[1417] The blockchain service circuit 7458 may be structured to
interface with a distributed ledger 7440. The data collection
circuit 7412 may be structured to receive data related to a
plurality of items of collateral 7404 or data related to
environments of the plurality of items of collateral 7402. The
valuation circuit 7444 may be structured to determine a value for
each of the plurality of items of collateral based on a valuation
model 7452 and the received data. The smart contract services
circuit 7422 may be structured to interpret a smart lending
contract 7431 for a loan, and to modify the smart lending contract
7431 by assigning, based on the determined value for each of the
plurality of items of collateral, at least a portion of the
plurality of items of collateral 7428 as security for the loan such
that the determined value of the of the plurality of items of
collateral is sufficient to provide security for the loan. The
blockchain service circuit 7458 may be further structured to record
the assigned at least a portion of items of collateral 7428 to an
entry in the distributed ledger 7440, wherein the entry is used to
record events relevant to the loan. Each of the blockchain service
circuit, the data collection circuit, the valuation circuit and the
smart contract circuit may further include a corresponding
application programming interface (API) component structured to
facilitate communication among the circuits of the system.
[1418] Modifying the smart lending contract 7431 may further
include specifying terms and conditions 7424 that govern an item
selected from the list consisting of a loan term, a loan condition,
a loan-related event, and a loan-related activity. The terms and
conditions 7424 may each include at least one member selected from
the group consisting of: a principal amount of the loan, a balance
of the loan, a fixed interest rate, a variable interest rate
description, a payment amount, a payment schedule, a balloon
payment schedule, a collateral specification, a collateral
substitution description, a description of at least one of the
parties, a guarantee description, a guarantor description, a
security description, a personal guarantee, a lien, a foreclosure
condition, a default condition, a consequence of default, a
covenant related to any one of the foregoing, and a duration of any
one of the foregoing.
[1419] The loan 7430 may include at least one loan type selected
from the loan types consisting of: an auto loan, an inventory loan,
a capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, and a subsidized loan.
[1420] The item of collateral may include at least one item
selected from the items consisting of: a vehicle, a ship, a plane,
a building, a home, a real estate property, an undeveloped land
property, a farm, a crop, a municipal facility, a warehouse, a set
of inventory, a commodity, a security, a currency, a token of
value, a ticket, a cryptocurrency, a consumable item, an edible
item, a beverage, a precious metal, an item of jewelry, a gemstone,
an item of intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, a
tool, an item of machinery, and an item of personal property.
[1421] The data collection circuit 7412 may be further structured
to receive outcome data 7410 related to the loan 7430 and a
corresponding item of collateral, and wherein the valuation circuit
7444 comprises an artificial intelligent circuit structured to
iteratively improve 7450 the valuation model 7452 based on the
outcome data 7410.
[1422] The valuation circuit 7444 may further include a market
value data collection circuit 7448 structured to monitor and report
marketplace information relevant to the value of at least one of
the plurality of items of collateral. The market value data
collection circuit 7448 may be further structured to monitor
pricing or financial data for items that are similar to the item of
collateral in at least one public marketplace.
[1423] The clustering circuit 7432 may be structured to identify a
set of offset items 7434 for use in valuing the item of collateral
based on similarity to an attribute of the collateral.
[1424] The attribute of the collateral may be selected from among a
list of attributes consisting of: a category of the collateral, an
age of the collateral, a condition of the collateral, a history of
the collateral, a storage condition of the collateral, and a
geolocation of the collateral.
[1425] The data collection circuit 7412 may be further structured
to interpret a condition 7411 of the item of collateral.
[1426] The data collection circuit may further include at least one
system selected from the systems consisting of: an Internet of
Things system, a camera system, a networked monitoring system, an
internet monitoring system, a mobile device system, a wearable
device system, a user interface system, and an interactive
crowdsourcing system.
[1427] The loan includes at least one loan type selected from the
loan types consisting of: an auto loan, an inventory loan, a
capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, and a subsidized loan.
[1428] A loan management circuit 7492 may be structured to
interpret an event relevant to the loan 7439, and to perform an
action 7438 related to the loan in response to the event relevant
to the loan.
[1429] The event relevant to the loan may include an event relevant
to at least one of: a value of the loan, a condition of collateral
of the loan, or an ownership of collateral of the loan.
[1430] The action related to the loan may include at least one of:
modifying the terms and conditions for the loan, providing a notice
to one of the parties, providing a required notice to a borrower of
the loan, and foreclosing on a property subject to the loan.
[1431] The corresponding API components of the circuits may further
include user interfaces structured to interact with a plurality of
users of the system.
[1432] The plurality of users may each include: one of the
plurality of parties, one of the plurality of entities, or a
representative of any one of the foregoing. At least one of the
plurality of users may include: a prospective party, a prospective
entity, or a representative of any one of the foregoing.
[1433] Referring to FIG. 75, an illustrative and non-limiting
example method for handling a loan 7500 is depicted. The example
method may include receiving data related to a plurality of items
of collateral (step 7502); setting a value for each of the
plurality of items of collateral (step 7504); assigning at least a
portion of the plurality of items of collateral as security for a
loan (step 7508); and recording the assigned at least a portion of
the plurality of items of collateral to an entry in a distributed
ledger, wherein the entry is used to record events relevant to the
loan (step 7510). A smart lending contract may be modified for the
loan (step 7512).
[1434] Terms and conditions may be specified for the loan (step
7514). The terms and conditions are each selected from the list
consisting of: a principal amount of debt, a balance of debt, a
fixed interest rate, a variable interest rate, a payment amount, a
payment schedule, a balloon payment schedule, a party, a guarantee,
a guarantor, a security, a personal guarantee, a lien, a duration,
a covenant, a foreclose condition, a default condition, and a
consequence of default.
[1435] Outcome data related to the loan may be received (step
7518). A valuation model may be iteratively improved based on the
outcome data and corresponding collateral (step 7520). Marketplace
information relevant to the value of at least one of the plurality
of items of collateral may be monitored (step 7522).
[1436] A set of items similar to one of the plurality of items of
collateral may be identified based on similarity to an attribute of
the one of the plurality of items of collateral (step 7524).
[1437] A condition of the one of the plurality of items of
collateral may be interpreted (step 7528).
[1438] Events related to a value of the one of the plurality of
items of collateral, a condition of the one of the plurality of
items of collateral, or an ownership of the one of the items of
collateral may be reported (step 7530).
[1439] An event relevant to: a value of one of the plurality of
items of collateral, a condition of one of the plurality of items
of collateral, or an ownership of one of the plurality of items of
collateral may be interpreted (step 7532); and an action related to
the secured loan in response to the event relevant to the one of
the plurality of items of collateral for said secured loan may be
performed (step 7534).
[1440] The loan-related action may be selected from among the
actions consisting of: offering a loan, accepting a loan,
underwriting a loan, setting an interest rate for a loan, deferring
a payment requirement, modifying an interest rate for a loan,
validating title for collateral, recording a change in title,
assessing the value of collateral, initiating inspection of
collateral, calling a loan, closing a loan, setting terms and
conditions for a loan, providing notices required to be provided to
a borrower, foreclosing on property subject to a loan, and
modifying terms and conditions for a loan.
[1441] Referring to FIG. 76, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 7600 is depicted. The example system may
include a controller 7601. The controller may include a data
collection circuit 7628 which may collect data such as collateral
data 7632, environmental data 7634 related to the collateral, and
the like from a variety of sources and systems such as: an Internet
of Things system, a camera system, a networked monitoring system,
an internet monitoring system, a mobile device system, a wearable
device system, a user interface system, and an interactive
crowdsourcing system. Based on the received data 7632, 7634 the
data collection circuit 7628 may identify a collateral event
7630.
[1442] The controller 7601 may also include a variety of AI
circuits 7644, including a valuation circuit 7602 which may, based
in part on the received data 7632, 7634, determine a value for an
item of collateral. The valuation circuit 7602 may include a market
value monitoring circuit 7606 structured to determine market data
regarding an item of collateral or an off-set item of collateral,
where the market data may contribute to the valuation for the item
of collateral. The AI circuits may also include a smart contract
services circuit 7610 to facilitate services related to a loan 7629
such as creating a smart contract 7622, identifying terms and
conditions 7624 for the smart contract 7622, identifying lender
priorities and tracking apportionment of value 7626 among lenders.
The smart contract services circuit 7610 may provide data to a
block chain service circuit 7636 which is able to create and modify
a loan entry 7627 on a distributed ledger 7625 where the loan entry
7627 may include terms and conditions, data regarding items of
collateral used to secure the loan, lender priority and
apportionment of value and the like. The AI circuits 7644 may also
include a collateral classification circuit 7640 which creates
groups of off-set items of collateral 7604 which share at least one
attribute with one of the items of collateral, where the common
attribute may be a category of the items, an age of the items, a
condition of the items, a history of the items, an ownership of the
items, a caretaker of the items, a security of the items, a
condition of an owner of the items, a lien on the items, a storage
condition of the items, a geolocation of the items, a
jurisdictional location of the items, and the like. The use of
off-set items of collateral 7642 may facilitate the market value
monitoring circuit 7606 in obtaining relevant market data and in
the overall determination of value for an item of collateral.
[1443] The data collection circuit 7628 may utilize the received
data and a determination of value for an item of collateral to
identify a collateral event 7630. Based on the collateral event
7630, an automated agent circuit 7646, may take an action 7648. The
action 7648 may be a loan-related action such as offering the loan,
accepting the loan, underwriting the loan, setting an interest rate
for a loan, deferring a payment requirement, modifying the interest
rate for the loan, calling the loan, closing the loan, setting
terms and conditions for the loan, providing notices required to be
provided to a borrower, foreclosing on property subject to the
loan, modifying terms and conditions for the loan, and the like.
The action 7648 may be a collateral-related action such as
validating title for the one of a set of items of collateral,
recording a change in title for one of a set of items of
collateral, assessing the value of the one of a set of items of
collateral, initiating inspection of one of a set of items of
collateral, initiating maintenance of one of a set of items of
collateral, initiating security for one of a set of items of
collateral, modifying terms and conditions for one of a set of
items of collateral, and the like.
[1444] Referring to FIG. 77, an illustrative and non-limiting
example method 7700 for loan creation and management is depicted.
The example method 7700 may include receiving data related to a set
of items of collateral (step 7702) that provide security for a loan
and receiving data related to an environment of one of a set of
items of collateral (step 7704). A smart lending contract for the
loan may be created (step 7706) and the set of items of collateral
may be recorded in the smart lending contract (step 7708). A
loan-entry may be recoded in a distributed ledger (step 7770) where
the loan entry includes the smart lending contract or a reference
to the smart contract.
[1445] The value for each of the set of items of collateral may be
determined (7772) and the value of the items of collateral may be
apportioned among lenders (step 7776) based on the priority of the
different lenders. The valuation model may be modified (step 7774)
based on a learning set including a set of valuation determinations
of a set of items of collateral and the outcomes of loans having
those items of collateral as security and the valuation of those
items of collateral.
[1446] A collateral event may be determined (step 7778) based on
received data or a valuation of one of the items of collateral. A
loan-related action may be performed in response to the determined
collateral event (step 7780) where the loan-related action includes
offering the loan, accepting the loan, underwriting the loan,
setting an interest rate for a loan, deferring a payment
requirement, modifying the interest rate for the loan, calling the
loan, closing the loan, setting terms and conditions for the loan,
providing notices required to be provided to a borrower,
foreclosing on property subject to the loan, modifying terms and
conditions for the loan, or the like.
[1447] A collateral-related action may be performed in response to
the determined collateral event (step 7782), where the
collateral-related action includes validating title for the one of
the set of items of collateral, recording a change in title for the
one of the set of items of collateral, assessing the value of the
one of the set of items of collateral, initiating inspection of the
one of the set of items of collateral, initiating maintenance of
the one of the set of items of collateral, initiating security for
the one of the set of items of collateral, modifying terms and
conditions for the one of the set of items of collateral, or the
like.
[1448] One or more group of off-set items of collateral may be
identified (step 7784) where each item in a group of off-set items
of collateral shares a common attribute with at least one of the
items of collateral. Marketplace information may then be monitored
for data related to off-set items of collateral (step 7786). The
monitored marketplace information regarding one or more off-set
items of collateral may be used to update a value of an item of
collateral (step 7788). The loan-entry in the distributed ledger
may be updated (7730) with the updated value of the item of
collateral.
[1449] Referring to FIG. 78, an example system 7800 for adaptive
intelligence and robotic process automation capabilities of a
transactional, financial and marketplace enablement is depicted.
The system 7800 may include a controller 7801 which may include a
plurality of AI circuits 7820. The plurality of AI circuits 7820
may include a smart contract services circuit 7810 to create and
modify a smart lending contract 7812 for a loan 7818. Smart lending
contracts 7812 may include the terms and conditions 7814 for the
loan 7818, a covenant specifying a required value of collateral,
information regarding a loan 7818, items of collateral, information
on lenders, including lender priorities including apportionment
7816 of the value of items of collateral among the lenders.
[1450] The plurality of AI circuits 7820 may include a valuation
circuit 7802 structured to determine one or more values 7808 for
items of collateral based on a valuation model 7809 and collateral
data 7840. The valuation circuit 7802 may include a collateral
classification circuit 7803 to identify items of off-set collateral
7807 based on common attributes with items of collateral used to
secure a loan 7818. A market value monitoring circuit 7806 may
receive marketplace information 7842 regarding items of collateral
and off-set items of collateral 7807. The marketplace information
7842 may be used by the valuation model 7809 in determining values
7808 for items of collateral. The valuation circuit 7802 may
further include a valuation model improvement circuit 7804 to
improve the valuation model 7809 used to determine values 7808. The
valuation model improvement circuit 7804 may utilize a training set
including previously determined values 7808 for items of collateral
and data regarding the outcome of loans for which those items of
collateral acted as security.
[1451] The plurality of AI circuits 7820 may include a loan
management circuit 7822 which may include a value comparison
circuit 7828 to compare a value 7808 of an item of collateral with
a required value of the item of collateral as specified in a
covenant of the loan, determining a collateral satisfaction value
7830. The smart contract services circuit 7810 may determine, in
response to the collateral satisfaction value 7830, a term or a
condition 7814 for a loan 7818, where the term of conditions 7814
is related to a loan component such as a loan party, a loan
collateral, a loan-related event, and a loan-related activity for
the smart lending contract 7812, and the like. The term of
condition may be a principal amount of the loan, a balance of the
loan, a fixed interest rate, a variable interest rate description,
a payment amount, a payment schedule, a balloon payment schedule, a
collateral specification, a collateral substitution description, a
description of a party, a guarantee description, a guarantor
description, a security description, a personal guarantee, a lien,
a foreclosure condition, a default condition, a consequence of
default, a covenant related to any one of the foregoing, a duration
of any one of the foregoing, and the like. The term of condition
may be a principal amount of debt, a balance of debt, a fixed
interest rate, a variable interest rate, a payment amount, a
payment schedule, a balloon payment schedule, a party, a guarantee,
a guarantor, a security, a personal guarantee, a lien, a duration,
a covenant, a foreclose condition, a default condition, a
consequence of default, and the like. The smart contract services
circuit 7810 may modify the smart lending contract 7812 to include
new terms or conditions 7814, such as those determined in response
to the collateral satisfaction value 7830.
[1452] The loan management circuit 7822 may also include an
automated agent circuit 7824 to take an action 7826 based on the
collateral satisfaction value 7830. The action 7826 may be a
collateral-related action such as validating title for the item of
collateral, recording a change in title for the item of collateral,
assessing the value of the item of collateral, initiating
inspection of the item of collateral, initiating maintenance of the
item of collateral, initiating security for the item of collateral,
modifying terms and conditions for the
[1453] item of collateral, and the like. The action 7826 may be a
loan-related action such as offering the loan, accepting the loan,
underwriting the loan, setting an interest rate for a loan,
deferring a payment requirement, modifying the interest rate for
the loan, calling the loan, closing the loan, setting terms and
conditions for the loan, providing notices required to be provided
to a borrower, foreclosing on property subject to the loan,
modifying terms and conditions for the loan, and the like.
[1454] The controller 7801 may also include a data collection
circuit 7832 to receive collateral data 7840 and determine a
collateral event 7834. The collateral event 7834 and collateral
data 7840 may then be reported by a reporting circuit 7836. A
blockchain service circuit 7838 may create and update blockchain
data 7825 where a copy of the smart lending contract 7812 is
stored.
[1455] Referring to FIG. 79, an illustrative and non-limiting
method for robotic process automation of transactional, financial
and marketplace activities is depicted. An example method may
include receiving data related to an item or set of items of
collateral (step 7902) where the item(s) of collateral are acting
as security for a loan. A value for the item of collateral is
determined (step 7904) based on received data and a valuation
model. A smart lending contract is created (step 7906) which
specifies information about the loan including a covenant
specifying a required value of collateral needed to secure the
loan.
[1456] The value of the item(s) of collateral may be compared to
the value of collateral specified in the covenant (step 7908) and a
collateral satisfaction value determined (step 7910), where the
collateral satisfaction value may be positive if the value of the
collateral exceeds the required value of collateral or negative if
the value of collateral is less than the required value of
collateral. A loan-related action may be implemented in response to
the collateral satisfaction value (step 7912). A term or condition
may be determined in response to the collateral satisfaction value
(step 7914) and the smart lending contract modified (step
7916).
[1457] The valuation model may be modified (step 7918) based on a
first set of valuation determinations for a first set of items of
collateral and a corresponding set of loan outcomes having the
first set of items of collateral as security, using a machine
learning system, a model-based system, a rule-based system, a deep
learning system, a neural network, a convolutional neural network,
a feed forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system, a
simulation system, a hybrid system of at least two of any of the
foregoing, and the like.
[1458] A group of off-set items of collateral may be identified
(step 7920) based on common attributes with the collateral such as
a category of the item of collateral, an age of the item of
collateral, a condition of the item of collateral, a history of the
item of collateral, an ownership of the item of collateral, a
caretaker of the item of collateral, a security of the item of
collateral, a condition of an owner of the item of collateral, a
lien on the item of collateral, a storage condition of the item of
collateral, a geolocation of the item of collateral, and a
jurisdictional location of the item of collateral. Marketplace
information such as may be monitored for data related to the
off-set collateral (step 7922) such as pricing or financial data
and the smart lending contract modified in response to the
marketplace information (step 7924). An action may be automatically
initiated (step 7926) based on the marketplace information. The
action may include modifying a term of the loan, issuing a notice
of default, initiating a foreclosure action modifying a conditions
of the loan, providing a notice to a party of the loan, providing a
required notice to a borrower of the loan, foreclosing on a
property subject to the loan, validating title for the item of
collateral, recording a change in title for the item of collateral,
assessing the value of the item of collateral, initiating
inspection of the item of collateral, initiating maintenance of the
item of collateral, initiating security for the item of collateral,
and modifying terms and conditions for the item of collateral, and
the like.
[1459] Referring to FIG. 80, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 8000 is depicted. The example system may
include a controller 8001 including a data collection circuit 8028
structured to receive collateral data 8032 regarding a plurality of
items of collateral used to secure a set of loans 8018. The data
collection circuit 8028 may include an Internet of Things system, a
camera system, a networked monitoring system, an internet
monitoring system, a mobile device system, a wearable device
system, a user interface system, an interactive crowdsourcing
system, and the like. The items of collateral may include a
vehicle, a ship, a plane, a building, a home, a real estate
property, an undeveloped land property, a farm, a crop, a municipal
facility, a warehouse, a set of inventory, a commodity, a security,
a currency, a token of value, a ticket, a cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, a tool, an item of machinery, an
item of personal property, and the like. The set of loans may
include an auto loan, an inventory loan, a capital equipment loan,
a bond for performance, a capital improvement loan, a building
loan, a loan backed by an account receivable, an invoice finance
arrangement, a factoring arrangement, a pay day loan, a refund
anticipation loan, a student loan, a syndicated loan, a title loan,
a home loan, a venture debt loan, a loan of intellectual property,
a loan of a contractual claim, a working capital loan, a small
business loan, a farm loan, a municipal bond, a subsidized loan,
and the like. The set of loans 8018 may be distributed among a
plurality of borrowers as means of diversifying the risk of the
loans.
[1460] The controller 8001 may also include a plurality of AI
circuits 8044, including a collateral classification circuit 8020,
to identify, from among the items of collateral, a group of
collateral 8022 which related by sharing a common attribute,
wherein the common attribute is among the received collateral data
8032, such as a type of the item of collateral, a category of the
item of collateral, a value of the item of collateral, a price of a
type of the item of collateral, a value of a type of the item of
collateral, a specification of the item of collateral, a product
feature set of the item of collateral, a model of the item of
collateral, a brand of the item of collateral, a manufacturer of
the item of collateral, an age of the item of collateral, a
liquidity of the item of collateral, a shelf-life of the item of
collateral, a useful life of the item of collateral, a condition of
the item of collateral, a valuation of the item of collateral, a
status of the item of collateral, a context of the item of
collateral, a state of the item of collateral, a storage location
of the item of collateral, a history of the item of collateral, an
ownership of the item of collateral, a caretaker of the item of
collateral, a security of the item of collateral, a condition of an
owner of the item of collateral, a lien on the item of collateral,
a storage condition of the item of collateral, a maintenance
history of the item of collateral, a usage history of the item of
collateral, an accident history of the item of collateral, a fault
history of the item of collateral, a history of ownership of the
item of collateral, an assessment of the item of collateral, a
geolocation of the item of collateral, a jurisdictional location of
the item of collateral, and the like. The collateral classification
circuit 8020 may also identify off-set collateral 8023 where items
of off-set collateral 8023 and the items of collateral share a
common attribute.
[1461] The reporting circuit 8034 may also report a collateral
event 8030 based on the collateral data 8032. An automated agent
circuit 8008 may automatically perform an action 8009 based on the
collateral event 8030. The action 8009 may be a collateral-related
action such as validating title for one of the plurality of items
of collateral, recording a change in title for one of the plurality
of items of collateral, assessing the value of one of the plurality
of items of collateral, initiating inspection of one of the
plurality of items of collateral, initiating maintenance of the one
of the plurality of items of collateral, initiating security for
one of the plurality of items of collateral, modifying terms and
conditions for one of the plurality of items of collateral, and the
like. The action 8009 may be a loan-related action such as offering
the loan, accepting the loan, underwriting the loan, setting an
interest rate for a loan, deferring a payment requirement,
modifying the interest rate for the loan, calling the loan, closing
the loan, setting terms and conditions for the loan, providing
notices required to be provided to a borrower, foreclosing on
property subject to the loan, modifying terms and conditions for
the loan, and the like.
[1462] The controller 8001 may also include a smart contract
services circuit 8010 to create a smart lending contract 8012 for
an individual loan or a set of loans 8018 where the smart lending
contract 8012 identifies a subset of collateral 8016, selected from
the group of related items of collateral 8022 sharing a common
attribute, to act as security for the set of loans 8018. The smart
contract services circuit 8010 may also redefine the subset of
collateral 8016 based on an updated value for an item of
collateral, thus rebalancing the items of collateral used for a set
of loans based on the values of the collateral items. The
identification of the subset of collateral 8016 may be identified
in real-time when the common attribute changes in real time (e.g. a
status of an item of collateral or whether collateral is in transit
during a defined time period). Further, the smart contract services
circuit 8010 may determine a term or condition 8014 for the loan
based on a value of one of the items of collateral, where the term
or the condition 8014 is related to a loan component such as a loan
party, a loan collateral, a loan-related event, and a loan-related
activity. The term or condition 8014 may be a principal amount of
the loan, a balance of the loan, a fixed interest rate, a variable
interest rate description, a payment amount, a payment schedule, a
balloon payment schedule, a collateral specification, a collateral
substitution description, a description of a party, a guarantee
description, a guarantor description, a security description, a
personal guarantee, a lien, a foreclosure condition, a default
condition, a consequence of default, a covenant related to any one
of the foregoing, a duration of any one of the foregoing, and the
like.
[1463] The controller may also include a valuation circuit 8002 to
determine a value 8040 for each item of collateral in the subset of
items collateral based on the received data and a valuation model
8042. A valuation model improvement circuit 8004 may modify the
valuation model 8042 based on a first set of valuation
determinations for a first set of items of collateral and a
corresponding set of loan outcomes having the first set of items of
collateral as security. The valuation model improvement circuit
8004 may include a machine learning system, a model-based system, a
rule-based system, a deep learning system, a neural network, a
convolutional neural network, a feed forward neural network, a
feedback neural network, a self-organizing map, a fuzzy logic
system, a random walk system, a random forest system, a
probabilistic system, a Bayesian system, a simulation system, a
hybrid system including at least two of the foregoing, or the like.
The valuation circuit 8002 may also include a market value data
collection circuit 8006 to monitor and report marketplace
information 8038 such as pricing or financial data relevant to
off-set collateral 8023 or a group of collateral 8022.
[1464] Referring to FIG. 81, a method 8100 for automated
transactional, financial and marketplace activities. A method may
include receiving data related to an item of collateral (step
8102), identifying a group of items of collateral (step 8104) where
the items in the group share a common attribute or feature,
identifying a subset of the group as security for a set of loans
(8108) and creating a smart lending contract (step 8110) for the
set of loans where the smart lending contract identifies the subset
of group acting as security. The common attribute shared by the
group of items of collateral may be in the received data.
[1465] The value of each item of collateral may be determined
(8112) using the received data and a valuation model. The subset of
collateral used as security may then be redefined based on the
value of the different items of collateral (8114). A term of
condition for at least one of the smart lending contracts may be
determined (8118) based on the value for at least one of the items
of collateral in the subset of the group and the smart lending
contract modified to include the determined term or condition
(8120). Further, in some embodiments, the valuation model may be
modified (8122) based on a first set of valuation determinations
for a first set of items of collateral and a corresponding set of
loan outcomes having the first set of items of collateral as
security.
[1466] A group of off-set items of collateral may be identified
(step 8124) where each member of the group of off-set items of
collateral and the group of the plurality of items share a common
attribute. An information marketplace may be monitored and
marketplace information reported (step 8126) for the group of
off-set items of collateral.
[1467] FIG. 82 depicts a system 8200 including a data collection
circuit 8224 structured to receive data 8202 related to a set of
parties to a loan 8212. The data collection circuit may be
structured to receive collateral-related data 8208 related to a set
of items of collateral 8214 acting as security for the loan and
determine a condition of the set of items of collateral, where the
change in the interest rate may be based on a condition of the set
of items of collateral. The item of collateral may be a vehicle, a
ship, a plane, a building, a home, a real estate property, an
undeveloped land property, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a beverage, a precious metal, an item of jewelry, a
gemstone, an item of intellectual property, an intellectual
property right, a contractual right, an antique, a fixture, an item
of furniture, a tool, an item of machinery, an item of personal
property, and the like. The received data may include an attribute
of the set of parties to the loan, where the change in the interest
rate may be based in part on the attribute. The data collection
circuit may include a system such as an Internet of Things circuit,
an image capture device, a networked monitoring circuit, an
internet monitoring circuit, a mobile device, a wearable device, a
user interface circuit, an interactive crowdsourcing circuit, and
the like. For instance, the data collection circuit may include an
Internet of Things circuit 8254 structured to monitor attributes of
the set of parties to the loan. The data collection circuit may
include a wearable device 8206 associated with at least one of the
set of parties, where the wearable device is structured to acquire
human-related data 8204, and where the received data includes at
least a portion of the human-related data. The data collection
circuit may include a user interface circuit 8226 structured to
receive data from the parties of the loan and provide the data from
at least one of the parties of the loan as a portion of the
received data. The data collection circuit may include an
interactive crowdsourcing circuit 8238 structured to solicit data
regarding at least one of the set of parties of the loan, receive
solicited data, and provide at least a subset of the solicited data
as a portion of the received data. The data collection circuit may
include an internet monitoring circuit 8240 structured to retrieve
data related to the parties of the loan from at least one publicly
available information site 8222. The system may include a smart
contract circuit 8232 structured to create a smart lending contract
8234 for the loan 8216. The loan may be a type selected from among
loan types such as an inventory loan, a capital equipment loan, a
bond for performance, a capital improvement loan, a building loan,
a loan backed by an account receivable, an invoice finance
arrangement, a factoring arrangement, a pay day loan, a refund
anticipation loan, a student loan, a syndicated loan, a title loan,
a home loan, a venture debt loan, a loan of intellectual property,
a loan of a contractual claim, a working capital loan, a small
business loan, a farm loan, a municipal bond, a subsidized loan,
and the like. The smart contract circuit may be structured to
determine a term or a condition 8218 for the smart lending contract
based on the attribute and modify the smart lending contract to
include the term or the condition. The term or condition may be
related to a loan component, such as a loan party, a loan
collateral, a loan-related event, a loan-related activity, and the
like. The term or condition may be a principal amount of the loan,
a balance of the loan, a fixed interest rate, a variable interest
rate description, a payment amount, a payment schedule, a balloon
payment schedule, a collateral specification, a collateral
substitution description, a description of a party, a guarantee
description, a guarantor description, a security description, a
personal guarantee, a lien, a foreclosure condition, a default
condition, a consequence of default, a covenant related to any one
of the foregoing, a duration of any one of the foregoing, and the
like. The system may include an automated agent circuit 8236
structured to automatically perform a loan-related action 8220 in
response to the received data, where the loan-related action is a
change in an interest rate for the loan, and where the smart
contract circuit may be further structured to update the smart
lending contract with the changed interest rate. The system may
include a valuation circuit 8228 structured to determine, such as
based on the received data and a valuation model 8230, a value for
the at least one of the set of items of collateral. The smart
contract circuit may be structured to determine a term or a
condition for the smart lending contract based on the value for the
at least one of the set of items of collateral and modify the smart
lending contract to include the term or the condition. The term or
the condition may be related to a loan component, such as a loan
party, a loan collateral, a loan-related event, a loan-related
activity, and the like. The term or the condition may be a
principal amount of the loan, a balance of the loan, a fixed
interest rate, a variable interest rate description, a payment
amount, a payment schedule, a balloon payment schedule, a
collateral specification, a collateral substitution description, a
description of a party, a guarantee description, a guarantor
description, a security description, a personal guarantee, a lien,
a foreclosure condition, a default condition, a consequence of
default, a covenant related to any one of the foregoing, a duration
of any one of the foregoing, and the like. The valuation circuit
may include a valuation model improvement circuit 8242, where the
valuation model improvement circuit may modify the valuation model,
such as based on a first set of valuation determinations 8244 for a
first set of items of collateral and a corresponding set of loan
outcomes having the first set of items of collateral as security.
The valuation model improvement circuit may include a one system
such as a machine learning system, a model-based system, a
rule-based system, a deep learning system, a neural network, a
convolutional neural network, a feed forward neural network, a
feedback neural network, a self-organizing map, a fuzzy logic
system, a random walk system, a random forest system, a
probabilistic system, a Bayesian system, a simulation system, a
hybrid system including at least two of the foregoing, and the
like. The change in the interest rate may be further based on the
value for the at least one of the set of items of collateral. The
valuation circuit may include a market value data collection
circuit 8246 structured to monitor and report marketplace
information 8248 for offset items of collateral relevant to the
value of the item of collateral. The market value data collection
circuit may be structured to monitor one of pricing or financial
data for the offset items of collateral in at least one public
marketplace and report the monitored one of pricing or financial
data. The system may include a collateral classification circuit
8250 structured to identify a group of off-set items of collateral
8252, where each member of the group of off-set items of collateral
and at least one of the set of items of collateral share a common
attribute. The common attribute may be a category of the item, an
age of the item, a condition of the item, a history of the item, an
ownership of the item, a caretaker of the item, a security of the
item, a condition of an owner of the item, a lien on the item, a
storage condition of the item, a geolocation of the item, a
jurisdictional location of the item, and the like.
[1468] FIG. 83 depicts a method 8300 including receiving data
related to at least one of a set of parties to a loan 8302,
creating a smart lending contract for the loan 8304, performing a
loan-related action in response to the received data, wherein the
loan-related action is a change in an interest rate for the loan
8308, and updating the smart lending contract with the changed
interest rate 8310. The method may further include receiving data
related to a set of items of collateral acting as security for the
loan 8314, determining a condition the set of items of collateral
8318, and performing a loan-related action in response to the
condition of the set of items of collateral, where the loan-related
action may be a change in interest rate for the loan 8320. The
method may further include receiving data related to a set of items
of collateral acting as security for the loan 8322, determining a
condition of at least one of the set of items of collateral 8324,
determining a term or a condition for the smart lending contract
based on the condition of the at least one of the set of items of
collateral 8328, and modifying the smart lending contract to
include the term or the condition 8330. The method may include
identifying a group of off-set items of collateral wherein each
member of the group of off-set items of collateral and at least one
of the set of items of collateral share a common attribute, and
monitoring the group of offset items of collateral in a public
marketplace, and further may report the monitored data. The method
may include changing, such as based on the monitored group of
off-set items of collateral, the interest rate of the loan secured
by at least one of the set of items of collateral.
[1469] FIG. 84 depicts a system 8400 including a data collection
circuit 8418 structured to acquire data 8402, from public sources
of information 8404 (e.g., a website, a news article, a social
network, crowdsourced information, and the like), related to at
least one party of a set of parties 8406 to a loan 8408 (e.g.,
primary lender, a secondary lender, a lending syndicate, a
corporate lender, a government lender, a bank lender, a secured
lender, bond issuer, a bond purchaser, an unsecured lender, a
guarantor, a provider of security, a borrower, a debtor, an
underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, an accountant, and the like).
The data collection circuit may be further structured to receive
collateral-related data 8410 related to a set of items of
collateral 8412 acting as security for the loan and to determine a
condition of at least one of the set of items of collateral,
wherein the change in the interest rate is further based on the
condition of the at least one of the set of items of collateral.
The acquired data may include a financial condition of the at least
one party of the set of parties to the loan. The financial
condition may be determined based on at least one attribute of the
at least one party of the set of parties to the loan, the attribute
selected from among the list of attributes consisting of: a
publicly stated valuation of the party, a set of property owned by
the party as indicated by public records, a valuation of a set of
property owned by the party, a bankruptcy condition of the party, a
foreclosure status of the party, a contractual default status of
the party, a regulatory violation status of the party, a criminal
status of the party, an export controls status of the party, an
embargo status of the party, a tariff status of the party, a tax
status of the party, a credit report of the party, a credit rating
of the party, a website rating of the party, a set of customer
reviews for a product of the party, a social network rating of the
party, a set of credentials of the party, a set of referrals of the
party, a set of testimonials for the party, a set of behavior of
the party, a location of the party, a geolocation of the party, a
judicial location of the party, and the like. The system may
include a smart contract circuit 8424 structured to create a smart
lending contract 8426 for the loan 8408. The smart contract circuit
may be structured to specify terms and conditions in the smart
lending contract, wherein one of a term or a condition in the smart
lending contract governs one of loan-related events or loan-related
activities. The system may include an automated agent circuit 8428
structured to automatically perform a loan-related action 8416 in
response to the acquired data, wherein the loan-related action is a
change in an interest rate for the loan, and wherein the smart
contract circuit is further structured to update the smart lending
contract with the changed interest rate. The automated agent
circuit may be structured to identify an event relevant to the loan
(e.g., a value of the loan, a condition of collateral of the loan,
or an ownership of collateral of the loan), based, at least in
part, on the received data. The automated agent circuit may be
structured to perform, in response to the event relevant to the
loan, an action selected from the list of actions, such as offering
the loan, accepting the loan, underwriting the loan, setting an
interest rate for the loan, deferring a payment requirement,
modifying an interest rate for the loan, validating title for at
least one of the set of items of collateral, assessing the value of
at least one of the set of items of collateral, initiating
inspection of at least one of the set of items of collateral,
setting or modifying terms and conditions 8414 for the loan (e.g.,
a principal amount of debt, a balance of debt, a fixed interest
rate, a variable interest rate, a payment amount, a payment
schedule, a balloon payment schedule, a party, a guarantee, a
guarantor, a security, a personal guarantee, a lien, a duration, a
covenant, a foreclose condition, a default condition, and a
consequence of default), providing a notice to one of the parties,
providing a required notice to a borrower of the loan, foreclosing
on a property subject to the loan, and the like. The loan may
include a loan type, such as an auto loan, an inventory loan, a
capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, a subsidized loan, and the like. The acquired
data may be related to the set of items of collateral such as a
vehicle, a ship, a plane, a building, a home, a real estate
property, an undeveloped land property, a farm, a crop, a municipal
facility, a warehouse, a set of inventory, a commodity, a security,
a currency, a token of value, a ticket, a cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, a tool, an item of machinery, an
item of personal property, and the like. The system may include a
valuation circuit 8420 structured to determine, based on the
acquired data and a valuation model 8422, a value for at least one
of the set of items of collateral. The valuation circuit may
include a valuation model improvement circuit 8430, where the
valuation model improvement circuit modifies the valuation model
based on a first set of valuation determinations 8432 for a first
set of items of collateral and a corresponding set of loan outcomes
having the first set of items of collateral as security. The
valuation model improvement circuit may include a machine learning
system, a model-based system, a rule-based system, a deep learning
system, a neural network, a convolutional neural network, a feed
forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system, a
simulation system, a hybrid system including at least two of the
foregoing, and the like. The smart contract circuit may be further
structured to determine a term or a condition for the smart lending
contract based on the value for the at least one of the set of
items of collateral and modify the smart lending contract to
include the term or the condition, modify a term or condition of
the loan based on the marketplace information for offset items of
collateral relevant to the value of the item of collateral, and the
like. The system may include a collateral classification circuit
8438 structured to identify a group of off-set items of collateral,
wherein each member of the group of off-set items 8440 of
collateral and at least one of the set of items of collateral share
a common attribute (e.g., a category of the item, an age of the
item, a condition of the item, a history of the item, an ownership
of the item, a caretaker of the item, a security of the item, a
condition of an owner of the item, a lien on the item, a storage
condition of the item, a geolocation of the item, a jurisdictional
location of the item, and the like). The valuation circuit may
further include a market value data collection circuit 8434
structured to monitor and report marketplace information 8436 for
offset items of collateral relevant to the value of the item of
collateral, monitor pricing or financial data for the offset items
of collateral in a public marketplace, and the like, and report the
monitored pricing or financial data.
[1470] FIG. 85 depicts a method 8500 including acquiring data, from
public sources, related to at least one of a set of parties to a
loan, where the public sources of information may be selected from
the list of information sources consisting of a website, a news
article, a social network, and crowdsourced information 8502. The
method may include creating a smart lending contract 8504. The
method may include performing a loan-related action in response to
the acquired data, wherein the loan-related action is a change in
an interest rate for the loan 8506. The method may include updating
the smart lending contract with the changed interest rate 8508. The
method may include receiving collateral-related data related to a
set of items of collateral acting as security for the loan 8510,
and determining a condition of at least one of the set of items of
collateral, wherein the change in the interest rate is further
based on the condition of the at least one of the set of items of
collateral 8512. The method may include identifying an event
relevant to the loan based, at least in part, on the
collateral-related data 8514, and performing, in response the event
relevant to the loan, an action 8518, such as offering the loan,
accepting the loan, underwriting the loan, setting an interest rate
for the loan, deferring a payment requirement, modifying an
interest rate for the loan, validating title for at least one of
the set of items of collateral, assessing a value of at least one
of the set of items of collateral, initiating inspection of at
least one of the set of items of collateral, setting or modifying
terms and conditions for the loan, providing a notice to one of the
parties, providing a required notice to a borrower of the loan,
foreclosing on a property subject to the loan, and the like. The
method may include determining, based on at least one of the
collateral-related data or the acquired data, and a valuation
model, a value for at least one of the set of items of collateral.
The method may include determining at least one of a term or a
condition for the smart lending contract based on the value for the
at least one of the set of items of collateral. The method may
include modifying the smart lending contract to include the at
least one of the term or the condition. The method may include
modifying the valuation model based on a first set of valuation
determinations for a first set of items of collateral and a
corresponding set of loan outcomes having the first set of items of
collateral as security. The method may include identifying a group
of off-set items of collateral, wherein each member of the group of
off-set items of collateral and at least one of the set of items of
collateral share a common attribute 8520, monitoring one of pricing
data or financial data for least one of the group off-set items of
collateral in at least one public marketplace 8522, reporting the
monitored data for the at least one of the group off-set items of
collateral 8524, and modifying a term or condition of the loan
based the reported monitored data 8528.
[1471] FIG. 86 depicts a system 8600 including a data collection
circuit 8620 structured to receive data 8602 relating to a status
8604 of a loan 8612 and data relating to a set of items of
collateral 8606 acting as security for the loan. The data
collection circuit may monitor one or more of the loan entities
with a system such as an Internet of Things system, a camera
system, a networked monitoring system, an internet monitoring
system, a mobile device system, a wearable device system, a user
interface system, and an interactive crowdsourcing system 8632. For
instance, an interactive crowdsourcing system may include a user
interface 8634, the user interface configured to solicit
information related to one or more of the loan entities from a
crowdsourcing site 8618, and where the user interface is structured
to allow one or more of the loan entities to input information one
or more of the loan entities. In another instance, a networked
monitoring system may include a network search circuit 8621
structured to search publicly available information sites for
information related one or more of the loan entities. The system
may include a blockchain service circuit 86144 structured to
maintain a secure historical ledger 8646 of events related to the
loan, such as to interpret a plurality of access control features
8608 corresponding to a plurality of parties 8610 associated with
the loan. The system may include a loan evaluation circuit 8648
structured to determine a loan status based on the received data.
The data collection circuit may receive data related to one or more
loan entities 8614, where the loan evaluation circuit may determine
compliance with a covenant based on the data related to the one or
more of the loan entities. The loan evaluation circuit may be
structured to determine a state of performance for a condition of
the loan based on the received data and a status of the one or more
of the loan entities, and wherein the determination of the loan
status is determined based in part on the status of the at least
one or more of the loan entities and the state of performance of
the condition for the loan. For instance, the condition of the loan
may relate to at least one of a payment performance and a
satisfaction on a covenant. The data collection circuit may include
a market data collection circuit 8636 structured to receive
financial data 8638 regarding at least one of the plurality of
parties associated with the loan. The loan evaluation circuit may
be structured to determine a financial condition of the least one
of the plurality of parties associated with the loan based on the
received financial data, where the at least one of the plurality of
parties may be a primary lender, a secondary lender, a lending
syndicate, a corporate lender, a government lender, a bank lender,
a secured lender, bond issuer, a bond purchaser, an unsecured
lender, a guarantor, a provider of security, a borrower, a debtor,
an underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, an accountant, and the like.
The received financial data may relate to an attribute of the
entity for one of the plurality of parties, such as a publicly
stated valuation of the party, a set of property owned by the party
as indicated by public records, a valuation of a set of property
owned by the party, a bankruptcy condition of the party, a
foreclosure status of the entity, a contractual default status of
the entity, a regulatory violation status of the entity, a criminal
status of the entity, an export controls status of the entity, an
embargo status of the entity, a tariff status of the entity, a tax
status of the entity, a credit report of the entity, a credit
rating of the entity, a website rating of the entity, a set of
customer reviews for a product of the entity, a social network
rating of the entity, a set of credentials of the entity, a set of
referrals of the entity, a set of testimonials for the entity, a
set of behavior of the entity, a location of the entity, a
geolocation of the entity, and the like. The system may include a
smart contract circuit 8626 structured to create a smart lending
contract 8628 for the loan. The smart contract circuit may be
structured to determine a term or a condition for the smart lending
contract based on the value for the at least one of the set of
items of collateral and modify the smart lending contract to
include the term or the condition, where the terms and conditions
may be a principal amount of debt, a balance of debt, a fixed
interest rate, a variable interest rate, a payment amount, a
payment schedule, a balloon payment schedule, a party, a guarantee,
a guarantor, a security, a personal guarantee, a lien, a duration,
a covenant, a foreclose condition, a default condition, a
consequence of default, and the like. The system may include an
automated agent circuit 8630 structured to perform a loan-action
8616 based on the loan status, where the blockchain service circuit
may be structured to update the historical ledger of events with
the loan action. The system may include a valuation circuit 8622
structured to determine, based on the received data and a valuation
model 8624, a value for at least one of the set of items of
collateral. The valuation circuit may include a valuation model
improvement circuit 8640, where the valuation model improvement
circuit modifies the valuation model based on a first set of
valuation determinations for a first set of items of collateral and
a corresponding set of loan outcomes having the first set of items
of collateral as security. The valuation model improvement circuit
may include a machine learning system, a model-based system, a
rule-based system, a deep learning system, a hybrid system, a
neural network, a convolutional neural network, a feed forward
neural network, a feedback neural network, a self-organizing map, a
fuzzy logic system, a random walk system, a random forest system, a
probabilistic system, a Bayesian system, and a simulation system.
The valuation circuit may include a market value data collection
circuit 8642 structured to monitor and report marketplace
information for offset items of collateral relevant to the value of
the item of collateral. The market value data collection circuit
may be further structured to monitor pricing or financial data for
the offset items of collateral in a public marketplace, such as to
report the monitored pricing or financial data. The smart contract
circuit may be further structured to modify a term or condition of
the loan based on the marketplace information for offset items of
collateral relevant to the value of the item of collateral. The
system may include a collateral classification circuit 8650
structured to identify a group of off-set items of collateral 8652,
where each member of the group of off-set items of collateral and
at least one of the set of items of collateral may share a common
attribute. The common attribute may be a category of the item of
collateral, an age of the item of collateral, a condition of the
item of collateral, a history of the item of collateral, an
ownership of the item of collateral, a caretaker of the item of
collateral, a security of the item of collateral, a condition of an
owner of the item of collateral, a lien on the item of collateral,
a storage condition of the item of collateral, a geolocation of the
item of collateral, a jurisdictional location of the item of
collateral, and the like.
[1472] FIG. 87 depicts a method 8700 including maintaining a secure
historical ledger of events related to a loan 8702, receiving data
relating to a status of the loan 8704, receiving data related to a
set of items of collateral acting as security of the loan 8708,
determining a status of the loan 8710, performing a loan-action
based on the loan status 8712 and updating the historical ledger of
events related to the loan 8714. The method may further include
receiving data related to one or more loan entities 8718 and
determining compliance with a covenant of the loan based on the
data received 8720. The method may further include determining a
state of performance for a condition of the loan, where the
determination of the loan status is based on part on the state of
performance of the condition of the loan. The method may further
include receiving financial data related to at least one party to
the loan. The method may further include determining a financial
condition of the at least one party to the loan based on the
financial data. The method may further include determining a value
for at least one set of items of collateral based on the received
data and a valuation model. The method may further include
determining at least one of a term or a condition for the loan
based on the value of the at least one of the items of collateral
8722 and modifying a smart lending contract to include the at least
one of the term or the condition 8724. The method may include 270
identifying a group of off-set items of collateral, where each
member of the group of off-set items of collateral and at least one
of the set of items of collateral share a common attribute 8728,
receiving data related to the group of off-set items of collateral,
wherein the determination of the value for the at least one set of
items of collateral is partially based on the received data related
to the group of off-set items of collateral 8730.
[1473] Referring to FIG. 88, an illustrative and non-limiting
example smart contract system for managing collateral for a loan
8800 is depicted. The example system may include a controller 8801.
The controller 8801 may include a data collection circuit 8812
structured to monitor a status of a loan 8830 and of a collateral
8828 for the loan, and several artificial intelligence circuits
including a smart contract circuit 8822 structured to process
information from the data collection circuit 8812 and automatically
initiate at least one of a substitution, a removal, or an addition
of one or items from the collateral for the loan based on the
information and a smart lending contract 8831 in response to at
least one of the status of the loan or the status of the collateral
for the loan; and a blockchain service circuit 8858 structured to
interpret a plurality of access control features 8880 corresponding
to at least one party associated with the loan and record the at
least one substitution, removal, or addition in a distributed
ledger 8840 for the loan. The data collection circuit may further
include at least one other system 8862 selected from the systems
consisting of: an Internet of Things system, a camera system, a
networked monitoring system, an internet monitoring system, a
mobile device system, a wearable device system, a user interface
system, and an interactive crowdsourcing system.
[1474] A status of the loan 8830 may be determined based on the
status of at least one of an entity (e.g. user 8806) related to the
loan and a state of a performance of a condition for the loan.
State of the performance of the condition may relate to at least
one of a payment performance or a satisfaction of a covenant for
the loan. The status of the loan may be determined based on a
status of at least one entity related to the loan and a state of
performance of a condition for the loan; and the performance of the
condition may relate to at least one of a payment performance or a
satisfaction of a covenant for the loan. The data collection
circuit 8812 may be further structured to determine compliance with
the covenant by monitoring the at least one entity. When the at
least one entity is a party to the loan, the data collection
circuit 8812 may monitor a financial condition of at least one
entity that is a party to the loan. The condition for the loan may
include a financial condition for the loan, and wherein the state
of performance of the financial condition may be determined based
on an attribute selected from the attributes consisting of: a
publicly stated valuation of the at least one entity, a property
owned by the at least one entity as indicated by public records, a
valuation of a property owned by the at least one entity, a
bankruptcy condition of the at least one entity, a foreclosure
status of the at least one entity, a contractual default status of
the at least one entity, a regulatory violation status of the at
least one entity, a criminal status of the at least one entity, an
export controls status of the at least one entity, an embargo
status of the at least one entity, a tariff status of the at least
one entity, a tax status of the at least one entity, a credit
report of the at least one entity, a credit rating of the at least
one entity, a website rating of the at least one entity, a
plurality of customer reviews for a product of the at least one
entity, a social network rating of the at least one entity, a
plurality of credentials of the at least one entity, a plurality of
referrals of the at least one entity, a plurality of testimonials
for the at least one entity, a behavior of the at least one entity,
a location of the at least one entity, a geolocation of the at
least one entity, and a relevant jurisdiction for the at least one
entity.
[1475] The party to the loan may be selected from the parties
consisting of: a primary lender, a secondary lender, a lending
syndicate, a corporate lender, a government lender, a bank lender,
a secured lender, bond issuer, a bond purchaser, an unsecured
lender, a guarantor, a provider of security, a borrower, a debtor,
an underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, and an accountant.
[1476] The data monitoring circuit 8812 may be further structured
to monitor the status of the collateral of the loan based on at
least one attribute of the collateral selected from the attributes
consisting of: a category of the collateral, an age of the
collateral, a condition of the collateral, a history of the
collateral, a storage condition of the collateral, and a
geolocation of the collateral.
[1477] The controller 88101 may include a valuation circuit 8844
which may be structured to use a valuation model 8852 to determine
a value for the collateral based on the status of the collateral
for the loan. The smart contract circuit 8822 may initiate the at
least one substitution, removal or addition of one or more items
from the collateral for the loan to maintain a value of collateral
within a predetermined range.
[1478] The valuation circuit 8844 may further include a
transactions outcome processing circuit 8864 structured to
interpret outcome data 8810 relating to a transaction in collateral
and iteratively improve 8850 the valuation model in response to the
outcome data.
[1479] The valuation circuit 8844 may further include a market
value data collection circuit 8848 structured to monitor and report
on marketplace information relevant to a value of collateral. The
market value data collection circuit 8848 may monitor pricing data
or financial data for an offset collateral item 8834 in at least
one public marketplace.
[1480] The market value data collection circuit 8848 is further
structured to construct a set of offset collateral items 8834 used
to value an item of collateral may be constructed using a
clustering circuit 8832 of the controller 88101 based on an
attribute of the collateral. The attributes may be selected from
among a category of the collateral, an age of the collateral, a
condition of the collateral, a history of the collateral, a storage
condition of the collateral, and a geolocation of the
collateral.
[1481] Terms and conditions 8824 for the loan may include at least
one member selected from the group consisting of: a principal
amount of debt, a balance of debt, a fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of collateral, a
specification of substitutability of collateral, a party, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition,
and a consequence of default.
[1482] The smart contract circuit may further include or be in
communication with a loan management circuit 8860 structured to
specify terms and conditions of the smart lending contract 8831
that governs at least one of loan terms and conditions, a
loan-related event 8839 or a loan-related activity or action
8838.
[1483] Referring to FIG. 89, an example smart contract method for
managing collateral for a loan is depicted. The example method may
include monitoring a status of a loan and of a collateral for the
loan (step 8902); automatically initiating at least one of a
substitution, a removal, or an addition of one or more items from
the collateral for the loan based on the information (step 8908);
and interpreting a plurality of access control features
corresponding to at least one party associated with the loan (step
8910) and recording the at least one substitution, removal, or
addition in a distributed ledger for the loan (step 8912). A status
of the loan may be determined based on the status of at least one
of an entity related to the loan and a state of a performance of a
condition for the loan.
[1484] The method may further include interpreting information from
the monitoring (step 8914) and determining a value with a valuation
model for a set of collateral based on at least one of the status
of the loan or the collateral for the loan (step 8918). The at
least one substitution, removal, or addition may be to maintain a
value of collateral within a predetermined range. The method may
further include interpreting outcome data relating to a transaction
of one of the collateral or an offset collateral (step 8920) and
iteratively improving the valuation model in response to the
outcome data (step 8922). The method may further include monitoring
and reporting on marketplace information relevant to a value of
collateral (step 8924).
[1485] The method may further include monitoring pricing data or
financial data for an offset collateral item in at least one public
marketplace (step 8928).
[1486] The method may further include specifying terms and
conditions of a smart contract that governs at least one of terms
and conditions for the loan, a loan-related event or a loan-related
activity (step 8930).
[1487] Referring to FIG. 90, an illustrative and non-limiting
example crowdsourcing system for validating conditions of
collateral or a guarantor for a loan 9000 is depicted. The example
system may include a controller 9001. The controller 9001 may
include a data collection circuit 9012, a user interface 9054, and
several artificial intelligence circuits including a smart contract
circuit 9022, robotic process automation circuit 9074, a
crowdsourcing request circuit 9060, a crowdsourcing communications
circuit 9062, a crowdsourcing publishing circuit 9064, and a
blockchain service circuit 9058.
[1488] The crowdsourcing request circuit 9060 may be structured to
configure at least one parameter of a crowdsourcing request 9068
related to obtaining information 9004 on a condition of a
collateral 9011 for a collateral 9002 for a loan 9030 or a
condition of a guarantor for the loan 9096. It may also enable a
workflow by which a human user enters the at least one parameter to
establish the crowdsourcing request. The at least one parameter may
include a type of requested information, the reward, and a
condition for receiving the reward. The reward may be selected from
selected from the rewards consisting of: a financial reward, a
token, a ticket, a contractual right, a cryptocurrency, a plurality
of reward points, a currency, a discount on a product or service,
and an access right.
[1489] The crowdsourcing publishing circuit 9064 may be configured
to publish the crowdsourcing request 9068 to a group of information
suppliers.
[1490] The crowdsourcing communications circuit 9062 may be
structured to collect and process at least one response 9072 from
the group of information suppliers 9070, and to provide a reward
9080 to at least one of the group of information suppliers in
response to a successful information supply event 9098.
[1491] The crowdsourcing communications circuit 9062 further
includes a smart contract circuit 9022 structured to manage the
reward 9080 by determining the successful information supply event
9098 in response to the at least one parameter configured for the
crowdsourcing request 9068, and to automatically allocate the
reward 9080 to the at least one of the group of information
suppliers 9070 in response to the successful information supply
event 9098. It may also be structured to process the at least one
response 9072 and, in response, automatically undertake an action
related to the loan. The action may be at least one of a
foreclosure action, a lien administration action, an interest-rate
setting action, a default initiation action, a substitution of
collateral, or a calling of the loan.
[1492] The loan 9030 may include at least one loan type selected
from the loan types consisting of: an auto loan, an inventory loan,
a capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, and a subsidized loan.
[1493] The crowdsourcing request circuit 9060 may be further
structured to configure at least one further parameter of the
crowdsourcing request 9068 to obtain information on a condition of
a collateral 9011 for the loan.
[1494] The collateral 9002 may include at least one item selected
from the items consisting of: a vehicle, a ship, a plane, a
building, a home, real estate property, undeveloped land, a farm, a
crop, a municipal facility, a warehouse, a set of inventory, a
commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a consumable item, an edible item, a beverage, a
precious metal, an item of jewelry, a gemstone, an item of
intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, and an item of
personal property.
[1495] The condition of collateral 9011 may be determined based on
an attribute selected from the attributes consisting of: a quality
of the collateral, a condition of the collateral, a status of a
title to the collateral, a status of a possession of the
collateral, and a status of a lien on the collateral. When the
collateral is an item, the condition may be determined based on an
attribute selected from the attributes consisting of: a new or used
status of the item, a type of the item, a category of the item, a
specification of the item, a product feature set of the item, a
model of the item, a brand of the item, a manufacturer of the item,
a status of the item, a context of the item, a state of the item, a
value of the item, a storage location of the item, a geolocation of
the item, an age of the item, a maintenance history of the item, a
usage history of the item, an accident history of the item, a fault
history of the item, an ownership of the item, an ownership history
of the item, a price of a type of the item, a value of a type of
the item, an assessment of the item, and a valuation of the
item.
[1496] The blockchain service circuit 9058 may be structured to
record identifying information and the at least one parameter of
the crowdsourcing request, the at least one response to the
crowdsourcing request, and a reward description in a distributed
ledger 9040.
[1497] The robotic process automation circuit 9074 may be
structured to, based on training on a training data set 9078
comprising human user interactions with at least one of the
crowdsourcing request circuit or the crowdsourcing communications
circuit, to configure the crowdsourcing request based on at least
one attribute of the loan. The at least one attribute of the loan
may be obtained from a smart contract circuit 9022 that manages the
loan. The training data set 9078 may further include outcomes from
a plurality of crowdsourcing requests.
[1498] The robotic process automation circuit 9074 may be further
structured to determine a reward 9080.
[1499] The robotic process automation circuit 9074 may be further
structured to determine at least one domain to which the
crowdsourcing publishing circuit 9064 publishes the crowdsourcing
request 9068.
[1500] Referring to FIG. 91, provided herein is a crowdsourcing
method for validating conditions of collateral or a guarantor for a
loan. At least one parameter of a crowdsourcing request may be
configured to obtain information on a condition of a collateral for
a loan or a condition of a guarantor for the loan (step 9102). The
crowdsourcing request may be published to a group of information
suppliers (step 9104). At least one response to the crowdsourcing
request may be collected and processed (step 9108). A reward may be
provided to at least one successful information supplier of the
group of information suppliers in response to a successful
information supply event (step 9110). A reward description may be
published to at least a portion of the group of information
suppliers in response to the successful information supply event
(step 9112). The reward may be automatically allocated to at least
one of the group of information suppliers in response to the
successful information supply event (step 9130). The method may
further include recording identifying information and the at least
one parameter of the crowdsourcing request, the at least one
response to the crowdsourcing request, and a reward description in
a distributed ledger for the crowdsourcing request (step 9114). A
graphical user interface may be configured to enable a workflow by
which a human user enters the at least one parameter to establish
the crowdsourcing request (step 9118). An action related to the
loan may be automatically undertaken in response to the successful
information supply event (step 9120). A robotic process automation
circuit may be trained on a training data set comprising a
plurality of outcomes corresponding to a plurality of the
crowdsourcing requests, and operating the robotic process
automation circuit to iteratively improve the crowdsourcing request
(step 9122). At least one attribute of the loan may be provided to
the robotic process automation circuit in order to configure the
crowdsourcing request (step 9124). Configuring the crowdsourcing
request may include determining a reward. At least one attribute of
the loan may be provided to the robotic process automation circuit
in order to determine at least one domain to which to publish the
crowdsourcing request (step 9128).
[1501] Referring to FIG. 92, an illustrative and non-limiting
example smart contract system for modifying a loan 9200 is
depicted. The example system may include a controller 9201. The
controller 9201 may include a data collection circuit 9212, a
valuation circuit 9244, and several artificial intelligence
circuits 9242 including a smart contract circuit 9222, a clustering
circuit 9232, a jurisdiction definition circuit 9298, and a loan
management circuit 9260. The data collection circuit 9212 may be
structured to determine location information corresponding to each
one of a plurality of entities involved in a loan. The jurisdiction
definition circuit 9298 may be structured to determine a
jurisdiction for at least one of the plurality of entities in
response to the location information. The smart contract circuit
9222 may be structured to automatically undertake a loan-related
action 9238 for the loan based at least in part on the jurisdiction
for at least one of the plurality of entities.
[1502] The smart contract circuit 9222 may be further structured to
automatically undertake the loan-related action in response to a
first one of the plurality of entities being in a first
jurisdiction, and a second one of the plurality of entities being
in a second jurisdiction.
[1503] The smart contract circuit 9222 may be further structured to
automatically undertake the loan-related action in response to one
of the plurality of entities moving from a first jurisdiction to a
second jurisdiction.
[1504] The loan-related action 9238 may include at least one
loan-related action selected from the loan-related actions
consisting of: offering the loan, accepting the loan, underwriting
the loan, setting an interest rate for the loan, deferring a
payment requirement, modifying an interest rate for the loan,
validating title for collateral, recording a change in title,
assessing a value of collateral, initiating inspection of
collateral, calling the loan, closing the loan, setting terms and
conditions for the loan, providing notices required to be provided
to a borrower, foreclosing on property subject to the loan, and
modifying terms and conditions for the loan.
[1505] The smart contract circuit 9222 may be further structured to
process a plurality of jurisdiction-specific regulatory
requirements 9268, such as requirements related to notice, and to
provide an appropriate notice to a borrower based on a jurisdiction
corresponding to at least one entity selected from the entities
consisting of a lender, a borrower, funds provided via the loan, a
repayment of the loan, or a collateral for the loan.
[1506] The smart contract circuit 9222 may be further structured to
process a plurality of jurisdiction-specific regulatory
requirements 9268, such as requirement related to foreclosure, and
to provide an appropriate foreclosure notice to a borrower based on
a jurisdiction of at least one of a lender, a borrower, funds
provided via the loan, a repayment of the loan, and a collateral
for the loan.
[1507] The smart contract circuit 9222 may be further structured to
process a plurality of jurisdiction-specific rules 9270 for setting
terms and conditions 9224 of the loan and to configure a smart
contract 9231 based on a jurisdiction corresponding to at least one
entity selected from the entities consisting of: a borrower, funds
provided via the loan, a repayment of the loan, and a collateral
for the loan.
[1508] The smart contract circuit 9222 may be further structured to
determine an interest rate for the loan to cause the loan to comply
with a maximum interest rate limitation applicable in a
jurisdiction corresponding to a selected one of the plurality of
entities.
[1509] The data collection circuit 9212 may be further structured
to monitor a condition of a collateral for the loan, and wherein
the smart contract circuit is further structured to determine the
interest rate for the loan in response to the condition of the
collateral for the loan.
[1510] The data collection circuit 9212 may be further structured
to monitor an attribute of at least one of the plurality of
entities that are party to the loan, and wherein the smart contract
circuit is further structured to determine the interest rate for
the loan in response to the attribute.
[1511] The smart contract circuit 9222 may further include a loan
management circuit 9260 for specifying terms and conditions of
smart contracts that govern at least one of loan terms and
conditions 9224, loan-related events 9239 or loan-related
activities 9272.
[1512] The loan may include at least one loan type selected from
the loan types consisting of: an auto loan, an inventory loan, a
capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring management,
a pay day loan, a refund anticipation loan, a student loan, a
syndicated loan, a title loan, a home loan, a venture debt loan, a
loan of intellectual property, a loan of a contractual claim, a
working capital loan, a small business loan, a farm loan, a
municipal bond, and a subsidized loan.
[1513] Terms and conditions for the loan may each include at least
one member selected from the group consisting of: a principal
amount of debt, a balance of debt, a fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of collateral, a
specification of substitutability of collateral, a party, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition,
and a consequence of default.
[1514] The data collection circuit 9212 may further include at
least one other system 9262 selected from the systems consisting
of: an Internet of Things system, a camera system, a networked
monitoring system, an internet monitoring system, a mobile device
system, a wearable device system, a user interface system, and an
interactive crowdsourcing system.
[1515] The valuation circuit 9244 may be structured to use a
valuation model 9252 to determine a value for a collateral for the
loan based on the jurisdiction corresponding to at least one of the
plurality of entities. The valuation model 9252 may be a
jurisdiction-specific valuation model, and wherein the jurisdiction
corresponding to at least one of the plurality of entities
comprises a jurisdiction corresponding to at least one entity
selected from the entities consisting of: a lender, a borrower,
funds provided pursuant to the loan, a delivery location of funds
provided pursuant to the loan, a payment of the loan, and a
collateral for the loan.
[1516] At least one of the terms and conditions for the loan may be
based on the value of the collateral for the loan.
[1517] The collateral may include at least one item selected from
the items consisting of: a vehicle, a ship, a plane, a building, a
home, real estate property, undeveloped land, a farm, a crop, a
municipal facility, a warehouse, a set of inventory, a commodity, a
security, a currency, a token of value, a ticket, a cryptocurrency,
a consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, an item of equipment, a tool, an
item of machinery, and an item of personal property.
[1518] The valuation circuit 9244 may further include a
transactions outcome processing circuit 9264 structured to
interpret outcome data relating to a transaction in collateral and
iteratively improve 9250 the valuation model in response to the
outcome data.
[1519] The valuation circuit 9244 may further include a market
value data collection circuit 9248 structured to monitor and report
on marketplace information relevant to a value of collateral. The
market value data collection circuit may monitor pricing or
financial data for an offset collateral item in at least one public
marketplace. A set of offset collateral items 9234 for valuing an
item of collateral may be constructed using the clustering circuit
9232 based on an attribute of the collateral. The attribute may be
selected from among a category of the collateral, an age of the
collateral, a condition of the collateral, a history of the
collateral, a storage condition of the collateral, and a
geolocation of the collateral.
[1520] Referring to FIG. 93, provided herein is a smart contract
method 9300 for modifying a loan. An example method may include
monitoring location information corresponding to each one of a
plurality of entities involved in a loan (step 9302); processing a
location information about the entities and automatically
undertaking a loan-related action for the loan based at least in
part on the location information (step 9304). The example method
includes processing a number of jurisdiction-specific regulatory
notice requirements and providing an appropriate notice to a
borrower based on a location of the lender, a borrower, funds
provided via the loan, a repayment of the loan, and/or a collateral
for the loan (step 9308). The example method includes processing a
number of jurisdiction-specific rules for setting terms and
conditions of the loan, and configuring a smart contract based on a
location of the lender, a borrower, funds provided via the loan, a
repayment of the loan, and/or a collateral for the loan (step
9310). The example method further includes determining an interest
rate of the loan to cause the loan to comply with a maximum
interest rate limitation applicable in a jurisdiction (step 9312).
The example method includes monitoring at least one of a condition
of a number of collateral items for the loan or an attribute of one
of the entities that are a party to the loan, where the condition
or the attribute is used to determine an interest rate (step 9314).
The example method includes specifying terms and conditions of
smart contract(s) that govern at least one of the terms and
conditions, loan-related events, or loan-related activities (step
9318). The example method includes interpreting the location
information and using a valuation model to determine a value for a
number of collateral items for the loan based on the location
information (step 9320). The example method includes interpreting
outcome data relating to a transaction in collateral, and
iteratively improving the valuation model in response to the
outcome data (step 9322). The example method includes monitoring
and reporting on marketplace information relevant to a value of
collateral (step 9324).
[1521] A plurality of jurisdiction-specific requirements based on a
jurisdiction of a relevant one of the plurality of entities may be
processed, and performing at least one operation may be selected
from the operations consisting of: providing an appropriate notice
to a borrower in response to the plurality of jurisdiction-specific
requirements comprising regulatory notice requirements; setting
specific rules for setting terms and conditions of the loan in
response to the plurality of jurisdiction-specific requirements
comprising jurisdiction-specific rules for terms and conditions of
the loan; determining an interest rate for the loan to cause the
loan to comply with a maximum interest rate limitation in response
to the plurality of jurisdiction-specific requirements comprising a
maximum interest rate limitation; and wherein the relevant one of
the plurality of entities comprises at least one entity selected
from the entities consisting of: a lender, a borrower, funds
provided pursuant to the loan, a repayment of the loan, and a
collateral for the loan (step 9308).
[1522] At least one of a condition of a plurality of collateral for
the loan or an attribute of at least one of the plurality of
entities that are party to the loan may be monitored, wherein the
condition or the attribute is used to determine an interest rate
(step 9314).
[1523] A valuation model may be operated to determine a value for a
collateral for the loan based on the jurisdiction for at least one
of the plurality of entities (step 9320).
[1524] Outcome data relating to a transaction in collateral may be
interpreted and the valuation model may be iteratively improved in
response to the outcome data (step 9322).
[1525] Referring now to FIG. 94, an illustrative and non-limiting
example smart contract system for modifying a loan 9400 is
depicted. The example system may include a controller 9401. The
controller 94101 may include a data collection circuit 9412, a
valuation circuit 9444, and several artificial intelligence
circuits 9442 including a smart contract circuit 9422, a clustering
circuit 9432, and a loan management circuit 9460.
[1526] The data collection circuit 9412 may be structured to
monitor and collect information about at least one entity 9498
involved in a loan 9430. The smart contract circuit 9422 may be
structured to automatically restructure a debt related to the loan
based on the monitored and collected information about the at least
one entity involved in the loan. The monitored and collected
information may include a condition of a collateral 9411 for the
loan, or according to at least one rule that is based on a covenant
of the loan and wherein the restructuring occurs upon an event that
is determined with respect to the at least one entity that relates
to the covenant, or restructuring may be based on an attribute 9494
of the at least one entity that is monitored by the data collection
circuit. The event may be a failure of collateral for the loan to
exceed a required fractional value of a remaining balance of the
loan, or a default of a buyer with respect to the covenant.
[1527] The smart contract circuit 9422 may be further structured to
determine the occurrence of an event based on a covenant of the
loan and the monitored and collected information about the at least
one entity involved in the loan, and to automatically restructure
the debt in response to the occurrence of the event.
[1528] The smart contract circuit 9422 may further include a loan
management circuit 9460 which may be structured to specify terms
and conditions of a smart contract that governs at least one of
loan terms and conditions 9424, a loan-related event 9439 or a
loan-related activity 9472.
[1529] The loan may include at least one loan type selected from
the loan types consisting of: an auto loan, an inventory loan, a
capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, and a subsidized loan.
[1530] Terms and conditions for the loan may include at least one
member selected from the group consisting of: a principal amount of
debt, a balance of debt, a fixed interest rate, a variable interest
rate, a payment amount, a payment schedule, a balloon payment
schedule, a specification of collateral, a specification of
substitutability of collateral, a party, a guarantee, a guarantor,
a security, a personal guarantee, a lien, a duration, a covenant, a
foreclose condition, a default condition, and a consequence of
default.
[1531] The data collection circuit 9412 may further include at
least one other system 9462 selected from the systems consisting
of: an Internet of Things system, a camera system, a networked
monitoring system, an internet monitoring system, a mobile device
system, a wearable device system, a user interface system, and an
interactive crowdsourcing system.
[1532] The valuation circuit 9444 may be structured to use a
valuation model 9452 to determine a value for a collateral based on
the monitored and collected information about the at least one
entity involved in the loan. The smart contract circuit may be
further structured to automatically restructure the debt based on
the value for the collateral.
[1533] The collateral may be at least one item selected from the
items consisting of: a vehicle, a ship, a plane, a building, a
home, real estate property, undeveloped land, a farm, a crop, a
municipal facility, a warehouse, a set of inventory, a commodity, a
security, a currency, a token of value, a ticket, a cryptocurrency,
a consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, an item of equipment, a tool, an
item of machinery, and an item of personal property.
[1534] The valuation circuit 9444 may further include a
transactions outcome processing circuit 9464 structured to
interpret outcome data 9410 relating to a transaction in collateral
and iteratively improve 9450 the valuation model in response to the
outcome data.
[1535] The valuation circuit 9444 may further include a market
value data collection circuit 9448 structured to monitor and report
on marketplace information relevant to a value of collateral. The
market value data collection circuit 9448 monitors pricing or
financial data for an offset collateral item 9434 in at least one
public marketplace. A set of offset collateral items 9434 for
valuing an item of collateral may be constructed using a clustering
circuit 9432 based on an attribute of the collateral. The attribute
may be selected from among a category of the collateral, an age of
the collateral, a condition of the collateral, a history of the
collateral, a storage condition of the collateral, and a
geolocation of the collateral.
[1536] Referring now to FIG. 95, an illustrative and non-limiting
example smart contract method for modifying a loan 9500 is
depicted. The method includes monitoring and collecting information
about at least one entity involved in a loan (step 9502);
processing information from the monitoring of the at least one
entity (step 9504); and automatically restructuring a debt related
to the loan based on the monitored and collected information about
the at least one entity (step 9508). Determining the occurrence of
an event may be based on a covenant of the loan and the monitored
and collected information about the at least one entity involved in
the loan, and automatically restructuring the debt in response to
the occurrence of the event (step 9509).
[1537] Terms and conditions of a smart contract that governs at
least one of loan terms and conditions, a loan-related event and a
loan-related activity may be specified (step 9510).
[1538] Operating a valuation model to determine a value for a
collateral based on the monitored and collected information about
the at least one entity involved in the loan (step 9512).
[1539] Outcome data relating to a transaction in collateral may be
interpreted and the valuation model may be iteratively improved in
response to the outcome data (step 9514).
[1540] The method may further include monitoring and reporting on
marketplace information relevant to a value of collateral (step
9518).
[1541] Pricing or financial data for an offset collateral item may
be monitored in at least one public marketplace (step 9520).
[1542] A set of offset collateral items for valuing an item of
collateral may be constructed using a similarity clustering
algorithm based on an attribute of the collateral (step 9522).
[1543] Referring now to FIG. 96, an illustrative and non-limiting
example smart contract system for modifying a loan 9600 is
depicted. The example system may include a controller 9601. The
controller 9601 may include a data collection circuit 9612, a
social networking input circuit 9644, a social network data
collection circuit 9632, and several artificial intelligence
circuits 9642 including a smart contract circuit 9622, a guarantee
validation circuit 9698, and a robotic process automation circuit
9648.
[1544] The social network data collection circuit 9632 may be
structured to collect data using a plurality of algorithms that are
configured to monitor social network information about an entity
9664 involved in a loan 9630 in response to the loan guarantee
parameter. The social networking input circuit 9644 may be
structured to interpret a loan guarantee parameter. The guarantee
validation circuit 9698 may be structured to validate a guarantee
for the loan in response to the monitored social network
information.
[1545] The loan guarantee parameter may include a financial
condition of the entity, wherein the entity is a guarantor for the
loan.
[1546] The guarantee validation circuit 9698 may be further
structured to determine the financial condition may be determined
based on at least one attribute selected from the attributes
consisting of: a publicly stated valuation of the entity, a
property owned by the entity as indicated by public records, a
valuation of a property owned by the entity, a bankruptcy condition
of the entity, a foreclosure status of the entity, a contractual
default status of the entity, a regulatory violation status of the
entity, a criminal status of the entity, an export controls status
of the entity, an embargo status of the entity, a tariff status of
the entity, a tax status of the entity, a credit report of the
entity, a credit rating of the entity, a website rating of the
entity, a plurality of customer reviews for a product of the
entity, a social network rating of the entity, a plurality of
credentials of the entity, a plurality of referrals of the entity,
a plurality of testimonials for the entity, a plurality of
behaviors of the entity, a location of the entity, a jurisdiction
of the entity, and a geolocation of the entity.
[1547] The loan may include at least one loan type selected from
the loan types consisting of: an auto loan, an inventory loan, a
capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, and a subsidized loan.
[1548] The data collection circuit 9612 may be structured to obtain
information about a condition 9611 of a collateral for the loan,
wherein the collateral comprises at least one item selected from
the items consisting of: a vehicle, a ship, a plane, a building, a
home, real estate property, undeveloped land, a farm, a crop, a
municipal facility, a warehouse, a set of inventory, a commodity, a
security, a currency, a token of value, a ticket, a cryptocurrency,
a consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, an item of equipment, a tool, an
item of machinery, and an item of personal property and wherein the
guarantee validation circuit is further structured to validate the
guarantee of the loan in response to the condition of the
collateral for the loan.
[1549] The condition 9611 of collateral may include a condition
attribute selected from the group consisting of a quality of the
collateral, a status of title to the collateral, a status of
possession of the collateral, a status of a lien on the collateral,
a new or used status, a type, a category, a specification, a
product feature set, a model, a brand, a manufacturer, a status, a
context, a state, a value, a storage location, a geolocation, an
age, a maintenance history, a usage history, an accident history, a
fault history, an ownership, an ownership history, a price, an
assessment, and a valuation. Conditions may be stored as collateral
data 9604.
[1550] The social networking input circuit 9644 may be further
structured to enable a workflow by which a human user enters the
loan guarantee parameter to establish a social network data
collection and monitoring request.
[1551] The smart contract circuit 9622 may be structured to
automatically undertake an action related to the loan in response
to the validation of the loan. The action may be related to the
loan is in response to the loan guarantee not being validated, and
wherein the action comprises at least one action selected from the
actions consisting of: a foreclosure action, a lien administration
action, an interest-rate adjustment action, a default initiation
action, a substitution of collateral, a calling of the loan, and
providing an alert to a second entity involved in the loan.
[1552] The robotic process automation circuit 9648 may be
structured to, based on iteratively training on a training data set
9646 comprising human user interactions with the social network
data collection circuit, configure the loan guarantee parameter
based on at least one attribute of the loan. The at least one
attribute of the loan 9630 may be obtained from a smart contract
circuit that manages the loan.
[1553] The training data set 9646 may further include outcomes from
a plurality of social network data collection and monitoring
requests performed by the social network data collection
circuit.
[1554] The robotic process automation circuit 9648 may be further
structured to determine at least one domain to which the social
network data collection circuit will apply.
[1555] Training may include training the robotic process automation
circuit 9648 to configure the plurality of algorithms.
[1556] Referring now to FIG. 97, an illustrative and non-limiting
example smart contract method for modifying a loan 9700 is
depicted. A loan guarantee parameter may be interpreted (step
9701). Data may be collected using a plurality of algorithms that
are configured to monitor social network information about an
entity involved in a loan in response to the loan guarantee
parameter (step 9702). A guarantee for the loan may be validated in
response to the monitored social network information (step 9704). A
workflow may be enabled by which a human user enters the loan
guarantee parameter to establish a social network data collection
and monitoring request (step 9708). In response to the validation
of the loan, an action related to the loan may be undertaken
automatically (step 9710). A robotic process automation circuit may
be iteratively trained to configure a data collection and
monitoring action based on at least one attribute of the loan,
wherein the robotic process automation circuit is trained on a
training data set comprising at least one of outcomes from or human
user interactions with the plurality of algorithms (step 9712). At
least one domain to which the plurality of algorithms will apply
may be determined (step 9714).
[1557] Referring to FIG. 98, an illustrative and non-limiting
example monitoring system for validating conditions of a guarantee
for a loan 9800 is depicted. The example system may include a
controller 9801. The controller 9801 may include an Internet of
Things data collection input circuit 9844, Internet of Things data
collection circuit 9832, and several artificial intelligence
circuits 9842 including a smart contract circuit 9822, a guarantee
validation circuit 9898, and a robotic process automation circuit
9848.
[1558] The Internet of Things data collection input circuit 9844
may be structured to interpret a loan guarantee parameter 9892. The
Internet of Things data collection circuit 9832 may be structured
to collect data using at least one algorithm that is configured to
monitor Internet of Things information collected from and about an
entity 9864 involved in a loan 9830 in response to the loan
guarantee parameter. The guarantee validation circuit 9898
structured to validate a guarantee for the loan in response to the
monitored IoT information.
[1559] The loan guarantee parameter 9892 may include a financial
condition of the entity, wherein the entity is a guarantor for the
loan. Monitored IoT information includes at least one of a publicly
stated valuation of the entity, a property owned by the entity as
indicated by public records, a valuation of a property owned by the
entity, a bankruptcy condition of the entity, a foreclosure status
of the entity, a contractual default status of the entity, a
regulatory violation status of the entity, a criminal status of an
entity, an export controls status of the entity, an embargo status
of the entity, a tariff status of the entity, a tax status of the
entity, a credit report of the entity, a credit rating of the
entity, a website rating of the entity, a plurality of customer
reviews for a product of the entity, a social network rating of the
entity, a plurality of credentials of the entity, a plurality of
referrals of the entity, a plurality of testimonials for the
entity, a plurality of behaviors of the entity, a location of the
entity, a jurisdiction of the entity, and a geolocation of the
entity.
[1560] The loan may include at least one loan type selected from
the loan types consisting of: an auto loan, an inventory loan, a
capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, and a subsidized loan.
[1561] The Internet of Things data collection circuit 9832 may be
further structured to obtain information about a condition of a
collateral for the loan, wherein the collateral comprises at least
one item selected from the items consisting of a vehicle, a ship, a
plane, a building, a home, a real estate property, an undeveloped
land, a farm, a crop, a municipal facility, a warehouse, a set of
inventory, a commodity, a security, a currency, a token of value, a
ticket, a cryptocurrency, a consumable item, an edible item, a
beverage, a precious metal, an item of jewelry, a gemstone, an item
of intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, and an item of
personal property, and wherein the guarantee validation circuit
9898 is further structured to validate the guarantee of the loan in
response to the condition of the collateral for the loan.
[1562] The condition 9811 of collateral may include a condition
attribute selected from the group consisting of a quality of the
collateral, a status of title to the collateral, a status of
possession of the collateral, a status of a lien on the collateral,
a new or used status, a type, a category, a specification, a
product feature set, a model, a brand, a manufacturer, a status, a
context, a state, a value, a storage location, a geolocation, an
age, a maintenance history, a usage history, an accident history, a
fault history, an ownership, an ownership history, a price, an
assessment, and a valuation.
[1563] The Internet of Things data collection input circuit 9844
may be further structured to enable a workflow by which a human
user enters the loan guarantee parameter 9892 to establish an
Internet of Things data collection request.
[1564] The smart contract circuit 9822 may be structured to
automatically undertake an action related to the loan in response
to the validation of the loan. The action related to the loan may
be in response to the loan guarantee not being validated, and
wherein the action comprises at least one action selected from the
actions consisting of: a foreclosure action, a lien administration
action, an interest-rate adjustment action, a default initiation
action, a substitution of collateral, a calling of the loan, and
providing an alert to second entity involved in the loan.
[1565] The robotic process automation circuit 9848 may be
structured to, based on iteratively training on a training data set
comprising human user interactions with the Internet of Things data
collection circuit, configure the loan guarantee parameter based on
at least one attribute of the loan. The at least one attribute of
the loan is obtained from a smart contract circuit that manage the
loan. The training data set 9846 may further include outcomes from
a plurality of Internet of Things data collection and monitoring
requests performed by the Internet of Things data collection
circuit.
[1566] The robotic process automation circuit 9848 may be further
structured to determine at least one domain to which the Internet
of Things data collection circuit will apply.
[1567] Training may include training the robotic process automation
circuit 9848 to configure the at least one algorithm.
[1568] Referring to FIG. 99, an illustrative and non-limiting
example monitoring method for validating conditions of a guarantee
for a loan 9900 is depicted. The example method may include
interpreting a loan guarantee parameter (step 9902); collecting
data using a plurality of algorithms that are configured to monitor
Internet of Things (IoT) information collected from and about an
entity involved in a loan in response to the loan guarantee
parameter (step 9904); and validating a guarantee for the loan in
response to the monitored IoT information (step 9905).
[1569] The loan guarantee parameter may be configured to obtain
information about a financial condition of the entity, wherein the
entity is a guarantor for the loan (step 9908). The at least one
algorithm may be configured to obtain information about a condition
of a collateral for the loan (step 9910), wherein the collateral
comprises at least one item selected from the items consisting of a
vehicle, a ship, a plane, a building, a home, a real estate
property, an undeveloped land, a farm, a crop, a municipal
facility, a warehouse, a set of inventory, a commodity, a security,
a currency, a token of value, a ticket, a cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, an item of intellectual property, an
intellectual property right, a contractual right, an antique, a
fixture, an item of furniture, an item of equipment, a tool, an
item of machinery, and an item of personal property; and validating
the guarantee for the loan further in response to the condition of
the collateral for the loan.
[1570] A workflow by which a human user enters the loan guarantee
parameter to establish an Internet of Things data collection
request may be enabled (step 9912).
[1571] An action related to the loan may be undertaken
automatically in response to the validation (step 9914).
[1572] The action related to the loan may be in response to the
loan guarantee not being validated, and wherein the action
comprises a foreclosure action.
[1573] The action related to the loan may be in response to the
loan guarantee not being validated, and wherein the action
comprises a lien administration action.
[1574] The action related to the loan may be in response to the
loan guarantee not being validated, and wherein the action
comprises an interest-rate adjustment action.
[1575] The action related to the loan may be in response to the
loan guarantee not being validated, and wherein the action
comprises a default initiation action.
[1576] The action related to the loan may be in response to the
loan guarantee not being validated, and wherein the action
comprises a substitution of collateral.
[1577] The action related to the loan may be in response to the
loan guarantee not being validated, and wherein the action
comprises a calling of the loan.
[1578] The action related to the loan may be in response to the
loan guarantee not being validated, and wherein the action
comprises providing an alert to a second entity involved in the
loan.
[1579] A robotic process automation circuit may be iteratively
trained to configure an Internet of Things data collection and
monitoring action based on at least one attribute of the loan,
wherein the robotic process automation circuit is trained on a
training data set comprising at least one of outcomes from or human
user interactions with the plurality of algorithms (step 9918).
[1580] At least one domain to which the at least one algorithm will
apply may be determined (step 9920). Training may include training
the robotic process automation circuit to configure the plurality
of algorithms.
[1581] The training data set may further include outcomes from a
set of IoT data collection and monitoring requests.
[1582] Referring now to FIG. 100, an illustrative and non-limiting
example robotic process automation system for negotiating a loan
10000 is depicted. The example system may include a controller
10001. The controller 10001 may include a data collection circuit
10012, a valuation circuit 10044, and several artificial
intelligence circuits 10042 including an automated loan
classification circuit 10032, a robotic process automation circuit
10060, a smart contract circuit 10084, and a clustering circuit
10082.
[1583] The data collection circuit 10012 may be structured to
collect a training set of interactions 10010 from at least one
entity 10078 related to at least one loan transaction. An automated
loan classification circuit 10032 may be trained on the training
set of interactions 10010 to classify a at least one loan
negotiation action. The robotic process automation circuit 10060
may be trained on a training set of a plurality of loan negotiation
actions 10074 classified by the automated loan classification
circuit 10032 and a plurality of loan transaction outcomes 10039 to
negotiate a terms and conditions 10024 of a new loan 10030 on
behalf of a party to the new loan.
[1584] The data collection circuit may further include at least one
other system 10062 selected from the systems consisting of: an
Internet of Things system, a camera system, a networked monitoring
system, an internet monitoring system, a mobile device system, a
wearable device system, a user interface system, and an interactive
crowdsourcing system. The at least one entity may be a party to the
at least one loan transaction and may be selected from the entities
consisting of: a primary lender, a secondary lender, a lending
syndicate, a corporate lender, a government lender, a bank lender,
a secured lender, bond issuer, a bond purchaser, an unsecured
lender, a guarantor, a provider of security, a borrower, a debtor,
an underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, and an accountant.
[1585] The automated loan classification circuit 10032 may include
a system selected from the systems consisting of: a machine
learning system, a model-based system, a rule-based system, a deep
learning system, a hybrid system, a neural network, a convolutional
neural network, a feed forward neural network, a feedback neural
network, a self-organizing map, a fuzzy logic system, a random walk
system, a random forest system, a probabilistic system, a Bayesian
system, and a simulation system.
[1586] The robotic process automation circuit 10060 may be further
trained on a plurality of interactions of parties with a plurality
of user interfaces involved in a plurality of lending
processes.
[1587] The smart contract circuit 10084 may be structured to
automatically configure a smart contract 8 for the new loan 10030
based on an outcome of the negotiation.
[1588] A distributed ledger 10080 may be associated with the new
loan 10030, wherein the distributed ledger 10080 is structured to
record at least one of an outcome and a negotiating event of the
negotiation.
[1589] The new loan may include at least one loan type selected
from the loan types consisting of: an auto loan, an inventory loan,
a capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, and a subsidized loan.
[1590] The valuation circuit 10044 may be structured to use a
valuation model 10052 to determine a value for a collateral for the
new loan. The collateral may include at least one item selected
from the items consisting of: a vehicle, a ship, a plane, a
building, a home, real estate property, undeveloped land, a farm, a
crop, a municipal facility, a warehouse, a set of inventory, a
commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a consumable item, an edible item, a beverage, a
precious metal, an item of jewelry, a gemstone, an item of
intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, and an item of
personal property.
[1591] The valuation circuit may further include a market value
data collection circuit 10048 structured to monitor and report on
marketplace information relevant to a value of the collateral. The
market value data collection circuit 10048 may monitor pricing or
financial data for an offset collateral item 10034 in at least one
public marketplace. A set of offset collateral items 10034 for
valuing the collateral may be constructed using a clustering
circuit 10082 based on an attribute of the collateral. The
attribute may be selected from among a category of the collateral,
an age of the collateral, a condition of the collateral, a history
of the collateral, a storage condition of the collateral, and a
geolocation of the collateral. The terms and conditions 10024 for
the new loan may include at least one member selected from the
group consisting of: a principal amount of debt, a balance of debt,
a fixed interest rate, a variable interest rate, a payment amount,
a payment schedule, a balloon payment schedule, a specification of
collateral, a specification of substitutability of collateral, a
party, a guarantee, a guarantor, a security, a personal guarantee,
a lien, a duration, a covenant, a foreclose condition, a default
condition, and a consequence of default.
[1592] Referring now to FIG. 101, an illustrative and non-limiting
example robotic process automation method for negotiating a loan
10000 is depicted. The example method may include collecting a
training set of interactions from at least one entity related to at
least one loan transaction (step 10102); training an automated loan
classification circuit on the training set of interactions to
classify a at least one loan negotiation action (step 10104); and
training a robotic process automation circuit on a training set of
a plurality of loan negotiation actions classified by the automated
loan classification circuit and a plurality of loan transaction
outcomes to negotiate a terms and conditions of a new loan on
behalf of a party to the new loan (step 10108).
[1593] The robotic process automation circuit may be trained on a
plurality of interactions of parties with a plurality of user
interfaces involved in a plurality of lending processes (step
10110).
[1594] A smart contract for the new loan may be configured based on
an outcome of the negotiation (step 10112).
[1595] At least one of an outcome and a negotiating event of the
negotiation may be recorded in a distributed ledger associated with
the new loan (step 10114).
[1596] A value for a collateral for the new loan may be determined
using a valuation model (step 10118).
[1597] An example method may further include monitoring and
reporting on marketplace information relevant to a value of the
collateral (step 10120).
[1598] A set of offset collateral items for valuing the collateral
may be constructed using a similarity clustering algorithm based on
an attribute of the collateral (step 10122).
[1599] Referring to FIG. 102, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 10200 is depicted. The example system may
include a data collection circuit 10206 which may collect data such
loan collection outcomes 10203, training set of loan interactions
10204 which may include collection of payments 10205 and the like.
The data may be collected from loan transactions 10219, loan data
10201, and entity information 10202 and the like. The data may be
collected from a variety of sources and systems such as: an
Internet of Things system, a camera system, a networked monitoring
system, an internet monitoring system, a mobile device system, a
wearable device system, a user interface system, and an interactive
crowdsourcing system. The loan collection outcomes 10203 may
include at least outcome such a response to a collection contact
event, a payment of a loan, a default of a borrower on a loan, a
bankruptcy of a borrower of a loan, an outcome of a collection
litigation, a financial yield of a set of collection actions, a
return on investment on collection, a measure of reputation of a
party involved in collection, and the like.
[1600] The system may also include an artificial intelligence
circuit 10210 that may be structured to classify a set of loan
collection actions 10209 based at least in part on the training set
of loan interactions 10204. The artificial intelligence circuit
10210 may include at least one system such as a machine learning
system, a model-based system, a rule-based system, a deep learning
system, a hybrid system, a neural network, a convolutional neural
network, a feed forward neural network, a feedback neural network a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system, a
simulation system, and the like.
[1601] The system may also include a robotic process automation
circuit 10213 structured to perform at least one loan collection
action 10211 on behalf of a party to a loan 10212 based at least in
part on the training set of loan interactions 10204 and the set of
loan collection outcomes 10203. The loan collection action 10211
undertaken by the robotic process automation circuit 10213 may be
at least one of a referral of a loan to an agent for collection,
configuration of a collection communication, scheduling of a
collection communication, configuration of content for a collection
communication, configuration of an offer to settle a loan,
termination of a collection action, deferral of a collection
action, configuration of an offer for an alternative payment
schedule, initiation of a litigation, initiation of a foreclosure,
initiation of a bankruptcy process, a repossession process,
placement of a lien on collateral, and the like. The party to a
loan 10212 may include least one such as a primary lender, a
secondary lender, a lending syndicate, a corporate lender, a
government lender, a bank lender, a secured lender, bond issuer, a
bond purchaser, an unsecured lender, a guarantor, a provider of
security, a borrower, a debtor, an underwriter, an inspector, an
assessor, an auditor, a valuation professional, a government
official, an accountant, and the like. Loans 10201 may include at
least one auto loan, an inventory loan, a capital equipment loan, a
bond for performance, a capital improvement loan, a building loan,
a loan backed by an account receivable, an invoice finance
arrangement, a factoring arrangement, a pay day loan, a refund
anticipation loan, a student loan, a syndicated loan, a title loan,
a home loan, a venture debt loan, a loan of intellectual property,
a loan of a contractual claim, a working capital loan, a small
business loan, a farm loan, a municipal bond, a subsidized loan and
the like.
[1602] The system may further include an interface circuit 10208
structured to receive interactions 10207 from one or more of the
entities 10202. In some embodiments the robotic process automation
circuit 10213 may be trained on the interactions 10207. The system
may further include a smart contract circuit 10218 structured to
determine completion of a negotiation of the loan collection action
10211 and modify a contract 10216 based on an outcome of the
negation 10217.
[1603] The system may further include a distributed ledger circuit
10215 structured to determine at least one of a collection outcome
10220 or an event 10221 associated with the loan collection action
10211. The distributed ledger circuit 10215 may be structured to
record, in a distributed ledger 10214 associated with the loan, the
event 10221 and/or the collection outcome 10220.
[1604] Referring to FIG. 103, an illustrative and non-limiting
example method 10300 is depicted. The example method 10300 may
include step 10301 for collecting a training set of loan
interactions and a set of loan collection outcomes among entities
for a set of loan transactions, wherein the training set of loan
interactions comprises a collection of a set of payments for a set
of loans. A set of loan collection actions based at least in part
the training set of loan interactions may be classified (step
10302). The method may further include the step 10303 of specifying
a loan collection action on behalf of a party to a loan based at
least in part on the training set of loan interactions and the set
of loan collection outcomes.
[1605] The method 10300 may further include the step 10304 of
determining completion of a negotiation of the loan collection
action. Based on the outcome of the negotiations a smart contract
may be modified in step 10305. The method may also include the step
10306 of determining at least one of a collection outcome or an
event associated with the loan collection action. The at least one
of the collection outcome or the event may be recorded in a
distributed ledger associate with the loan in step 10307.
[1606] Referring to FIG. 104, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 10400 is depicted. The example system may
include a data collection circuit 10406 structured to collect a
training set of loan interactions between entities 10402, wherein
the training set of loan interactions may include a set of loan
refinancing activities 10403 and a set of loan refinancing outcomes
10404. The system may include an artificial intelligence circuit
10410 structured to classify the set of loan refinancing
activities, wherein the artificial intelligence circuit is trained
on the training set of loan interactions. The system may include a
robotic process automation circuit 10413 structured to perform a
second loan refinancing activity 10411 on behalf of a party to a
second loan 10412, wherein the robotic process automation circuit
is trained on the set of loan refinancing activities and the set of
loan refinancing outcomes. The example system may include a data
collection circuit 10406 which may collect data such as a training
set of loan interactions between entities 10402. Data related to
the set of loan interactions between entities 10402 may include
data related to loan refinancing activities 10403 and loan
refinancing outcomes 10404. The data may be collected from loan
data 10401, information about entities 10402, and the like. The
data may be collected from a variety of sources and systems such
as: an Internet of Things system, a camera system, a networked
monitoring system, an internet monitoring system, a mobile device
system, a wearable device system, a user interface system, and an
interactive crowdsourcing system. The loan refinancing activity
10403 may include at least one activity such as initiating an offer
to refinance, initiating a request to refinance, configuring a
refinancing interest rate, configuring a refinancing payment
schedule, configuring a refinancing balance, configuring collateral
for a refinancing, managing use of proceeds of a refinancing,
removing or placing a lien associated with a refinancing, verifying
title for a refinancing, managing an inspection process, populating
an application, negotiating terms and conditions for a refinancing,
closing a refinancing, and the like.
[1607] The system may also include an artificial intelligence
circuit 10410 that may be structured to classify the set of loan
refinancing activities 10409 based at least in part on the training
set of loan interactions 10405. The artificial intelligence circuit
10410 may include at least one system such as a machine learning
system, a model-based system, a rule-based system, a deep learning
system, a hybrid system, a neural network, a convolutional neural
network, a feed forward neural network, a feedback neural network a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system, a
simulation system, and the like.
[1608] The system may also include a robotic process automation
circuit 10413 structured to perform a second loan refinancing
activity 10411 on behalf of a party to a second loan 10412 based at
least in part on the set of loan refinancing activities 10403 and
the set of loan refinancing outcomes 10404. The party to a second
loan 10412 may include least one such as a primary lender, a
secondary lender, a lending syndicate, a corporate lender, a
government lender, a bank lender, a secured lender, bond issuer, a
bond purchaser, an unsecured lender, a guarantor, a provider of
security, a borrower, a debtor, an underwriter, an inspector, an
assessor, an auditor, a valuation professional, a government
official, an accountant, and the like.
[1609] The second loan 10419 may include at least one auto loan, an
inventory loan, a capital equipment loan, a bond for performance, a
capital improvement loan, a building loan, a loan backed by an
account receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, a subsidized loan and the like.
[1610] The system may further include an interface circuit 10408
structured to receive interactions 10407 from one or more of the
entities 10402. In some embodiments the robotic process automation
circuit 10413 may be trained on the interactions 10407. The system
may further include a smart contract circuit 10418 structured to
determine completion of the second loan refinancing activity 10411
and modify a smart refinance contract 10417 based on an outcome of
the second loan refinancing activity 10411.
[1611] The system may further include a distributed ledger circuit
10416 structured to determine an event 10415 associated with the
second loan refinancing activity 10411. The distributed ledger
circuit 10416 may be structured to record, in a distributed ledger
10414 associated with the second loan 10419, the event 10415
associated with the second loan refinancing activity 10411.
[1612] Referring to FIG. 105, an illustrative and non-limiting
example method 10500 is depicted. The example method 10500 may
include step 10501 for collecting a training set of loan
interactions between entities, wherein the training set of loan
interactions comprises a set of loan refinancing activities and a
set of loan refinancing outcomes. A set of loan refinancing
activities based at least in part the training set of loan
interactions may be classified (step 10502). The method may further
include the step 10503 of specifying a second loan refinancing
activity on behalf of a party to a second loan based at least in
part on the set of loan refinancing activities and the set of loan
refinancing outcomes.
[1613] The method 10500 may further include the step 10504 of
determining completion of the second loan refinancing activity.
Based on the outcome of the second loan refinancing activity a
smart refinance contract may be modified in step 10505. The method
may also include the step 10506 of determining an event associated
with the second loan refinancing activity. The event associated
with the second loan refinancing activity may be recorded in a
distributed ledger associate with the second loan in step
10507.
[1614] Referring to FIG. 106, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 10600 is depicted. The example system may
include a data collection circuit 10605 which may collect data such
as a training set of loan interactions 10604 between entities which
may include a set of loan consolidation transactions 10603 and the
like. The data may be collected from loan data 10601, information
re. entities 10602, and the like. The data may be collected from a
variety of sources and systems such as: an Internet of Things
system, a camera system, a networked monitoring system, an internet
monitoring system, a mobile device system, a wearable device
system, a user interface system, and a crowdsourcing system.
[1615] The system may also include an artificial intelligence
circuit 10610 that may be structured to classify a set of loans as
candidates for consolidation 10608 based at least in part on the
training set of loan interactions 10604. The artificial
intelligence circuit 10610 may include at least one system such as
a machine learning system, a model-based system, a rule-based
system, a deep learning system, a hybrid system, a neural network,
a convolutional neural network, a feed forward neural network, a
feedback neural network a self-organizing map, a fuzzy logic
system, a random walk system, a random forest system, a
probabilistic system, a Bayesian system, a simulation system, and
the like.
[1616] The system may also include a robotic process automation
circuit 10613 structured to manage a consolidation of at least a
subset of the set of loans 10611 on behalf of a party to the loan
consolidation 10612 based at least in part on the training set of
loan consolidation transactions 10603. Managing the consolidation
may include identification of loans from a set of candidate loans,
preparation of a consolidation offer, preparation of a
consolidation plan, preparation of content communicating a
consolidation offer, scheduling a consolidation offer,
communicating a consolidation offer, negotiating a modification of
a consolidation offer, preparing a consolidation agreement,
executing a consolidation agreement, modifying collateral for a set
of loans, handling an application workflow for consolidation,
managing an inspection, managing an assessment, setting an interest
rate, deferring a payment requirement, setting a payment schedule,
or closing a consolidation agreement.
[1617] The artificial intelligence circuit may further include a
model 10609 that may be used to classify loans are candidates for
consolidation 10608. The model 10609 may process attributes of
entities, the attributes may include identity of a party, interest
rate, payment balance, payment terms, payment schedule, type of
loan, type of collateral, financial condition of party, payment
status, condition of collateral, value of collateral, and the
like.
[1618] The party to a loan consolidation 10612 may include least
one such as a primary lender, a secondary lender, a lending
syndicate, a corporate lender, a government lender, a bank lender,
a secured lender, bond issuer, a bond purchaser, an unsecured
lender, a guarantor, a provider of security, a borrower, a debtor,
an underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, an accountant, and the
like.
[1619] Loans 10601 may include at least one auto loan, an inventory
loan, a capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, a subsidized loan and the like.
[1620] The system may further include an interface circuit 10607
structured to receive interactions 10606 from one or more of the
entities 10602. In some embodiments the robotic process automation
circuit 10613 may be trained on the interactions 10606. The system
may further include a smart contract circuit 10620 structured to
determine a completion of a negotiations of the consolidation and
modify a contract 10618 based on an outcome of the negotiation
10619.
[1621] The system may further include a distributed ledger circuit
10617 structured to determine at least one of an outcome 10615 or a
negotiation event 10616 associated with the consolidation. The
distributed ledger circuit 10617 may be structured to record, in a
distributed ledger 10614 associated with the loan, the event 10616
and/or the outcome 10615.
[1622] Referring to FIG. 107, an illustrative and non-limiting
example method 10700 is depicted. The example method 10700 may
include step 10701 collecting a training set of loan interactions
between entities, wherein the training set of loan interactions
comprises a set of loan consolidation transactions. A set of loans
as candidates for consolidation based at least in part on the
training set of loan interactions may be classified (step 10702).
The method may further include the step 10703 of managing a
consolidation of at least a subset of the set of loans on behalf of
a party to the consolidation based at least in part on the set of
loan consolidation transactions.
[1623] The method 10700 may further include the step 10704 of
determining completion of a negotiation of the consolidation of at
least one loan from the subset of the set of loans. Based on the
outcome of the negotiations a smart contract may be modified in
step 10705. The method may also include the step 10706 of
determining at least one of an outcome and a negotiation event
associated with the consolidation of at least the subset of the set
of loans. The at least one of the outcome and the negotiation event
may be recorded in a distributed ledger associate with the
consolidation in step 10707.
[1624] Referring to FIG. 108, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 10800 is depicted. The example system may
include a data collection circuit 10805 which may collect data
information about entities 10802 involved in a set of factoring
loans 10801 and a training set of interactions 10804 between
entities for a set of factoring loan transactions 10803. The data
may be collected from a variety of sources and systems such as: an
Internet of Things system, a camera system, a networked monitoring
system, an internet monitoring system, a mobile device system, a
wearable device system, a user interface system, and a
crowdsourcing system.
[1625] The system may also include an artificial intelligence
circuit 10811 that may be structured to classify entities 10808
involved in the set of factoring loans based at least in part on
the training set of interactions 10804. The artificial intelligence
circuit 10811 may include at least one system such as a machine
learning system, a model-based system, a rule-based system, a deep
learning system, a hybrid system, a neural network, a convolutional
neural network, a feed forward neural network, a feedback neural
network a self-organizing map, a fuzzy logic system, a random walk
system, a random forest system, a probabilistic system, a Bayesian
system, a simulation system, and the like.
[1626] The system may also include a robotic process automation
circuit 10813 structured to manage a factoring loan 10812 based at
least in part on the factoring loan transactions 10803. Managing
the factoring loan may include managing at least one of a set of
assets for factoring, identification of loans for factoring from a
set of candidate loans, preparation of a factoring offer,
preparation of a factoring plan, preparation of content
communicating a factoring offer, scheduling a factoring offer,
communicating a factoring offer, negotiating a modification of a
factoring offer, preparing a factoring agreement, executing a
factoring agreement, modifying collateral for a set of factoring
loans, handing transfer of a set of accounts receivable, handling
an application workflow for factoring, managing an inspection,
managing an assessment of a set of assets to be factored, setting
an interest rate, deferring a payment requirement, setting a
payment schedule, or dosing a factoring agreement.
[1627] The artificial intelligence circuit 10811 may further
include a model 10809 that may be used to process attributes of
entities involved in the set of factoring loans, the attributes may
include assets used for factoring, identity of a party, interest
rate, payment balance, payment terms, payment schedule, type of
loan, type of collateral, financial condition of party, payment
status, condition of collateral, or value of collateral. The assets
used for factoring may include a set of accounts receivable 10810.
At least one entity of the entities 10802 may be a party to at
least one factoring loan transactions 10803. The party may include
least one such as a primary lender, a secondary lender, a lending
syndicate, a corporate lender, a government lender, a bank lender,
a secured lender, bond issuer, a bond purchaser, an unsecured
lender, a guarantor, a provider of security, a borrower, a debtor,
an underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, an accountant, and the
like.
[1628] The system may further include an interface circuit 10807
structured to receive interactions 10806 from one or more of the
entities 10802. In some embodiments the robotic process automation
circuit 10813 may be trained on the interactions 10806.
[1629] The system may further include a smart contract circuit
10820 structured to determine a completion of a negotiations of the
factoring loan and modify a contract 10818 based on an outcome of
the negotiation 10819.
[1630] The system may further include a distributed ledger circuit
10817 structured to determine at least one of an outcome 10815 or a
negotiation event 10816 associated with the negotiation of the
factoring loan. The distributed ledger circuit 10817 may be
structured to record, in a distributed ledger 10814 associated with
the factoring loan, the event 10816 and/or the outcome 10815.
[1631] Referring to FIG. 109, an illustrative and non-limiting
example method 10900 is depicted. The example method 10900 may
include step 10901 collecting information about entities involved
in a set of factoring loans and a training set of interactions
between entities for a set of factoring loan transactions. Entities
involved in the set of factoring loans may be classified based at
least in part on the training set of loan interactions (step
10902). The method may further include the step 10903 of managing a
factoring loan based at least in part on the set of factoring loan
interactions.
[1632] The method 10900 may further include the step 10904 of
determining completion of a negotiation of the factoring loan.
Based on the outcome of the negotiations a smart contract may be
modified in step 10905. The method may also include the step 10906
of determining at least one of an outcome and a negotiation event
associated with the negotiation of the factoring loan. The at least
one of the outcome and the negotiation event may be recorded in a
distributed ledger associate with the factoring loan in step
10907.
[1633] Referring to FIG. 110, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 11000 is depicted. The example system may
include a data collection circuit 11006 which may collect data
information about entities 11002 involved in a set of mortgage loan
activities 11005 and a training set of interactions 11004 between
entities for a set of mortgage loan transactions 11003. The data
may be collected from a variety of sources and systems such as: an
Internet of Things system, a camera system, a networked monitoring
system, an internet monitoring system, a mobile device system, a
wearable device system, a user interface system, and a
crowdsourcing system.
[1634] The system may also include an artificial intelligence
circuit 11010 that may be structured to classify entities 11009
involved in the set of mortgage loan activities based at least in
part on the training set of interactions 11004. The artificial
intelligence circuit 11010 may include at least one system such as
a machine learning system, a model-based system, a rule-based
system, a deep learning system, a hybrid system, a neural network,
a convolutional neural network, a feed forward neural network, a
feedback neural network a self-organizing map, a fuzzy logic
system, a random walk system, a random forest system, a
probabilistic system, a Bayesian system, a simulation system, and
the like.
[1635] The system may also include a robotic process automation
circuit 11012 structured to broker a mortgage loan 11011 based at
least in part on at least one of the set of mortgage loan
activities 11005 and the training set of interactions 11004. The
set of mortgage loan activities 11005 and/or the set of mortgage
loan transactions 11003 may include activities selected from a
group consisting of: among marketing activity, identification of a
set of prospective borrowers, identification of property,
identification of collateral, qualification of borrower, title
search, title verification, property assessment, property
inspection, property valuation, income verification, borrower
demographic analysis, identification of capital providers,
determination of available interest rates, determination of
available payment terms and conditions, analysis of existing
mortgage, comparative analysis of existing and new mortgage terms,
completion of application workflow, population of fields of
application, preparation of mortgage agreement, completion of
schedule to mortgage agreement, negotiation of mortgage terms and
conditions with capital provider, negotiation of mortgage terms and
conditions with borrower, transfer of title, placement of lien, or
closing of mortgage agreement.
[1636] The artificial intelligence circuit 11010 may further
include a model that may be used to process attributes of entities
involved in the set of mortgage loan activities, the attributes may
properties that are subject to mortgages, assets used for
collateral, identity of a party, interest rate, payment balance,
payment terms, payment schedule, type of mortgage, type of
property, financial condition of party, payment status, condition
of property, or value of property. In embodiments, brokering the
mortgage loan comprises at least one activity such as managing at
least one of a property that is subject to a mortgage,
identification of candidate mortgages from a set of borrower
situations, preparation of a mortgage offer, preparation of content
communicating a mortgage offer, scheduling a mortgage offer,
communicating a mortgage offer, negotiating a modification of a
mortgage offer, preparing a mortgage agreement, executing a
mortgage agreement, modifying collateral for a set of mortgage
loans, handing transfer of a lien, handling an application
workflow, managing an inspection, managing an assessment of a set
of assets to be subject to a mortgage, setting an interest rate,
deferring a payment requirement, setting a payment schedule,
closing a mortgage agreement, and the like
[1637] In embodiments at least one entity of the entities 11002 may
be a party to at least one mortgage loan transactions of the set of
mortgage loan transactions 11003. The party may include least one
such as a primary lender, a secondary lender, a lending syndicate,
a corporate lender, a government lender, a bank lender, a secured
lender, bond issuer, a bond purchaser, an unsecured lender, a
guarantor, a provider of security, a borrower, a debtor, an
underwriter, an inspector, an assessor, an auditor, a valuation
professional, a government official, an accountant, and the
like.
[1638] The system may further include an interface circuit 11008
structured to receive interactions 11007 from one or more of the
entities 11002. In some embodiments the robotic process automation
circuit 11012 may be trained on the interactions 11007.
[1639] The system may further include a smart contract circuit
11019 structured to determine a completion of a negotiations of the
mortgage loan and modify a smart contract 11017 based on an outcome
of the negotiation 11018.
[1640] The system may further include a distributed ledger circuit
11016 structured to determine at least one of an outcome 11014 or a
negotiation event 11015 associated with the negotiation of the
mortgage loan. The distributed ledger circuit 11016 may be
structured to record, in a distributed ledger 11013 associated with
the mortgage loan, the event 11015 and/or the outcome 11014.
[1641] Referring to FIG. 111, an illustrative and non-limiting
example method 11100 is depicted. The example method 11100 may
include step 11101 collecting information about entities involved
in a set of mortgage loan activities and a training set of
interactions between entities for a set of mortgage loan
transactions. Entities involved in the set of factoring loans may
be classified based at least in part on the training set of loan
interactions (step 11102). The method may further include the step
11103 of brokering a mortgage loan based at least in part on at
least one of the set of mortgage loan activities and the training
set of interactions.
[1642] The method 11100 may further include the step 11104 of
determining completion of a negotiation of the mortgage loan. Based
on the outcome of the negotiations a smart contract may be modified
in step 11105. The method may also include the step 11106 of
determining at least one of an outcome and a negotiation event
associated with the negotiation of the mortgage loan. The at least
one of the outcome and the negotiation event may be recorded in a
distributed ledger associate with the mortgage loan in step
11107.
[1643] Referring to FIG. 112, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 11200 is depicted. The example system may
include a data collection circuit 11208 which may collect data
about entities 11205 involved in a set of debt transactions 11201,
training data set of outcomes 11206 related to the entities, and a
training set of debt management activities 11207. The data may be
collected from a variety of sources and systems such as: Internet
of Things devices, a set of environmental condition sensors, a set
of crowdsourcing services, a set of social network analytic
services, or a set of algorithms for querying network domains, and
the like.
[1644] The system may also include a condition classifying circuit
11214 that may be structured to classify a condition 11211 of at
least one entity of the entities 11205. The condition classifying
circuit 11214 may include a model 11212 and a set of artificial
intelligence circuits 11213. The model 11212 may be trained using
the training data set of outcomes 11206 related to the entities.
The artificial intelligence circuits 11213 may include at least one
system such as machine learning system, a model-based system, a
rule-based system, a deep learning system, a hybrid system, a
neural network, a convolutional neural network, a feed forward
neural network, a feedback neural network, a self-organizing map, a
fuzzy logic system, a random walk system, a random forest system, a
probabilistic system, a Bayesian system, or a simulation
system.
[1645] The system may also include an automated debt management
circuit 11216 structured to manage an action related to a debt
11215. The automated debt management circuit 11216 may be trained
on the training set of debt management activities 11207.
[1646] In embodiments, at least one debt transaction of the set of
debt transactions 11201 may be include an auto loan, an inventory
loan, a capital equipment loan, a bond for performance, a capital
improvement loan, a building loan, a loan backed by an account
receivable, an invoice finance arrangement, a factoring
arrangement, a pay day loan, a refund anticipation loan, a student
loan, a syndicated loan, a title loan, a home loan, a venture debt
loan, a loan of intellectual property, a loan of a contractual
claim, a working capital loan, a small business loan, a farm loan,
a municipal bond, a subsidized loan, and the like.
[1647] In embodiments, the entities 11205 involved in the set of
debt transactions may include at least one of set of parties 11202
and a set of assets 11204. The assets 11204 may include a municipal
asset, a vehicle, a ship, a plane, a building, a home, real estate
property, undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a beverage, a precious metal, an item of jewelry, a
gemstone, intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, or an item of
personal property. The system may further include a set of sensors
11203 positioned on at least one asset 11204 from the set of
assets, on a container for least one asset from the set of assets,
and on a package for at least one asset from the set of assets,
wherein the set of sensors configured to associate sensor
information sensed by the set of sensors with a unique identifier
for the at least one asset from the set of assets. The sensors
11203 may include image, temperature, pressure, humidity, velocity,
acceleration, rotational, torque, weight, chemical, magnetic field,
electrical field, or position sensors.
[1648] In embodiments, the system may further include a set of
block chain circuits 11224 structured to receive information from
the data collection circuit 11208 and the set of sensors 11203 and
storing the information in a blockchain 11226. The access to the
blockchain 11226 may be provided via a secure access control
interface circuit 11223.
[1649] An automated agent circuit 11225 may be structured to
process events relevant to at least one of a value, a condition,
and an ownership of at least one asset of the set of assets and
further structured to undertake a set of actions related to a debt
transaction to which the asset is related.
[1650] The system may further include an interface circuit 11210
structured to receive interactions 11209 from at least one of the
entities 11205. In embodiments the automated debt management
circuit 11216 may be trained on the interactions 11209. In some
embodiments the system may further include a market value data
collection circuit 11218 structured to monitor and report
marketplace information 11217 relevant to a value of a of at least
one asset of a set of assets 11204. The market value data
collection circuit 11218 may be further structured to monitor at
least one pricing and financial data for items that are similar to
at least one asset in the set of assets in at least one public
marketplace. A set of similar items for valuing at least one asset
from the set of assets may be constructed using a similarity
clustering algorithm based on attributes of the assets. In
embodiments, at least one attribute of the attributes of the assets
may include a category of assets, asset age, asset condition, asset
history, asset storage, geolocation of assets, and the like.
[1651] In embodiments, the system may further include a smart
contract circuit 11222 structured to manage a smart contract 11219
for a debt transaction 11221. The smart contract circuit 11222 may
be further structured to establish a set of terms and conditions
11220 for the debt transaction 11221. At least one of the terms and
conditions may include a principal amount of debt, a balance of
debt, a fixed interest rate, a variable interest rate, a payment
amount, a payment schedule, a balloon payment schedule, a
specification of collateral, a specification of substitutability of
collateral, a party, a guarantee, a guarantor, a security, a
personal guarantee, a lien, a duration, a covenant, a foreclose
condition, a default condition, a consequence of default, and the
like.
[1652] In embodiments at least one action related to a debt 11215
may include offering a debt transaction, underwriting a debt
transaction, setting an interest rate, deferring a payment
requirement, modifying an interest rate, validating title, managing
inspection, recording a change in title, assessing the value of an
asset, calling a loan, closing a transaction, setting terms and
conditions for a transaction, providing notices required to be
provided, foreclosing on a set of assets, modifying terms and
conditions, setting a rating for an entity, syndicating debt, or
consolidating debt. At least one debt management activity from the
training set of debt management activities 11207 may include
offering a debt transaction, underwriting a debt transaction,
setting an interest rate, deferring a payment requirement,
modifying an interest rate, validating title, managing inspection,
recording a change in title, assessing a value of an asset, calling
a loan, closing a transaction, setting terms and conditions for a
transaction, providing notices required to be provided, foreclosing
on a set of assets, modifying terms and conditions, setting a
rating for an entity, syndicating debt, or consolidating debt.
[1653] Referring to FIG. 113, an illustrative and non-limiting
example method 11300 is depicted. The example method 11300 may
include step 11301 collecting information about entities involved
in a set of debt transactions, training data set of outcomes
related to the entities, and a training set of debt management
activities. The example method may further include classifying a
condition of at least one entity of the entities based at least in
part the training data set of outcomes related to the entities
(step 11302). The example method may further include managing an
action related to a debt based at least in part on the training set
of debt management activities (step 11303). The example method may
further include receiving information from a set of sensors
positioned on at least one asset (step 11304). The example method
may further include storing the information in a blockchain,
wherein access to the blockchain is provided via a secure access
control interface for a party for a debt transaction involving the
at least one asset from the set of assets (step 11305). In step
11306 the method may include processing events relevant to at least
one of a value, a condition, or an ownership of at least one asset
of the set of assets. In step 11307 the method may include
processing a set of actions related to a debt transaction to which
the asset is related. In embodiments the method may further include
receiving interactions from at least one of the entities (step
11308), monitoring and reporting marketplace information relevant
to a value of a of at least one asset of a set of assets (step
11309), constructing using a similarity clustering algorithm based
on attributes of the assets a set of similar items for valuing at
least one asset from the set of assets (step 11310), managing a
smart contract for a debt transaction (step 11311) and establishing
a set of terms and conditions for the smart contract for the debt
transaction (step 11312).
[1654] Referring to FIG. 114, an illustrative and non-limiting
example system for adaptive intelligence and robotic process
automation capabilities 11400 is depicted.
[1655] The example system may include a crowdsourcing data
collection circuit 11405 structured to collect information about
entities 11403 involved in a set of bond transactions 11402 and a
training data set of outcomes related to the entities 11403. The
system may further include a condition classifying circuit 11411
structured to classify a condition of a set of issuers 11408 using
the information from the crowdsourcing data collection circuit
11405 and a model 11409. The model 11409 may be trained using the
training data set of outcomes 11404 related to the set of issuers.
The example system may further include an automated agent circuit
11419 structured to perform an action related to a debt transaction
in response to the classified condition of at least one issuer of
the set of issuers. In embodiments, at least one entity 11403 may
include a set of issuers, a set of bonds, a set of parties, or a
set of assets. At least one issuer may include a municipality, a
corporation, a contractor, a government entity, a non-governmental
entity, or a non-profit entity. At least one bond may include a
municipal bond, a government bond, a treasury bond, an asset-backed
bond, or a corporate bond.
[1656] In embodiments, the condition classified 11408 by the
condition classifying circuit 11411 may include a default
condition, a foreclosure condition, a condition indicating
violation of a covenant, a financial risk condition, a behavioral
risk condition, a policy risk condition, a financial health
condition, a physical defect condition, a physical health
condition, an entity risk condition, an entity health condition, or
the like. The crowdsourcing data collection circuit 11411 may be
structured to enable a user interface 11407 by which a user may
configure a crowdsourcing request 11406 for information relevant to
the condition about the set of issuers.
[1657] The system may further include a configurable data
collection and monitoring circuit 11413 structured to monitor at
least one issuer from the set of issuers 11412. The configurable
data collection and monitoring circuit 11413 may include a system
such as: Internet of Things devices, a set of environmental
condition sensors, a set of social network analytic services, or a
set of algorithms for querying network domains. The configurable
data collection and monitoring circuit 11413 mat be structured to
monitor an at least one environment such as: a municipal
environment, a corporate environment, a securities trading
environment, a real property environment, a commercial facility, a
warehousing facility, a transportation environment, a manufacturing
environment, a storage environment, a home, or a vehicle.
[1658] In embodiments a set of bonds associated with the set of
bond transactions 11402 may be backed by a set of assets 11401. At
least one asset 11401 may include a municipal asset, a vehicle, a
ship, a plane, a building, a home, real estate property,
undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a beverage, a precious metal, an item of jewelry, a
gemstone, intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, an item of
personal property, or the like.
[1659] In embodiments, the system may further include an automated
agent circuit 11419 structured to processes events relevant to at
least one of a value, a condition, or an ownership of at least one
asset of the at least one issuer of the set of issuers, and to
perform the action related to the debt transaction in response to
at least one of the processed events.
[1660] The action 11418 may include offering a debt transaction,
underwriting a debt transaction, setting an interest rate,
deferring a payment requirement, modifying an interest rate,
validating title, managing inspection, recording a change in title,
assessing the value of an asset, calling a loan, closing a
transaction, setting terms and conditions for a transaction,
providing notices required to be provided, foreclosing on a set of
assets, modifying terms and conditions, setting a rating for an
entity, syndicating debt, consolidating debt, and the like. The
condition classifying circuit 11411 may include a system such as: a
machine learning system, a model-based system, a rule-based system,
a deep learning system, a hybrid system, a neural network, a
convolutional neural network, a feed forward neural network, a
feedback neural network, a self-organizing map, a fuzzy logic
system, a random walk system, a random forest system, a
probabilistic system, a Bayesian system, or a simulation
system.
[1661] In embodiments the system may further include an automated
bond management circuit 11427 configured to manage an action
related to the bond 11424 related to the at least one issuer of the
set of issuers. The automated bond management circuit 11427 may be
trained on a training set of bond management activities 11426. The
automated bond management circuit 11427 may be further trained on a
set of interactions of parties 11425 with a set of user interfaces
involved in a set of bond transaction activities. At least one bond
transaction may include a debt transaction, underwriting a debt
transaction, setting an interest rate, deferring a payment
requirement, modifying an interest rate, validating title, managing
inspection, recording a change in title, assessing the value of an
asset, calling a loan, closing a transaction, setting terms and
conditions for a transaction, providing notices required to be
provided, foreclosing on a set of assets, modifying terms and
conditions, setting a rating for an entity, syndicating debt,
consolidating debt, or the like.
[1662] In embodiments the system may further include a market value
data collection circuit 11417 structured to monitor and reports on
marketplace information 11414 relevant to a value of at least one
of the issuer or a set of assets. Reporting may include reporting
on: a municipal asset, a vehicle, a ship, a plane, a building, a
home, real estate property, undeveloped land, a farm, a crop, a
municipal facility, a warehouse, a set of inventory, a commodity, a
security, a currency, a token of value, a ticket, a cryptocurrency,
a consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, intellectual property, an intellectual
property right, a contractual right, an antique, a fixture, an item
of furniture, an item of equipment, a tool, an item of machinery,
or an item of personal property. The market value data collection
circuit 11417 may be structured to monitor pricing 11416 or
financial data 11415 for items that are similar to the assets in at
least one public marketplace. The market value data collection
circuit 11417 may be further structured to construct a set of
similar items for valuing the assets using a similarity clustering
algorithm based on attributes of the assets. At least one attribute
from the attributes may be selected from: a category of the assets,
asset age, asset condition, asset history, asset storage, or
geolocation of assets.
[1663] In embodiments, the system may further include a smart
contract circuit 11423 structured for managing a smart contract
11420 for a bond transaction 11422 in response to the classified
condition of the at least one issuer of the set of issuers. The
smart contract circuit 11423 may be structured to determine terms
and conditions 11421 for the bond. At least one term and condition
11421 may include a principal amount of debt, a balance of debt, a
fixed interest rate, a variable interest rate, a payment amount, a
payment schedule, a balloon payment schedule, a specification of
assets that back the bond, a specification of substitutability of
assets, a party, an issuer, a purchaser, a guarantee, a guarantor,
a security, a personal guarantee, a lien, a duration, a covenant, a
foreclose condition, a default condition, a consequence of default,
and the like.
[1664] Referring to FIG. 115, an illustrative and non-limiting
example method 11500 is depicted. The example method 11500 may
include step 11501 of collecting information about entities
involved in a set of bond transactions of a set of bonds and a
training data set of outcomes related to the entities. The method
may further include the step 11502 of classifying a condition of a
set of issuers using the collected information and a model, wherein
the model is trained using the training data set of outcomes
related to the set of issuers. The method may further include
processing events relevant to at least one of a value, a condition,
or an ownership of at least one asset of the set of assets (step
11503). The method may further include the steps 11504 of
performing an action related to a debt transaction to which the
asset is related, 11505 managing an action related to the bond
based at least in part a training set of bond management
activities, 11506 monitoring and reporting on marketplace
information relevant to a value of at least one of the issuer and a
set of assets, 11507 managing a smart contract for a bond
transaction, and 11508 determining terms and conditions for the
smart contract for at least one bond.
[1665] Referring now to FIG. 116, an illustrative and non-limiting
example system for monitoring a condition of an issuer for a bond
11600 is depicted. The example system may include a controller
11601. The controller 11601 may include a data collection circuit
11612, a market value data collection circuit 11656, a social
networking input circuit 11644, a social network data collection
circuit 11632, and several artificial intelligence circuits 11642
including a smart contract circuit 11622, an automated bond
management circuit 11650, a condition classifying circuit 11646, a
clustering circuit 11662, and an event processing circuit
11652.
[1666] The social network data collection circuit 11632 may be
structured to collect information about at least one entity 11664
involved in at least one transaction 11630 comprising at least one
bond; and a condition classifying circuit 11646 may be structured
to classify a condition of the at least one entity in accordance
with a model 11674 and based on information from the social network
data collection circuit, wherein the model is trained using a
training data set 11654 of a plurality of outcomes related to the
at least one entity. The at least one entity may be selected from
the entities consisting of: a bond issuer, a bond, a party, and an
asset. The bond issuer may be selected from the bond issuers
consisting of: a municipality, a corporation, a contractor, a
government entity, a non-governmental entity, and a non-profit
entity. The bond may be selected from the entities consisting of: a
municipal bond, a government bond, a treasury bond, an asset-backed
bond, and a corporate bond.
[1667] The condition classified by the condition classifying
circuit 11648 may be at least one of a default condition, a
foreclosure condition, a condition indicating violation of a
covenant, a financial risk condition, a behavioral risk condition,
a policy risk condition, a financial health condition, a physical
defect condition, a physical health condition, an entity risk
condition or an entity health condition.
[1668] The social network data collection circuit 11632 may further
include a social networking input circuit 11644 which may be
structured to receive input from a user used to configure a query
for information about the at least one entity.
[1669] The data collection circuit 11612 may be structured to
monitor at least one of an Internet of Things device, an
environmental condition sensor, a crowdsourcing request circuit, a
crowdsourcing communication circuit, a crowdsourcing publishing
circuit, and an algorithm for querying network domains.
[1670] The data collection circuit 11612 may be further structured
to monitor an environment selected from the group consisting of: a
municipal environment, a corporate environment, a securities
trading environment, a real property environment, a commercial
facility, a warehousing facility, a transportation environment, a
manufacturing environment, a storage environment, a home, and a
vehicle.
[1671] The at least one bond is backed by at least one asset. The
at least one asset may be selected from the assets consisting of: a
municipal asset, a vehicle, a ship, a plane, a building, a home,
real estate property, undeveloped land, a farm, a crop, a municipal
facility, a warehouse, a set of inventory, a commodity, a security,
a currency, a token of value, a ticket, a cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, intellectual property, an intellectual
property right, a contractual right, an antique, a fixture, an item
of furniture, an item of equipment, a tool, an item of machinery,
and an item of personal property.
[1672] The event processing circuit 11652 may be structured to
process an event relevant to at least one of a value, a condition
and an ownership of the at least one asset and undertake an action
related to the at least one transaction. The action may be selected
from the actions consisting of: a bond transaction, underwriting a
bond transaction, setting an interest rate, deferring a payment
requirement, modifying an interest rate, validating title, managing
inspection, recording a change in title, assessing the value of an
asset, calling a loan, closing a transaction, setting terms and
conditions for a transaction, providing notices required to be
provided, foreclosing on a set of assets, modifying terms and
conditions, setting a rating for an entity, syndicating bonds, and
consolidating bonds.
[1673] The condition classifying circuit 11648 may further include
a system selected from the systems consisting of: a machine
learning system, a model-based system, a rule-based system, a deep
learning system, a hybrid system, a neural network, a convolutional
neural network, a feed forward neural network, a feedback neural
network, a self-organizing map, a fuzzy logic system, a random walk
system, a random forest system, a probabilistic system, a Bayesian
system, and a simulation system.
[1674] The automated bond management circuit 11650 may be
structured to manage an action related to the at least one bond,
wherein the automated bond management circuit is trained on a
training data set of a plurality of bond management activities.
[1675] The automated bond management circuit 11650 may be trained
on a plurality of interactions of parties with a plurality of user
interfaces involved in a plurality of bond transaction activities.
The plurality of bond transaction activities may be selected from
the bond transaction activities consisting of: offering a bond
transaction, underwriting a bond transaction, setting an interest
rate, deferring a payment requirement, modifying an interest rate,
validating title, managing inspection, recording a change in title,
assessing a value of an asset, calling a loan, closing a
transaction, setting terms and conditions for a transaction,
providing notices required to be provided, foreclosing on a set of
assets, modifying terms and conditions, setting a rating for an
entity, syndicating bonds, and consolidating bonds.
[1676] The market value data collection circuit 11656 may be
structured to monitor and report on marketplace information
relevant to a value of at least one of a bond issuer, the at least
one bond, and an asset. The asset may be selected from the assets
consisting of: a municipal asset, a vehicle, a ship, a plane, a
building, a home, real estate property, undeveloped land, a farm, a
crop, a municipal facility, a warehouse, a set of inventory, a
commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a consumable item, an edible item, a beverage, a
precious metal, an item of jewelry, a gemstone, intellectual
property, an intellectual property right, a contractual right, an
antique, a fixture, an item of furniture, an item of equipment, a
tool, an item of machinery, and an item of personal property.
[1677] The market value data collection circuit 11656 may be
further structured to monitor pricing or financial data for an
offset asset item in at least one public marketplace.
[1678] A set of offset asset items 11658 for valuing the asset may
be constructed using a clustering circuit 11662 based on an
attribute of the asset. The attribute may be selected from the
attributes consisting of: a category, an asset age, an asset
condition, an asset history, an asset storage, and a
geolocation.
[1679] The smart contract circuit 11622 may be structured to manage
a smart contract for the at least one transaction. The smart
contract circuit may be further structured to determine a terms and
conditions for the at least one bond.
[1680] The terms and conditions may be selected from the group
consisting of: a principal amount of debt, a balance of debt, a
fixed interest rate, a variable interest rate, a payment amount, a
payment schedule, a balloon payment schedule, a specification of
assets that back the at least one bond, a specification of
substitutability of assets, a party, an issuer, a purchaser, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition,
and a consequence of default.
[1681] Referring now to FIG. 117, an illustrative and non-limiting
example method for monitoring a condition of an issuer for a bond
11700 is depicted. An example method may include collecting social
network information about at least one entity involved in at least
one transaction comprising at least one bond 11702; and classifying
a condition of the at least one entity in accordance with a model
and based on the social network information, wherein the model is
trained using a training data set of a plurality of outcomes
related to the at least one entity 11704.
[1682] An event relevant to at least one of a value, a condition
and an ownership of at least one asset may be processed 11708. An
action related to the at least one transaction may be undertaken in
response to the event 11710. An automated bond management circuit
may be trained on a training set of a plurality of bond management
activities to manage an action related to the at least one bond
11712. An example method may further include monitoring and
reporting on marketplace information relevant to a value of at
least one of a bond issuer, the at least one bond, and an asset
11714.
[1683] Referring now to FIG. 118, an illustrative and non-limiting
example system for monitoring a condition of an issuer for a bond
11800 is depicted. The example system may include a controller
11801. The controller 11801 may include a data collection circuit
11812, a market value data collection circuit 11856, an Internet of
Things input circuit 11844, an Internet of Things data collection
circuit 11832, and several artificial intelligence circuits 11842
including a smart contract circuit 11822, an automated bond
management circuit 11850, a condition classifying circuit 11846, a
clustering circuit 11862, and an event processing circuit
11852.
[1684] The Internet of Things data collection circuit 11832 may be
structured to collect information about at least one entity 11864
involved in at least one transaction 11830 comprising at least one
bond; and a condition classifying circuit 11846 may be structured
to classify a condition of the at least one entity in accordance
with a model 11874 and based on information from the Internet of
Things data collection circuit, wherein the model is trained using
a training data set 11854 of a plurality of outcomes related to the
at least one entity. The at least one entity may be selected from
the entities consisting of: a bond issuer, a bond, a party, and an
asset. The bond issuer may be selected from the bond issuers
consisting of: a municipality, a corporation, a contractor, a
government entity, a non-governmental entity, and a non-profit
entity. The bond may be selected from the entities consisting of: a
municipal bond, a government bond, a treasury bond, an asset-backed
bond, and a corporate bond.
[1685] The condition classified by the condition classifying
circuit 11848 may be at least one of a default condition, a
foreclosure condition, a condition indicating violation of a
covenant, a financial risk condition, a behavioral risk condition,
a policy risk condition, a financial health condition, a physical
defect condition, a physical health condition, an entity risk
condition or an entity health condition.
[1686] The Internet of Things data collection circuit 11832 may
further include an Internet of Things input circuit 11844 which may
be structured to receive input from a user used to configure a
query for information about the at least one entity.
[1687] The data collection circuit 11812 may be structured to
monitor at least one of an Internet of Things device, an
environmental condition sensor, a crowdsourcing request circuit, a
crowdsourcing communication circuit, a crowdsourcing publishing
circuit, and an algorithm for querying network domains.
[1688] The data collection circuit 11812 may be further structured
to monitor an environment selected from the group consisting of: a
municipal environment, a corporate environment, a securities
trading environment, a real property environment, a commercial
facility, a warehousing facility, a transportation environment, a
manufacturing environment, a storage environment, a home, and a
vehicle.
[1689] The at least one bond is backed by at least one asset. The
at least one asset may be selected from the assets consisting of: a
municipal asset, a vehicle, a ship, a plane, a building, a home,
real estate property, undeveloped land, a farm, a crop, a municipal
facility, a warehouse, a set of inventory, a commodity, a security,
a currency, a token of value, a ticket, a cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an
item of jewelry, a gemstone, intellectual property, an intellectual
property right, a contractual right, an antique, a fixture, an item
of furniture, an item of equipment, a tool, an item of machinery,
and an item of personal property.
[1690] The event processing circuit 11852 may be structured to
process an event relevant to at least one of a value, a condition
and an ownership of the at least one asset and undertake an action
related to the at least one transaction. The action may be selected
from the actions consisting of: a bond transaction, underwriting a
bond transaction, setting an interest rate, deferring a payment
requirement, modifying an interest rate, validating title, managing
inspection, recording a change in title, assessing the value of an
asset, calling a loan, closing a transaction, setting terms and
conditions for a transaction, providing notices required to be
provided, foreclosing on a set of assets, modifying terms and
conditions, setting a rating for an entity, syndicating bonds, and
consolidating bonds.
[1691] The condition classifying circuit 11848 may further include
a system selected from the systems consisting of: a machine
learning system, a model-based system, a rule-based system, a deep
learning system, a hybrid system, a neural network, a convolutional
neural network, a feed forward neural network, a feedback neural
network, a self-organizing map, a fuzzy logic system, a random walk
system, a random forest system, a probabilistic system, a Bayesian
system, and a simulation system.
[1692] The automated bond management circuit 11850 may be
structured to manage an action related to the at least one bond,
wherein the automated bond management circuit is trained on a
training data set of a plurality of bond management activities.
[1693] The automated bond management circuit 11850 may be trained
on a plurality of interactions of parties with a plurality of user
interfaces involved in a plurality of bond transaction activities.
The plurality of bond transaction activities may be selected from
the bond transaction activities consisting of: offering a bond
transaction, underwriting a bond transaction, setting an interest
rate, deferring a payment requirement, modifying an interest rate,
validating title, managing inspection, recording a change in title,
assessing a value of an asset, calling a loan, closing a
transaction, setting terms and conditions for a transaction,
providing notices
[1694] required to be provided, foreclosing on a set of assets,
modifying terms and conditions, setting a rating for an entity,
syndicating bonds, and consolidating bonds.
[1695] The market value data collection circuit 11856 may be
structured to monitor and report on marketplace information
relevant to a value of at least one of a bond issuer, the at least
one bond, and an asset. The asset may be selected from the assets
consisting of: a municipal asset, a vehicle, a ship, a plane, a
building, a home, real estate property, undeveloped land, a farm, a
crop, a municipal facility, a warehouse, a set of inventory, a
commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a consumable item, an edible item, a beverage, a
precious metal, an item of jewelry, a gemstone, intellectual
property, an intellectual property right, a contractual right, an
antique, a fixture, an item of furniture, an item of equipment, a
tool, an item of machinery, and an item of personal property.
[1696] The market value data collection circuit 11856 may be
further structured to monitor pricing or financial data for an
offset asset item in at least one public marketplace.
[1697] A set of offset asset items 11858 for valuing the asset may
be constructed using a clustering circuit 11862 based on an
attribute of the asset. The attribute may be selected from the
attributes consisting of: a category, an asset age, an asset
condition, an asset history, an asset storage, and a
geolocation.
[1698] The smart contract circuit 11822 may be structured to manage
a smart contract for the at least one transaction. The smart
contract circuit may be further structured to determine a terms and
conditions for the at least one bond.
[1699] The terms and conditions may be selected from the group
consisting of: a principal amount of debt, a balance of debt, a
fixed interest rate, a variable interest rate, a payment amount, a
payment schedule, a balloon payment schedule, a specification of
assets that back the at least one bond, a specification of
substitutability of assets, a party, an issuer, a purchaser, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition,
and a consequence of default.
[1700] Referring now to FIG. 119, an illustrative and non-limiting
example method for monitoring a condition of an issuer for a bond
11900 is depicted. An example method may include collecting
Internet of Things information about at least one entity involved
in at least one transaction comprising at least one bond 11902; and
classifying a condition of the at least one entity in accordance
with a model and based on the Internet of Things information,
wherein the model is trained using a training data set of a
plurality of outcomes related to the at least one entity 11904.
[1701] An event relevant to at least one of a value, a condition
and an ownership of at least one asset may be processed 11908. An
action related to the at least one transaction may be undertaken in
response to the event 11910. An automated bond management circuit
may be trained on a training set of a plurality of bond management
activities to manage an action related to the at least one bond
11912. An example method may further include monitoring and
reporting on marketplace information relevant to a value of at
least one of a bond issuer, the at least one bond, and an asset
11914.
[1702] FIG. 120 depicts a system 12000 including an Internet of
Things data collection circuit 12014 structured to collect
information about an entity 12002 (e.g., where an entity may be a
subsidized loan, a party, a subsidy, a guarantor, a subsidizing
party, a collateral, and the like, where a party may be least one
of a municipality, a corporation, a contractor, a government
entity, a non-governmental entity, and a non-profit entity)
involved in a subsidized loan transaction 12004. In embodiments,
the Internet of Things data collection circuit may include a user
interface 12016 structured to enable a user to configure a query
for information about the at least one entity. The system may
include a condition classifying circuit 12018 that may include a
model 12020 structured to classify a parameter 12006 of a
subsidized loan 12008 (e.g., municipal subsidized loan, a
government subsidized loan, a student loan, an asset-backed
subsidized loan, or a corporate subsidized loan) involved in a
subsidized loan transaction, such as based on the information from
the Internet of Things data collection circuit. In embodiments, the
condition classifying circuit may include a machine learning
system, a model-based system, a rule-based system, a deep learning
system, a hybrid system, a neural network, a convolutional neural
network, a feed forward neural network, a feedback neural network,
a self-organizing map, a fuzzy logic system, a random walk system,
a random forest system, a probabilistic system, a Bayesian system,
a simulation system, and the like. The subsidized loan may be
backed by an asset, such as a municipal asset, a vehicle, a ship, a
plane, a building, a home, real estate property, undeveloped land,
a farm, a crop, a municipal facility, a warehouse, a set of
inventory, a commodity, a security, a currency, a token of value, a
ticket, a cryptocurrency, a consumable item, an edible item, a
beverage, a precious metal, an item of jewelry, a gemstone,
intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, an item of
personal property, and the like. The condition classified by the
condition classifying circuit may be a default condition, a
foreclosure condition, a condition indicating violation of a
covenant, a financial risk condition, a behavioral risk condition,
a contractual performance condition, a policy risk condition, a
financial health condition, a physical defect condition, a physical
health condition, an entity risk condition, an entity health
condition, and the like. The model may be trained using a training
data set of a plurality of outcomes 12010 related to the subsidized
loan. For instance, the subsidized loan may be a student loan and
the condition classifying circuit may classify a progress of a
student toward a degree, a participation of a student in a
non-profit activity, a participation of a student in a public
interest activity, and the like. The system may include a smart
contract circuit 12022 structured to automatically modify terms and
conditions 12012 of the subsidized loan, such as based on the
classified parameter from the condition classifying circuit. The
system may include a configurable data collection and circuit 12024
structured to monitor the entity, such as further including a
social network analytic circuit 12030, an environmental condition
circuit 12032, a crowdsourcing circuit 12034, and an algorithm for
querying a network domain 12036, where the configurable data
collection and circuit may monitor an environment selected from an
environment, such as a municipal environment, an educational
environment, a corporate environment, a securities trading
environment, a real property environment, a commercial facility, a
warehousing facility, a transportation environment, a manufacturing
environment, a storage environment, a home, a vehicle, and the
like. The system may include an automated agent 12026 structured to
process an event relevant to a value, a condition and an ownership
of the asset and undertake an action related to the subsidized loan
transaction to which the asset is related, wherein the action may
be a subsidized loan transaction, underwriting a subsidized loan
transaction, setting an interest rate, deferring a payment
requirement, modifying an interest rate, validating a title,
managing an inspection, recording a change in a title, assessing
the value of an asset, calling a loan, closing a transaction,
setting terms and conditions for a transaction, providing notices
required to be provided, foreclosing on a set of assets, modifying
terms and conditions, setting a rating for an entity, syndicating a
subsidized loan, consolidating a subsidized loan, and the like. The
system may include an automated subsidized loan management circuit
12038 structured to manage an action related to the at least one
subsidized loan, wherein the automated subsidized loan management
circuit is trained on a training set of subsidized loan management
activities. For instance, the automated subsidized loan management
circuit may be trained on a plurality of interactions of parties
with a plurality of user interfaces involved in a plurality of
subsidized loan transaction activities, where the plurality of
subsidized loan transaction activities may be selected from the
activities consisting of offering a subsidized loan transaction,
underwriting a subsidized loan transaction, setting an interest
rate, deferring a payment requirement, modifying an interest rate,
validating a title, managing an inspection, recording a change in a
title, assessing a value of an asset, calling a loan, closing a
transaction, setting terms and conditions for a transaction,
providing notices required to be provided, foreclosing on a set of
assets, modifying terms and conditions, setting a rating for an
entity, syndicating a subsidized loan, and consolidating a
subsidized loan. The system may include a blockchain service
circuit 12040 structured to record the modified set of terms and
conditions for a subsidized loan, such as in a distributed ledger
12042. The system may include a market value data collection
circuit 12028 structured to monitor and report on marketplace
information relevant to a value of an issuer, a subsidized loan, an
asset, and the like, where reporting may be on an asset selected
from the assets consisting of a municipal asset, a vehicle, a ship,
a plane, a building, a home, real estate property, undeveloped
land, a farm, a crop, a municipal facility, a warehouse, a set of
inventory, a commodity, a security, a currency, a token of value, a
ticket, a cryptocurrency, a consumable item, an edible item, a
beverage, a precious metal, an item of jewelry, a gemstone,
intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, and an item of
personal property. The market value data collection circuit may be
further structured to monitor pricing or financial data for an
offset asset item in a public marketplace. A set of offset asset
items for valuing the asset may be constructed using a clustering
circuit based on an attribute of the asset, where the attribute may
be a category, an asset age, an asset condition, an asset history,
an asset storage, a geolocation, and the like. The smart contract
circuit may be structured to manage a smart contract for a
subsidized loan transaction, where the smart contract circuit may
set terms and conditions for the subsidized loan, where the terms
and conditions for the subsidized loan that are specified and
managed by the smart contract circuit may include a principal
amount of debt, a balance of debt, a fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of assets that back the
at least one subsidized loan, a specification of substitutability
of assets, a party, an issuer, a purchaser, a guarantee, a
guarantor, a security, a personal guarantee, a lien, a duration, a
covenant, a foreclose condition, a default condition, a consequence
of default, and the like.
[1703] FIG. 121 depicts a method 12100 including collecting
information about an entity involved in a subsidized loan
transaction 12102. The method may include classifying a parameter
of a subsidized loan involved in the subsidized loan transaction
based on the information using a model trained on a training data
set of a plurality of outcomes related to the at least one
subsidized loan 12104. The method may include automatically
modifying terms and conditions of the subsidized loan based on the
classified parameter 12108. The method may include processing an
event relevant to a value, a condition and an ownership of an asset
and undertaking an action related to the subsidized loan
transaction to which the asset is related 12110. The method may
include recording the modified set of terms and conditions for the
subsidized loan in a distributed ledger 12112. The method may
include monitoring and reporting on marketplace information
relevant to a value of an issuer, the subsidized loan, the asset,
and the like.
[1704] FIG. 122 depicts a system 12200 including a social network
analytic data collection circuit 12214 structured to collect social
network information about an entity 12202 (e.g., where an entity
may be a subsidized loan, a party, a subsidy, a guarantor, a
subsidizing party, a collateral, and the like, where a party may be
least one of a municipality, a corporation, a contractor, a
government entity, a non-governmental entity, and a non-profit
entity) involved in a subsidized loan transaction 12204. In
embodiments, the social network analytic data collection circuit
may include a user interface 12216 structured to enable a user to
configure a query for information about the at least one entity,
wherein, in response to the query, the social network analytic data
collection circuit may initiate at least one algorithm that
searches and retrieves data from at least one social network based
on the query. The system may include a condition classifying
circuit 12218 that may include a model 12220 structured to classify
a parameter 12206 of a subsidized loan 12208 (e.g., municipal
subsidized loan, a government subsidized loan, a student loan, an
asset-backed subsidized loan, or a corporate subsidized loan)
involved in a subsidized loan transaction, such as based on the
social network information from the social network analytic data
collection circuit. In embodiments, the condition classifying
circuit may include a machine learning system, a model-based
system, a rule-based system, a deep learning system, a hybrid
system, a neural network, a convolutional neural network, a feed
forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system, a
simulation system, and the like. The subsidized loan may be backed
by an asset, such as a municipal asset, a vehicle, a ship, a plane,
a building, a home, real estate property, undeveloped land, a farm,
a crop, a municipal facility, a warehouse, a set of inventory, a
commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a consumable item, an edible item, a beverage, a
precious metal, an item of jewelry, a gemstone, intellectual
property, an intellectual property right, a contractual right, an
antique, a fixture, an item of furniture, an item of equipment, a
tool, an item of machinery, an item of personal property, and the
like. The parameter classified by the condition classifying circuit
may be a default condition, a foreclosure condition, a condition
indicating violation of a covenant, a financial risk condition, a
behavioral risk condition, a contractual performance condition, a
policy risk condition, a financial health condition, a physical
defect condition, a physical health condition, an entity risk
condition, an entity health condition, and the like. The model may
be trained using a training data set of a plurality of outcomes
12210 related to the subsidized loan. For instance, the subsidized
loan may be a student loan and the condition classifying circuit
may classify a progress of a student toward a degree, a
participation of a student in a non-profit activity, a
participation of a student in a public interest activity, and the
like. The system may include a smart contract circuit 12222
structured to automatically modify terms and conditions 12212 of
the subsidized loan, such as based on the classified parameter. The
system may include a configurable data collection and circuit 12224
structured to monitor the entity, such as further including a
social network analytic circuit 12230, an environmental condition
circuit 12232, a crowdsourcing circuit 12234, and an algorithm for
querying a network domain 12236, where the configurable data
collection and circuit may monitor an environment selected from an
environment, such as a municipal environment, an educational
environment, a corporate environment, a securities trading
environment, a real property environment, a commercial facility, a
warehousing facility, a transportation environment, a manufacturing
environment, a storage environment, a home, a vehicle, and the
like. The system may include an automated agent 12226 structured to
process an event relevant to a value, a condition and an ownership
of the asset and undertake an action related to the subsidized loan
transaction to which the asset is related, wherein the action may
be a subsidized loan transaction, underwriting a subsidized loan
transaction, setting an interest rate, deferring a payment
requirement, modifying an interest rate, validating a title,
managing an inspection, recording a change in a title, assessing
the value of an asset, calling a loan, closing a transaction,
setting terms and conditions for a transaction, providing notices
required to be provided, foreclosing on a set of assets, modifying
terms and conditions, setting a rating for an entity, syndicating a
subsidized loan, consolidating a subsidized loan, and the like. The
system may include an automated subsidized loan management circuit
12238 structured to manage an action related to the at least one
subsidized loan, wherein the automated subsidized loan management
circuit is trained on a training set of subsidized loan management
activities. For instance, the automated subsidized loan management
circuit may be trained on a plurality of interactions of parties
with a plurality of user interfaces involved in a plurality of
subsidized loan transaction activities, where the plurality of
subsidized loan transaction activities may be selected from the
activities consisting of offering a subsidized loan transaction,
underwriting a subsidized loan transaction, setting an interest
rate, deferring a payment requirement, modifying an interest rate,
validating a title, managing an inspection, recording a change in a
title, assessing a value of an asset, calling a loan, closing a
transaction, setting terms and conditions for a transaction,
providing notices required to be provided, foreclosing on a set of
assets, modifying terms and conditions, setting a rating for an
entity, syndicating a subsidized loan, and consolidating a
subsidized loan. The system may include a blockchain service
circuit 12240 structured to record the modified set of terms and
conditions for a subsidized loan, such as in a distributed ledger
12242. The system may include a market value data collection
circuit 12228 structured to monitor and report on marketplace
information relevant to a value of an issuer, a subsidized loan, an
asset, and the like, where reporting may be on an asset selected
from the assets consisting of a municipal asset, a vehicle, a ship,
a plane, a building, a home, real estate property, undeveloped
land, a farm, a crop, a municipal facility, a warehouse, a set of
inventory, a commodity, a security, a currency, a token of value, a
ticket, a cryptocurrency, a consumable item, an edible item, a
beverage, a precious metal, an item of jewelry, a gemstone,
intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, and an item of
personal property. The market value data collection circuit may be
further structured to monitor pricing or financial data for an
offset asset item in a public marketplace. A set of offset asset
items for valuing the asset may be constructed using a clustering
circuit based on an attribute of the asset, where the attribute may
be a category, an asset age, an asset condition, an asset history,
an asset storage, a geolocation, and the like. The smart contract
circuit may be structured to manage a smart contract for a
subsidized loan transaction, where the smart contract circuit may
set terms and conditions for the subsidized loan, where the terms
and conditions for the subsidized loan that are specified and
managed by the smart contract circuit may include a principal
amount of debt, a balance of debt, a fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a
balloon payment schedule, a specification of assets that back the
at least one subsidized loan, a specification of substitutability
of assets, a party, an issuer, a purchaser, a guarantee, a
guarantor, a security, a personal guarantee, a lien, a duration, a
covenant, a foreclose condition, a default condition, a consequence
of default, and the like.
[1705] FIG. 123 depicts a method 12300 including collecting social
network information about an entity involved in a subsidized loan
transaction 12302. The method may include classifying a parameter
of a subsidized loan involved in the subsidized loan transaction
based on the social network information using a model trained on a
training data set of a plurality of outcomes related to the at
least one subsidized loan 12304. The method may include
automatically modifying terms and conditions of the subsidized loan
based on the classified parameter 12308. The method may include
processing an event relevant to a value, a condition and an
ownership of an asset and undertaking an action related to the
subsidized loan transaction to which the asset is related 12310.
The method may include recording the modified set of terms and
conditions for the subsidized loan in a distributed ledger 12312.
The method may include monitoring and reporting on marketplace
information relevant to a value of an issuer, the subsidized loan,
the asset, and the like.
[1706] FIG. 124 depicts a system 12400 for automating handling of a
subsidized loan including a crowdsourcing services circuit 12425
structured to collect information related to a set of entities
12402 involved in a set of subsidized loan transactions 12404. The
set of entities may include entities such as a set of subsidized
loans, a set of parties 12416, a set of subsidies, a set of
guarantors, a set of subsidizing parties, a set of collateral, and
the like. A set of subsidizing parties may include a municipality,
a corporation, a contractor, a government entity, a
non-governmental entity, and a non-profit entity, and the like. The
loan may be a student loan and the condition classifying circuit
classifies at least one of the progress of a student toward a
degree, the participation of a student in a non-profit activity,
the participation of the student in a public interest activity, and
the like. The crowdsourcing services circuit may be further
structured with a user interface 12420 by which a user may
configure a query for information about the set of entities and the
crowdsourcing services circuit automatically configures a
crowdsourcing request based on the query. The set of subsidized
loans may be backed by a set of assets 12412, such as a municipal
asset, a vehicle, a ship, a plane, a building, a home, real estate
property, undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of inventory, a commodity, a security, a currency,
a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a beverage, a precious metal, an item of jewelry, a
gemstone, intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, an item of
personal property, and the like. An example system may include a
condition classifying circuit 12422 including a model 12424 and an
artificial intelligence services circuit 12436 structured to
classify a set of parameters 12406 of the set of subsidized loans
12410 involved in the transactions based on information from
crowdsourcing services circuit, where the model may be trained
using a training data set of outcomes 12414 related to subsidized
loans. The set of subsidized loans may include at least one of a
municipal subsidized loan, a government subsidized loan, a student
loan, an asset-backed subsidized loan, and a corporate subsidized
loan. The condition classified by the condition classifying circuit
may be a default condition, a foreclosure condition, a condition
indicating violation of a covenant, a financial risk condition, a
behavioral risk condition, a contractual performance condition, a
policy risk condition, a financial health condition, a physical
defect condition, a physical health condition, an entity risk
condition, an entity health condition, and the like. The artificial
intelligence services circuit may a machine learning system, a
model-based system, a rule-based system, a deep learning system, a
hybrid system, a neural network, a convolutional neural network, a
feed forward neural network, a feedback neural network, a
self-organizing map, a fuzzy logic system, a random walk system, a
random forest system, a probabilistic system, a Bayesian system, a
simulation system, and the like. An example system may include a
smart contract circuit 12426 for automatically modifying the terms
and conditions 12418 of a subsidized loan based on the classified
set of parameters from the condition classifying circuit. The smart
contract services circuit may be utilized for managing a smart
contract for the subsidized loan transaction, set terms and
conditions for the subsidized loan, and the like. In embodiments,
the set of terms and conditions for the debt transaction that are
specified and managed by the smart contract services circuit may be
selected from among a principal amount of debt, a balance of debt,
a fixed interest rate, a variable interest rate, a payment amount,
a payment schedule, a balloon payment schedule, a specification of
assets that back the subsidized loan, a specification of
substitutability of assets, a party, an issuer, a purchaser, a
guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition,
and a consequence of default. An example system may include a
configurable data collection and monitoring services circuit 12428
for monitoring the entities such as a set of Internet of Things
services, a set of environmental condition sensors, a set of social
network analytic services, a set of algorithms for querying network
domains, and the like. The configurable data collection and
monitoring services circuit may be further structured to monitor an
environment such as a municipal environment, an educational
environment, a corporate environment, a securities trading
environment, a real property environment, a commercial facility, a
warehousing facility, a transportation environment, a manufacturing
environment, a storage environment, a home, a vehicle, and the
like. An example system may include an automated agent circuit
12430 structured to process events relevant to at least one of the
value, the condition, and the ownership of the assets and
undertakes an action related to a subsidized loan transaction to
which the asset is related, such as where the action may be a
subsidized loan transaction, underwriting a subsidized loan
transaction, setting an interest rate, deferring a payment
requirement, modifying an interest rate, validating title, managing
inspection, recording a change in title, assessing the value of an
asset, calling a loan, closing a transaction, setting terms and
conditions for a transaction, providing notices required to be
provided, foreclosing on a set of assets, modifying terms and
conditions, setting a rating for an entity, syndicating subsidized
loans, consolidating subsidized loans, and the like. An example
system may include an automated subsidized loan management circuit
12438 structured to manage an action related to the subsidized
loan, where the automated subsidized loan management circuit may be
trained on a training set of subsidized loan management activities.
The automated subsidized loan management circuit may be trained on
a set of interactions of parties with a set of user interfaces
involved in a set of subsidized loan transaction activities, such
as offering a subsidized loan transaction, underwriting a
subsidized loan transaction, setting an interest rate, deferring a
payment requirement, modifying an interest rate, validating title,
managing inspection, recording a change in title, assessing the
value of an asset, calling a loan, closing a transaction, setting
terms and conditions for a transaction providing notices required
to be provided, foreclosing on a set of assets, modifying terms and
conditions, setting a rating for an entity, syndicating subsidized
loans, consolidating subsidized loans, and the like. An example
system may include a blockchain services circuit 12440 structured
to record the modified set of terms and conditions for the set of
subsidized loans in a distributed ledger. An example system may
include a market value data collection service circuit 12432
structured to monitor and report on marketplace information 12434
relevant to the value of at least one of a party, a set of
subsidized loans, and a set of assets, where reporting may be on a
set of assets such as one of a municipal asset, a vehicle, a ship,
a plane, a building, a home, real estate property, undeveloped
land, a farm, a crop, a municipal facility, a warehouse, a set of
inventory, a commodity, a security, a currency, a token of value, a
ticket, a cryptocurrency, a consumable item, an edible item, a
beverage, a precious metal, an item of jewelry, a gemstone,
intellectual property, an intellectual property right, a
contractual right, an antique, a fixture, an item of furniture, an
item of equipment, a tool, an item of machinery, and an item of
personal property. The market value data collection service circuit
may be further structured to monitor pricing or financial data for
items that are similar to the assets in at least one public
marketplace. In embodiments, a set of similar items for valuing the
assets may be constructed using a similarity clustering algorithm
12442 based on the attributes of the assets, such as from among a
category of the assets, asset age, asset condition, asset history,
asset storage, geolocation of assets, and the like.
[1707] FIG. 125 depicts a method 12500 for automating handling of a
subsidized loan including collecting information related to a set
of entities involved in a set of subsidized loan transactions
12502, classifying a set of parameters of the set of subsidized
loans involved in the transactions based on an artificial
intelligence service, a model, and information from a crowdsourcing
service, where the model is trained using a training data set of
outcomes related to subsidized loans 12504; and modifying terms and
conditions of a subsidized loan based on the classified set of
parameters 12508. The set of entities may include entities among a
set of subsidized loans, a set of parties, a set of subsidies, a
set of guarantors, a set of subsidizing parties, and a set of
collateral 12510. A set of subsidizing parties may include a
municipality, a corporation, a contractor, a government entity, a
non-governmental entity, and a non-profit entity 12512. The set of
subsidized loans may include a municipal subsidized loan, a
government subsidized loan, a student loan, an asset-backed
subsidized loan, and a corporate subsidized loan 12514. The loan
may be a student loan where the condition classifying system
classifies at least one of the progress of a student toward a
degree, the participation of a student in a non-profit activity,
and the participation of the student in a public interest activity
12518.
[1708] FIG. 126 depicts a system including an asset identification
service circuit 12612 structured to interpret assets 12624
corresponding to a financial entity 12622 configured to take
custody of the assets (e.g., identifying assets for which a bank
may take custody), where an identity management service circuit
12614 may be structured to authenticate identifiers 12628 (e.g.,
including a credential 12630) corresponding to actionable entities
12626 (e.g., an owner, a beneficiary, an agent, a trustee, a
custodian, and the like) entitled to take action with respect to
the assets. For example, a group of financial entities may have
permissions with respect to actions to be taken with respect to an
asset. A blockchain service circuit 12616 may be structured to
store a plurality of asset control features 12632 in a blockchain
structure 12618, where the blockchain structure may include a
distributed ledger configuration 12620. For instance, transactional
events may be stored in a distributed ledger in the blockchain
structure where the financial entity and actionable entities may
have distributed access through the blockchain structure to share
and distribute the asset events. A financial management circuit
12610 may be structured to communicate the interpreted assets and
authenticated identifiers to the blockchain service circuit for
storage in the blockchain structure as asset control features,
wherein the asset control features are recorded in the distributed
ledger configuration as asset events 12634 (e.g., a transfer of
title, death of an owner, disability of an owner, bankruptcy of an
owner, foreclosure, placement of a lien, use of assets as
collateral, designation of a beneficiary, undertaking a loan
against assets, providing a notice with respect to assets,
inspection of assets, assessment of assets, reporting on assets for
taxation purposes, allocation of ownership of assets, disposal of
assets, sale of assets, purchase of assets, a designation of an
ownership status, and the like). A data collection circuit 12602
may be structured to monitor the interpretation of the plurality of
assets, authentication of the plurality of identifiers, and the
recording of asset events, where t data collection circuit may be
communicatively coupled with an Internet of Things system, a camera
system, a networked monitoring system, an internet monitoring
system, a mobile device system, a wearable device system, a user
interface system, and an interactive crowdsourcing system. A smart
contract circuit 12604 may be structured to manage the custody of
the assets, where an asset event related to the plurality of assets
may be managed by the smart contract circuit based on terms and
conditions 12608 embodied in a smart contract configuration 12606
and based on data collected by the data collection service circuit.
In embodiments, the asset identification service circuit, identity
management service circuit, blockchain service circuit, and the
financial management circuit may include a corresponding
application programming interface (API) component structured to
facilitate communication among the circuits of the system, such as
where the corresponding API components of the circuits further
include user interfaces structured to interact with users of the
system.
[1709] FIG. 127 depicts a method including interpreting assets
corresponding to a financial entity configured to take custody of
the plurality of assets 12702, such as where the interpreting of
the assets may include identifying the plurality of assets for
which a financial entity is responsible for taking custody. The
method may include authenticating identifiers (e.g., including a
credential) corresponding to actionable entities (e.g., owner, a
beneficiary, an agent, a trustee, and a custodian) entitled to take
action with respect to the plurality of assets 12704, such as where
authenticating the identifiers includes verifying the identifiers
corresponding to actionable entities are entitled to take action
with respect to the assets. The method may include storing a
plurality of asset control features in a blockchain structure
(e.g., including a distributed ledger configuration) 12708 (e.g.,
the blockchain structure may be provided in conjunction with a
block-chain marketplace, utilize an automated blockchain-based
transaction application, the blockchain structure may be a
distributed blockchain structure across a plurality of asset nodes,
and the like). The method may include communicating the interpreted
assets and authenticated identifiers for storage in the blockchain
structure as asset control features, where the asset control
features may be recorded in the distributed ledger configuration as
asset events 12710. The method may include monitoring the
interpretation of the assets, authentication of the identifiers,
and the recording of asset events 12712, such as where asset events
may include transfer of title, death of an owner, disability of an
owner, bankruptcy of an owner, foreclosure, placement of a lien,
use of assets as collateral, designation of a beneficiary,
undertaking a loan against assets, providing a notice with respect
to assets, inspection of assets, assessment of assets, reporting on
assets for taxation purposes, allocation of ownership of assets,
disposal of assets, sale of assets, purchase of assets, and
designation of an ownership status. In embodiments, monitoring may
be executed by an Internet of Things system, a camera system, a
networked monitoring system, an internet monitoring system, a
mobile device system, a wearable device system, a user interface
system, an interactive crowdsourcing system, and the like. The
method may include managing the custody of the assets, where an
asset event related to the plurality of assets may be based on
terms and conditions embodied in a smart contract configuration and
based on data collected by a data collection service circuit 12714.
The method may include sharing and distributing the asset events
with the plurality of actionable entities 12718. The method may
include storing asset transaction data in the blockchain structure
based on interactions between actionable entities 12720. An asset
may include a virtual asset tag where interpreting the assets
comprises identifying the virtual asset tag (e.g., storing of the
asset control features may include storing virtual asset tag data,
such as where the virtual asset tag data is location data, tracking
data, and the like. For instance, an identifier corresponding to
the financial entity or actionable entities may be stored as
virtual asset tag data.
[1710] FIG. 128 depicts a system 12800 including a lending
agreement storage circuit 12802 structured to store a lending
agreement data 12804 including a lending agreement 12814, wherein
the lending agreement may include a lending condition data 12816.
In embodiments, the lending condition data may include a terms and
condition data 12818 of the at least one lending agreement related
to a foreclosure condition 12822 on an asset 12820 that provides a
collateral condition 12824 related to a collateral asset 12826,
such as for securing a repayment obligation 12828 of the lending
agreement. The system may include a data collection services
circuit 12806 structured to monitor the lending condition data and
to detect a default condition 12808 based on a change to the
lending condition data. Further, the data collection services
circuit may include an Internet of Things system, a camera system,
a networked monitoring system, an internet monitoring system, a
mobile device system, a wearable device system, a user interface
system, and an interactive crowdsourcing system. The system may
include a smart contract services circuit 12810 structured to, when
the default condition is detected by the data collection services
circuit, interpret the default condition 12812 and communicate a
default condition indication 12830, such as to initiate a
foreclosure procedure 12832 based on the collateral condition. For
instance, the foreclosure procedure may configure and initiate a
listing of the collateral asset on a public auction site, configure
and deliver a set of transport instructions for the collateral
asset, configure a set of instructions for a drone to transport the
collateral asset, configure a set of instructions for a robotic
device to transport the collateral asset, initiate a process for
automatically substituting a set of substitute collateral, initiate
a collateral tracking procedure, initiates a collateral valuation
process, initiates a message to a borrower initiating a negotiation
regarding the foreclosure, and the like. The default condition
indication may be communicated to a smart lock and a smart
container to lock the collateral asset. The negotiation may be
managed by a robotic process automation system trained on a
training set of foreclosure negotiations, and may relate to
modification of interest rate, payment terms, collateral for the
lending agreement, and the like. In embodiments, each of the
lending agreement storage circuit, data collection services
circuit, and smart contract services circuit may further include a
corresponding application programming interface (API) component
structured to facilitate communication among the circuits of the
system, where the corresponding API components of the circuits may
include user interfaces structured to interact with a plurality of
users of the system.
[1711] FIG. 129 depicts a method 12900 for facilitating foreclosure
on collateral, the method including storing a lending agreement
data including a lending agreement, where the lending agreement may
include a lending condition data, such as where the lending
condition data includes a terms and condition data of the lending
agreement related to a foreclosure condition on an asset that
provides a collateral condition related to a collateral asset for
securing a repayment obligation of the at least one lending
agreement 12902. The method may include monitoring the lending
condition data and to detect a default condition based on a change
to the lending condition data 12904. The method may include
interpreting the default condition 12908 and communicating a
default condition indication that initiates a foreclosure procedure
based on the collateral condition 12910. For instance, the
foreclosure procedure may configure and initiate a listing of the
collateral asset on a public auction site, configure and deliver a
set of transport instructions for the collateral asset, configure a
set of instructions for a drone to transport the collateral asset,
configure a set of instructions for a robotic device to transport
the collateral asset, initiate a process for automatically
substituting a set of substitute collateral, initiate a collateral
tracking procedure, initiates a collateral valuation process,
initiates a message to a borrower initiating a negotiation
regarding the foreclosure, and the like 12914. The default
condition indication may be communicated to a smart lock and a
smart container to lock the collateral asset 12912. The negotiation
may be managed by a robotic process automation system trained on a
training set of foreclosure negotiations 12918, and may relate to
modification of interest rate, payment terms, collateral for the
lending agreement, and the like. In embodiments, communications may
be provided by a corresponding application programming interface
(API) 12920, where the corresponding API may include user
interfaces structured to interact with a plurality of users.
Artificial Intelligence Embodiments
[1712] Referring to FIGS. 4-31, in embodiments of the present
disclosure, including ones involving artificial intelligence 3448,
adaptive intelligent systems 3304, robotic process automation 3422,
expert systems, self-organization, machine learning, training of
models, and the like, may benefit from the use of a neural net,
such as a neural net trained for pattern recognition, for
prediction, for optimization based on a set of desired outcomes,
for classification or recognition of one or more parameters,
features characteristics, or phenomena, for support of autonomous
control, and other purposes. References to artificial intelligence,
expert systems, models, adaptive intelligence, and/or neural
networks throughout this disclosure should be understood to
optionally encompass use of a wide range of different types of
neural networks, machine learning systems, artificial intelligence
systems, and the like as particular embodiments permit, such as
feed forward neural networks, radial basis function neural
networks, self-organizing neural networks (e.g., Kohonen
self-organizing neural networks), recurrent neural networks,
modular neural networks, artificial neural networks, physical
neural networks, multi-layered neural networks, convolutional
neural networks, hybrids of neural networks with other expert
systems (e.g., hybrid fuzzy logic--neural network systems),
Autoencoder neural networks, probabilistic neural networks, time
delay neural networks, convolutional neural networks, regulatory
feedback neural networks, radial basis function neural networks,
recurrent neural networks, Hopfield neural networks, Boltzmann
machine neural networks, self-organizing map (SOM) neural networks,
learning vector quantization (LVQ) neural networks, fully recurrent
neural networks, simple recurrent neural networks, echo state
neural networks, long short-term memory neural networks,
bi-directional neural networks, hierarchical neural networks,
stochastic neural networks, genetic scale RNN neural networks,
committee of machines neural networks, associative neural networks,
physical neural networks, instantaneously trained neural networks,
spiking neural networks, neocognitron neural networks, dynamic
neural networks, cascading neural networks, neuro-fuzzy neural
networks, compositional pattern-producing neural networks, memory
neural networks, hierarchical temporal memory neural networks, deep
feed forward neural networks, gated recurrent unit (GCU) neural
networks, auto encoder neural networks, variational auto encoder
neural networks, de-noising auto encoder neural networks, sparse
auto-encoder neural networks, Markov chain neural networks,
restricted Boltzmann machine neural networks, deep belief neural
networks, deep convolutional neural networks, de-convolutional
neural networks, deep convolutional inverse graphics neural
networks, generative adversarial neural networks, liquid state
machine neural networks, extreme learning machine neural networks,
echo state neural networks, deep residual neural networks, support
vector machine neural networks, neural Turing machine neural
networks, and/or holographic associative memory neural networks, or
hybrids or combinations of the foregoing, or combinations with
other expert systems, such as rule-based systems, model-based
systems (including ones based on physical models, statistical
models, flow-based models, biological models, biomimetic models,
and the like).
[1713] The foregoing neural networks may have a variety of nodes or
neurons, which may perform a variety of functions on inputs, such
as inputs received from sensors or other data sources, including
other nodes. Functions may involve weights, features, feature
vectors, and the like. Neurons may include perceptrons, neurons
that mimic biological functions (such as of the human senses of
touch, vision, taste, hearing, and smell), and the like. Continuous
neurons, such as with sigmoidal activation, may be used in the
context of various forms of neural net, such as where back
propagation is involved.
[1714] In many embodiments, an expert system or neural network may
be trained, such as by a human operator or supervisor, or based on
a data set, model, or the like. Training may include presenting the
neural network with one or more training data sets that represent
values, such as sensor data, event data, parameter data, and other
types of data (including the many types described throughout this
disclosure), as well as one or more indicators of an outcome, such
as an outcome of a process, an outcome of a calculation, an outcome
of an event, an outcome of an activity, or the like. Training may
include training in optimization, such as training a neural network
to optimize one or more systems based on one or more optimization
approaches, such as Bayesian approaches, parametric Bayes
classifier approaches, k-nearest-neighbor classifier approaches,
iterative approaches, interpolation approaches, Pareto optimization
approaches, algorithmic approaches, and the like. Feedback may be
provided in a process of variation and selection, such as with a
genetic algorithm that evolves one or more solutions based on
feedback through a series of rounds.
[1715] In embodiments, a plurality of neural networks may be
deployed in a cloud platform that receives data streams and other
inputs collected (such as by mobile data collectors) in one or more
transactional environments and transmitted to the cloud platform
over one or more networks, including using network coding to
provide efficient transmission. In the cloud platform, optionally
using massively parallel computational capability, a plurality of
different neural networks of various types (including modular
forms, structure-adaptive forms, hybrids, and the like) may be used
to undertake prediction, classification, control functions, and
provide other outputs as described in connection with expert
systems disclosed throughout this disclosure. The different neural
networks may be structured to compete with each other (optionally
including use evolutionary algorithms, genetic algorithms, or the
like), such that an appropriate type of neural network, with
appropriate input sets, weights, node types and functions, and the
like, may be selected, such as by an expert system, for a specific
task involved in a given context, workflow, environment process,
system, or the like.
[1716] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
feed forward neural network, which moves information in one
direction, such as from a data input, like a data source related to
at least one resource or parameter related to a transactional
environment, such as any of the data sources mentioned throughout
this disclosure, through a series of neurons or nodes, to an
output. Data may move from the input nodes to the output nodes,
optionally passing through one or more hidden nodes, without loops.
In embodiments, feed forward neural networks may be constructed
with various types of units, such as binary McCulloch-Pitts
neurons, the simplest of which is a perceptron.
[1717] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
capsule neural network, such as for prediction, classification, or
control functions with respect to a transactional environment, such
as relating to one or more of the machines and automated systems
described throughout this disclosure.
[1718] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
radial basis function (RBF) neural network, which may be preferred
in some situations involving interpolation in a multi-dimensional
space (such as where interpolation is helpful in optimizing a
multi-dimensional function, such as for optimizing a data
marketplace as described here, optimizing the efficiency or output
of a power generation system, a factory system, or the like, or
other situation involving multiple dimensions. In embodiments, each
neuron in the RBF neural network stores an example from a training
set as a "prototype." Linearity involved in the functioning of this
neural network offers RBF the advantage of not typically suffering
from problems with local minima or maxima.
[1719] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
radial basis function (RBF) neural network, such as one that
employs a distance criterion with respect to a center (e.g., a
Gaussian function). A radial basis function may be applied as a
replacement for a hidden layer, such as a sigmoidal hidden layer
transform, in a multi-layer perceptron. An RBF network may have two
layers, such as where an input is mapped onto each RBF in a hidden
layer. In embodiments, an output layer may comprise a linear
combination of hidden layer values representing, for example, a
mean predicted output. The output layer value may provide an output
that is the same as or similar to that of a regression model in
statistics. In classification problems, the output layer may be a
sigmoid function of a linear combination of hidden layer values,
representing a posterior probability. Performance in both cases is
often improved by shrinkage techniques, such as ridge regression in
classical statistics. This corresponds to a prior belief in small
parameter values (and therefore smooth output functions) in a
Bayesian framework. RBF networks may avoid local minima, because
the only parameters that are adjusted in the learning process are
the linear mapping from hidden layer to output layer. Linearity
ensures that the error surface is quadratic and therefore has a
single minimum. In regression problems, this can be found in one
matrix operation. In classification problems, the fixed
non-linearity introduced by the sigmoid output function may be
handled using an iteratively re-weighted least squares function or
the like.
[1720] RBF networks may use kernel methods such as support vector
machines (SVM) and Gaussian processes (where the RBF is the kernel
function). A non-linear kernel function may be used to project the
input data into a space where the learning problem can be solved
using a linear model.
[1721] In embodiments, an RBF neural network may include an input
layer, a hidden layer and a summation layer. In the input layer,
one neuron appears in the input layer for each predictor variable.
In the case of categorical variables, N-1 neurons are used, where N
is the number of categories. The input neurons may, in embodiments,
standardize the value ranges by subtracting the median and dividing
by the interquartile range. The input neurons may then feed the
values to each of the neurons in the hidden layer. In the hidden
layer, a variable number of neurons may be used (determined by the
training process). Each neuron may consist of a radial basis
function that is centered on a point with as many dimensions as a
number of predictor variables. The spread (e.g., radius) of the RBF
function may be different for each dimension. The centers and
spreads may be determined by training. When presented with a vector
of input values from the input layer, a hidden neuron may compute a
Euclidean distance of the test case from the neuron's center point
and then apply the RBF kernel function to this distance, such as
using the spread values. The resulting value may then be passed to
the summation layer. In the summation layer, the value coming out
of a neuron in the hidden layer may be multiplied by a weight
associated with the neuron and may add to the weighted values of
other neurons. This sum becomes the output. For classification
problems, one output is produced (with a separate set of weights
and summation units) for each target category. The value output for
a category is the probability that the case being evaluated has
that category. In training of an RBF, various parameters may be
determined, such as the number of neurons in a hidden layer, the
coordinates of the center of each hidden-layer function, the spread
of each function in each dimension, and the weights applied to
outputs as they pass to the summation layer. Training may be used
by clustering algorithms (such as k-means clustering), by
evolutionary approaches, and the like.
[1722] In embodiments, a recurrent neural network may have a
time-varying, real-valued (more than just zero or one) activation
(output). Each connection may have a modifiable real-valued weight.
Some of the nodes are called labeled nodes, some output nodes, and
others hidden nodes. For supervised learning in discrete time
settings, training sequences of real-valued input vectors may
become sequences of activations of the input nodes, one input
vector at a time. At each time step, each non-input unit may
compute its current activation as a nonlinear function of the
weighted sum of the activations of all units from which it receives
connections. The system can explicitly activate (independent of
incoming signals) some output units at certain time steps.
[1723] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
self-organizing neural network, such as a Kohonen self-organizing
neural network, such as for visualization of views of data, such as
low-dimensional views of high-dimensional data. The self-organizing
neural network may apply competitive learning to a set of input
data, such as from one or more sensors or other data inputs from or
associated with a transactional environment, including any machine
or component that relates to the transactional environment. In
embodiments, the self-organizing neural network may be used to
identify structures in data, such as unlabeled data, such as in
data sensed from a range of data sources about or sensors in or
about in a transactional environment, where sources of the data are
unknown (such as where events may be coming from any of a range of
unknown sources). The self-organizing neural network may organize
structures or patterns in the data, such that they can be
recognized, analyzed, and labeled, such as identifying market
behavior structures as corresponding to other events and
signals.
[1724] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
recurrent neural network, which may allow for a bi-directional flow
of data, such as where connected units (e.g., neurons or nodes)
form a directed cycle. Such a network may be used to model or
exhibit dynamic temporal behavior, such as involved in dynamic
systems, such as a wide variety of the automation systems, machines
and devices described throughout this disclosure, such as an
automated agent interacting with a marketplace for purposes of
collecting data, testing spot market transactions, execution
transactions, and the like, where dynamic system behavior involves
complex interactions that a user may desire to understand, predict,
control and/or optimize. For example, the recurrent neural network
may be used to anticipate the state of a market, such as one
involving a dynamic process or action, such as a change in state of
a resource that is traded in or that enables a marketplace of
transactional environment. In embodiments, the recurrent neural
network may use internal memory to process a sequence of inputs,
such as from other nodes and/or from sensors and other data inputs
from or about the transactional environment, of the various types
described herein. In embodiments, the recurrent neural network may
also be used for pattern recognition, such as for recognizing a
machine, component, agent, or other item based on a behavioral
signature, a profile, a set of feature vectors (such as in an audio
file or image), or the like. In a non-limiting example, a recurrent
neural network may recognize a shift in an operational mode of a
marketplace or machine by learning to classify the shift from a
training data set consisting of a stream of data from one or more
data sources of sensors applied to or about one or more
resources.
[1725] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
modular neural network, which may comprise a series of independent
neural networks (such as ones of various types described herein)
that are moderated by an intermediary. Each of the independent
neural networks in the modular neural network may work with
separate inputs, accomplishing subtasks that make up the task the
modular network as whole is intended to perform. For example, a
modular neural network may comprise a recurrent neural network for
pattern recognition, such as to recognize what type of machine or
system is being sensed by one or more sensors that are provided as
input channels to the modular network and an RBF neural network for
optimizing the behavior of the machine or system once understood.
The intermediary may accept inputs of each of the individual neural
networks, process them, and create output for the modular neural
network, such an appropriate control parameter, a prediction of
state, or the like.
[1726] Combinations among any of the pairs, triplets, or larger
combinations, of the various neural network types described herein,
are encompassed by the present disclosure. This may include
combinations where an expert system uses one neural network for
recognizing a pattern (e.g., a pattern indicating a problem or
fault condition) and a different neural network for self-organizing
an activity or workflow based on the recognized pattern (such as
providing an output governing autonomous control of a system in
response to the recognized condition or pattern). This may also
include combinations where an expert system uses one neural network
for classifying an item (e.g., identifying a machine, a component,
or an operational mode) and a different neural network for
predicting a state of the item (e.g., a fault state, an operational
state, an anticipated state, a maintenance state, or the like).
Modular neural networks may also include situations where an expert
system uses one neural network for determining a state or context
(such as a state of a machine, a process, a work flow, a
marketplace, a storage system, a network, a data collector, or the
like) and a different neural network for self-organizing a process
involving the state or context (e.g., a data storage process, a
network coding process, a network selection process, a data
marketplace process, a power generation process, a manufacturing
process, a refining process, a digging process, a boring process,
or other process described herein).
[1727] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
physical neural network where one or more hardware elements is used
to perform or simulate neural behavior. In embodiments, one or more
hardware neurons may be configured to stream voltage values,
current values, or the like that represent sensor data, such as to
calculate information from analog sensor inputs representing energy
consumption, energy production, or the like, such as by one or more
machines providing energy or consuming energy for one or more
transactions. One or more hardware nodes may be configured to
stream output data resulting from the activity of the neural net.
Hardware nodes, which may comprise one or more chips,
microprocessors, integrated circuits, programmable logic
controllers, application-specific integrated circuits,
field-programmable gate arrays, or the like, may be provided to
optimize the machine that is producing or consuming energy, or to
optimize another parameter of some part of a neural net of any of
the types described herein. Hardware nodes may include hardware for
acceleration of calculations (such as dedicated processors for
performing basic or more sophisticated calculations on input data
to provide outputs, dedicated processors for filtering or
compressing data, dedicated processors for de-compressing data,
dedicated processors for compression of specific file or data types
(e.g., for handling image data, video streams, acoustic signals,
thermal images, heat maps, or the like), and the like. A physical
neural network may be embodied in a data collector, including one
that may be reconfigured by switching or routing inputs in varying
configurations, such as to provide different neural net
configurations within the data collector for handling different
types of inputs (with the switching and configuration optionally
under control of an expert system, which may include a
software-based neural net located on the data collector or
remotely). A physical, or at least partially physical, neural
network may include physical hardware nodes located in a storage
system, such as for storing data within a machine, a data storage
system, a distributed ledger, a mobile device, a server, a cloud
resource, or in a transactional environment, such as for
accelerating input/output functions to one or more storage elements
that supply data to or take data from the neural net. A physical,
or at least partially physical, neural network may include physical
hardware nodes located in a network, such as for transmitting data
within, to or from an industrial environment, such as for
accelerating input/output functions to one or more network nodes in
the net, accelerating relay functions, or the like. In embodiments,
of a physical neural network, an electrically adjustable resistance
material may be used for emulating the function of a neural
synapse. In embodiments, the physical hardware emulates the
neurons, and software emulates the neural network between the
neurons. In embodiments, neural networks complement conventional
algorithmic computers. They are versatile and can be trained to
perform appropriate functions without the need for any
instructions, such as classification functions, optimization
functions, pattern recognition functions, control functions,
selection functions, evolution functions, and others.
[1728] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
multilayered feed forward neural network, such as for complex
pattern classification of one or more items, phenomena, modes,
states, or the like. In embodiments, a multilayered feed forward
neural network may be trained by an optimization technical, such as
a genetic algorithm, such as to explore a large and complex space
of options to find an optimum, or near-optimum, global solution.
For example, one or more genetic algorithms may be used to train a
multilayered feed forward neural network to classify complex
phenomena, such as to recognize complex operational modes of
machines, such as modes involving complex interactions among
machines (including interference effects, resonance effects, and
the like), modes involving non-linear phenomena, modes involving
critical faults, such as where multiple, simultaneous faults occur,
making root cause analysis difficult, and others. In embodiments, a
multilayered feed forward neural network may be used to classify
results from monitoring of a marketplace, such as monitoring
systems, such as automated agents, that operate within the
marketplace, as well as monitoring resources that enable the
marketplace, such as computing, networking, energy, data storage,
energy storage, and other resources.
[1729] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
feed-forward, back-propagation multi-layer perceptron (MLP) neural
network, such as for handling one or more remote sensing
applications, such as for taking inputs from sensors distributed
throughout various transactional environments. In embodiments, the
MLP neural network may be used for classification of transactional
environments and resource environments, such as lending markets,
spot markets, forward markets, energy markets, renewable energy
credit (REC) markets, networking markets, advertising markets,
spectrum markets, ticketing markets, rewards markets, compute
markets, and others mentioned throughout this disclosure, as well
as physical resources and environments that produce them, such as
energy resources (including renewable energy environments, mining
environments, exploration environments, drilling environments, and
the like, including classification of geological structures
(including underground features and above ground features),
classification of materials (including fluids, minerals, metals,
and the like), and other problems. This may include fuzzy
classification.
[1730] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
structure-adaptive neural network, where the structure of a neural
network is adapted, such as based on a rule, a sensed condition, a
contextual parameter, or the like. For example, if a neural network
does not converge on a solution, such as classifying an item or
arriving at a prediction, when acting on a set of inputs after some
amount of training, the neural network may be modified, such as
from a feed forward neural network to a recurrent neural network,
such as by switching data paths between some subset of nodes from
unidirectional to bi-directional data paths. The structure
adaptation may occur under control of an expert system, such as to
trigger adaptation upon occurrence of a trigger, rule or event,
such as recognizing occurrence of a threshold (such as an absence
of a convergence to a solution within a given amount of time) or
recognizing a phenomenon as requiring different or additional
structure (such as recognizing that a system is varying dynamically
or in a non-linear fashion). In one non-limiting example, an expert
system may switch from a simple neural network structure like a
feed forward neural network to a more complex neural network
structure like a recurrent neural network, a convolutional neural
network, or the like upon receiving an indication that a
continuously variable transmission is being used to drive a
generator, turbine, or the like in a system being analyzed.
[1731] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
autoencoder, autoassociator or Diabolo neural network, which may be
similar to a multilayer perceptron (MLLP) neural network, such as
where there may be an input layer, an output layer and one or more
hidden layers connecting them. However, the output layer in the
auto-encoder may have the same number of units as the input layer,
where the purpose of the MLP neural network is to reconstruct its
own inputs (rather than just emitting a target value).
[1732] Therefore, the auto encoders may operate as an unsupervised
learning model. An auto encoder may be used, for example, for
unsupervised learning of efficient codings, such as for
dimensionality reduction, for learning generative models of data,
and the like. In embodiments, an auto-encoding neural network may
be used to self-learn an efficient network coding for transmission
of analog sensor data from a machine over one or more networks or
of digital data from one or more data sources. In embodiments, an
auto-encoding neural network may be used to self-learn an efficient
storage approach for storage of streams of data.
[1733] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
probabilistic neural network (PNN), which in embodiments may
comprise a multi-layer (e.g., four-layer) feed forward neural
network, where layers may include input layers, hidden layers,
pattern/summation layers and an output layer. In an embodiment of a
PNN algorithm, a parent probability distribution function (PDF) of
each class may be approximated, such as by a Parzen window and/or a
non-parametric function. Then, using the PDF of each class, the
class probability of a new input is estimated, and Bayes' rule may
be employed, such as to allocate it to the class with the highest
posterior probability. A PNN may embody a Bayesian network and may
use a statistical algorithm or analytic technique, such as Kernel
Fisher discriminant analysis technique. The PNN may be used for
classification and pattern recognition in any of a wide range of
embodiments disclosed herein. In one non-limiting example, a
probabilistic neural network may be used to predict a fault
condition of an engine based on collection of data inputs from
sensors and instruments for the engine.
[1734] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
time delay neural network (TDNN), which may comprise a feed forward
architecture for sequential data that recognizes features
independent of sequence position. In embodiments, to account for
time shifts in data, delays are added to one or more inputs, or
between one or more nodes, so that multiple data points (from
distinct points in time) are analyzed together. A time delay neural
network may form part of a larger pattern recognition system, such
as using a perceptron network. In embodiments, a TDNN may be
trained with supervised learning, such as where connection weights
are trained with back propagation or under feedback. In
embodiments, a TDNN may be used to process sensor data from
distinct streams, such as a stream of velocity data, a stream of
acceleration data, a stream of temperature data, a stream of
pressure data, and the like, where time delays are used to align
the data streams in time, such as to help understand patterns that
involve understanding of the various streams (e.g., changes in
price patterns in spot or forward markets).
[1735] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
convolutional neural network (referred to in some cases as a CNN, a
ConvNet, a shift invariant neural network, or a space invariant
neural network), wherein the units are connected in a pattern
similar to the visual cortex of the human brain. Neurons may
respond to stimuli in a restricted region of space, referred to as
a receptive field. Receptive fields may partially overlap, such
that they collectively cover the entire (e.g., visual) field. Node
responses can be calculated mathematically, such as by a
convolution operation, such as using multilayer perceptrons that
use minimal preprocessing. A convolutional neural network may be
used for recognition within images and video streams, such as for
recognizing a type of machine in a large environment using a camera
system disposed on a mobile data collector, such as on a drone or
mobile robot. In embodiments, a convolutional neural network may be
used to provide a recommendation based on data inputs, including
sensor inputs and other contextual information, such as
recommending a route for a mobile data collector. In embodiments, a
convolutional neural network may be used for processing inputs,
such as for natural language processing of instructions provided by
one or more parties involved in a workflow in an environment. In
embodiments, a convolutional neural network may be deployed with a
large number of neurons (e.g., 100,000, 500,000 or more), with
multiple (e.g., 4, 5, 6 or more) layers, and with many (e.g.,
millions) of parameters. A convolutional neural net may use one or
more convolutional nets.
[1736] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
regulatory feedback network, such as for recognizing emergent
phenomena (such as new types of behavior not previously understood
in a transactional environment).
[1737] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
learning vector quantization neural net (LVQ). Prototypical
representatives of the classes may parameterize, together with an
appropriate distance measure, in a distance-based classification
scheme.
[1738] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
echo state network (ESN), which may comprise a recurrent neural
network with a sparsely connected, random hidden layer. The weights
of output neurons may be changed (e.g., the weights may be trained
based on feedback). In embodiments, an ESN may be used to handle
time series patterns, such as, in an example, recognizing a pattern
of events associated with a market, such as pattern of price
changes in response to stimuli.
[1739] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
Bi-directional, recurrent neural network (BRNN), such as using a
finite sequence of values (e.g., voltage values from a sensor) to
predict or label each element of the sequence based on both the
past and the future context of the element. This may be done by
adding the outputs of two RNNs, such as one processing the sequence
from left to right, the other one from right to left. The combined
outputs are the predictions of target signals, such as ones
provided by a teacher or supervisor. A bi-directional RNN may be
combined with a long short-term memory RNN.
[1740] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
hierarchical RNN that connects elements in various ways to
decompose hierarchical behavior, such as into useful subprograms.
In embodiments, a hierarchical RNN may be used to manage one or
more hierarchical templates for data collection in a transactional
environment.
[1741] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
stochastic neural network, which may introduce random variations
into the network. Such random variations can be viewed as a form of
statistical sampling, such as Monte Carlo sampling.
[1742] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
genetic scale recurrent neural network. In such embodiments, a RNN
(often a LSTM) is used where a series is decomposed into a number
of scales where every scale informs the primary length between two
consecutive points. A first order scale consists of a normal RL'JN,
a second order consists of all points separated by two indices and
so on. The Nth order RNN connects the first and last node. The
outputs from all the various scales may be treated as a committee
of members, and the associated scores may be used genetically for
the next iteration.
[1743] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
committee of machines (CoM), comprising a collection of different
neural networks that together "vote" on a given example. Because
neural networks may suffer from local minima, starting with the
same architecture and training, but using randomly different
initial weights often gives different results. A CoM tends to
stabilize the result.
[1744] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
associative neural network (ASNN), such as involving an extension
of committee of machines that combines multiple feed forward neural
networks and a k-nearest neighbor technique. It may use the
correlation between ensemble responses as a measure of distance
amid the analyzed cases for the kNN. This corrects the bias of the
neural network ensemble. An associative neural network may have a
memory that can coincide with a training set. If new data become
available, the network instantly improves its predictive ability
and provides data approximation (self-learns) without retraining.
Another important feature of ASNN is the possibility to interpret
neural network results by analysis of correlations between data
cases in the space of models.
[1745] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
instantaneously trained neural network (ITNN), where the weights of
the hidden and the output layers are mapped directly from training
vector data.
[1746] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
spiking neural network, which may explicitly consider the timing of
inputs. The network input and output may be represented as a series
of spikes (such as a delta function or more complex shapes). SNNs
can process information in the time domain (e.g., signals that vary
over time, such as signals involving dynamic behavior of markets or
transactional environments). They are often implemented as
recurrent networks.
[1747] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
dynamic neural network that addresses nonlinear multivariate
behavior and includes learning of time-dependent behavior, such as
transient phenomena and delay effects. Transients may include
behavior of shifting market variables, such as prices, available
quantities, available counterparties, and the like.
[1748] In embodiments, cascade correlation may be used as an
architecture and supervised learning algorithm, supplementing
adjustment of the weights in a network of fixed topology.
Cascade-correlation may begin with a minimal network, then
automatically trains and add new hidden units one by one, creating
a multi-layer structure. Once a new hidden unit has been added to
the network, its input-side weights may be frozen. This unit then
becomes a permanent feature-detector in the network, available for
producing outputs or for creating other, more complex feature
detectors. The cascade-correlation architecture may learn quickly,
determine its own size and topology, and retain the structures it
has built even if the training set changes and requires no
back-propagation.
[1749] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
neuro-fuzzy network, such as involving a fuzzy inference system in
the body of an artificial neural network. Depending on the type,
several layers may simulate the processes involved in a fuzzy
inference, such as fuzzification, inference, aggregation and
defuzzification. Embedding a fuzzy system in a general structure of
a neural net as the benefit of using available training methods to
find the parameters of a fuzzy system.
[1750] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
compositional pattern-producing network (CPPN), such as a variation
of an associative neural network (ANN) that differs the set of
activation functions and how they are applied. While typical ANNs
often contain only sigmoid functions (and sometimes Gaussian
functions), CPPNs can include both types of functions and many
others. Furthermore, CPPNs may be applied across the entire space
of possible inputs, so that they can represent a complete image.
Since they are compositions of functions, CPPNs in effect encode
images at infinite resolution and can be sampled for a particular
display at whatever resolution is optimal.
[1751] This type of network can add new patterns without
re-training. In embodiments, methods and systems described herein
that involve an expert system or self-organization capability may
use a one-shot associative memory network, such as by creating a
specific memory structure, which assigns each new pattern to an
orthogonal plane using adjacently connected hierarchical
arrays.
[1752] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
hierarchical temporal memory (HTM) neural network, such as
involving the structural and algorithmic properties of the
neocortex. HTM may use a biomimetic model based on
memory-prediction theory. HTM may be used to discover and infer the
high-level causes of observed input patterns and sequences.
[1753] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
holographic associative memory (HAM) neural network, which may
comprise an analog, correlation-based, associative,
stimulus-response system. Information may be mapped onto the phase
orientation of complex numbers. The memory is effective for
associative memory tasks, generalization and pattern recognition
with changeable attention.
[1754] In embodiments, various embodiments involving network coding
may be used to code transmission data among network nodes in neural
net, such as where nodes are located in one or more data collectors
or machines in a transactional environment.
[1755] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software,
program codes, and/or instructions on a processor. The present
disclosure may be implemented as a method on the machine, as a
system or apparatus as part of or in relation to the machine, or as
a computer program product embodied in a computer readable medium
executing on one or more of the machines. In embodiments, the
processor may be part of a server, cloud server, client, network
infrastructure, mobile computing platform, stationary computing
platform, or other computing platform. A processor may be any kind
of computational or processing device capable of executing program
instructions, codes, binary instructions and the like. The
processor may be or may include a signal processor, digital
processor, embedded processor, microprocessor or any variant such
as a co-processor (math co-processor, graphic co-processor,
communication co-processor and the like) and the like that may
directly or indirectly facilitate execution of program code or
program instructions stored thereon. In addition, the processor may
enable execution of multiple programs, threads, and codes. The
threads may be executed simultaneously to enhance the performance
of the processor and to facilitate simultaneous operations of the
application. By way of implementation, methods, program codes,
program instructions and the like described herein may be
implemented in one or more thread. The thread may spawn other
threads that may have assigned priorities associated with them; the
processor may execute these threads based on priority or any other
order based on instructions provided in the program code. The
processor, or any machine utilizing one, may include non-transitory
memory that stores methods, codes, instructions and programs as
described herein and elsewhere. The processor may access a
non-transitory storage medium through an interface that may store
methods, codes, and instructions as described herein and elsewhere.
The storage medium associated with the processor for storing
methods, programs, codes, program instructions or other type of
instructions capable of being executed by the computing or
processing device may include but may not be limited to one or more
of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache
and the like.
[1756] A processor may include one or more cores that may enhance
speed and performance of a multiprocessor. In embodiments, the
process may be a dual core processor, quad core processors, other
chip-level multiprocessor and the like that combine two or more
independent cores (called a die).
[1757] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software
on a server, client, firewall, gateway, hub, router, or other such
computer and/or networking hardware. The software program may be
associated with a server that may include a file server, print
server, domain server, internet server, intranet server, cloud
server, and other variants such as secondary server, host server,
distributed server and the like. The server may include one or more
of memories, processors, computer readable media, storage media,
ports (physical and virtual), communication devices, and interfaces
capable of accessing other servers, clients, machines, and devices
through a wired or a wireless medium, and the like. The methods,
programs, or codes as described herein and elsewhere may be
executed by the server. In addition, other devices required for
execution of methods as described in this application may be
considered as a part of the infrastructure associated with the
server.
[1758] The server may provide an interface to other devices
including, without limitation, clients, other servers, printers,
database servers, print servers, file servers, communication
servers, distributed servers, social networks, and the like.
Additionally, this coupling and/or connection may facilitate remote
execution of program across the network. The networking of some or
all of these devices may facilitate parallel processing of a
program or method at one or more location without deviating from
the scope of the disclosure. In addition, any of the devices
attached to the server through an interface may include at least
one storage medium capable of storing methods, programs, code
and/or instructions. A central repository may provide program
instructions to be executed on different devices. In this
implementation, the remote repository may act as a storage medium
for program code, instructions, and programs.
[1759] The software program may be associated with a client that
may include a file client, print client, domain client, internet
client, intranet client and other variants such as secondary
client, host client, distributed client and the like. The client
may include one or more of memories, processors, computer readable
media, storage media, ports (physical and virtual), communication
devices, and interfaces capable of accessing other clients,
servers, machines, and devices through a wired or a wireless
medium, and the like. The methods, programs, or codes as described
herein and elsewhere may be executed by the client. In addition,
other devices required for execution of methods as described in
this application may be considered as a part of the infrastructure
associated with the client.
[1760] The client may provide an interface to other devices
including, without limitation, servers, other clients, printers,
database servers, print servers, file servers, communication
servers, distributed servers and the like. Additionally, this
coupling and/or connection may facilitate remote execution of
program across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more location without deviating from the scope of the
disclosure. In addition, any of the devices attached to the client
through an interface may include at least one storage medium
capable of storing methods, programs, applications, code and/or
instructions. A central repository may provide program instructions
to be executed on different devices. In this implementation, the
remote repository may act as a storage medium for program code,
instructions, and programs.
[1761] The methods and systems described herein may be deployed in
part or in whole through network infrastructures. The network
infrastructure may include elements such as computing devices,
servers, routers, hubs, firewalls, clients, personal computers,
communication devices, routing devices and other active and passive
devices, modules and/or components as known in the art. The
computing and/or non-computing device(s) associated with the
network infrastructure may include, apart from other components, a
storage medium such as flash memory, buffer, stack, RAM, ROM and
the like. The processes, methods, program codes, instructions
described herein and elsewhere may be executed by one or more of
the network infrastructural elements. The methods and systems
described herein may be adapted for use with any kind of private,
community, or hybrid cloud computing network or cloud computing
environment, including those which involve features of software as
a service (SaaS), platform as a service (PaaS), and/or
infrastructure as a service (IaaS).
[1762] The methods, program codes, and instructions described
herein and elsewhere may be implemented on a cellular network
having multiple cells. The cellular network may either be frequency
division multiple access (FDMA) network or code division multiple
access (CDMA) network. The cellular network may include mobile
devices, cell sites, base stations, repeaters, antennas, towers,
and the like. The cell network may be a GSM, GPRS, 3G, EVDO, mesh,
or other network types.
[1763] The methods, program codes, and instructions described
herein and elsewhere may be implemented on or through mobile
devices. The mobile devices may include navigation devices, cell
phones, mobile phones, mobile personal digital assistants, laptops,
palmtops, netbooks, pagers, electronic books readers, music players
and the like. These devices may include, apart from other
components, a storage medium such as a flash memory, buffer, RAM,
ROM and one or more computing devices. The computing devices
associated with mobile devices may be enabled to execute program
codes, methods, and instructions stored thereon. Alternatively, the
mobile devices may be configured to execute instructions in
collaboration with other devices. The mobile devices may
communicate with base stations interfaced with servers and
configured to execute program codes. The mobile devices may
communicate on a peer-to-peer network, mesh network, or other
communications network. The program code may be stored on the
storage medium associated with the server and executed by a
computing device embedded within the server. The base station may
include a computing device and a storage medium. The storage device
may store program codes and instructions executed by the computing
devices associated with the base station.
[1764] The computer software, program codes, and/or instructions
may be stored and/or accessed on machine readable media that may
include: computer components, devices, and recording media that
retain digital data used for computing for some interval of time;
semiconductor storage known as random access memory (RAM); mass
storage typically for more permanent storage, such as optical
discs, forms of magnetic storage like hard disks, tapes, drums,
cards and other types; processor registers, cache memory, volatile
memory, non-volatile memory; optical storage such as CD, DVD;
removable media such as flash memory (e.g., USB sticks or keys),
floppy disks, magnetic tape, paper tape, punch cards, standalone
RAM disks, Zip drives, removable mass storage, off-line, and the
like; other computer memory such as dynamic memory, static memory,
read/write storage, mutable storage, read only, random access,
sequential access, location addressable, file addressable, content
addressable, network attached storage, storage area network, bar
codes, magnetic ink, and the like.
[1765] The methods and systems described herein may transform
physical and/or intangible items from one state to another. The
methods and systems described herein may also transform data
representing physical and/or intangible items from one state to
another.
[1766] The elements described and depicted herein, including in
flow charts and block diagrams throughout the figures, imply
logical boundaries between the elements. However, according to
software or hardware engineering practices, the depicted elements
and the functions thereof may be implemented on machines through
computer executable media having a processor capable of executing
program instructions stored thereon as a monolithic software
structure, as standalone software modules, or as modules that
employ external routines, code, services, and so forth, or any
combination of these, and all such implementations may be within
the scope of the present disclosure. Examples of such machines may
include, but may not be limited to, personal digital assistants,
laptops, personal computers, mobile phones, other handheld
computing devices, medical equipment, wired or wireless
communication devices, transducers, chips, calculators, satellites,
tablet PCs, electronic books, gadgets, electronic devices, devices
having artificial intelligence, computing devices, networking
equipment, servers, routers and the like. Furthermore, the elements
depicted in the flow chart and block diagrams or any other logical
component may be implemented on a machine capable of executing
program instructions. Thus, while the foregoing drawings and
descriptions set forth functional aspects of the disclosed systems,
no particular arrangement of software for implementing these
functional aspects should be inferred from these descriptions
unless explicitly stated or otherwise clear from the context.
Similarly, it will be appreciated that the various steps identified
and described above may be varied, and that the order of steps may
be adapted to particular applications of the techniques disclosed
herein. All such variations and modifications are intended to fall
within the scope of this disclosure. As such, the depiction and/or
description of an order for various steps should not be understood
to require a particular order of execution for those steps, unless
required by a particular application, or explicitly stated or
otherwise clear from the context.
[1767] The methods and/or processes described above, and steps
associated therewith, may be realized in hardware, software or any
combination of hardware and software suitable for a particular
application. The hardware may include a general-purpose computer
and/or dedicated computing device or specific computing device or
particular aspect or component of a specific computing device. The
processes may be realized in one or more microprocessors,
microcontrollers, embedded microcontrollers, programmable digital
signal processors or other programmable device, along with internal
and/or external memory. The processes may also, or instead, be
embodied in an application specific integrated circuit, a
programmable gate array, programmable array logic, or any other
device or combination of devices that may be configured to process
electronic signals. It will further be appreciated that one or more
of the processes may be realized as a computer executable code
capable of being executed on a machine-readable medium.
[1768] The computer executable code may be created using a
structured programming language such as C, an object oriented
programming language such as C++, or any other high-level or
low-level programming language (including assembly languages,
hardware description languages, and database programming languages
and technologies) that may be stored, compiled or interpreted to
run on one of the above devices, as well as heterogeneous
combinations of processors, processor architectures, or
combinations of different hardware and software, or any other
machine capable of executing program instructions.
[1769] Thus, in one aspect, methods described above and
combinations thereof may be embodied in computer executable code
that, when executing on one or more computing devices, performs the
steps thereof. In another aspect, the methods may be embodied in
systems that perform the steps thereof, and may be distributed
across devices in a number of ways, or all of the functionality may
be integrated into a dedicated, standalone device or other
hardware. In another aspect, the means for performing the steps
associated with the processes described above may include any of
the hardware and/or software described above. All such permutations
and combinations are intended to fall within the scope of the
present disclosure.
[1770] While the disclosure has been disclosed in connection with
the preferred embodiments shown and described in detail, various
modifications and improvements thereon will become readily apparent
to those skilled in the art. Accordingly, the spirit and scope of
the present disclosure is not to be limited by the foregoing
examples, but is to be understood in the broadest sense allowable
by law.
[1771] The use of the terms "a" and "an" and "the" and similar
referents in the context of describing the disclosure (especially
in the context of the following claims) is to be construed to cover
both the singular and the plural, unless otherwise indicated herein
or clearly contradicted by context. The terms "comprising,"
"having," "including," and "containing" are to be construed as
open-ended terms (i.e., meaning "including, but not limited to,")
unless otherwise noted. Recitation of ranges of values herein are
merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range,
unless otherwise indicated herein, and each separate value is
incorporated into the specification as if it were individually
recited herein. All methods described herein may be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (e.g., "such as") provided herein, is
intended merely to better illuminate the disclosure and does not
pose a limitation on the scope of the disclosure unless otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element as essential to the practice of
the disclosure.
[1772] While the foregoing written description enables one skilled
to make and use what is considered presently to be the best mode
thereof, those skilled in the art will understand and appreciate
the existence of variations, combinations, and equivalents of the
specific embodiment, method, and examples herein. The disclosure
should therefore not be limited by the above described embodiment,
method, and examples, but by all embodiments and methods within the
scope and spirit of the disclosure.
[1773] Any element in a claim that does not explicitly state "means
for" performing a specified function, or "step for" performing a
specified function, is not to be interpreted as a "means" or "step"
clause as specified in 35 U.S.C. .sctn. 112(f). In particular, any
use of "step of" in the claims is not intended to invoke the
provision of 35 U.S.C. .sctn. 112(f). The term "set" as used herein
refers to a group having one or more members.
[1774] Persons skilled in the art may appreciate that numerous
design configurations may be possible to enjoy the functional
benefits of the inventive systems. Thus, given the wide variety of
configurations and arrangements of embodiments of the present
invention the scope of the invention is reflected by the breadth of
the claims below rather than narrowed by the embodiments described
above.
* * * * *
References