U.S. patent application number 14/729545 was filed with the patent office on 2015-12-10 for composite cognitive insights.
This patent application is currently assigned to COGNITIVE SCALE, INC.. The applicant listed for this patent is COGNITIVE SCALE, INC.. Invention is credited to Dilum Ranatunga, Matthew Sanchez.
Application Number | 20150356437 14/729545 |
Document ID | / |
Family ID | 54769729 |
Filed Date | 2015-12-10 |
United States Patent
Application |
20150356437 |
Kind Code |
A1 |
Sanchez; Matthew ; et
al. |
December 10, 2015 |
Composite Cognitive Insights
Abstract
A system comprising: a processor; a data bus coupled to the
processor; and a non-transitory, computer-readable storage medium
embodying computer program code, the non-transitory,
computer-readable storage medium being coupled to the data bus. The
computer program code interacting with a plurality of computer
operations and comprising instructions executable by the processor
and configured for: receiving streams of data from a plurality of
data sources; processing the streams of data from the plurality of
data sources, the processing the streams of data from the plurality
of data sources performing data enriching and generating a
sub-graph for incorporation into a cognitive graph; processing the
cognitive graph, the processing the cognitive graph providing a
plurality of individual cognitive insights; and, generating a
composite cognitive insight, the composite cognitive insight being
composed of the plurality of individual cognitive insights.
Inventors: |
Sanchez; Matthew; (Austin,
TX) ; Ranatunga; Dilum; (Austin, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
COGNITIVE SCALE, INC. |
Austin |
TX |
US |
|
|
Assignee: |
COGNITIVE SCALE, INC.
Austin
TX
|
Family ID: |
54769729 |
Appl. No.: |
14/729545 |
Filed: |
June 3, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62009626 |
Jun 9, 2014 |
|
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Current U.S.
Class: |
706/55 |
Current CPC
Class: |
G06F 16/2465 20190101;
G06F 16/9024 20190101; H04W 4/025 20130101; G06N 5/022 20130101;
G06F 16/287 20190101; G06F 16/24568 20190101; G06F 16/955 20190101;
G06F 16/972 20190101 |
International
Class: |
G06N 5/04 20060101
G06N005/04; G06N 99/00 20060101 G06N099/00 |
Claims
1. A system comprising: a processor; a data bus coupled to the
processor; and a non-transitory, computer-readable storage medium
embodying computer program code, the non-transitory,
computer-readable storage medium being coupled to the data bus, the
computer program code interacting with a plurality of computer
operations and comprising instructions executable by the processor
and configured for: receiving streams of data from a plurality of
data sources; processing the streams of data from the plurality of
data sources, the processing the streams of data from the plurality
of data sources performing data enriching and generating a
sub-graph for incorporation into a cognitive graph; processing the
cognitive graph, the processing the cognitive graph providing a
plurality of individual cognitive insights; and, generating a
composite cognitive insight, the composite cognitive insight being
composed of the plurality of individual cognitive insights.
2. The system of claim 1, wherein: the plurality of individual
cognitive insights used when composing the composite cognitive
insight are related to a particular composite cognitive insight
session.
3. The system of claim 1, wherein: the composite cognitive insight
comprises a contextually-relevant composite insight.
4. The system of claim 1, wherein the instructions executable by
the processor further comprise instructions for: presenting the
contextually-relevant composite insight to a user; determining
whether the user provides feedback regarding the
contextually-relevant composite insight; and, generating a
contextually-relevant question responsive to user provided
feedback.
5. The system of claim 1, wherein the instructions executable by
the processor further comprise instructions for: determining
whether the user provides response to the contextually-relevant
question; and, providing additional cognitive insights responsive
to the response to the contextually-relevant question.
6. The system of claim 1, wherein: a plurality of insight agents
orchestrate the processing of the cognitive graph to provide the
plurality of individual cognitive insights used when composing the
composite cognitive insight.
7. A non-transitory, computer-readable storage medium embodying
computer program code, the computer program code comprising
computer executable instructions configured for: receiving streams
of data from a plurality of data sources; processing the streams of
data from the plurality of data sources, the processing the streams
of data from the plurality of data sources performing data
enriching and generating a sub-graph for incorporation into a
cognitive graph; processing the cognitive graph, the processing the
cognitive graph providing a plurality of individual cognitive
insights; and, generating a composite cognitive insight, the
composite cognitive insight being composed of the plurality of
individual cognitive insights.
8. The non-transitory, computer-readable storage medium of claim 7,
wherein: the plurality of individual cognitive insights used when
composing the composite cognitive insight are related to a
particular composite cognitive insight session.
9. The non-transitory, computer-readable storage medium of claim 7,
wherein: the composite cognitive insight comprises a
contextually-relevant composite insight.
10. The non-transitory, computer-readable storage medium of claim
7, wherein the instructions executable by the processor further
comprise instructions for: presenting the contextually-relevant
composite insight to a user; determining whether the user provides
feedback regarding the contextually-relevant composite insight;
and, generating a contextually-relevant question responsive to user
provided feedback.
11. The non-transitory, computer-readable storage medium of claim
7, wherein the instructions executable by the processor further
comprise instructions for: determining whether the user provides
response to the contextually-relevant question; and, providing
additional cognitive insights responsive to the response to the
contextually-relevant question.
12. The non-transitory, computer-readable storage medium of claim
7, wherein a plurality of insight agents orchestrate the processing
of the cognitive graph to provide the plurality of individual
cognitive insights used when composing the composite cognitive
insight.
13. The non-transitory, computer-readable storage medium of claim
7, wherein the computer executable instructions are deployable to a
client system from a server system at a remote location.
14. The non-transitory, computer-readable storage medium of claim
7, wherein the computer executable instructions are provided by a
service provider to a user on an on-demand basis.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit under 35 U.S.C.
.sctn.119(e) of U.S. Provisional Application No. 62/009,626, filed
Jun. 9, 2014, entitled "Cognitive Information Processing System
Environment." U.S. Provisional Application No. 62/009,626 includes
exemplary systems and methods and is incorporated by reference in
its entirety.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates in general to the field of
computers and similar technologies, and in particular to software
utilized in this field. Still more particularly, it relates to a
method, system and computer-usable medium for performing cognitive
inference and learning operations.
[0004] 2. Description of the Related Art
[0005] In general, "big data" refers to a collection of datasets so
large and complex that they become difficult to process using
typical database management tools and traditional data processing
approaches. These datasets can originate from a wide variety of
sources, including computer systems, mobile devices, credit card
transactions, television broadcasts, and medical equipment, as well
as infrastructures associated with cities, sensor-equipped
buildings and factories, and transportation systems. Challenges
commonly associated with big data, which may be a combination of
structured, unstructured, and semi-structured data, include its
capture, curation, storage, search, sharing, analysis and
visualization. In combination, these challenges make it difficult
to efficiently process large quantities of data within tolerable
time intervals.
[0006] Nonetheless, big data analytics hold the promise of
extracting insights by uncovering difficult-to-discover patterns
and connections, as well as providing assistance in making complex
decisions by analyzing different and potentially conflicting
options. As such, individuals and organizations alike can be
provided new opportunities to innovate, compete, and capture
value.
[0007] One aspect of big data is "dark data," which generally
refers to data that is either not collected, neglected, or
underutilized. Examples of data that is not currently being
collected includes location data prior to the emergence of
companies such as Foursquare or social data prior to the advent
companies such as Facebook. An example of data that is being
collected, but is difficult to access at the right time and place,
includes data associated with the side effects of certain spider
bites while on a camping trip. As another example, data that is
collected and available, but has not yet been productized of fully
utilized, may include disease insights from population-wide
healthcare records and social media feeds. As a result, a case can
be made that dark data may in fact be of higher value than big data
in general, especially as it can likely provide actionable insights
when it is combined with readily-available data.
SUMMARY OF THE INVENTION
[0008] A method, system and computer-usable medium are disclosed
for cognitive inference and learning operations.
[0009] In one embodiment, the invention relates to a system
comprising: a processor; a data bus coupled to the processor; and a
non-transitory, computer-readable storage medium embodying computer
program code, the non-transitory, computer-readable storage medium
being coupled to the data bus. The computer program code
interacting with a plurality of computer operations and comprising
instructions executable by the processor and configured for:
receiving streams of data from a plurality of data sources;
processing the streams of data from the plurality of data sources,
the processing the streams of data from the plurality of data
sources performing data enriching and generating a sub-graph for
incorporation into a cognitive graph; processing the cognitive
graph, the processing the cognitive graph providing a plurality of
individual cognitive insights; and, generating a composite
cognitive insight, the composite cognitive insight being composed
of the plurality of individual cognitive insights.
[0010] In another embodiment, the invention relates to a
non-transitory, computer-readable storage medium embodying computer
program code, the computer program code comprising computer
executable instructions configured for: receiving streams of data
from a plurality of data sources; processing the streams of data
from the plurality of data sources, the processing the streams of
data from the plurality of data sources performing data enriching
and generating a sub-graph for incorporation into a cognitive
graph; processing the cognitive graph, the processing the cognitive
graph providing a plurality of individual cognitive insights; and,
generating a composite cognitive insight, the composite cognitive
insight being composed of the plurality of individual cognitive
insights.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The present invention may be better understood, and its
numerous objects, features and advantages made apparent to those
skilled in the art by referencing the accompanying drawings. The
use of the same reference number throughout the several figures
designates a like or similar element.
[0012] FIG. 1 depicts an exemplary client computer in which the
present invention may be implemented;
[0013] FIG. 2 is a simplified block diagram of a cognitive
inference and learning system (CILS);
[0014] FIG. 3 is a simplified block diagram of a CILS reference
model implemented in accordance with an embodiment of the
invention;
[0015] FIGS. 4a through 4c depict additional components of the CILS
reference model shown in FIG. 3;
[0016] FIG. 5 is a simplified process diagram of CILS
operations;
[0017] FIG. 6 depicts the lifecycle of CILS agents implemented to
perform CILS operations;
[0018] FIG. 7 is a simplified block diagram of a plurality of
cognitive platforms implemented in a hybrid cloud environment;
[0019] FIGS. 8a and 8b show a simplified process flow diagram of
composite cognitive insight generation and feedback operations;
and
[0020] FIG. 9 is a generalized flowchart of the performance of
composite cognitive insight generation and feedback operations.
DETAILED DESCRIPTION
[0021] A method, system and computer-usable medium are disclosed
for cognitive inference and learning operations. The present
invention may be a system, a method, and/or a computer program
product. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0022] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0023] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0024] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0025] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0026] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0027] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0028] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0029] FIG. 1 is a generalized illustration of an information
processing system 100 that can be used to implement the system and
method of the present invention. The information processing system
100 includes a processor (e.g., central processor unit or "CPU")
102, input/output (I/O) devices 104, such as a display, a keyboard,
a mouse, and associated controllers, a hard drive or disk storage
106, and various other subsystems 108. In various embodiments, the
information processing system 100 also includes network port 110
operable to connect to a network 140, which is likewise accessible
by a service provider server 142. The information processing system
100 likewise includes system memory 112, which is interconnected to
the foregoing via one or more buses 114. System memory 112 further
comprises operating system (OS) 116 and in various embodiments may
also comprise cognitive inference and learning system (CILS) 118.
In these and other embodiments, the CILS 118 may likewise comprise
invention modules 120. In one embodiment, the information
processing system 100 is able to download the CILS 118 from the
service provider server 142. In another embodiment, the CILS 118 is
provided as a service from the service provider server 142.
[0030] In various embodiments, the CILS 118 is implemented to
perform various cognitive computing operations described in greater
detail herein. As used herein, cognitive computing broadly refers
to a class of computing involving self-learning systems that use
techniques such as spatial navigation, machine vision, and pattern
recognition to increasingly mimic the way the human brain works. To
be more specific, earlier approaches to computing typically solved
problems by executing a set of instructions codified within
software. In contrast, cognitive computing approaches are
data-driven, sense-making, insight-extracting, problem-solving
systems that have more in common with the structure of the human
brain than with the architecture of contemporary,
instruction-driven computers.
[0031] To further differentiate these distinctions, traditional
computers must first be programmed by humans to perform specific
tasks, while cognitive systems learn from their interactions with
data and humans alike, and in a sense, program themselves to
perform new tasks. To summarize the difference between the two,
traditional computers are designed to calculate rapidly. Cognitive
systems are designed to quickly draw inferences from data and gain
new knowledge.
[0032] Cognitive systems achieve these abilities by combining
various aspects of artificial intelligence, natural language
processing, dynamic learning, and hypothesis generation to render
vast quantities of intelligible data to assist humans in making
better decisions. As such, cognitive systems can be characterized
as having the ability to interact naturally with people to extend
what either humans, or machines, could do on their own.
Furthermore, they are typically able to process natural language,
multi-structured data, and experience much in the same way as
humans. Moreover, they are also typically able to learn a knowledge
domain based upon the best available data and get better, and more
immersive, over time.
[0033] It will be appreciated that more data is currently being
produced every day than was recently produced by human beings from
the beginning of recorded time. Deep within this ever-growing mass
of data is a class of data known as "dark data," which includes
neglected information, ambient signals, and insights that can
assist organizations and individuals in augmenting their
intelligence and deliver actionable insights through the
implementation of cognitive applications. As used herein, cognitive
applications, or "cognitive apps," broadly refer to cloud-based,
big data interpretive applications that learn from user engagement
and data interactions. Such cognitive applications extract patterns
and insights from dark data sources that are currently almost
completely opaque. Examples of such dark data include disease
insights from population-wide healthcare records and social media
feeds, or from new sources of information, such as sensors
monitoring pollution in delicate marine environments.
[0034] Over time, it is anticipated that cognitive applications
will fundamentally change the ways in which many organizations
operate as they invert current issues associated with data volume
and variety to enable a smart, interactive data supply chain.
Ultimately, cognitive applications hold the promise of receiving a
user query and immediately providing a data-driven answer from a
masked data supply chain in response. As they evolve, it is
likewise anticipated that cognitive applications may enable a new
class of "sixth sense" applications that intelligently detect and
learn from relevant data and events to offer insights, predictions
and advice rather than wait for commands. Just as web and mobile
applications changed the way people access data, cognitive
applications may change the way people listen to, and become
empowered by, multi-structured data such as emails, social media
feeds, doctors notes, transaction records, and call logs.
[0035] However, the evolution of such cognitive applications has
associated challenges, such as how to detect events, ideas, images,
and other content that may be of interest. For example, assuming
that the role and preferences of a given user are known, how is the
most relevant information discovered, prioritized, and summarized
from large streams of multi-structured data such as news feeds,
blogs, social media, structured data, and various knowledge bases?
To further the example, what can a healthcare executive be told
about their competitor's market share? Other challenges include the
creation of a contextually-appropriate visual summary of responses
to questions or queries.
[0036] FIG. 2 is a simplified block diagram of a cognitive
inference and learning system (CILS) implemented in accordance with
an embodiment of the invention. In various embodiments, the CILS
118 is implemented to incorporate a variety of processes, including
semantic analysis 202, goal optimization 204, collaborative
filtering 206, common sense reasoning 208, natural language
processing 210, summarization 212, temporal/spatial reasoning 214,
and entity resolution 216 to generate cognitive insights.
[0037] As used herein, semantic analysis 202 broadly refers to
performing various analysis operations to achieve a semantic level
of understanding about language by relating syntactic structures.
In various embodiments, various syntactic structures are related
from the levels of phrases, clauses, sentences and paragraphs, to
the level of the body of content as a whole and to its
language-independent meaning. In certain embodiments, the semantic
analysis 202 process includes processing a target sentence to parse
it into its individual parts of speech, tag sentence elements that
are related to predetermined items of interest, identify
dependencies between individual words, and perform co-reference
resolution. For example, if a sentence states that the author
really likes the hamburgers served by a particular restaurant, then
the name of the "particular restaurant" is co-referenced to
"hamburgers."
[0038] As likewise used herein, goal optimization 204 broadly
refers to performing multi-criteria decision making operations to
achieve a given goal or target objective. In various embodiments,
one or more goal optimization 204 processes are implemented by the
CILS 118 to define predetermined goals, which in turn contribute to
the generation of a cognitive insight. For example, goals for
planning a vacation trip may include low cost (e.g., transportation
and accommodations), location (e.g., by the beach), and speed
(e.g., short travel time). In this example, it will be appreciated
that certain goals may be in conflict with another. As a result, a
cognitive insight provided by the CILS 118 to a traveler may
indicate that hotel accommodations by a beach may cost more than
they care to spend.
[0039] Collaborative filtering 206, as used herein, broadly refers
to the process of filtering for information or patterns through the
collaborative involvement of multiple agents, viewpoints, data
sources, and so forth. The application of such collaborative
filtering 206 processes typically involves very large and different
kinds of data sets, including sensing and monitoring data,
financial data, and user data of various kinds Collaborative
filtering 206 may also refer to the process of making automatic
predictions associated with predetermined interests of a user by
collecting preferences or other information from many users. For
example, if person `A` has the same opinion as a person `B` for a
given issue `x`, then an assertion can be made that person `A` is
more likely to have the same opinion as person `B` opinion on a
different issue `y` than to have the same opinion on issue `y` as a
randomly chosen person. In various embodiments, the collaborative
filtering 206 process is implemented with various recommendation
engines familiar to those of skill in the art to make
recommendations.
[0040] As used herein, common sense reasoning 208 broadly refers to
simulating the human ability to make deductions from common facts
they inherently know. Such deductions may be made from inherent
knowledge about the physical properties, purpose, intentions and
possible behavior of ordinary things, such as people, animals,
objects, devices, and so on. In various embodiments, common sense
reasoning 208 processes are implemented to assist the CILS 118 in
understanding and disambiguating words within a predetermined
context. In certain embodiments, the common sense reasoning 208
processes are implemented to allow the CILS 118 to generate text or
phrases related to a target word or phrase to perform deeper
searches for the same terms. It will be appreciated that if the
context of a word is better understood, then a common sense
understanding of the word can then be used to assist in finding
better or more accurate information. In certain embodiments, this
better or more accurate understanding of the context of a word, and
its related information, allows the CILS 118 to make more accurate
deductions, which are in turn used to generate cognitive
insights.
[0041] As likewise used herein, natural language processing (NLP)
210 broadly refers to interactions with a system, such as the CILS
118, through the use of human, or natural, languages. In various
embodiments, various NLP 210 processes are implemented by the CILS
118 to achieve natural language understanding, which enables it to
not only derive meaning from human or natural language input, but
to also generate natural language output.
[0042] Summarization 212, as used herein, broadly refers to
processing a set of information, organizing and ranking it, and
then generating a corresponding summary. As an example, a news
article may be processed to identify its primary topic and
associated observations, which are then extracted, ranked, and then
presented to the user. As another example, page ranking operations
may be performed on the same news article to identify individual
sentences, rank them, order them, and determine which of the
sentences are most impactful in describing the article and its
content. As yet another example, a structured data record, such as
a patient's electronic medical record (EMR), may be processed using
the summarization 212 process to generate sentences and phrases
that describes the content of the EMR. In various embodiments,
various summarization 212 processes are implemented by the CILS 118
to generate summarizations of content streams, which are in turn
used to generate cognitive insights.
[0043] As used herein, temporal/spatial reasoning 214 broadly
refers to reasoning based upon qualitative abstractions of temporal
and spatial aspects of common sense knowledge, described in greater
detail herein. For example, it is not uncommon for a predetermined
set of data to change over time. Likewise, other attributes, such
as its associated metadata, may likewise change over time. As a
result, these changes may affect the context of the data. To
further the example, the context of asking someone what they
believe they should be doing at 3:00 in the afternoon during the
workday while they are at work may be quite different that asking
the same user the same question at 3:00 on a Sunday afternoon when
they are at home. In various embodiments, various temporal/spatial
reasoning 214 processes are implemented by the CILS 118 to
determine the context of queries, and associated data, which are in
turn used to generate cognitive insights.
[0044] As likewise used herein, entity resolution 216 broadly
refers to the process of finding elements in a set of data that
refer to the same entity across different data sources (e.g.,
structured, non-structured, streams, devices, etc.), where the
target entity does not share a common identifier. In various
embodiments, the entity resolution 216 process is implemented by
the CILS 118 to identify significant nouns, adjectives, phrases or
sentence elements that represent various predetermined entities
within one or more domains. From the foregoing, it will be
appreciated that the implementation of one or more of the semantic
analysis 202, goal optimization 204, collaborative filtering 206,
common sense reasoning 208, natural language processing 210,
summarization 212, temporal/spatial reasoning 214, and entity
resolution 216 processes by the CILS 118 can facilitate the
generation of a semantic, cognitive model.
[0045] In various embodiments, the CILS 118 receives ambient
signals 220, curated data 222, and learned knowledge, which is then
processed by the CILS 118 to generate one or more cognitive graphs
226. In turn, the one or more cognitive graphs 226 are further used
by the CILS 118 to generate cognitive insight streams, which are
then delivered to one or more destinations 230, as described in
greater detail herein.
[0046] As used herein, ambient signals 220 broadly refer to input
signals, or other data streams, that may contain data providing
additional insight or context to the curated data 222 and learned
knowledge 224 received by the CILS 118. For example, ambient
signals may allow the CILS 118 to understand that a user is
currently using their mobile device, at location `x`, at time `y`,
doing activity `z`. To further the example, there is a difference
between the user using their mobile device while they are on an
airplane versus using their mobile device after landing at an
airport and walking between one terminal and another. To extend the
example even further, ambient signals may add additional context,
such as the user is in the middle of a three leg trip and has two
hours before their next flight. Further, they may be in terminal
A1, but their next flight is out of C1, it is lunchtime, and they
want to know the best place to eat. Given the available time the
user has, their current location, restaurants that are proximate to
their predicted route, and other factors such as food preferences,
the CILS 118 can perform various cognitive operations and provide a
recommendation for where the user can eat.
[0047] In various embodiments, the curated data 222 may include
structured, unstructured, social, public, private, streaming,
device or other types of data described in greater detail herein.
In certain embodiments, the learned knowledge 224 is based upon
past observations and feedback from the presentation of prior
cognitive insight streams and recommendations. In various
embodiments, the learned knowledge 224 is provided via a feedback
look that provides the learned knowledge 224 in the form of a
learning stream of data.
[0048] As likewise used herein, a cognitive graph 226 refers to a
representation of expert knowledge, associated with individuals and
groups over a period of time, to depict relationships between
people, places, and things using words, ideas, audio and images. As
such, it is a machine-readable formalism for knowledge
representation that provides a common framework allowing data and
knowledge to be shared and reused across user, application,
organization, and community boundaries.
[0049] In various embodiments, the information contained in, and
referenced by, a cognitive graph 226 is derived from many sources
(e.g., public, private, social, device), such as curated data 222.
In certain of these embodiments, the cognitive graph 226 assists in
the identification and organization of information associated with
how people, places and things are related to one other. In various
embodiments, the cognitive graph 226 enables automated agents,
described in greater detail herein, to access the Web more
intelligently, enumerate inferences through utilization of curated,
structured data 222, and provide answers to questions by serving as
a computational knowledge engine.
[0050] In certain embodiments, the cognitive graph 226 not only
elicits and maps expert knowledge by deriving associations from
data, it also renders higher level insights and accounts for
knowledge creation through collaborative knowledge modeling. In
various embodiments, the cognitive graph 226 is a machine-readable,
declarative memory system that stores and learns both episodic
memory (e.g., specific personal experiences associated with an
individual or entity), and semantic memory, which stores factual
information (e.g., geo location of an airport or restaurant).
[0051] For example, the cognitive graph 226 may know that a given
airport is a place, and that there is a list of related places such
as hotels, restaurants and departure gates. Furthermore, the
cognitive graph 226 may know that people such as business
travelers, families and college students use the airport to board
flights from various carriers, eat at various restaurants, or shop
at certain retail stores. The cognitive graph 226 may also have
knowledge about the key attributes from various retail rating sites
that travelers have used to describe the food and their experience
at various venues in the airport over the past six months.
[0052] In certain embodiments, the cognitive insight stream 228 is
bidirectional, and supports flows of information both too and from
destinations 230. In these embodiments, the first flow is generated
in response to receiving a query, and subsequently delivered to one
or more destinations 230. The second flow is generated in response
to detecting information about a user of one or more of the
destinations 230. Such use results in the provision of information
to the CILS 118. In response, the CILS 118 processes that
information, in the context of what it knows about the user, and
provides additional information to the user, such as a
recommendation. In various embodiments, the cognitive insight
stream 228 is configured to be provided in a "push" stream
configuration familiar to those of skill in the art. In certain
embodiments, the cognitive insight stream 228 is implemented to use
natural language approaches familiar to skilled practitioners of
the art to support interactions with a user.
[0053] In various embodiments, the cognitive insight stream 228 may
include a stream of visualized insights. As used herein, visualized
insights broadly refers to cognitive insights that are presented in
a visual manner, such as a map, an infographic, images, and so
forth. In certain embodiments, these visualized insights may
include various cognitive insights, such as "What happened?", "What
do I know about it?", "What is likely to happen next?", or "What
should I do about it?" In these embodiments, the cognitive insight
stream is generated by various cognitive agents, which are applied
to various sources, datasets, and cognitive graphs. As used herein,
a cognitive agent broadly refers to a computer program that
performs a task with minimum specific directions from users and
learns from each interaction with data and human users.
[0054] In various embodiments, the CILS 118 delivers Cognition as a
Service (CaaS). As such, it provides a cloud-based development and
execution platform that allow various cognitive applications and
services to function more intelligently and intuitively. In certain
embodiments, cognitive applications powered by the CILS 118 are
able to think and interact with users as intelligent virtual
assistants. As a result, users are able to interact with such
cognitive applications by asking them questions and giving them
commands. In response, these cognitive applications will be able to
assist the user in completing tasks and managing their work more
efficiently.
[0055] In these and other embodiments, the CILS 118 can operate as
an analytics platform to process big data, and dark data as well,
to provide data analytics through a public, private or hybrid cloud
environment. As used herein, cloud analytics broadly refers to a
service model wherein data sources, data models, processing
applications, computing power, analytic models, and sharing or
storage of results are implemented within a cloud environment to
perform one or more aspects of analytics.
[0056] In various embodiments, users submit queries and computation
requests in a natural language format to the CILS 118. In response,
they are provided with a ranked list of relevant answers and
aggregated information with useful links and pertinent
visualizations through a graphical representation. In these
embodiments, the cognitive graph 226 generates semantic and
temporal maps to reflect the organization of unstructured data and
to facilitate meaningful learning from potentially millions of
lines of text, much in the same way as arbitrary syllables strung
together create meaning through the concept of language.
[0057] FIG. 3 is a simplified block diagram of a cognitive
inference and learning system (CILS) reference model implemented in
accordance with an embodiment of the invention. In this embodiment,
the CILS reference model is associated with the CILS 118 shown in
FIG. 2. As shown in FIG. 3, the CILS 118 includes client
applications 302, application accelerators 306, a cognitive
platform 310, and cloud infrastructure 340. In various embodiments,
the client applications 302 include cognitive applications 304,
which are implemented to understand and adapt to the user, not the
other way around, by natively accepting and understanding human
forms of communication, such as natural language text, audio,
images, video, and so forth.
[0058] In these and other embodiments, the cognitive applications
304 possess situational and temporal awareness based upon ambient
signals from users and data, which facilitates understanding the
user's intent, content, context and meaning to drive goal-driven
dialogs and outcomes. Further, they are designed to gain knowledge
over time from a wide variety of structured, non-structured, and
device data sources, continuously interpreting and autonomously
reprogramming themselves to better understand a given domain. As
such, they are well-suited to support human decision making, by
proactively providing trusted advice, offers and recommendations
while respecting user privacy and permissions.
[0059] In various embodiments, the application accelerators 306
include a cognitive application framework 308. In certain
embodiments, the application accelerators 306 and the cognitive
application framework 308 support various plug-ins and components
that facilitate the creation of client applications 302 and
cognitive applications 304. In various embodiments, the application
accelerators 306 include widgets, user interface (UI) components,
reports, charts, and back-end integration components familiar to
those of skill in the art.
[0060] As likewise shown in FIG. 3, the cognitive platform 310
includes a management console 312, a development environment 314,
application program interfaces (APIs) 316, sourcing agents 318, a
cognitive engine 320, destination agents 336, and platform data
338, all of which are described in greater detail herein. In
various embodiments, the management console 312 is implemented to
manage accounts and projects, along with user-specific metadata
that is used to drive processes and operations within the cognitive
platform 310 for a predetermined project.
[0061] In certain embodiments, the development environment 314 is
implemented to create custom extensions to the CILS 118 shown in
FIG. 2. In various embodiments, the development environment 314 is
implemented for the development of a custom application, which may
subsequently be deployed in a public, private or hybrid cloud
environment. In certain embodiments, the development environment
314 is implemented for the development of a custom sourcing agent,
a custom bridging agent, a custom destination agent, or various
analytics applications or extensions.
[0062] In various embodiments, the APIs 316 are implemented to
build and manage predetermined cognitive applications 304,
described in greater detail herein, which are then executed on the
cognitive platform 310 to generate cognitive insights. Likewise,
the sourcing agents 318 are implemented in various embodiments to
source a variety of multi-site, multi-structured source streams of
data described in greater detail herein. In various embodiments,
the cognitive engine 320 includes a dataset engine 322, a graph
query engine 326, an insight/learning engine 330, and foundation
components 334. In certain embodiments, the dataset engine 322 is
implemented to establish and maintain a dynamic data ingestion and
enrichment pipeline. In these and other embodiments, the dataset
engine 322 may be implemented to orchestrate one or more sourcing
agents 318 to source data. Once the data is sourced, the data set
engine 322 performs data enriching and other data processing
operations, described in greater detail herein, and generates one
or more sub-graphs that are subsequently incorporated into a target
cognitive graph.
[0063] In various embodiments, the graph query engine 326 is
implemented to receive and process queries such that they can be
bridged into a cognitive graph, as described in greater detail
herein, through the use of a bridging agent. In certain
embodiments, the graph query engine 326 performs various natural
language processing (NLP), familiar to skilled practitioners of the
art, to process the queries. In various embodiments, the
insight/learning engine 330 is implemented to encapsulate a
predetermined algorithm, which is then applied to a cognitive graph
to generate a result, such as a cognitive insight or a
recommendation. In certain embodiments, one or more such algorithms
may contribute to answering a specific question and provide
additional cognitive insights or recommendations. In various
embodiments, two or more of the dataset engine 322, the graph query
engine 326, and the insight/learning engine 330 may be implemented
to operate collaboratively to generate a cognitive insight or
recommendation. In certain embodiments, one or more of the dataset
engine 322, the graph query engine 326, and the insight/learning
engine 330 may operate autonomously to generate a cognitive insight
or recommendation.
[0064] The foundation components 334 shown in FIG. 3 include
various reusable components, familiar to those of skill in the art,
which are used in various embodiments to enable the dataset engine
322, the graph query engine 326, and the insight/learning engine
330 to perform their respective operations and processes. Examples
of such foundation components 334 include natural language
processing (NLP) components and core algorithms, such as cognitive
algorithms.
[0065] In various embodiments, the platform data 338 includes
various data repositories, described in greater detail herein, that
are accessed by the cognitive platform 310 to generate cognitive
insights. In various embodiments, the destination agents 336 are
implemented to publish cognitive insights to a consumer of
cognitive insight data. Examples of such consumers of cognitive
insight data include target databases, business intelligence
applications, and mobile applications. It will be appreciated that
many such examples of cognitive insight data consumers are possible
and the foregoing is not intended to limit the spirit, scope or
intent of the invention. In various embodiments, as described in
greater detail herein, the cloud infrastructure 340 includes
cognitive cloud management 342 components and cloud analytics
infrastructure components 344.
[0066] FIGS. 4a through 4c depict additional cognitive inference
and learning system (CILS) components implemented in accordance
with an embodiment of the CILS reference model shown in FIG. 3. In
this embodiment, the CILS reference model includes client
applications 302, application accelerators 306, a cognitive
platform 310, and cloud infrastructure 340. As shown in FIG. 4a,
the client applications 302 include cognitive applications 304. In
various embodiments, the cognitive applications 304 are implemented
natively accept and understand human forms of communication, such
as natural language text, audio, images, video, and so forth. In
certain embodiments, the cognitive applications 304 may include
healthcare 402, business performance 403, travel 404, and various
other 405 applications familiar to skilled practitioners of the
art. As such, the foregoing is only provided as examples of such
cognitive applications 304 and is not intended to limit the intent,
spirit of scope of the invention.
[0067] In various embodiments, the application accelerators 306
include a cognitive application framework 308. In certain
embodiments, the application accelerators 308 and the cognitive
application framework 308 support various plug-ins and components
that facilitate the creation of client applications 302 and
cognitive applications 304. In various embodiments, the application
accelerators 306 include widgets, user interface (UI) components,
reports, charts, and back-end integration components familiar to
those of skill in the art. It will be appreciated that many such
application accelerators 306 are possible and their provided
functionality, selection, provision and support are a matter of
design choice. As such, the application accelerators 306 described
in greater detail herein are not intended to limit the spirit,
scope or intent of the invention.
[0068] As shown in FIGS. 4a and 4b, the cognitive platform 310
includes a management console 312, a development environment 314,
application program interfaces (APIs) 316, sourcing agents 318, a
cognitive engine 320, destination agents 336, platform data 338,
and a crawl framework 452. In various embodiments, the management
console 312 is implemented to manage accounts and projects, along
with management metadata 461 that is used to drive processes and
operations within the cognitive platform 310 for a predetermined
project.
[0069] In various embodiments, the management console 312 is
implemented to run various services on the cognitive platform 310.
In certain embodiments, the management console 312 is implemented
to manage the configuration of the cognitive platform 310. In
certain embodiments, the management console 312 is implemented to
establish the development environment 314. In various embodiments,
the management console 312 may be implemented to manage the
development environment 314 once it is established. Skilled
practitioners of the art will realize that many such embodiments
are possible and the foregoing is not intended to limit the spirit,
scope or intent of the invention.
[0070] In various embodiments, the development environment 314 is
implemented to create custom extensions to the CILS 118 shown in
FIG. 2. In these and other embodiments, the development environment
314 is implemented to support various programming languages, such
as Python, Java, R, and others familiar to skilled practitioners of
the art. In various embodiments, the development environment 314 is
implemented to allow one or more of these various programming
languages to create a variety of analytic models and applications.
As an example, the development environment 314 may be implemented
to support the R programming language, which in turn can be used to
create an analytic model that is then hosted on the cognitive
platform 310.
[0071] In certain embodiments, the development environment 314 is
implemented for the development of various custom applications or
extensions related to the cognitive platform 310, which may
subsequently be deployed in a public, private or hybrid cloud
environment. In various embodiments, the development environment
314 is implemented for the development of various custom sourcing
agents 318, custom enrichment agents 425, custom bridging agents
429, custom insight agents 433, custom destination agents 336, and
custom learning agents 434, which are described in greater detail
herein.
[0072] In various embodiments, the APIs 316 are implemented to
build and manage predetermined cognitive applications 304,
described in greater detail herein, which are then executed on the
cognitive platform 310 to generate cognitive insights. In these
embodiments, the APIs 316 may include one or more of a project and
dataset API 408, a cognitive search API 409, a cognitive insight
API 410, and other APIs. The selection of the individual APIs 316
implemented in various embodiments is a matter design choice and
the foregoing is not intended to limit the spirit, scope or intent
of the invention.
[0073] In various embodiments, the project and dataset API 408 is
implemented with the management console 312 to enable the
management of a variety of data and metadata associated with
various cognitive insight projects and user accounts hosted or
supported by the cognitive platform 310. In one embodiment, the
data and metadata managed by the project and dataset API 408 are
associated with billing information familiar to those of skill in
the art. In one embodiment, the project and dataset API 408 is used
to access a data stream that is created, configured and
orchestrated, as described in greater detail herein, by the dataset
engine 322.
[0074] In various embodiments, the cognitive search API 409 uses
natural language processes familiar to those of skill in the art to
search a target cognitive graph. Likewise, the cognitive insight
API 410 is implemented in various embodiments to configure the
insight/learning engine 330 to provide access to predetermined
outputs from one or more cognitive graph algorithms that are
executing in the cognitive platform 310. In certain embodiments,
the cognitive insight API 410 is implemented to subscribe to, or
request, such predetermined outputs.
[0075] In various embodiments, the sourcing agents 318 may include
a batch upload 414 agent, an API connectors 415 agent, a real-time
streams 416 agent, a Structured Query Language (SQL)/Not Only SQL
(NoSQL) databases 417 agent, a message engines 418 agent, and one
or more custom sourcing 420 agents. Skilled practitioners of the
art will realize that other types of sourcing agents 318 may be
used in various embodiments and the foregoing is not intended to
limit the spirit, scope or intent of the invention. In various
embodiments, the sourcing agents 318 are implemented to source a
variety of multi-site, multi-structured source streams of data
described in greater detail herein. In certain embodiments, each of
the sourcing agents 318 has a corresponding API.
[0076] In various embodiments, the batch uploading 414 agent is
implemented for batch uploading of data to the cognitive platform
310. In these embodiments, the uploaded data may include a single
data element, a single data record or file, or a plurality of data
records or files. In certain embodiments, the data may be uploaded
from more than one source and the uploaded data may be in a
homogenous or heterogeneous form. In various embodiments, the API
connectors 415 agent is implemented to manage interactions with one
or more predetermined APIs that are external to the cognitive
platform 310. As an example, Associated Press.RTM. may have their
own API for news stories, Expedia.RTM. for travel information, or
the National Weather Service for weather information. In these
examples, the API connectors 415 agent would be implemented to
determine how to respectively interact with each organization's API
such that the cognitive platform 310 can receive information.
[0077] In various embodiments, the real-time streams 416 agent is
implemented to receive various streams of data, such as social
media streams (e.g., Twitter feeds) or other data streams (e.g.,
device data streams). In these embodiments, the streams of data are
received in near-real-time. In certain embodiments, the data
streams include temporal attributes. As an example, as data is
added to a blog file, it is time-stamped to create temporal data.
Other examples of a temporal data stream include Twitter feeds,
stock ticker streams, device location streams from a device that is
tracking location, medical devices tracking a patient's vital
signs, and intelligent thermostats used to improve energy
efficiency for homes.
[0078] In certain embodiments, the temporal attributes define a
time window, which can be correlated to various elements of data
contained in the stream. For example, as a given time window
changes, associated data may have a corresponding change. In
various embodiments, the temporal attributes do not define a time
window. As an example, a social media feed may not have
predetermined time windows, yet it is still temporal. As a result,
the social media feed can be processed to determine what happened
in the last 24 hours, what happened in the last hour, what happened
in the last 15 minutes, and then determine related subject matter
that is trending.
[0079] In various embodiments, the SQL/NoSQL databases 417 agent is
implemented to interact with one or more target databases familiar
to those of skill in the art. For example, the target database may
include a SQL, NoSQL, delimited flat file, or other form of
database. In various embodiments, the message engines 418 agent is
implemented to provide data to the cognitive platform 310 from one
or more message engines, such as a message queue (MQ) system, a
message bus, a message broker, an enterprise service bus (ESB), and
so forth. Skilled practitioners of the art will realize that there
are many such examples of message engines with which the message
engines 418 agent may interact and the foregoing is not intended to
limit the spirit, scope or intent of the invention.
[0080] In various embodiments, the custom sourcing agents 420,
which are purpose-built, are developed through the use of the
development environment 314, described in greater detail herein.
Examples of custom sourcing agents 420 include sourcing agents for
various electronic medical record (EMR) systems at various
healthcare facilities. Such EMR systems typically collect a variety
of healthcare information, much of it the same, yet it may be
collected, stored and provided in different ways. In this example,
the custom sourcing agents 420 allow the cognitive platform 310 to
receive information from each disparate healthcare source.
[0081] In various embodiments, the cognitive engine 320 includes a
dataset engine 322, a graph engine 326, an insight/learning engine
330, learning agents 434, and foundation components 334. In these
and other embodiments, the dataset engine 322 is implemented as
described in greater detail to establish and maintain a dynamic
data ingestion and enrichment pipeline. In various embodiments, the
dataset engine 322 may include a pipelines 422 component, an
enrichment 423 component, a storage component 424, and one or more
enrichment agents 425.
[0082] In various embodiments, the pipelines 422 component is
implemented to ingest various data provided by the sourcing agents
318. Once ingested, this data is converted by the pipelines 422
component into streams of data for processing. In certain
embodiments, these managed streams are provided to the enrichment
423 component, which performs data enrichment operations familiar
to those of skill in the art. As an example, a data stream may be
sourced from Associated Press.RTM. by a sourcing agent 318 and
provided to the dataset engine 322. The pipelines 422 component
receives the data stream and routes it to the enrichment 423
component, which then enriches the data stream by performing
sentiment analysis, geotagging, and entity detection operations to
generate an enriched data stream. In certain embodiments, the
enrichment operations include filtering operations familiar to
skilled practitioners of the art. To further the preceding example,
the Associated Press.RTM. data stream may be filtered by a
predetermined geography attribute to generate an enriched data
stream.
[0083] The enriched data stream is then subsequently stored, as
described in greater detail herein, in a predetermined location. In
various embodiments, the enriched data stream is cached by the
storage 424 component to provide a local version of the enriched
data stream. In certain embodiments, the cached, enriched data
stream is implemented to be "replayed" by the cognitive engine 320.
In one embodiment, the replaying of the cached, enriched data
stream allows incremental ingestion of the enriched data stream
instead of ingesting the entire enriched data stream at one time.
In various embodiments, one or more enrichment agents 425 are
implemented to be invoked by the enrichment component 423 to
perform one or more enrichment operations described in greater
detail herein.
[0084] In various embodiments, the graph query engine 326 is
implemented to receive and process queries such that they can be
bridged into a cognitive graph, as described in greater detail
herein, through the use of a bridging agent. In these embodiments,
the graph query engine 326 may include a query 426 component, a
translate 427 component, a bridge 428 component, and one or more
bridging agents 429.
[0085] In various embodiments, the query 426 component is
implemented to support natural language queries. In these and other
embodiments, the query 426 component receives queries, processes
them (e.g., using NLP processes), and then maps the processed query
to a target cognitive graph. In various embodiments, the translate
427 component is implemented to convert the processed queries
provided by the query 426 component into a form that can be used to
query a target cognitive graph. To further differentiate the
distinction between the functionality respectively provided by the
query 426 and translate 427 components, the query 426 component is
oriented toward understanding a query from a user. In contrast, the
translate 427 component is oriented to translating a query that is
understood into a form that can be used to query a cognitive
graph.
[0086] In various embodiments, the bridge 428 component is
implemented to generate an answer to a query provided by the
translate 427 component. In certain embodiments, the bridge 428
component is implemented to provide domain-specific responses when
bridging a translated query to a cognitive graph. For example, the
same query bridged to a target cognitive graph by the bridge 428
component may result in different answers for different domains,
dependent upon domain-specific bridging operations performed by the
bridge 428 component.
[0087] To further differentiate the distinction between the
translate 427 component and the bridging 428 component, the
translate 427 component relates to a general domain translation of
a question. In contrast, the bridging 428 component allows the
question to be asked in the context of a specific domain (e.g.,
healthcare, travel, etc.), given what is known about the data. In
certain embodiments, the bridging 428 component is implemented to
process what is known about the translated query, in the context of
the user, to provide an answer that is relevant to a specific
domain.
[0088] As an example, a user may ask, "Where should I eat today?"
If the user has been prescribed a particular health regimen, the
bridging 428 component may suggest a restaurant with a "heart
healthy" menu. However, if the user is a business traveler, the
bridging 428 component may suggest the nearest restaurant that has
the user's favorite food. In various embodiments, the bridging 428
component may provide answers, or suggestions, that are composed
and ranked according to a specific domain of use. In various
embodiments, the bridging agent 429 is implemented to interact with
the bridging component 428 to perform bridging operations described
in greater detail herein. In these embodiments, the bridging agent
interprets a translated query generated by the query 426 component
within a predetermined user context, and then maps it to
predetermined nodes and links within a target cognitive graph.
[0089] In various embodiments, the insight/learning engine 330 is
implemented to encapsulate a predetermined algorithm, which is then
applied to a target cognitive graph to generate a result, such as a
cognitive insight or a recommendation. In certain embodiments, one
or more such algorithms may contribute to answering a specific
question and provide additional cognitive insights or
recommendations. In these and other embodiments, the
insight/learning engine 330 is implemented to perform
insight/learning operations, described in greater detail herein. In
various embodiments, the insight/learning engine 330 may include a
discover/visibility 430 component, a predict 431 component, a
rank/recommend 432 component, and one or more insight 433
agents.
[0090] In various embodiments, the discover/visibility 430
component is implemented to provide detailed information related to
a predetermined topic, such as a subject or an event, along with
associated historical information. In certain embodiments, the
predict 431 component is implemented to perform predictive
operations to provide insight into what may next occur for a
predetermined topic. In various embodiments, the rank/recommend 432
component is implemented to perform ranking and recommendation
operations to provide a user prioritized recommendations associated
with a provided cognitive insight.
[0091] In certain embodiments, the insight/learning engine 330 may
include additional components. For example the additional
components may include classification algorithms, clustering
algorithms, and so forth. Skilled practitioners of the art will
realize that many such additional components are possible and that
the foregoing is not intended to limit the spirit, scope or intent
of the invention. In various embodiments, the insights agents 433
are implemented to create a visual data story, highlighting
user-specific insights, relationships and recommendations. As a
result, it can share, operationalize, or track business insights in
various embodiments. In various embodiments, the learning agent 434
work in the background to continually update the cognitive graph,
as described in greater detail herein, from each unique interaction
with data and users.
[0092] In various embodiments, the destination agents 336 are
implemented to publish cognitive insights to a consumer of
cognitive insight data. Examples of such consumers of cognitive
insight data include target databases, business intelligence
applications, and mobile applications. In various embodiments, the
destination agents 336 may include a Hypertext Transfer Protocol
(HTTP) stream 440 agent, an API connectors 441 agent, a databases
442 agent, a message engines 443 agent, a mobile push notification
444 agent, and one or more custom destination 446 agents. Skilled
practitioners of the art will realize that other types of
destination agents 318 may be used in various embodiments and the
foregoing is not intended to limit the spirit, scope or intent of
the invention. In certain embodiments, each of the destination
agents 318 has a corresponding API.
[0093] In various embodiments, the HTTP stream 440 agent is
implemented for providing various HTTP streams of cognitive insight
data to a predetermined cognitive data consumer. In these
embodiments, the provided HTTP streams may include various HTTP
data elements familiar to those of skill in the art. In certain
embodiments, the HTTP streams of data are provided in
near-real-time. In various embodiments, the API connectors 441
agent is implemented to manage interactions with one or more
predetermined APIs that are external to the cognitive platform 310.
As an example, various target databases, business intelligence
applications, and mobile applications may each have their own
unique API.
[0094] In various embodiments, the databases 442 agent is
implemented for provision of cognitive insight data to one or more
target databases familiar to those of skill in the art. For
example, the target database may include a SQL, NoSQL, delimited
flat file, or other form of database. In these embodiments, the
provided cognitive insight data may include a single data element,
a single data record or file, or a plurality of data records or
files. In certain embodiments, the data may be provided to more
than one cognitive data consumer and the provided data may be in a
homogenous or heterogeneous form. In various embodiments, the
message engines 443 agent is implemented to provide cognitive
insight data to one or more message engines, such as a message
queue (MQ) system, a message bus, a message broker, an enterprise
service bus (ESB), and so forth. Skilled practitioners of the art
will realize that there are many such examples of message engines
with which the message engines 443 agent may interact and the
foregoing is not intended to limit the spirit, scope or intent of
the invention.
[0095] In various embodiments, the custom destination agents 420,
which are purpose-built, are developed through the use of the
development environment 314, described in greater detail herein.
Examples of custom destination agents 420 include destination
agents for various electronic medical record (EMR) systems at
various healthcare facilities. Such EMR systems typically collect a
variety of healthcare information, much of it the same, yet it may
be collected, stored and provided in different ways. In this
example, the custom destination agents 420 allow such EMR systems
to receive cognitive insight data in a form they can use.
[0096] In various embodiments, data that has been cleansed,
normalized and enriched by the dataset engine, as described in
greater detail herein, is provided by a destination agent 336 to a
predetermined destination, likewise described in greater detail
herein. In these embodiments, neither the graph query engine 326
nor the insight/learning engine 330 are implemented to perform
their respective functions.
[0097] In various embodiments, the foundation components 334 are
implemented to enable the dataset engine 322, the graph query
engine 326, and the insight/learning engine 330 to perform their
respective operations and processes. In these and other
embodiments, the foundation components 334 may include an NLP core
436 component, an NLP services 437 component, and a dynamic
pipeline engine 438. In various embodiments, the NLP core 436
component is implemented to provide a set of predetermined NLP
components for performing various NLP operations described in
greater detail herein.
[0098] In these embodiments, certain of these NLP core components
are surfaced through the NLP services 437 component, while some are
used as libraries. Examples of operations that are performed with
such components include dependency parsing, parts-of-speech
tagging, sentence pattern detection, and so forth. In various
embodiments, the NLP services 437 component is implemented to
provide various internal NLP services, which are used to perform
entity detection, summarization, and other operations, likewise
described in greater detail herein. In these embodiments, the NLP
services 437 component is implemented to interact with the NLP core
436 component to provide predetermined NLP services, such as
summarizing a target paragraph.
[0099] In various embodiments, the dynamic pipeline engine 438 is
implemented to interact with the dataset engine 322 to perform
various operations related to receiving one or more sets of data
from one or more sourcing agents, apply enrichment to the data, and
then provide the enriched data to a predetermined destination. In
these and other embodiments, the dynamic pipeline engine 438
manages the distribution of these various operations to a
predetermined compute cluster and tracks versioning of the data as
it is processed across various distributed computing resources. In
certain embodiments, the dynamic pipeline engine 438 is implemented
to perform data sovereignty management operations to maintain
sovereignty of the data.
[0100] In various embodiments, the platform data 338 includes
various data repositories, described in greater detail herein, that
are accessed by the cognitive platform 310 to generate cognitive
insights. In these embodiments, the platform data 338 repositories
may include repositories of dataset metadata 456, cognitive graphs
457, models 459, crawl data 460, and management metadata 461. In
various embodiments, the dataset metadata 456 is associated with
curated data 458 contained in the repository of cognitive graphs
457. In these and other embodiments, the repository of dataset
metadata 456 contains dataset metadata that supports operations
performed by the storage 424 component of the dataset engine 322.
For example, if a Mongo.RTM. NoSQL database with ten million items
is being processed, and the cognitive platform 310 fails after
ingesting nine million of the items, then the dataset metadata 456
may be able to provide a checkpoint that allows ingestion to
continue at the point of failure instead restarting the ingestion
process.
[0101] Those of skill in the art will realize that the use of such
dataset metadata 456 in various embodiments allows the dataset
engine 322 to be stateful. In certain embodiments, the dataset
metadata 456 allows support of versioning. For example versioning
may be used to track versions of modifications made to data, such
as in data enrichment processes described in greater detail herein.
As another example, geotagging information may have been applied to
a set of data during a first enrichment process, which creates a
first version of enriched data. Adding sentiment data to the same
million records during a second enrichment process creates a second
version of enriched data. In this example, the dataset metadata
stored in the dataset metadata 456 provides tracking of the
different versions of the enriched data and the differences between
the two.
[0102] In various embodiments, the repository of cognitive graphs
457 is implemented to store cognitive graphs generated, accessed,
and updated by the cognitive engine 320 in the process of
generating cognitive insights. In various embodiments, the
repository of cognitive graphs 457 may include one or more
repositories of curated data 458, described in greater detail
herein. In certain embodiments, the repositories of curated data
458 includes data that has been curated by one or more users,
machine operations, or a combination of the two, by performing
various sourcing, filtering, and enriching operations described in
greater detail herein. In these and other embodiments, the curated
data 458 is ingested by the cognitive platform 310 and then
processed, as likewise described in greater detail herein, to
generate cognitive insights. In various embodiments, the repository
of models 459 is implemented to store models that are generated,
accessed, and updated by the cognitive engine 320 in the process of
generating cognitive insights. As used herein, models broadly refer
to machine learning models. In certain embodiments, the models
include one or more statistical models.
[0103] In various embodiments, the crawl framework 452 is
implemented to support various crawlers 454 familiar to skilled
practitioners of the art. In certain embodiments, the crawlers 454
are custom configured for various target domains. For example,
different crawlers 454 may be used for various travel forums,
travel blogs, travel news and other travel sites. In various
embodiments, data collected by the crawlers 454 is provided by the
crawl framework 452 to the repository of crawl data 460. In these
embodiments, the collected crawl data is processed and then stored
in a normalized form in the repository of crawl data 460. The
normalized data is then provided to SQL/NoSQL database 417 agent,
which in turn provides it to the dataset engine 322. In one
embodiment, the crawl database 460 is a NoSQL database, such as
Mongo.RTM..
[0104] In various embodiments, the repository of management
metadata 461 is implemented to store user-specific metadata used by
the management console 312 to manage accounts (e.g., billing
information) and projects. In certain embodiments, the
user-specific metadata stored in the repository of management
metadata 461 is used by the management console 312 to drive
processes and operations within the cognitive platform 310 for a
predetermined project. In various embodiments, the user-specific
metadata stored in the repository of management metadata 461 is
used to enforce data sovereignty. It will be appreciated that many
such embodiments are possible and the foregoing is not intended to
limit the spirit, scope or intent of the invention.
[0105] Referring now to FIG. 4c, the cloud infrastructure 340 may
include a cognitive cloud management 342 component and a cloud
analytics infrastructure 344 component in various embodiments.
Current examples of a cloud infrastructure 340 include Amazon Web
Services (AWS.RTM.), available from Amazon.com.RTM. of Seattle,
Wash., IBM.RTM. Softlayer, available from International Business
Machines of Armonk, N.Y., and Nebula/Openstack, a joint project
between Rackspace Hosting.RTM., of Windcrest, Tex., and the
National Aeronautics and Space Administration (NASA). In these
embodiments, the cognitive cloud management 342 component may
include a management playbooks 468 sub-component, a cognitive cloud
management console 469 sub-component, a data console 470
sub-component, an asset repository 471 sub-component. In certain
embodiments, the cognitive cloud management 342 component may
include various other sub-components.
[0106] In various embodiments, the management playbooks 468
sub-component is implemented to automate the creation and
management of the cloud analytics infrastructure 344 component
along with various other operations and processes related to the
cloud infrastructure 340. As used herein, "management playbooks"
broadly refers to any set of instructions or data, such as scripts
and configuration data, that is implemented by the management
playbooks 468 sub-component to perform its associated operations
and processes.
[0107] In various embodiments, the cognitive cloud management
console 469 sub-component is implemented to provide a user
visibility and management controls related to the cloud analytics
infrastructure 344 component along with various other operations
and processes related to the cloud infrastructure 340. In various
embodiments, the data console 470 sub-component is implemented to
manage platform data 338, described in greater detail herein. In
various embodiments, the asset repository 471 sub-component is
implemented to provide access to various cognitive cloud
infrastructure assets, such as asset configurations, machine
images, and cognitive insight stack configurations.
[0108] In various embodiments, the cloud analytics infrastructure
344 component may include a data grid 472 sub-component, a
distributed compute engine 474 sub-component, and a compute cluster
management 476 sub-component. In these embodiments, the cloud
analytics infrastructure 344 component may also include a
distributed object storage 478 sub-component, a distributed full
text search 480 sub-component, a document database 482
sub-component, a graph database 484 sub-component, and various
other sub-components. In various embodiments, the data grid 472
sub-component is implemented to provide distributed and shared
memory that allows the sharing of objects across various data
structures. One example of a data grid 472 sub-component is Redis,
an open-source, networked, in-memory, key-value data store, with
optional durability, written in ANSI C. In various embodiments, the
distributed compute engine 474 sub-component is implemented to
allow the cognitive platform 310 to perform various cognitive
insight operations and processes in a distributed computing
environment. Examples of such cognitive insight operations and
processes include batch operations and streaming analytics
processes.
[0109] In various embodiments, the compute cluster management 476
sub-component is implemented to manage various computing resources
as a compute cluster. One such example of such a compute cluster
management 476 sub-component is Mesos/Nimbus, a cluster management
platform that manages distributed hardware resources into a single
pool of resources that can be used by application frameworks to
efficiently manage workload distribution for both batch jobs and
long-running services. In various embodiments, the distributed
object storage 478 sub-component is implemented to manage the
physical storage and retrieval of distributed objects (e.g., binary
file, image, text, etc.) in a cloud environment. Examples of a
distributed object storage 478 sub-component include Amazon 53t,
available from Amazon.com of Seattle, Wash., and Swift, an open
source, scalable and redundant storage system.
[0110] In various embodiments, the distributed full text search 480
sub-component is implemented to perform various full text search
operations familiar to those of skill in the art within a cloud
environment. In various embodiments, the document database 482
sub-component is implemented to manage the physical storage and
retrieval of structured data in a cloud environment. Examples of
such structured data include social, public, private, and device
data, as described in greater detail herein. In certain
embodiments, the structured data includes data that is implemented
in the JavaScript Object Notation (JSON) format. One example of a
document database 482 sub-component is Mongo, an open source
cross-platform document-oriented database. In various embodiments,
the graph database 484 sub-component is implemented to manage the
physical storage and retrieval of cognitive graphs. One example of
a graph database 484 sub-component is GraphDB, an open source graph
database familiar to those of skill in the art.
[0111] FIG. 5 is a simplified process diagram of cognitive
inference and learning system (CILS) operations performed in
accordance with an embodiment of the invention. In various
embodiments, these CILS operations may include a perceive 506
phase, a relate 508 phase, an operate 510 phase, a process and
execute 512 phase, and a learn 514 phase. In these and other
embodiments, the CILS 118 shown in FIG. 2 is implemented to mimic
cognitive processes associated with the human brain. In various
embodiments, the CILS operations are performed through the
implementation of a cognitive platform 310, described in greater
detail herein. In these and other embodiments, the cognitive
platform 310 may be implemented within a cloud analytics
infrastructure 344, which in turn is implemented within a cloud
infrastructure 340, likewise described in greater detail
herein.
[0112] In various embodiments, multi-site, multi-structured source
streams 504 are provided by sourcing agents, as described in
greater detail herein. In these embodiments, the source streams 504
are dynamically ingested in real-time during the perceive 506
phase, and based upon a predetermined context, extraction, parsing,
and tagging operations are performed on language, text and images
contained in the source streams 504. Automatic feature extraction
and modeling operations are then performed with the previously
processed source streams 504 during the relate 508 phase to
generate queries to identify related data (i.e., corpus
expansion).
[0113] In various embodiments, operations are performed during the
operate 510 phase to discover, summarize and prioritize various
concepts, which are in turn used to generate actionable
recommendations and notifications associated with predetermined
plan-based optimization goals. The resulting actionable
recommendations and notifications are then processed during the
process and execute 512 phase to provide cognitive insights, such
as recommendations, to various predetermined destinations and
associated application programming interfaces (APIs) 524.
[0114] In various embodiments, features from newly-observed data
are automatically extracted from user feedback during the learn 514
phase to improve various analytical models. In these embodiments,
the learn 514 phase includes feedback on observations generated
during the relate 508 phase, which is provided to the perceive 506
phase. Likewise, feedback on decisions resulting from operations
performed during the operate 510 phase, and feedback on results
resulting from operations performed during the process and execute
512 phase, are also provided to the perceive 506 phase.
[0115] In various embodiments, user interactions result from
operations performed during the process and execute 512 phase. In
these embodiments, data associated with the user interactions are
provided to the perceive 506 phase as unfolding interactions 522,
which include events that occur external to the CILS operations
described in greater detail herein. As an example, a first query
from a user may be submitted to the CILS system, which in turn
generates a first cognitive insight, which is then provided to the
user. In response, the user may respond by providing a first
response, or perhaps a second query, either of which is provided in
the same context as the first query. The CILS receives the first
response or second query, performs various CILS operations, and
provides the user a second cognitive insight. As before, the user
may respond with a second response or a third query, again in the
context of the first query. Once again, the CILS performs various
CILS operations and provides the user a third cognitive insight,
and so forth. In this example, the provision of cognitive insights
to the user, and their various associated responses, results in
unfolding interactions 522, which in turn result in a stateful
dialog that evolves over time. Skilled practitioners of the art
will likewise realize that such unfolding interactions 522, occur
outside of the CILS operations performed by the cognitive platform
310.
[0116] FIG. 6 depicts the lifecycle of CILS agents implemented in
accordance with an embodiment of the invention to perform CILS
operations. In various embodiments, the CILS agents lifecycle 602
may include implementation of a sourcing 318 agent, an enrichment
425 agent, a bridging 429 agent, an insight 433 agent, a
destination 336 agent, and a learning 434 agent. In these
embodiments, the sourcing 318 agent is implemented to source a
variety of multi-site, multi-structured source streams of data
described in greater detail herein. These sourced data streams are
then provided to an enrichment 425 agent, which then invokes an
enrichment component to perform enrichment operations to generate
enriched data streams, likewise described in greater detail
herein.
[0117] The enriched data streams are then provided to a bridging
429 agent, which is used to perform bridging operations described
in greater detail herein. In turn, the results of the bridging
operations are provided to an insight 433 agent, which is
implemented as described in greater detail herein to create a
visual data story, highlighting user-specific insights,
relationships and recommendations. The resulting visual data story
is then provided to a destination 336 agent, which is implemented
to publish cognitive insights to a consumer of cognitive insight
data, likewise as described in greater detail herein. In response,
the consumer of cognitive insight data provides feedback to a
learning 434 agent, which is implemented as described in greater
detail herein to provide the feedback to the sourcing agent 318, at
which point the CILS agents lifecycle 602 is continued. From the
foregoing, skilled practitioners of the art will recognize that
each iteration of the cognitive agents lifecycle 602 provides more
informed cognitive insights.
[0118] FIG. 7 is a simplified block diagram of a plurality of
cognitive platforms implemented in accordance with an embodiment of
the invention within a hybrid cloud infrastructure. In this
embodiment, the hybrid cloud infrastructure 740 includes a
cognitive cloud management 342 component, a hosted 704 cognitive
cloud environment, and a private 706 network environment. As shown
in FIG. 7, the hosted 704 cognitive cloud environment includes a
hosted 710 cognitive platform, such as the cognitive platform 310
shown in FIGS. 3, 4a, and 4b. In various embodiments, the hosted
704 cognitive cloud environment may also include a hosted 718
universal knowledge repository and one or more repositories of
curated public data 714 and licensed data 716. Likewise, the hosted
710 cognitive platform may also include a hosted 712 analytics
infrastructure, such as the cloud analytics infrastructure 344
shown in FIGS. 3 and 4c.
[0119] As likewise shown in FIG. 7, the private 706 network
environment includes a private 720 cognitive platform, such as the
cognitive platform 310 shown in FIGS. 3, 4a, and 4b. In various
embodiments, the private 706 network cognitive cloud environment
may also include a private 728 universal knowledge repository and
one or more repositories of application data 724 and private data
726. Likewise, the private 720 cognitive platform may also include
a private 722 analytics infrastructure, such as the cloud analytics
infrastructure 344 shown in FIGS. 3 and 4c. In certain embodiments,
the private 706 network environment may have one or more private
736 cognitive applications implemented to interact with the private
720 cognitive platform.
[0120] As used herein, a universal knowledge repository broadly
refers to a collection of knowledge elements that can be used in
various embodiments to generate one or more cognitive insights
described in greater detail herein. In various embodiments, these
knowledge elements may include facts (e.g., milk is a dairy
product), information (e.g., an answer to a question), descriptions
(e.g., the color of an automobile), skills (e.g., the ability to
install plumbing fixtures), and other classes of knowledge familiar
to those of skill in the art. In these embodiments, the knowledge
elements may be explicit or implicit. As an example, the fact that
water freezes at zero degrees centigrade would be an explicit
knowledge element, while the fact that an automobile mechanic knows
how to repair an automobile would be an implicit knowledge
element.
[0121] In certain embodiments, the knowledge elements within a
universal knowledge repository may also include statements,
assertions, beliefs, perceptions, preferences, sentiments,
attitudes or opinions associated with a person or a group. As an
example, user `A` may prefer the pizza served by a first
restaurant, while user `B` may prefer the pizza served by a second
restaurant. Furthermore, both user `A` and `B` are firmly of the
opinion that the first and second restaurants respectively serve
the very best pizza available. In this example, the respective
preferences and opinions of users `A` and B' regarding the first
and second restaurant may be included in the universal knowledge
repository 880 as they are not contradictory. Instead, they are
simply knowledge elements respectively associated with the two
users and can be used in various embodiments for the generation of
various cognitive insights, as described in greater detail
herein.
[0122] In various embodiments, individual knowledge elements
respectively associated with the hosted 718 and private 728
universal knowledge repositories may be distributed. In one
embodiment, the distributed knowledge elements may be stored in a
plurality of data stores familiar to skilled practitioners of the
art. In this embodiment, the distributed knowledge elements may be
logically unified for various implementations of the hosted 718 and
private 728 universal knowledge repositories. In certain
embodiments, the hosted 718 and private 728 universal knowledge
repositories may be respectively implemented in the form of a
hosted or private universal cognitive graph. In these embodiments,
nodes within the hosted or private universal graph contain one or
more knowledge elements.
[0123] In various embodiments, a secure tunnel 730, such as a
virtual private network (VPN) tunnel, is implemented to allow the
hosted 710 cognitive platform and the private 720 cognitive
platform to communicate with one another. In these various
embodiments, the ability to communicate with one another allows the
hosted 710 and private 720 cognitive platforms to work
collaboratively when generating cognitive insights described in
greater detail herein. In various embodiments, the hosted 710
cognitive platform accesses knowledge elements stored in the hosted
718 universal knowledge repository and data stored in the
repositories of curated public data 714 and licensed data 716 to
generate various cognitive insights. In certain embodiments, the
resulting cognitive insights are then provided to the private 720
cognitive platform, which in turn provides them to the one or more
private cognitive applications 736.
[0124] In various embodiments, the private 720 cognitive platform
accesses knowledge elements stored in the private 728 universal
knowledge repository and data stored in the repositories of
application data 724 and private data 726 to generate various
cognitive insights. In turn, the resulting cognitive insights are
then provided to the one or more private cognitive applications
736. In certain embodiments, the private 720 cognitive platform
accesses knowledge elements stored in the hosted 718 and private
728 universal knowledge repositories and data stored in the
repositories of curated public data 714, licensed data 716,
application data 724 and private data 726 to generate various
cognitive insights. In these embodiments, the resulting cognitive
insights are in turn provided to the one or more private cognitive
applications 736.
[0125] In various embodiments, the secure tunnel 730 is implemented
for the hosted 710 cognitive platform to provide 732 predetermined
data and knowledge elements to the private 720 cognitive platform.
In one embodiment, the provision 732 of predetermined knowledge
elements allows the hosted 718 universal knowledge repository to be
replicated as the private 728 universal knowledge repository. In
another embodiment, the provision 732 of predetermined knowledge
elements allows the hosted 718 universal knowledge repository to
provide updates 734 to the private 728 universal knowledge
repository. In certain embodiments, the updates 734 to the private
728 universal knowledge repository do not overwrite other data.
Instead, the updates 734 are simply added to the private 728
universal knowledge repository.
[0126] In one embodiment, knowledge elements that are added to the
private 728 universal knowledge repository are not provided to the
hosted 718 universal knowledge repository. As an example, an
airline may not wish to share private information related to its
customer's flights, the price paid for tickets, their awards
program status, and so forth. In another embodiment, predetermined
knowledge elements that are added to the private 728 universal
knowledge repository may be provided to the hosted 718 universal
knowledge repository. As an example, the operator of the private
720 cognitive platform may decide to license predetermined
knowledge elements stored in the private 728 universal knowledge
repository to the operator of the hosted 710 cognitive platform. To
continue the example, certain knowledge elements stored in the
private 728 universal knowledge repository may be anonymized prior
to being provided for inclusion in the hosted 718 universal
knowledge repository. In one embodiment, only private knowledge
elements are stored in the private 728 universal knowledge
repository. In this embodiment, the private 720 cognitive platform
may use knowledge elements stored in both the hosted 718 and
private 728 universal knowledge repositories to generate cognitive
insights. Skilled practitioners of the art will recognize that many
such embodiments are possible and the foregoing is not intended to
limit the spirit, scope or intent of the invention.
[0127] FIG. 8 is a simplified process flow diagram of composite
cognitive insight generation and feedback operations performed in
accordance with an embodiment of the invention. As used herein, a
composite cognitive insight broadly refers to a set of cognitive
insights generated as a result of orchestrating a predetermined set
of independent cognitive agents, referred to herein as insight
agents. In various embodiments, the insight agents use a cognitive
graph, such as an application cognitive graph 882 as their data
source to respectively generate individual cognitive insights. As
used herein, an application cognitive graph 882 broadly refers to a
cognitive graph that is associated with a predetermined cognitive
application 304. In certain embodiments, different cognitive
applications 304 may interact with different application cognitive
graphs 882 to generate individual cognitive insights for a user. In
various embodiments, the resulting individual cognitive insights
are then composed to generate a set of cognitive composite
insights, which in turn is provided to a user in the form of a
cognitive insight summary 848.
[0128] In various embodiments, the orchestration of the selected
insight agents is performed by the cognitive insight/learning
engine 330 shown in FIGS. 3 and 4a. In certain embodiments, a
predetermined subset of insight agents is selected to provide
composite cognitive insights to satisfy a graph query 844, a
contextual situation, or some combination thereof. For example, it
may be determined, as described in greater detail herein, that a
particular subset of insight agents may be suited to provide a
composite cognitive insight related to a particular user of a
particular device, at a particular location, at a particular time,
for a particular purpose.
[0129] In certain embodiments, the insight agents are selected for
orchestration as a result of receiving direct or indirect input
data 842 from a user. In various embodiments, the direct user input
may be a natural language inquiry. In certain embodiments, the
indirect user input data 842 may include the location of a user's
device or the purpose for which it is being used. As an example,
the Geographical Positioning System (GPS) coordinates of the
location of a user's mobile device may be received as indirect user
input data 842. As another example, a user may be using the
integrated camera of their mobile device to take a photograph of a
location, such as a restaurant, or an item, such as a food product.
In certain embodiments, the direct or indirect user input data 842
may include personal information that can be used to identify the
user. Skilled practitioners of the art will recognize that many
such embodiments are possible and the foregoing is not intended to
limit the spirit, scope or intent of the invention.
[0130] In various embodiments, composite cognitive insight
generation and feedback operations may be performed in various
phases. In this embodiment, these phases include a data lifecycle
840 phase, a learning 838 phase, and an application/insight
composition 840 phase. In the data lifecycle 836 phase, a
predetermined instantiation of a cognitive platform 810 sources
social data 812, public data 814, licensed data 816, and
proprietary data 818 from various sources as described in greater
detail herein. In various embodiments, an example of a cognitive
platform 810 instantiation is the cognitive platform 310 shown in
FIGS. 3, 4a, and 4b. In this embodiment, the instantiation of a
cognitive platform 810 includes a source 806 component, a process
808 component, a deliver 810 component, a cleanse 820 component, an
enrich 822 component, a filter/transform 824 component, and a
repair/reject 826 component. Likewise, as shown in FIG. 8, the
process 808 component includes a repository of models 828,
described in greater detail herein.
[0131] In various embodiments, the process 808 component is
implemented to perform various composite insight generation and
other processing operations described in greater detail herein. In
these embodiments, the process 808 component is implemented to
interact with the source 806 component, which in turn is
implemented to perform various data sourcing operations described
in greater detail herein. In various embodiments, the sourcing
operations are performed by one or more sourcing agents, as
likewise described in greater detail herein. The resulting sourced
data is then provided to the process 808 component. In turn, the
process 808 component is implemented to interact with the cleanse
820 component, which is implemented to perform various data
cleansing operations familiar to those of skill in the art. As an
example, the cleanse 820 component may perform data normalization
or pruning operations, likewise known to skilled practitioners of
the art. In certain embodiments, the cleanse 820 component may be
implemented to interact with the repair/reject 826 component, which
in turn is implemented to perform various data repair or data
rejection operations known to those of skill in the art.
[0132] Once data cleansing, repair and rejection operations are
completed, the process 808 component is implemented to interact
with the enrich 822 component, which is implemented in various
embodiments to perform various data enrichment operations described
in greater detail herein. Once data enrichment operations have been
completed, the process 808 component is likewise implemented to
interact with the filter/transform 824 component, which in turn is
implemented to perform data filtering and transformation operations
described in greater detail herein.
[0133] In various embodiments, the process 808 component is
implemented to generate various models, described in greater detail
herein, which are stored in the repository of models 828. The
process 808 component is likewise implemented in various
embodiments to use the sourced data to generate one or more
cognitive graphs, such as an application cognitive graph 882, as
described in greater detail herein. In various embodiments, the
process 808 component is implemented to gain an understanding of
the data sourced from the sources of social data 812, public data
814, licensed data 816, and proprietary data 818, which assist in
the automated generation of the application cognitive graph
882.
[0134] The process 808 component is likewise implemented in various
embodiments to perform bridging 846 operations, described in
greater detail herein, to access the application cognitive graph
882. In certain embodiments, the bridging 846 operations are
performed by bridging agents, likewise described in greater detail
herein. In various embodiments, the application cognitive graph 882
is accessed by the process 808 component during the learning 836
phase of the composite cognitive insight generation operations.
[0135] In various embodiments, a cognitive application 304 is
implemented to receive input data 842 associated with an individual
user or a group of users. In these embodiments, the input data 842
may be direct, such as a user query or mouse click, or indirect,
such as the current time or Geographical Positioning System (GPS)
data received from a mobile device associated with a user. In
various embodiments, the indirect input data 842 may include
contextual data, described in greater detail herein. Once it is
received, the input data 842 is then submitted by the cognitive
application 304 to a graph query engine 326 during the
application/insight composition 840 phase. In turn, the graph query
engine 326 processes the submitted input data 842 to generate a
graph query 844, as described in greater detail herein. The graph
query 844 is then used to query the application cognitive graph
882, which results in the generation of one or more composite
cognitive insights, likewise described in greater detail herein. In
certain embodiments, the graph query 844 uses predetermined
knowledge elements stored in the universal knowledge repository 880
when querying the application cognitive graph 882 to generate the
one or more composite insights.
[0136] In various embodiments, a set of contextually-related
interactions between a cognitive application 304 and the
application cognitive graph 882 are represented as a corresponding
set of nodes in a predetermined cognitive session graph, which is
then stored in a repository of cognitive session graphs `1` through
`n` 852. As used herein, a cognitive session graph broadly refers
to a cognitive graph whose nodes are associated with a cognitive
session. As used herein, a cognitive session broadly refers to a
predetermined user, group of users, theme, topic, issue, question,
intent, goal, objective, task, assignment, process, situation,
requirement, condition, responsibility, location, period of time,
or any combination thereof.
[0137] As an example, the application cognitive graph 882 may be
unaware of a particular user's preferences, which are likely stored
in a corresponding user profile. To further the example, a user may
typically choose a particular brand or manufacturer when shopping
for a given type of product, such as cookware. A record of each
query regarding that brand of cookware, or its selection, is
iteratively stored in a predetermined cognitive session graph that
is associated with the user and stored in a repository of cognitive
session graphs `1` through `n` 852. As a result, the preference of
that brand of cookware is ranked higher, and is presented in
response to contextually-related queries, even when the preferred
brand of cookware is not explicitly referenced by the user. To
continue the example, the user may make a number of queries over a
period of days or weeks, yet the queries are all associated with
the same cognitive session graph that is associated with the user
and stored in a repository of cognitive session graphs `1` through
`n` 852, regardless of when each query is made.
[0138] As another example, a user queries a cognitive application
304 during business hours to locate an upscale restaurant located
close their place of business. As a result, a first cognitive
session graph stored in a repository of cognitive session graphs
`1` through `n` 852 is associated with the user's query, which
results in the provision of composite cognitive insights related to
restaurants suitable for business meetings. To continue the
example, the same user queries the same cognitive application 304
during the weekend to locate a casual restaurant located close to
their home. As a result, a second cognitive session graph stored in
a repository of cognitive session graphs `1` through `n` 852 is
associated with the user's query, which results in the provision of
composite cognitive insights related to restaurants suitable for
family meals. In these examples, the first and second cognitive
session graphs are both associated with the same user, but for two
different purposes, which results in the provision of two different
sets of composite cognitive insights.
[0139] As yet another example, a group of customer support
representatives is tasked with resolving technical issues customers
may have with a predetermined product. In this example, the product
and the group of customer support representatives are collectively
associated with a predetermined cognitive session graph stored in a
repository of cognitive session graphs `1` through `n` 852. To
continue the example, individual customer support representatives
may submit queries related to the product to a cognitive
application 304, such as a knowledge base application. In response,
a predetermined cognitive session graph stored in a repository of
cognitive session graphs `1` through `n` 852 is used, along with
the universal knowledge repository 880 and application cognitive
graph 882, to generate individual or composite cognitive insights
to resolve a technical issue for a customer. In this example, the
cognitive application 304 may be queried by the individual customer
support representatives at different times during some
predetermined time interval, yet the same cognitive session graph
stored in a repository of cognitive session graphs `1` through `n`
852 is used to generate composite cognitive insights related to the
product.
[0140] In various embodiments, each cognitive session graph
associated with a user and stored in a repository of cognitive
session graphs `1` through `n` 852 includes one or more direct or
indirect user queries represented as nodes, and the time at which
they were asked, which are in turn linked 854 to nodes that appear
in the application cognitive graph 882. In certain embodiments,
each individual session graph that is associated with the user and
stored in a repository of cognitive session graphs `1` through `n`
852 introduces edges that are not already present in the
application cognitive graph 882. More specifically, each of the
session graphs that is associated with the user and stored in a
repository of cognitive session graphs `1` through `n` 852
establishes various relationships that the application cognitive
graph 882 does not already have.
[0141] In various embodiments, individual graph queries 844
associated with a predetermined session graph stored in a
repository of cognitive session graphs `1` through `n` 852 are
likewise provided to predetermined insight agents to perform
various kinds of analyses. In certain embodiments, each insight
agent performs a different kind of analysis. In various
embodiments, different insight agents may perform the same, or
similar, analyses. In certain embodiments, different agents
performing the same or similar analyses may be competing between
themselves.
[0142] For example, a user may be a realtor that has a young, upper
middle-class, urban-oriented clientele that typically enjoys eating
at trendy restaurants that are in walking distance of where they
live. As a result, the realtor may be interested in knowing about
new or popular restaurants that are in walking distance of their
property listings that have a young, middle-class clientele. In
this example, the user's queries may result the assignment of
predetermined insight agents to perform analysis of various social
media interactions to identify such restaurants that have received
favorable reviews. To continue the example, the resulting composite
insights may be provided as a ranked list of candidate restaurants
that may be suitable venues for the realtor to meet his
clients.
[0143] In various embodiments, the process 808 component is
implemented to provide these composite cognitive insights to the
deliver 810 component, which in turn is implemented to deliver the
composite cognitive insights in the form of a cognitive insight
summary 848 to the cognitive application 304. In these embodiments,
the cognitive platform 810 is implemented to interact with an
insight front-end 856 component, which provides a composite insight
and feedback interface with the cognitive application 304. In
certain embodiments, the insight front-end 856 component includes
an insight Application Program Interface (API) 858 and a feedback
API 860, described in greater detail herein. In these embodiments,
the insight API 858 is implemented to convey the cognitive insight
summary 848 to the cognitive application 304. Likewise, the
feedback API 860 is used to convey associated direct or indirect
user feedback 862 to the cognitive platform 810. In certain
embodiments, the feedback API 860 provides the direct or indirect
user feedback 862 to the repository of models 828 described in
greater detail herein.
[0144] To continue the preceding example, the user may have
received a list of candidate restaurants that may be suitable
venues for meeting his clients. However, one of his clients has a
pet that they like to take with them wherever they go. As a result,
the user provides feedback 862 that he is looking for a restaurant
that is pet-friendly. The provided feedback 862 is in turn provided
to the insight agents to identify candidate restaurants that are
also pet-friendly. In this example, the feedback 862 is stored in
the appropriate cognitive session graph stored in a repository of
cognitive session graphs `1` through `n` 852 associated with the
user and their original query.
[0145] In various embodiments, as described in the descriptive text
associated with FIG. 5, learning operations are iteratively
performed during the learning 838 phase to provide more accurate
and useful composite cognitive insights. In certain of these
embodiments, feedback 862 received from the user is stored in a
predetermined cognitive session graph that is associated with the
user and stored in a repository of cognitive session graphs `1`
through `n` 852, which is then used to provide more accurate
composite cognitive insights in response to subsequent
contextually-relevant queries from the user.
[0146] As an example, composite cognitive insights provided by a
particular insight agent related to a first subject may not be
relevant or particularly useful to a user of the cognitive
application 304. As a result, the user provides feedback 862 to
that effect, which in turn is stored in the appropriate cognitive
session graph that is associated with the user and stored in a
repository of cognitive session graphs `1` through `n` 852.
Accordingly, subsequent insights provided by the insight agent
related the first subject may be ranked lower, or not provided,
within a cognitive insight summary 848 to the user. Conversely, the
same insight agent may provide excellent insights related to a
second subject, resulting in positive feedback 862 being received
from the user. The positive feedback 862 is likewise stored in the
appropriate cognitive session graph that is associated with the
user and stored in a repository of cognitive session graphs `1`
through `n` 852. As a result, subsequent insights provided by the
insight agent related to the second subject may be ranked higher
within a cognitive insight summary 848 provided to the user.
[0147] In various embodiments, the composite insights provided in
each cognitive insight summary 848 to the cognitive application
304, and corresponding feedback 862 received from a user in return,
is provided in the form of one or more insight streams 864 to an
associated cognitive session graph stored in a repository of
cognitive session graphs `1` through `n` 852. In these and other
embodiments, the insight streams 864 may contain information
related to the user of the cognitive application 304, the time and
date of the provided composite cognitive insights and related
feedback 862, the location of the user, and the device used by the
user.
[0148] As an example, a query related to upcoming activities that
is received at 10:00 AM on a Saturday morning from a user's home
may return composite cognitive insights related to entertainment
performances scheduled for the weekend. Conversely, the same query
received at the same time on a Monday morning from a user's office
may return composite insights related to business functions
scheduled during the work week. In various embodiments, the
information contained in the insight streams 864 is used to rank
the composite cognitive insights provided in the cognitive insight
summary 848. In certain embodiments, the composite cognitive
insights are continually re-ranked as additional insight streams
864 are received. Skilled practitioners of the art will recognize
that many such embodiments are possible and the foregoing is not
intended to limit the spirit, scope or intent of the invention.
[0149] FIG. 9 is a generalized flowchart of composite cognitive
insight and feedback operations performed in accordance with an
embodiment of the invention. In this embodiment, composite
cognitive insight and feedback operations are begun in step 902,
followed by a cognitive application requesting an Application
Programming Interface (API) key from a cognitive platform,
described in greater detail herein, in step 906. The method by
which the API key is requested, generated and provided to the
cognitive application is a matter of design choice.
[0150] A cognitive session token is then issued to the cognitive
application in step 906, followed by a cognitive session token
being returned to the cognitive application in step 908 to
establish a composite cognitive insight session. As used herein, a
composite cognitive insight session broadly refers to a session
with a cognitive application, described in greater detail herein,
where composite cognitive insights are generated and presented to a
user. In various embodiments, the composite cognitive insight
session may also include the receipt of feedback from the user,
described in greater detail herein. In one embodiment, the
cognitive session token is used to establish a composite cognitive
insight session that generates a new cognitive session graph. In
another embodiment, the cognitive session token is used to
establish a composite cognitive insight session that appends
composite cognitive insights and user feedback to an existing
cognitive session graph associated with the user.
[0151] In various embodiments, the cognitive session token enables
the cognitive application to interact with a cognitive session
graph associated with the cognitive session token. In these
embodiments, the composite cognitive insight session is
perpetuated. For example, a given composite cognitive insight
session may last months or even years. In certain embodiments, the
cognitive session token expires after a predetermined period of
time. In these embodiments, the cognitive session token is no
longer valid once the predetermined period of time expires. The
method by which the period of time is determined, and monitored, is
a matter of design choice.
[0152] Contextually-relevant composite cognitive insights are then
generated and presented, as described in greater detail herein, to
the user in step 910. In various embodiments, composite cognitive
insights presented to the user are stored in the cognitive session
graph associated with the cognitive session token. A determination
is then made in step 912 if feedback is received from the user. If
not, then a determination is made in step 924 whether to end
composite cognitive insights and feedback operations. If not, then
a determination is made in step 926 whether the cognitive session
token for the target session has expired. If not, then the process
is continued, proceeding with step 910. Otherwise, the process is
continued, proceeding with step 904. However, if it is determined
in step 924 not to end composite cognitive insights and feedback
operations, then composite cognitive insights and feedback
operations are ended in step 928.
[0153] However, if it was determined in step 912 that feedback was
received from the user then the cognitive application provides the
feedback, as described in greater detail herein, to the cognitive
platform in step 914. A determination is then made in step 916
whether to use the provided feedback to generate
contextually-relevant questions for provision to the user. If not,
then the process is continued, proceeding with step 924. Otherwise,
the cognitive platform uses the provided feedback in step 918 to
generate contextually-relevant questions. Then, in step 922, the
cognitive platform provides the contextually-relevant questions,
along with additional composite insights, to the cognitive
application. In turn, the cognitive application provides the
contextually-relevant questions and additional composite cognitive
insights to the user in step 922 and the process is continued,
proceeding with step 912.
[0154] Although the present invention has been described in detail,
it should be understood that various changes, substitutions and
alterations can be made hereto without departing from the spirit
and scope of the invention as defined by the appended claims.
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