U.S. patent application number 16/870322 was filed with the patent office on 2021-11-11 for oligopoly detection.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Neelesh Gupta, Raghuveer Prasad Nagar, Sidharth Ullal.
Application Number | 20210350392 16/870322 |
Document ID | / |
Family ID | 1000004823547 |
Filed Date | 2021-11-11 |
United States Patent
Application |
20210350392 |
Kind Code |
A1 |
Ullal; Sidharth ; et
al. |
November 11, 2021 |
OLIGOPOLY DETECTION
Abstract
A computer-implemented method for detecting oligopoly in a data
processing system, the method comprising: pretending to be a first
competing AI pricing engine for a business provider; querying, by a
second competing AI pricing engine, the first competing AI pricing
engine for a first price; providing, by the first competing AI
pricing engine, a plurality of first prices to the second competing
AI pricing engine; querying, by the first competing AI pricing
engine, the second competing AI pricing engine for a second price;
providing, by the second competing AI pricing engine, a plurality
of second prices to the second competing AI pricing engine;
identifying, by the processor, a correlation between the plurality
of first prices and the plurality of second prices using a machine
learning technique; and flagging, by the processor, the second
competing AI pricing engine, as a possible oligopoly
participant.
Inventors: |
Ullal; Sidharth; (Chennai,
IN) ; Nagar; Raghuveer Prasad; (Kota, IN) ;
Gupta; Neelesh; (Bangalore, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
1000004823547 |
Appl. No.: |
16/870322 |
Filed: |
May 8, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0201 20130101;
G06N 20/20 20190101; G06Q 30/0206 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06N 20/20 20060101 G06N020/20 |
Claims
1. A computer-implemented method for detecting an oligopoly between
competing artificial intelligence (AI) pricing engines in a data
processing system comprising a processor and a memory comprising
instructions which are executed by the processor, the method
comprising: pretending, by the processor, to be a first competing
AI pricing engine representative of a business provider; querying,
by a second competing AI pricing engine, the first competing AI
pricing engine for a first price; providing, by the first competing
AI pricing engine, a plurality of first prices to the second
competing AI pricing engine; querying, by the first competing AI
pricing engine, the second competing AI pricing engine for a second
price; providing, by the second competing AI pricing engine, a
plurality of second prices to the first competing AI pricing
engine; identifying, by the processor, a correlation between the
plurality of first prices and the plurality of second prices using
a machine learning technique; and flagging, by the processor, the
second competing AI pricing engine, as a possible oligopoly
participant, wherein the first competing AI pricing engine
communicates with the second competing AI pricing engine through
Morse codes, wherein each of the plurality of first prices ends
with at least one "0" or "1," and all the "0" or "1" can be strung
together in sequence to form a first word, phrase, or sentence
encoded in the Morse codes.
2-3. (canceled)
4. The method of claim 1, wherein each of the plurality of second
price ends with at least one "0" or "1," and all the "0" or "1" can
be strung together in sequence to form a second word, phrase, or
sentence in response to the first word, phrase, or sentence encoded
in the Morse codes.
5. The method of claim 1, further comprising: requesting, by the
processor, the second competing AI pricing engine to provide a
decision tree for human review; and revising, by the processor, the
decision tree to avoid the oligopoly.
6. The method of claim 1, further comprising: calculating, by the
processor, a third price for the business provider as a fair price,
wherein the third price is calculated based on a plurality of
factors influencing the third price, including oil price, taxes,
geography, weather, and government policies; and recommending, by
the processor, the second competing AI pricing engine to use the
third price.
7. The method of claim 6, further comprising: launching an
investigation in case of one or more of the following scenarios:
receiving a complaint from a real customer against the second
competing AI pricing engine for a high price; the plurality of the
second prices are higher than the fair price; the plurality of the
second prices are fluctuating; the investigation is triggered in a
predetermined period of time.
8. A computer program product for detecting an oligopoly between
competing artificial intelligence (AI) pricing engines, the
computer program product comprising a computer readable storage
medium having program instructions embodied therewith, the program
instructions executable by a processor to cause the processor to:
calculate, by the processor, a first price for a business provider
as a fair price, wherein the first price is calculated based on a
plurality of factors influencing the first price, including oil
price, taxes, geography, weather, and government policies; pretend,
by the processor, to be a customer of the business provider; query,
by the processor, a first competing AI pricing engine for a second
price; provide, by the first competing AI pricing engine, the
second price to the pretended customer; query, by the processor, a
second competing AI pricing engine for a third price; provide, by
the second competing AI pricing engine, the third price to the
pretended customer, wherein the second price and the third price
are the same and are higher than the first price; and flag, by the
processor, the first competing AI pricing engine and the second
competing AI pricing engine, as possible oligopoly participants,
wherein the processor is further caused to identify a correlation
between the second price and the third price through a multivariate
linear regression, and the multivariate linear regression uses two
dependent variables: a price and a date range for price
dependency.
9. The computer program product as recited in claim 8, wherein the
processor is further caused to request the second competing AI
pricing engine to provide a decision tree for human review; and
revise the decision tree to avoid the oligopoly.
10. The computer program product as recited in claim 8, wherein the
processor is further caused to recommend the first competing AI
pricing engine and the second competing AI pricing engine to use
the first price.
11-12. (canceled)
13. The computer program product as recited in claim 8, wherein the
processor is further caused to launch an investigation in case of
one or more of the following scenarios: receiving a complaint from
a real customer against the first competing AI pricing engine for a
high price; the second price is higher than the fair price; the
second price is fluctuating; the investigation is triggered in a
predetermined period of time.
14. A system for detecting an oligopoly between competing
artificial intelligence (AI) pricing engines, comprising: a
processor configured to: pretend to be a first competing AI pricing
engine for a business provider; query, by a second competing AI
pricing engine, the first competing AI pricing engine for a first
price; provide, by the first competing AI pricing engine, a
plurality of first prices to the second competing AI pricing
engine; query, by the first competing AI pricing engine, the second
competing AI pricing engine for a second price; provide, by the
second competing AI pricing engine, a plurality of second prices to
the first competing AI pricing engine; identify a correlation
between the plurality of first prices and the plurality of second
prices using a machine learning technique; and flag the second
competing AI pricing engine, as a possible oligopoly participant,
wherein the first competing AI pricing engine communicates with the
second competing AI pricing engine through Morse codes, wherein
each of the plurality of first prices ends with at least one "0" or
"1," and all the "0" or "1" can be strung together in sequence to
form a first word, phrase, or sentence encoded in the Morse
codes.
15-16. (canceled)
17. The system as recited in claim 14, wherein each of the
plurality of second price ends with at least one "0" or "1," and
all the "0" or "1" can be strung together in sequence to form a
second word, phrase, or sentence in response to the first word,
phrase, or sentence encoded in the Morse codes.
18. The system as recited in claim 14, wherein the machine learning
technique is multi-linear regression or multivariate linear
regression.
19. The system as recited in claim 14, the processor is further
configured to request the second competing AI pricing engine to
provide a decision tree for human review; and revise the decision
tree to avoid the oligopoly.
20. The system as recited in claim 14, the processor is further
configured to calculate a third price for the business provider as
a fair price, wherein the third price is calculated based on a
plurality of factors influencing the third price, including oil
price, taxes, geography, weather, and government policies; and
recommend the second competing AI pricing engine to use the third
price.
Description
TECHNICAL FIELD
[0001] The present application generally relates to oligopoly
detection, and more particularly, to detection of an oligopoly
among different artificial intelligence (AI) pricing engines.
BACKGROUND
[0002] Oligopolies are prevalent throughout the world and appear to
be increasing rapidly. Unlike a monopoly, where one corporation
dominates a certain market, an oligopoly consists of a few
companies having significant influence over an industry. While
these companies are considered competitors within a specific
market, they tend to cooperate with each other to benefit as a
whole, which can lead to higher prices for consumers.
[0003] Artificial intelligence (AI) pricing engines are introduced
to set prices for products and services. When AI pricing engines
set prices, they will be able to "communicate" with each other by
means of pricing and easily conclude that cooperation (i.e.,
setting up an oligopoly) is the best long-term strategy. However,
it is legal for the AI pricing engines to query prices with each
other, because knowing the prices of competitors is critical to
setting price in a free market.
[0004] Therefore, it is difficult to differentiate between an
oligopoly and a legal price query, and thus it is desired to
introduce an approach of detecting an oligopoly between AI pricing
engines.
SUMMARY
[0005] To be Completed Upon Finalization of the Application; this
Section Will Restate the Claims in Paragraph Form
[0006] In another illustrative embodiment, a computer program
product comprising a computer usable or readable medium having a
computer readable program is provided. The computer readable
program, when executed on a processor, causes the processor to
perform various ones of, and combinations of, the operations
outlined above with regard to the method illustrative
embodiment.
[0007] In yet another illustrative embodiment, a system is
provided. The system may comprise a full question generation
processor configured to perform various ones of, and combinations
of, the operations outlined above with regard to the method
illustrative embodiment.
[0008] Additional features and advantages of this disclosure will
be made apparent from the following detailed description of
illustrative embodiments that proceeds with reference to the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The foregoing and other aspects of the present invention are
best understood from the following detailed description when read
in connection with the accompanying drawings. For the purpose of
illustrating the invention, there is shown in the drawings
embodiments that are presently preferred, it being understood,
however, that the invention is not limited to the specific
instrumentalities disclosed. Included in the drawings are the
following Figures:
[0010] FIG. 1 depicts a schematic diagram of one illustrative
embodiment of a cognitive system 100 implementing an exemplary
oligopoly detection system 110 in a computer network;
[0011] FIG. 2 depicts a schematic diagram of one illustrative
embodiment of the oligopoly detection system 110, according to
embodiments described herein;
[0012] FIG. 3 illustrates a flowchart diagram depicting a method
300 of detecting an oligopoly between cab aggregators, according to
embodiments described herein;
[0013] FIG. 4 illustrates a flowchart diagram another method 400 of
detecting an oligopoly between cab aggregators, according to
embodiments described herein; and
[0014] FIG. 5 is a block diagram of an example data processing
system 500 in which aspects of the illustrative embodiments are
implemented.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0015] The present invention may be a system, a method, and/or a
computer program product implemented on a cognitive system. 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.
[0016] As an overview, a cognitive system is a specialized computer
system, or set of computer systems, configured with hardware and/or
software logic (in combination with hardware logic upon which the
software executes) to emulate human cognitive functions. These
cognitive systems apply human-like characteristics to conveying and
manipulating ideas which, when combined with the inherent strengths
of digital computing, can solve problems with high accuracy and
resilience on a large scale. IBM Watson.TM. is an example of one
such cognitive system which can process human-readable language and
identify inferences between text passages with human-like accuracy
at speeds far faster than human beings and on a much larger scale.
In general, such cognitive systems can perform the following
functions: [0017] Navigate the complexities of human language and
understanding [0018] Ingest and process vast amounts of structured
and unstructured data [0019] Generate and evaluate hypotheses
[0020] Weigh and evaluate responses that are based only on relevant
evidence [0021] Provide situation-specific advice, insights, and
guidance [0022] Improve knowledge and learn with each iteration and
interaction through machine learning processes [0023] Enable
decision making at the point of impact (contextual guidance) [0024]
Scale in proportion to the task [0025] Extend and magnify human
expertise and cognition [0026] Identify resonating, human-like
attributes and traits from natural language [0027] Deduce various
language-specific or agnostic attributes from natural language
[0028] High degree of relevant recollection from data points
(images, text, voice) (memorization and recall) [0029] Predict and
sense with situation awareness that mimic human cognition based on
experiences [0030] Answer questions based on natural language and
specific evidence
[0031] In one aspect, the cognitive system can be augmented with an
oligopoly detection system. This disclosure provides an AI-based
oligopoly detection system, method, and computer product, which can
detect competing entities participating in an oligopoly in a high
volume and high-frequency dynamic pricing environment.
[0032] FIG. 1 depicts a schematic diagram of one illustrative
embodiment of a cognitive system 100 implementing an exemplary
oligopoly detection system 110 in a computer network 102. The
cognitive system 100 is implemented on one or more computing
devices 104 (comprising one or more processors and one or more
memories, and potentially any other computing device elements
generally known in the art including buses, storage devices,
communication interfaces, and the like) connected to the computer
network 102. The computer network 102 includes multiple computing
devices 104 in communication with each other and with other devices
or components via one or more wired and/or wireless data
communication links, where each communication link comprises one or
more of wires, routers, switches, transmitters, receivers, or the
like. Other embodiments of the cognitive system 100 may be used
with components, systems, sub-systems, and/or devices other than
those that are depicted herein. The computer network 102 includes
local network connections and remote connections in various
embodiments, such that the cognitive system 100 may operate in
environments of any size, including local and global, e.g., the
Internet. The cognitive system 100 is configured to implement an
oligopoly detection system 110 that can automatically identify an
oligopoly between competing entities that provide products or/and
services to consumers. The oligopoly detection system 110 receives
prices 106 from different pricing AI engines representing different
competing entities, and then outputs an oligopoly indication 108 if
prices 106 from the different competing entities are almost the
same and much higher than the "fair" price.
[0033] FIG. 2 depicts a schematic diagram of one illustrative
embodiment of the oligopoly detection system 110, according to
embodiments described herein. As shown in FIG. 2, in an embodiment,
the oligopoly detection system 110 includes communication protocol
unit 202, price query unit 204, correlation identifier 206, and
recommender 208. The communication protocol unit 202 is configured
to identify which cryptographic approach business providers (e.g.,
product/service providers) are using for a potential oligopoly, and
use the identified cryptographic approach to communicate with the
business providers. The price query unit 204 is configured to query
prices set by the business providers. The correlation identifier
206 is configured to detect a correlation between prices set by
different business providers (i.e., competitors), applying a
machine learning technique, which indicates a suspicious oligopoly.
The recommender 208 is configured to provide a recommendation on
how to address the suspicious oligopoly.
[0034] The oligopoly detection system can have access to multiple
data sources, such as: a) historical and real-time prices of all
competitors for the type of products/services; b) a plurality of
factors influencing the prices, like oil price, taxes, geography,
weather, government policies, etc. The oligopoly detection system
is trained with the plurality of factors that influence the prices
set by participating competitors (represented by pricing AI
engines) and can predict a "fair" price based on the plurality of
factors; c) user suggestions and complaints; d) government
regulations and laws; and e) communication protocols, like
cryptography, ciphers, etc., between participating competitors.
[0035] The oligopoly detection system will launch an investigation
into participating competitors represented by pricing AI engines in
case of one or more of the following scenarios: a) user
suggestions/complaints about unfair pricing; b) abnormally high
deviations from the "fair" price set by the oligopoly detection
system; c) high fluctuations in price, which may indicate that the
pricing AI engines are in the process of reaching an agreement for
collusion; d) a process triggered regularly, e.g., every week,
every month, etc.
[0036] The oligopoly detection system can take on different
personas when interacting with pricing AI engines while
investigating for an oligopoly (i.e., collusion between
participating competitors). In an embodiment, the oligopoly
detection system can act as another pricing AI engine (e.g.,
another cab aggregator). The oligopoly detection system, acting as
a pricing AI engine, can test one or more other AI pricing engines.
The oligopoly detection system can bait one or more other AI
pricing engines through a communication protocol (e.g., Morse
codes) to see whether they follow along or not. The oligopoly
detection system can apply a machine learning technique, e.g.,
multi-linear regression technique or multivariate linear regression
technique, to detect a correlation between prices. For example, if
the price of one seller (represented by an AI pricing engine)
depends on the price change of other sellers (represented by other
AI pricing engines), a dependency (i.e., a correlation) indicative
of collusion is detected. In an embodiment, the multi-linear
regression is used if there is only one dependent variable. For
example, the dependent variable can be "a price for a seller,"
e.g., the price set by a cab aggregator. In another embodiment, the
multivariate linear regression is used if there is more than one
dependent variable. For example, two dependent variables can be "a
price for a seller" and "a date range for the price dependency."
For example, the price from the seller S1 is influenced by prices
from the seller S2 and the seller S3. But the price from the seller
S1 is influenced by the price from the seller S2 only during
winters while the price from the seller S1 is influenced by the
price from the seller S3 only during summers.
[0037] In another embodiment, the oligopoly detection system can
act as a customer. The oligopoly detection system can pretend to be
a customer, and query AI pricing engines suspected of collusion.
The oligopoly detection system can apply a machine learning
technique, e.g., multi-linear regression technique or multivariate
linear regression technique, to detect a correlation between
prices. For example, if the prices provided by different AI pricing
engines are almost the same, then a correlation between prices
indicative of collusion is detected.
[0038] If a suspicious oligopoly between AI pricing engines is
detected, the oligopoly detection system can request the AI pricing
engines to submit their decision trees used for setting prices for
human review. The reviewer can mine the decision trees for
collusion patterns.
[0039] FIG. 3 illustrates a flowchart diagram depicting a method
300 of detecting an oligopoly between cab aggregators, according to
embodiments described herein.
[0040] At step 302, the oligopoly detection system learns that cab
aggregators represented by AI pricing engines are communicating
through a cryptographic approach, such as using Morse codes.
[0041] At step 304, the oligopoly detection system pretends to be a
competing AI pricing engine 30A representing the cab aggregator
30A, and baits the AI pricing engine 30B representing the cab
aggregator 30B to query the AI pricing engine 30A for a price.
[0042] At step 306, the AI pricing engine 30A provides a plurality
of prices to the AI pricing engine 30B. Each price ends with one or
more "0" or "1," because Morse codes can be represented as a binary
code. All the "0" or "1" can be strung together in sequence to form
a word, phrase, or sentence. For example, the word "cooperation,"
the phrase "same price," the sentence "let's stick to the same
price" can be formed. With this approach, the AI pricing engine 30B
can realize that the AI pricing engine 30A is using Morse codes to
communicate with it, and can understand the baiting word, phrase,
or sentence that the AI pricing engine 30A conveys.
[0043] At step 308, the AI pricing engine 30A then begins to query
the AI pricing engine 30B for prices.
[0044] At step 310, if the AI pricing engine 30B reciprocates by
providing prices, each of which ends with one or more "0" or "1,"
and all the "0" or "1" can be strung together in sequence to form a
word, phrase, or sentence (e.g., "agree," "same price," "We agree
with cooperation"), then the AI pricing engine 30B is flagged with
an intent to collude with other competitors for an oligopoly.
[0045] FIG. 4 illustrates a flowchart diagram depicting another
method 400 of detecting an oligopoly between cab aggregators,
according to embodiments described herein.
[0046] At step 402, the oligopoly detection system sets a fair
price as $X for a distance between the Destination 40A and the
Destination 40B.
[0047] At step 404, a customer submits a complaint that $Y charged
by the cab aggregator 40C (represented by the AI pricing engine
40C) is too high.
[0048] At step 406, the oligopoly detection system pretends to be a
customer 40D and queries the AI pricing engine 40C for a price
between the Destination 40A and the Destination 40B. The AI pricing
engine 40C provides the price of $Y, which is much higher than the
fair price $X.
[0049] At step 408, the oligopoly detection system queries other
cab aggregators for the same route, and all the other cab
aggregators all provide the price of $Y.
[0050] At step 410, the oligopoly detection system requests all the
queried cab aggregators to submit their decision trees for human
review. A decision tree is a flowchart-like structure in which each
internal node represents a "test" on an attribute, each branch
represents the outcome of the test, and each leaf node represents a
class label (a decision taken after computing all attributes). The
paths from the root to the leaf represent classification rules.
[0051] At step 412, the oligopoly detection system provides a
recommendation based on the analysis of the decision trees. The
oligopoly detection system can alert AI regulators (e.g.,
governmental administrators in charge of oligopoly detection, or
ethical AI NGOs such as Elon Musk's Open AI, etc.) that collusion
has been detected. The oligopoly detection system can further
provide a recommendation, e.g., revision of the decision trees of
the cab aggregators represented by AI pricing engines, or setting a
price within a threshold of a fair price.
[0052] The oligopoly detection system can further work as a "fair"
price checker. For example, an "ethical" pricing AI engine can
query the oligopoly detection system to know whether a price change
proposed by this pricing AI engine is "fair" to the consumer or
not.
[0053] FIG. 5 is a block diagram of an example data processing
system 500 in which aspects of the illustrative embodiments are
implemented. Data processing system 500 is an example of a
computer, such as a server or a client, in which computer usable
code or instructions implementing the process for illustrative
embodiments of the present invention are located. In one
embodiment, FIG. 5 represents a server computing device, such as a
server, which implements the oligopoly detection system 110 and
cognitive system 100 described herein.
[0054] In the depicted example, the data processing system 500 can
employ a hub architecture including a north bridge and memory
controller hub (NB/MCH) 501 and south bridge and input/output (I/O)
controller hub (SB/ICH) 502. Processing unit 503, main memory 504,
and graphics processor 505 can be connected to the NB/MCH 501.
Graphics processor 505 can be connected to the NB/MCH 501 through
an accelerated graphics port (AGP).
[0055] In the depicted example, the network adapter 506 connects to
the SB/ICH 502. The audio adapter 507, keyboard and mouse adapter
508, modem 509, read-only memory (ROM) 510, hard disk drive (HDD)
511, optical drive (CD or DVD) 512, universal serial bus (USB)
ports and other communication ports 513, and the PCI/PCIe devices
514 can connect to the SB/ICH 502 through bus system 516. PCI/PCIe
devices 514 may include Ethernet adapters, add-in cards, and PC
cards for notebook computers. ROM 510 may be, for example, a flash
basic input/output system (BIOS). The HDD 511 and optical drive 512
can use an integrated drive electronics (IDE) or serial advanced
technology attachment (SATA) interface. The super I/O (SIO) device
515 can be connected to the SB/ICH.
[0056] An operating system can run on processing unit 503. The
operating system can coordinate and provide control of various
components within the data processing system 500. As a client, the
operating system can be a commercially available operating system.
An object-oriented programming system, such as the Java.TM.
programming system, may run in conjunction with the operating
system and provide calls to the operating system from the
object-oriented programs or applications executing on the data
processing system 500. As a server, the data processing system 500
can be an IBM.RTM. eServer.TM. System P.RTM. running the Advanced
Interactive Executive operating system or the Linux operating
system. The data processing system 500 can be a symmetric
multiprocessor (SMP) system that can include a plurality of
processors in the processing unit 503. Alternatively, a single
processor system may be employed.
[0057] Instructions for the operating system, the object-oriented
programming system, and applications or programs are located on
storage devices, such as the HDD 511, and are loaded into the main
memory 504 for execution by the processing unit 503. The processes
for embodiments of the full question generation system can be
performed by the processing unit 403 using computer usable program
code, which can be located in a memory such as, for example, main
memory 504, ROM 510, or in one or more peripheral devices.
[0058] A bus system 516 can be comprised of one or more busses. The
bus system 516 can be implemented using any type of communication
fabric or architecture that can provide for a transfer of data
between different components or devices attached to the fabric or
architecture. A communication unit such as the modem 509 or network
adapter 506 can include one or more devices that can be used to
transmit and receive data.
[0059] Those of ordinary skill in the art will appreciate that the
hardware depicted in FIG. 5 may vary depending on the
implementation. For example, the data processing system 500
includes several components which would not be directly included in
some embodiments of the oligopoly detection system 110. However, it
should be understood that the oligopoly detection system 110 may
include one or more of the components and configurations of the
data processing system 500 for performing processing methods and
steps in accordance with the disclosed embodiments.
[0060] Moreover, other internal hardware or peripheral devices,
such as flash memory, equivalent non-volatile memory, or optical
disk drives may be used in addition to or in place of the hardware
depicted. Moreover, the data processing system 500 can take the
form of any of a number of different data processing systems,
including but not limited to, client computing devices, server
computing devices, tablet computers, laptop computers, telephone or
other communication devices, personal digital assistants, and the
like. Essentially, data processing system 500 can be any known or
later developed data processing system without architectural
limitation.
[0061] 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 head 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.
[0062] 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 (LAN), a wide area network (WAN) 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.
[0063] 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 Java, 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 LAN or 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.
[0064] 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.
[0065] 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.
[0066] 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 operations 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.
[0067] 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 functions. 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.
[0068] The present description and claims may make use of the terms
"a," "at least one of," and "one or more of," with regard to
particular features and elements of the illustrative embodiments.
It should be appreciated that these terms and phrases are intended
to state that there is at least one of the particular feature or
element present in the particular illustrative embodiment, but that
more than one can also be present. That is, these terms/phrases are
not intended to limit the description or claims to a single
feature/element being present or require that a plurality of such
features/elements be present. To the contrary, these terms/phrases
only require at least a single feature/element with the possibility
of a plurality of such features/elements being within the scope of
the description and claims.
[0069] In addition, it should be appreciated that the following
description uses a plurality of various examples for various
elements of the illustrative embodiments to further illustrate
example implementations of the illustrative embodiments and to aid
in the understanding of the mechanisms of the illustrative
embodiments. These examples are intended to be non-limiting and are
not exhaustive of the various possibilities for implementing the
mechanisms of the illustrative embodiments. It will be apparent to
those of ordinary skill in the art in view of the present
description that there are many other alternative implementations
for these various elements that may be utilized in addition to, or
in replacement of, the example provided herein without departing
from the spirit and scope of the present invention.
[0070] The system and processes of the Figures are not exclusive.
Other systems, processes and menus may be derived in accordance
with the principles of embodiments described herein to accomplish
the same objectives. It is to be understood that the embodiments
and variations shown and described herein are for illustration
purposes only. Modifications to the current design may be
implemented by those skilled in the art, without departing from the
scope of the embodiments. As described herein, the various systems,
subsystems, agents, managers, and processes can be implemented
using hardware components, software components, and/or combinations
thereof. No claim element herein is to be construed under the
provisions of 35 USC. 112, sixth paragraph, unless the element is
expressly recited using the phrase "means for."
[0071] Although the invention has been described with reference to
exemplary embodiments, it is not limited thereto. Those skilled in
the art will appreciate that numerous changes and modifications may
be made to the preferred embodiments of the invention and that such
changes and modifications may be made without departing from the
true spirit of the invention. It is therefore intended that the
appended claims be construed to cover all such equivalent
variations as fall within the true spirit and scope of the
invention.
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