U.S. patent application number 14/734498 was filed with the patent office on 2015-12-10 for cognitive media commerce.
This patent application is currently assigned to Cognitive Scale, Inc.. The applicant listed for this patent is Cognitive Scale, Inc.. Invention is credited to Neeraj Chawla, Manoj Saxena, Joshua L. Segars.
Application Number | 20150356443 14/734498 |
Document ID | / |
Family ID | 54769729 |
Filed Date | 2015-12-10 |
United States Patent
Application |
20150356443 |
Kind Code |
A1 |
Chawla; Neeraj ; et
al. |
December 10, 2015 |
Cognitive Media Commerce
Abstract
A method, system and computer-usable medium for providing
composite cognitive insights comprising 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;
generating a composite cognitive insight, the composite cognitive
insight being composed of the plurality of individual cognitive
insights; and, providing the composite cognitive insight to a user
via a set of cognitive media content, the set of cognitive media
content comprising a commercial offer content element.
Inventors: |
Chawla; Neeraj; (Austin,
TX) ; Segars; Joshua L.; (Cedar Park, TX) ;
Saxena; Manoj; (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.: |
14/734498 |
Filed: |
June 9, 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: |
H04W 4/025 20130101;
G06F 16/2465 20190101; G06N 5/022 20130101; G06F 16/9024 20190101;
G06F 16/955 20190101; G06F 16/24568 20190101; G06F 16/287 20190101;
G06F 16/972 20190101 |
International
Class: |
G06N 5/04 20060101
G06N005/04; G06Q 30/02 20060101 G06Q030/02; G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer-implementable method for providing composite
cognitive insights comprising: 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; generating
a composite cognitive insight, the composite cognitive insight
being composed of the plurality of individual cognitive insights;
and, providing the composite cognitive insight to a user via a set
of cognitive media content, the set of cognitive media content
comprising a commercial offer content element.
2. The method of claim 1, wherein: the set of cognitive media
content further comprises at least one of a cognitive media content
element, a non-commercial content element and a cognitive
advertising content element.
3. The method of claim 1, wherein: the composite cognitive insight
comprises a contextually-relevant composite insight; and, the set
of cognitive media content comprises content related to the
contextually-relevant insight.
4. The method of claim 1, further comprising: referencing a
cognitive persona when generating the composite cognitive
insight.
5. The method of claim 1, further comprising: annotating the
commercial offer content element to indicate an association with
another of the set of cognitive media content.
6. The method of claim 1, wherein: the set of cognitive media
content are presented via a cognitive insight summary; and, the
commercial offer content element is presented more prominently than
at least some other elements of the set of cognitive media content
presented via the cognitive insight summary.
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 generating and using
cognitive insights in cognitive commerce.
[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 method for
providing composite cognitive insights comprising 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; generating a composite cognitive insight, the composite
cognitive insight being composed of the plurality of individual
cognitive insights; and, providing the composite cognitive insight
to a user via a set of cognitive media content, the set of
cognitive media content comprising a commercial offer content
element.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] 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.
[0011] FIG. 1 depicts an exemplary client computer in which the
present invention may be implemented;
[0012] FIG. 2 is a simplified block diagram of a cognitive
inference and learning system (CILS);
[0013] FIG. 3 is a simplified block diagram of a CILS reference
model implemented in accordance with an embodiment of the
invention;
[0014] FIGS. 4a through 4c depict additional components of the CILS
reference model shown in FIG. 3;
[0015] FIG. 5 is a simplified process diagram of CILS
operations;
[0016] FIG. 6 depicts the lifecycle of CILS agents implemented to
perform CILS operations;
[0017] FIG. 7 is a simplified block diagram of a plurality of
cognitive platforms implemented in a hybrid cloud environment;
[0018] FIG. 8 depicts a cognitive persona defined by a first set of
nodes in a cognitive graph;
[0019] FIG. 9 depicts a cognitive profile defined by the addition
of a second set of nodes to the first set of nodes shown in FIG.
8;
[0020] FIG. 10 depicts a cognitive persona defined by a first set
of nodes in a weighted cognitive graph;
[0021] FIG. 11 depicts a cognitive profile defined by the addition
of a second set of nodes to the first set of nodes shown in FIG.
10;
[0022] FIGS. 12a and 12b are a simplified process flow diagram
showing the use of cognitive personas and cognitive profiles to
generate composite cognitive insights;
[0023] FIGS. 13a and 13b are a simplified process flow diagram
showing the use of cognitive insight summaries for presenting
composite cognitive insights to a user during a cognitive media
session; and
[0024] FIGS. 14a through 14c are a generalized flowchart of the
performance of cognitive media content management operations to
present composite cognitive insights to a user.
DETAILED DESCRIPTION
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] 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.
[0030] 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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.
[0038] 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.
[0039] 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.
[0040] 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.
[0041] 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."
[0042] 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.
[0043] 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.
[0044] 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.
[0045] 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.
[0046] 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.
[0047] 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.
[0048] 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.
[0049] 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.
[0050] 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.
[0051] 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.
[0052] 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.
[0053] 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.
[0054] 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).
[0055] 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.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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 may include a query 426 component, a
translate 427 component, a bridge 428 component, and one or more
bridging agents 429.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] 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.
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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.
[0105] 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.
[0106] 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.
[0107] 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..
[0108] 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.
[0109] 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.
[0110] 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.
[0111] 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.
[0112] 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.
[0113] 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
S3.RTM., available from Amazon.com of Seattle, Wash., and Swift, an
open source, scalable and redundant storage system.
[0114] 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.
[0115] 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.
[0116] 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).
[0117] 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.
[0118] 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.
[0119] 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.
[0120] 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.
[0121] 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.
[0122] 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.
[0123] 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.
[0124] 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.
[0125] 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.
[0126] 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.
[0127] 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.
[0128] 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.
[0129] 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.
[0130] 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.
[0131] FIG. 8 depicts a cognitive persona defined in accordance
with an embodiment of the invention by a first set of nodes in a
cognitive graph. As used herein, a cognitive persona broadly refers
to an archetype user model that represents a common set of
attributes associated with a hypothesized group of users. In
various embodiments, the common set of attributes may be described
through the use of demographic, geographic, psychographic,
behavioristic, and other information. As an example, the
demographic information may include age brackets (e.g., 25 to 34
years old), gender, marital status (e.g., single, married,
divorced, etc.), family size, income brackets, occupational
classifications, educational achievement, and so forth. Likewise,
the geographic information may include the cognitive persona's
typical living and working locations (e.g., rural, semi-rural,
suburban, urban, etc.) as well as characteristics associated with
individual locations (e.g., parochial, cosmopolitan, population
density, etc.).
[0132] The psychographic information may likewise include
information related to social class (e.g., upper, middle, lower,
etc.), lifestyle (e.g., active, healthy, sedentary, reclusive,
etc.), interests (e.g., music, art, sports, etc.), and activities
(e.g., hobbies, travel, going to movies or the theatre, etc.).
Other psychographic information may be related to opinions,
attitudes (e.g., conservative, liberal, etc.), preferences,
motivations (e.g., living sustainably, exploring new locations,
etc.), and personality characteristics (e.g., extroverted,
introverted, etc.) Likewise, the behavioristic information may
include information related to knowledge and attitude towards
various manufacturers or organizations and the products or services
they may provide. To continue the example, the behavioristic
information may be related to brand loyalty, interest in purchasing
a product or using a service, usage rates, perceived benefits, and
so forth. Skilled practitioners of the art will recognize that many
such attributes are possible and the foregoing is not intended to
limit the spirit, scope or intent of the invention.
[0133] In various embodiments, one or more cognitive personas may
be associated with a predetermined user. In certain embodiments, a
predetermined cognitive persona is selected and then used by a
cognitive inference and learning system (CILS) to generate one or
more composite cognitive insights as described in greater detail
herein. In these embodiments, the composite cognitive insights that
are generated for a user as a result of using a first cognitive
persona may be different than the composite cognitive insights that
are generated as a result of using a second cognitive persona. In
various embodiments, provision of the composite cognitive insights
results in the CILS receiving feedback information from various
individual users and other sources. In one embodiment, the feedback
information is used to revise or modify the cognitive persona. In
another embodiment, the feedback information is used to create a
new cognitive persona. In yet another embodiment, the feedback
information is used to create one or more associated cognitive
personas, which inherit a common set of attributes from a source
cognitive persona. In one embodiment, the feedback information is
used to create a new cognitive persona that combines attributes
from two or more source cognitive personas. In another embodiment,
the feedback information is used to create a cognitive profile,
described in greater detail herein, based upon the cognitive
persona. Those of skill in 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.
[0134] In this embodiment, a cognitive persona 802 is defined by
attributes A.sub.1 804, A.sub.2 806, A.sub.3 808, A.sub.4 810,
A.sub.5 812, A.sub.6 814, A.sub.7 816, which are respectively
associated with a set of corresponding nodes in cognitive graph
800. As shown in FIG. 8, the cognitive persona 802 is associated
with attributes A.sub.1 804 and A.sub.4 810, which are in turn
respectively associated with attributes A.sub.2 806, A.sub.3 808,
A.sub.5 812, and A.sub.6 814. Likewise, attributes A.sub.1 804 and
A.sub.4 810 are associated with each other as well as with
attribute A.sub.7 816.
[0135] As an example, the cognitive persona 802 may represent a
teacher of theatrical arts who also has an interest in history. In
this example, attribute A.sub.1 804 may be a demographic attribute
representing the profession of teaching theatrical arts, while
attribute A.sub.4 810 may be a psychographic attribute associated
with an interest in history. To continue the example, demographic
attributes A.sub.2 806 and A.sub.3 808 may respectively be
associated with teaching stage and film aspects of theatrical arts,
while psychographic attributes A.sub.5 812 and A.sub.6 814 may
respectively associated with an interest in European and American
history. Likewise, attribute A.sub.7 816 may be associated with
period costumes, which relates to both teaching theatrical arts and
an interest in history. In certain embodiments, an attribute may be
associated with two or more classes of attributes. For example,
attribute A.sub.7 816 may be a demographic attribute, a
psychographic attribute, or both. In various embodiments, the
cognitive persona 802 may be defined by additional attributes than
those shown in FIG. 8. In certain embodiments, the cognitive
persona 802 may be defined by fewer attributes than those shown in
FIG. 8.
[0136] FIG. 9 depicts a cognitive profile defined in accordance
with an embodiment of the invention by the addition of a second set
of nodes to the first set of nodes in the cognitive graph shown in
FIG. 8. As used herein, a cognitive profile refers to an instance
of a cognitive persona that references personal data associated
with a predetermined user. In various embodiments, the personal
data may include the user's name, physical address, email address,
social network ID, credit card number, Social Security Number
(SSN), age, gender, marital status, occupation, employer, income,
education, skills, knowledge, interests, preferences, likes and
dislikes, goals and plans, and so forth. In certain embodiments,
the personal data may include data associated with the user's
interaction with a cognitive inference and learning system (CILS)
and related composite cognitive insights that are generated and
provided to the user. In various embodiments, the personal data may
be distributed. In certain of these embodiments, predetermined
subsets of the distributed personal data may be logically
aggregated to generate one or more cognitive profiles, each of
which is associated with 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.
[0137] In this embodiment, a cognitive profile 902 is defined by
the addition of attributes A.sub.8 918, A.sub.9 920, A.sub.10 922,
A.sub.11 924 to attributes A.sub.1 804, A.sub.2 806, A.sub.3 808,
A.sub.4 810, A.sub.5 812, A.sub.6 814, A.sub.7 816, all of which
are respectively associated with a set of corresponding nodes in
cognitive graph 900. As shown in FIG. 9, the cognitive profile 902
is associated with attributes A.sub.1 804 and A.sub.4 810, which
are in turn respectively associated with attributes A.sub.2 806,
A.sub.3 808, A.sub.5 812, and A.sub.6 814. Likewise, attributes
A.sub.1 804 and A.sub.4 810 are associated with each other as well
as with attribute A.sub.7 816. As likewise shown in FIG. 9,
attribute A.sub.7 816 is associated with attributes A.sub.9 920 and
A.sub.11 924, both of which are associated with attribute A.sub.10
922. Likewise, attribute A.sub.11 924 is associated with attribute
A.sub.3 808, while attribute A.sub.8 918 is associated with
attributes A.sub.6 814, A.sub.9 920 and A.sub.11 924.
[0138] To continue the example described in the descriptive text
associated with FIG. 8, psychographic attributes A.sub.8 918,
A.sub.9 920, and A.sub.10 922 may respectively be associated with
the Union, the Civil War, and the battle of Gettysburg. Likewise,
attribute A.sub.11 924 may be a demographic attribute, a
psychographic attribute, or both as it is associated with attribute
A.sub.7 816, which may also be a demographic attribute, a
psychographic attribute, or both. In various embodiments, the
cognitive profile 902 may be defined by additional attributes than
those shown in FIG. 9. In certain embodiments, the cognitive
profile 902 may be defined by fewer attributes than those shown in
FIG. 9.
[0139] FIG. 10 depicts a cognitive persona defined in accordance
with an embodiment of the invention by a first set of nodes in a
weighted cognitive graph. In this embodiment, a cognitive persona
102 is defined by attributes A.sub.1 804, A.sub.2 806, A.sub.3 808,
A.sub.4 810, A.sub.5 812, A.sub.6 814, A.sub.7 816, which are
respectively associated with a set of corresponding nodes in a
weighted cognitive graph 1000. In various embodiments, an attribute
weight (e.g., attribute weights AW.sub.1 1032, AW.sub.2 1034,
AW.sub.3 1036, AW.sub.4 1038, AW.sub.5 1040, AW.sub.6 1042,
AW.sub.7 1044, AW.sub.8 1046, and AW.sub.9 1048), is used to
represent a relevance value between two attributes. For example, a
higher numeric value (e.g., `5.0`) associated with an attribute
weight may indicate a higher degree of relevance between two
attributes, while a lower numeric value (e.g., `0.5`) may indicate
a lower degree of relevance.
[0140] As shown in FIG. 10, the degree of relevance between the
persona 1002 and attributes A.sub.1 804 and A.sub.4 810 is
respectively indicated by attribute weights AW.sub.1 1032 and
AW.sub.4 1038. Likewise, the degree of relevance between attribute
A.sub.1 804 and attributes A.sub.2 806 and A.sub.3 808 is
respectively indicated by attribute weights AW.sub.2 1034 and
AW.sub.3 1036. As likewise show in FIG. 10, the degree of relevance
between attribute A.sub.4 810 and attributes A.sub.5 812 and
A.sub.6 814 is respectively indicated by attribute weights AW.sub.5
1040 and AW.sub.6 1042. Likewise, the degree of relevance between
attributes A.sub.1 804 and A.sub.4 810 is represented by attribute
weight AW.sub.7 1044, while the degree of relevance between
attribute A.sub.7 816 and attributes A.sub.1 804 and A.sub.4 810 is
respectively represented by attribute weights AW.sub.8 1046 and
AW.sub.9 1048.
[0141] In various embodiments, the numeric value associated with
predetermined attribute weights (e.g., attribute weights AW.sub.1
1032, AW.sub.2 1034, AW.sub.3 1036, AW.sub.4 1038, AW.sub.5 1040,
AW.sub.6 1042, AW.sub.7 1044, AW.sub.8 1046, and AW.sub.9 1048) may
change as a result of the performance of composite cognitive
insight and feedback operations described in greater detail herein.
In one embodiment, the changed numeric values associated with the
predetermined attribute weights may be used to modify an existing
cognitive persona. In another embodiment, the changed numeric
values associated with the predetermined attribute weights may be
used to generate a new cognitive persona. In yet another
embodiment, the changed numeric values associated with the
predetermined attribute weights may be used to generate a cognitive
profile.
[0142] FIG. 11 depicts a cognitive profile defined in accordance
with an embodiment of the invention by the addition of a second set
of nodes to the first set of nodes shown in FIG. 10. In this
embodiment, a cognitive profile 1102 is defined by the addition of
attributes A.sub.8 918, A.sub.9 920, A.sub.10 922, A.sub.11 924 to
attributes A.sub.1 804, A.sub.2 806, A.sub.3 808, A.sub.4 810,
A.sub.5 812, A.sub.6 814, A.sub.7 816, all of which are
respectively associated with a set of corresponding nodes in a
weighted cognitive graph 1100. As shown in FIG. 11, the cognitive
profile 1102 is associated with attributes A.sub.1 804 and A.sub.4
810, which are in turn respectively associated with attributes
A.sub.2 806, A.sub.3 808, A.sub.5 812, and A.sub.6 814. Likewise,
attributes A.sub.1 804 and A.sub.4 810 are associated with each
other as well as with attribute A.sub.7 816. As likewise shown in
FIG. 11, attribute A.sub.7 816 is associated with attributes
A.sub.9 920 and A.sub.11 924, both of which are associated with
attribute A.sub.10 922. Likewise, attribute A.sub.11 924 is
associated with attribute A.sub.3 808, while attribute A.sub.8 918
is associated with attributes A.sub.6 814, A.sub.9 920 and A.sub.11
924.
[0143] As shown in FIG. 11, the degree of relevance between
attribute A.sub.6 814 and A.sub.8 918 is represented by attribute
weight AW.sub.10 1150, while the degree of relevance between
attribute A.sub.8 918 and attributes A.sub.9 920 and A.sub.10 922
is respectively indicated by attribute weights AW.sub.11 1152 and
AW.sub.12 1154. Likewise, the degree of relevance between attribute
A.sub.10 922 and attributes A.sub.9 920 and A.sub.11 924 is
respectively indicated by attribute weights AW.sub.13 1156 and
AW.sub.15 1160. As likewise shown in FIG. 11, the degree of
relevance between attribute A.sub.7 816 and attributes A.sub.9 920
and A.sub.11 924 is respectively indicated by attribute weights
AW.sub.14 1158 and AW.sub.16 1162, while the degree of relevance
between attributes A.sub.11 924 and A.sub.3 808 is represented by
attribute weight AW.sub.17 1164.
[0144] In various embodiments, the numeric value associated with
predetermined attribute weights may change as a result of the
performance of composite cognitive insight and feedback operations
described in greater detail herein. In one embodiment, the changed
numeric values associated with the predetermined attribute weights
may be used to modify an existing cognitive profile. In another
embodiment, the changed numeric values associated with the
predetermined attribute weights may be used to generate a new
cognitive profile.
[0145] FIGS. 12a and 12b are a simplified process flow diagram
showing the use of cognitive personas and cognitive profiles
implemented in accordance with an embodiment of the invention to
generate composite cognitive insights. 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 1282, as their data source
to respectively generate individual cognitive insights. As used
herein, an application cognitive graph 1282 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 1282 to generate individual cognitive insights for a user.
In various embodiments, the resulting individual cognitive insights
are then composed to generate a set of composite cognitive
insights, which in turn is provided to a user in the form of a
cognitive insight summary 1248.
[0146] 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 1244, 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.
[0147] In certain embodiments, the insight agents are selected for
orchestration as a result of receiving direct or indirect input
from a user. In various embodiments, the direct user input may be a
natural language inquiry. In certain embodiments, the indirect user
input 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. 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 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.
[0148] 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
1240 phase, a learning 1238 phase, and an application/insight
composition 1240 phase. In the data lifecycle 1236 phase, a
predetermined instantiation of a cognitive platform 1210 sources
social data 1212, public data 1214, licensed data 1216, and
proprietary data 1218 from various sources as described in greater
detail herein. In various embodiments, an example of a cognitive
platform 1210 instantiation is the cognitive platform 310 shown in
FIGS. 3, 4a, and 4b. In this embodiment, the instantiation of a
cognitive platform 1210 includes a source 1206 component, a process
1208 component, a deliver 1210 component, a cleanse 1220 component,
an enrich 1222 component, a filter/transform 1224 component, and a
repair/reject 1226 component. Likewise, as shown in FIG. 12a, the
process 1208 component includes a repository of models 1228,
described in greater detail herein.
[0149] In various embodiments, the process 1208 component is
implemented to perform various composite insight generation and
other processing operations described in greater detail herein. In
these embodiments, the process 1208 component is implemented to
interact with the source 1208 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 1208 component. In turn, the
process 1208 component is implemented to interact with the cleanse
1220 component, which is implemented to perform various data
cleansing operations familiar to those of skill in the art. As an
example, the cleanse 1220 component may perform data normalization
or pruning operations, likewise known to skilled practitioners of
the art. In certain embodiments, the cleanse 1220 component may be
implemented to interact with the repair/reject 1226 component,
which in turn is implemented to perform various data repair or data
rejection operations known to those of skill in the art.
[0150] Once data cleansing, repair and rejection operations are
completed, the process 1208 component is implemented to interact
with the enrich 1222 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 1208 component is likewise implemented to
interact with the filter/transform 1224 component, which in turn is
implemented to perform data filtering and transformation operations
described in greater detail herein.
[0151] In various embodiments, the process 1208 component is
implemented to generate various models, described in greater detail
herein, which are stored in the repository of models 1228. The
process 1208 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 1282, as
described in greater detail herein. In various embodiments, the
process 1208 component is implemented to gain an understanding of
the data sourced from the sources of social data 1212, public data
1214, licensed data 1216, and proprietary data 1218, which assist
in the automated generation of the application cognitive graph
1282.
[0152] The process 1208 component is likewise implemented in
various embodiments to perform bridging 1246 operations, described
in greater detail herein, to access the application cognitive graph
1282. In certain embodiments, the bridging 1246 operations are
performed by bridging agents, likewise described in greater detail
herein. In various embodiments, the application cognitive graph
1282 is accessed by the process 1208 component during the learning
1236 phase of the composite cognitive insight generation
operations.
[0153] In various embodiments, a cognitive application 304 is
implemented to receive input data associated with an individual
user or a group of users. In these embodiments, the input data 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 may include contextual data,
described in greater detail herein. Once it is received, the input
data is then submitted 1242 by the cognitive application 304 to a
graph query engine 326 during the application/insight composition
1240 phase. In turn, the graph query engine 326 processes the
submitted 1242 input data to generate a graph query 1244, as
described in greater detail herein. The graph query 1244 is then
used to query the application cognitive graph 1282, which results
in the generation of one or more composite cognitive insights,
likewise described in greater detail herein. In certain
embodiments, the graph query 1244 uses predetermined knowledge
elements stored in the universal knowledge repository 1280 when
querying the application cognitive graph 1282 to generate the one
or more composite cognitive insights.
[0154] In various embodiments, the graph query 1244 results in the
selection of a predetermined cognitive persona, described in
greater detail herein, from a repository of cognitive personas `1`
through `n` 1272, according to a set of contextual information
associated with a user. In certain embodiments, the universal
knowledge repository 1280 includes the repository of personas `1`
through `n` 1272. In various embodiments, individual nodes within
predetermined cognitive personas stored in the repository of
personas `1` through `n` 1272 are linked 954 to corresponding nodes
in the universal knowledge repository 1280. In certain embodiments,
predetermined nodes within the universal knowledge repository 1280
are likewise linked to predetermined nodes within the cognitive
application graph 1282.
[0155] As used herein, contextual information broadly refers to
information associated with a location, a point in time, a user
role, an activity, a circumstance, an interest, a desire, a
perception, an objective, or a combination thereof. In certain
embodiments, the contextual information is likewise used in
combination with the selected cognitive persona to generate one or
more composite cognitive insights for a user. In various
embodiments, the composite cognitive insights that are generated
for a user as a result of using a first set of contextual
information may be different than the composite cognitive insights
that are generated as a result of using a second set of contextual
information.
[0156] As an example, a user may have two associated cognitive
personas, "purchasing agent" and "retail shopper," which are
respectively selected according to two sets of contextual
information. In this example, the "purchasing agent" cognitive
persona may be selected according to a first set of contextual
information associated with the user performing business purchasing
activities in their office during business hours, with the
objective of finding the best price for a particular commercial
inventory item. Conversely, the "retail shopper" cognitive persona
may be selected according to a second set of contextual information
associated with the user performing cognitive personal shopping
activities in their home over a weekend, with the objective of
finding a decorative item that most closely matches their current
furnishings. As a result, the composite cognitive insights
generated as a result of combining the first cognitive persona with
the first set of contextual information will likely be different
than the composite cognitive insights generated as a result of
combining the second cognitive persona with the second set of
contextual information.
[0157] In various embodiments, the graph query 1244 results in the
selection of a predetermined cognitive profile, described in
greater detail herein, from a repository of cognitive profiles `1`
through `n` 1274 according to identification information associated
with a user. The method by which the identification information is
determined is a matter of design choice. In certain embodiments,
set of contextual information associated with a user is used to
select the cognitive profile is selected from the repository of
cognitive profiles `1` through `n` 1274. In various embodiments,
one or more cognitive profiles may be associated with a
predetermined user. In these embodiments, a predetermined cognitive
profile is selected and then used by a CILS to generate one or more
composite cognitive insights for the user as described in greater
detail herein. In certain of these embodiments, the selected
cognitive profile provides a basis for adaptive changes to the
CILS, and by extension, the composite cognitive insights it
generates.
[0158] In various embodiments, provision of the composite cognitive
insights results in the CILS receiving feedback 1262 information
related to an individual user. In one embodiment, the feedback 1262
information is used to revise or modify a cognitive persona. In
another embodiment, the feedback 1262 information is used to revise
or modify the cognitive profile associated with a user. In yet
another embodiment, the feedback 1262 information is used to create
a new cognitive profile, which in turn is stored in the repository
of cognitive profiles `1` through `n` 1274. In still yet another
embodiment, the feedback 1262 information is used to create one or
more associated cognitive profiles, which inherit a common set of
attributes from a source cognitive profile. In another embodiment,
the feedback 1262 information is used to create a new cognitive
profile that combines attributes from two or more source cognitive
profiles. In various embodiments, these persona and profile
management operations 1276 are performed through interactions
between the cognitive application 304, the repository of cognitive
personas `1` through `n` 1272, the repository of cognitive profiles
`1` through `n` 1274, the universal knowledge repository 1280, or
some combination thereof. Those of skill in 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.
[0159] In various embodiments, a cognitive profile associated with
a user may be either static or dynamic. As used herein, a static
cognitive profile refers to a cognitive profile that contains
identification information associated with a user that changes on
an infrequent basis. As an example, a user's name, Social Security
Number (SSN), or passport number may not change, although their
age, address or employer may change over time. To continue the
example, the user may likewise have a variety of financial account
identifiers, social network identifiers, and various travel awards
program identifiers which change infrequently.
[0160] As likewise used herein, a dynamic cognitive profile refers
to a cognitive profile that contains information associated with a
user that changes on a dynamic basis. For example, a user's
interests and activities may evolve over time, which may be
evidenced by associated interactions with the CILS. In various
embodiments, these interactions result in the provision of
associated composite cognitive insights to the user. In these
embodiments, the user's interactions with the CILS, and the
resulting composite cognitive insights that are generated, are used
to update the dynamic cognitive profile on an ongoing basis to
provide an up-to-date representation of the user in the context of
the cognitive profile used to generate the composite cognitive
insights.
[0161] In various embodiments, a cognitive profile, whether static
or dynamic, is selected according to a set of contextual
information associated with a user. In certain embodiments, the
contextual information is likewise used in combination with the
selected cognitive profile to generate one or more composite
cognitive insights for the user. In these embodiments, the
composite cognitive insights that are generated as a result of
using a first set of contextual information with the selected
cognitive profile may be different than the composite cognitive
insights that are generated as a result of using a second set of
contextual information.
[0162] As an example, a user may have two associated cognitive
profiles, "runner" and "foodie," which are respectively selected
according to two sets of contextual information. In this example,
the "runner" cognitive profile may be selected according to a first
set of contextual information associated with the user being out of
town on business travel and wanting to find a convenient place to
run close to where they are staying. To continue this example, two
composite cognitive insights may be generated and provided to the
user in the form of a cognitive insight summary 1248. The first may
be suggesting a running trail the user has used before and liked,
but needs directions to find again. The second may be suggesting a
new running trail that is equally convenient, but wasn't available
the last time the user was in town.
[0163] Conversely, the "foodie" cognitive profile may be selected
according to a second set of contextual information associated with
the user being at home and expressing an interest in trying either
a new restaurant or an innovative cuisine. To further continue this
example, the user's "foodie" cognitive profile may be processed by
the CILS to determine which restaurants and cuisines the user has
tried in the last eighteen months. As a result, two composite
cognitive insights may be generated and provided to the user in the
form of a cognitive insight summary 1248. The first may be a
suggestion for a new restaurant that is serving a cuisine the user
has enjoyed in the past. The second may be a suggestion for a
restaurant familiar to the user that is promoting a seasonal menu
featuring Asian fusion dishes, which the user has not tried before.
Those of skill in the art will realize that the composite cognitive
insights generated as a result of combining the first cognitive
profile with the first set of contextual information will likely be
different than the composite cognitive insights generated as a
result of combining the second cognitive profile with the second
set of contextual information.
[0164] In various embodiments, a user's cognitive profile, whether
static or dynamic, may reference data that is proprietary to the
user, an organization, or a combination thereof. As used herein,
proprietary data broadly refers to data that is owned, controlled,
or a combination thereof, by an individual user or an organization,
which is deemed important enough that it gives competitive
advantage to that individual or organization. In certain
embodiments, the organization may be a governmental, non-profit,
academic or social entity, a manufacturer, a wholesaler, a
retailer, a service provider, an operator of a cognitive inference
and learning system (CILS), and others.
[0165] In various embodiments, an organization may or may not grant
a user the right to obtain a copy of certain proprietary
information referenced by their cognitive profile. In certain
embodiments, a first organization may or may not grant a user the
right to obtain a copy of certain proprietary information
referenced by their cognitive profile and provide it to a second
organization. As an example, the user may not be granted the right
to provide travel detail information (e.g., travel dates and
destinations, etc.) associated with an awards program provided by a
first travel services provider (e.g., an airline, a hotel chain, a
cruise ship line, etc.) to a second travel services provider. In
various embodiments, the user may or may not grant a first
organization the right to provide a copy of certain proprietary
information referenced by their cognitive profile to a second
organization. Those of skill in 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.
[0166] In various embodiments, a set of contextually-related
interactions between a cognitive application 304 and the
application cognitive graph 1282 are represented as a corresponding
set of nodes in a predetermined cognitive session graph, which is
then stored in a repository of session graphs `1` through `n` 1252.
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.
[0167] As an example, the application cognitive graph 1282 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 session graph that is
associated with the user and stored in a repository of session
graphs `1` through `n` 1252. 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 session graphs `1` through `n` 1252, regardless
of when each query is made.
[0168] 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 session graphs `1` through
`n` 1252 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
session graphs `1` through `n` 1252 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.
[0169] 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 session graphs `1` through `n` 1252. 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 session graphs
`1` through `n` 1252 is used, along with the universal knowledge
repository 880 and application cognitive graph 1282, 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 1252 stored in a
repository of session graphs `1` through `n` is used to generate
composite cognitive insights related to the product.
[0170] In various embodiments, each cognitive session graph
associated with a user and stored in a repository of session graphs
`1` through `n` 1252 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 1254 to nodes that appear in the
application cognitive graph 1282. In certain embodiments, each
individual session graph that is associated with the user and
stored in a repository of session graphs `1` through `n` 1252
introduces edges that are not already present in the application
cognitive graph 1282. More specifically, each of the session graphs
that is associated with the user and stored in a repository of
session graphs `1` through `n` 1252 establishes various
relationships that the application cognitive graph 1282 does not
already have.
[0171] In various embodiments, individual cognitive profiles in the
repository of profiles `1` through `n` 1274 are respectively stored
as session graphs in the repository of session graphs 1252. In
these embodiments, predetermined nodes within each of the
individual cognitive profiles are linked 1254 to predetermined
nodes within corresponding cognitive session graphs stored in the
repository of cognitive session graphs `1` through `n` 1254. In
certain embodiments, individual nodes within each of the cognitive
profiles are likewise linked 1254 to corresponding nodes within
various cognitive personas stored in the repository of cognitive
personas `1` through `n` 1272.
[0172] In various embodiments, individual graph queries 1244
associated with a predetermined session graph stored in a
repository of session graphs `1` through `n` 1254 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.
[0173] 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.
[0174] In various embodiments, the process 1208 component is
implemented to provide these composite cognitive insights to the
deliver 1210 component, which in turn is implemented to deliver the
composite cognitive insights in the form of a cognitive insight
summary 1248 to the cognitive application 304. In these
embodiments, the cognitive platform 1210 is implemented to interact
with an insight front-end 1256 component, which provides a
composite insight and feedback interface with the cognitive
application 304. In certain embodiments, the insight front-end 1256
component includes an insight Application Program Interface (API)
1258 and a feedback API 1260, described in greater detail herein.
In these embodiments, the insight API 1258 is implemented to convey
the cognitive insight summary 1248 to the cognitive application
304. Likewise, the feedback API 1260 is used to convey associated
direct or indirect user feedback 1262 to the cognitive platform
1210. In certain embodiments, the feedback API 1260 provides the
direct or indirect user feedback 1262 to the repository of models
1228 described in greater detail herein.
[0175] 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 1262 that he is looking for a restaurant
that is pet-friendly. The provided feedback 1262 is in turn
provided to the insight agents to identify candidate restaurants
that are also pet-friendly. In this example, the feedback 1262 is
stored in the appropriate session graph 1252 associated with the
user and their original query.
[0176] In various embodiments, as described in the descriptive text
associated with FIG. 5, learning operations are iteratively
performed during the learning 1238 phase to provide more accurate
and useful composite cognitive insights. In certain of these
embodiments, feedback 1262 received from the user is stored in a
predetermined session graph 1252 that is associated with the user
and stored in a repository of session graphs `1` through `n`, which
is then used to provide more accurate composite cognitive insights
in response to subsequent contextually-relevant queries from the
user.
[0177] 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 1262 to
that effect, which in turn is stored in the appropriate session
graph that is associated with the user and stored in a repository
of session graphs `1` through `n` 1252. Accordingly, subsequent
insights provided by the insight agent related the first subject
may be ranked lower, or not provided, within a cognitive insight
summary 1248 provided to the user. Conversely, the same insight
agent may provide excellent insights related to a second subject,
resulting in positive feedback 1262 being received from the user.
The positive feedback 1262 is likewise stored in the appropriate
session graph that is associated with the user and stored in a
repository of session graphs `1` through `n` 1252. As a result,
subsequent insights provided by the insight agent related to the
second subject may be ranked higher within a cognitive insight
summary 1248 provided to the user.
[0178] In various embodiments, the composite insights provided in
each cognitive insight summary 1248 to the cognitive application
304, and corresponding feedback 1262 received from a user in
return, is provided to an associated session graph 1252 in the form
of one or more insight streams 1264. In these and other
embodiments, the insight streams 1264 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 1262, the location of the user, and the device used by the
user.
[0179] 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 cognitive insights related to business
functions scheduled during the work week. In various embodiments,
the information contained in the insight streams 1264 is used to
rank the composite cognitive insights provided in the cognitive
insight summary 1248. In certain embodiments, the composite
cognitive insights are continually re-ranked as additional insight
streams 1264 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.
[0180] FIGS. 13a and 13b are a simplified process flow diagram
showing the use of cognitive insight summaries implemented in
accordance with an embodiment of the invention to present composite
cognitive insights to a user during a cognitive media session. As
used herein, a cognitive media session refers to a cognitive
session, described in greater detail herein, where a user is
provided a first set of cognitive media content, and in response to
user input, is subsequently provided a second set of cognitive
media content in the form of composite cognitive insights.
Cognitive media content, as used herein, broadly refers to content
that is associated with other cognitive media content, which in
various embodiments is provided to the user in the form of one or
more composite cognitive insights.
[0181] In various embodiments, the set of cognitive insights may be
generated by referencing a cognitive persona associated with one or
more consumers of the cognitive media content. In certain
embodiments, a cognitive profile may be generated by referencing a
cognitive persona associated with one or more consumers of the
cognitive media content. In various embodiments, a corpus of
cognitive media content may be preprocessed by a Cognitive
Inference and Learning System (CILS) to generate a set of composite
cognitive insights associated with one or more personas. In certain
embodiments, the corpus of cognitive media content may be
iteratively processed by the CILS to generate a set of composite
cognitive insights associated with an individual cognitive profile.
In one embodiment, the corpus of cognitive media content is
iteratively processed by the CILS whenever the individual cognitive
profile is updated or revised. In another embodiment, the updating
or revision of the individual cognitive profile is performed in
response to receipt of feedback 1262 information from the consumer
of cognitive media content associated with an individual cognitive
profile stored in the repository of cognitive profiles `1` through
`n` 1274.
[0182] In various embodiments, composite cognitive insights are
provided in the form of a cognitive insight summary 1248. As used
herein, a cognitive insight summary 1248 broadly refers to a
predetermined set of composite cognitive insights provided to a
user. In various embodiments, the set of composite cognitive
insights may be ranked within the cognitive insight summary 1248
according to their relevance. In these embodiments, the relevance
may be determined through the use of direct and indirect input data
1242, the application cognitive graph 1282, the universal knowledge
repository 1280, and feedback 1262 information, or any combination
thereof. In these same embodiments, the relevance may likewise be
determined through the use of one or more cognitive session graphs
stored in the repository of cognitive session graphs `1` through
`n` 1252. Likewise, one or more cognitive personas or profiles
respectively stored in the repositories of cognitive personas `1`
through `n` 1272 or cognitive profiles `1` through `n` 1274, or any
combination of the foregoing, may be used to determine the
relevance.
[0183] In various embodiments, the cognitive media content used to
provide a composite cognitive insight may include individual
cognitive media content elements. In these embodiments, the
cognitive media content elements may include non-commercial content
elements, advertising content elements, non-commercial offer
content elements, commercial offer content elements, or any
combination thereof. In this embodiment, non-commercial (NC)
content elements NC `1` 1304 through `n` 1306, advertising (AD)
content elements AD `1` 1308 through `n` 1310, non-commercial offer
(NO) content elements NC `1` 1312 through `n` 1314, and commercial
offer (CO) content elements CO `1` 1316 through `n` 1318 are stored
in a repository of cognitive media content 1302.
[0184] As used herein, a non-commercial content element broadly
refers to a content element that is used in the provision of a
composite cognitive insight without the intent or expectation of
achieving a commercial objective. One example of a non-commercial
content element may be an article on the history of wine making in
the Medoc region of France. Likewise, photographs of vineyards in
the Medoc region or recipes for notable cuisine in the area would
be other examples of non-commercial content elements. As likewise
used herein, an advertising content element broadly refers to a
content element that is used in the provision of a composite
cognitive insight with the intent of persuading the recipient to
take or continue a predetermined action associated with a
commercial or non-commercial offer, a social, political or
religious ideology, or a public service. To continue the preceding
example, an advertising content element may be an advertisement
encouraging the purchase of a certain style of wine made by a
particular winemaker in the Medoc region. Another example of an
advertising content element would be a public service announcement
encouraging citizens to wear their seatbelts when driving. Yet
another example of an advertising content element would be a
political campaign advertisement encouraging voters to vote for a
particular candidate or legislation. In various embodiments,
advertising content elements AD `1` 1308 through `n` 1310 are
selected from the repository of cognitive media content 1302 by a
cognitive platform implemented with an advertising engine 1320.
[0185] As used herein, a non-commercial offer content element
broadly refers to a content element that is used in the provision
of a composite cognitive insight without the intent or expectation
of achieving a commercial objective. For example, a community may
offer free tours of historical landmarks. Another example of a
non-commercial offer element would be an energy audit of a
homeowner's home, combined with advice on how to reduce energy
consumption. As likewise used herein, a commercial offer content
element broadly refers to a content element that is used in the
provision of a composite cognitive insight with the intent or
expectation of initiating a commercial transaction. For example, a
commercial offer content element may be an offer to sell a set of
wine glasses made by a particular manufacturer at an attractive
price. Another example of a commercial offer content element would
be an offer to provide lessons at a culinary institute. In various
embodiments, commercial offer content elements CO `1` 1316 through
`n` 1318 are selected from the repository of cognitive media
content 1302 by a cognitive platform implemented with a commerce
engine 1322. Those of skill in the art will recognize that many
such examples of cognitive media element are possible and the
foregoing is not intended to limit the spirit, scope or intent of
the invention.
[0186] In various embodiments, predetermined media is used to
provide the first set of cognitive media content to the user,
receive user input in response, and provide the second set of
cognitive media content in the form of composite cognitive insights
during the cognitive media session. As used herein, media broadly
refers to any medium used to communicate information or data, such
as a composite cognitive insight in the form of a cognitive insight
summary 1248, to one or more users. In various embodiments, the
medium may be used to communicate a cognitive insight summary 1248
in a predetermined form, such as tactile (e.g., braille), textual,
graphical, audio, video, or some combination thereof.
[0187] In various embodiments, one or more subsets of the first set
of cognitive media content are annotated to indicate an association
with corresponding second sets of cognitive media content. In these
embodiments, the user provides user input to select an annotated
subset of the first set of cognitive media content, and in return,
receives a corresponding second set of cognitive media content in
the form of one or more composite cognitive insights. In various
embodiments, the user input may include one or more user gestures,
such as a keystroke, a mouse click. In certain embodiments, the
user input may include other user gestures, such as a finger tap or
swipe on a touch-sensitive screen. In various embodiments, the user
input may include one or more voice commands, such as "tell me
more" or "more detail please."
[0188] As an example, a first set of cognitive media content
containing a body of text may be presented to a user. In this
example, the first set of cognitive media content may be annotated
with one or more visual attributes (e.g., bolding, color,
underlining, etc.) applied to certain portions of the text, whether
it is a single letter, a word, a sentence, a paragraph, or an
entire manuscript. In various embodiments, the visual attributes
may be implemented to indicate an association between certain
portions of the text with different types of content elements. For
example, a color attribute of blue may indicate an association with
a non-commercial content element, while a color attribute of green
may signify an association with a commercial offer content
element.
[0189] As another example, a first set of cognitive media content
containing a streaming video may be presented to a user. In this
example, certain portions of the streaming video may be annotated
with one or more icons, which appear at predetermined times during
playback of the streaming video. In various embodiments, the
appearance of an icon may indicate an association between certain
portions of the annotated video stream and different types of
content elements. For example, one icon may represent an
association with an advertising content element while a different
icon may represent an association with a commercial offer content
element.
[0190] As yet another example, a first set of cognitive media
content containing an audio stream may be presented to a user. In
this example, certain portions of the audio stream may be annotated
with one or more audio cues, which are heard at predetermined times
during playback of the audio stream. In various embodiments, the
occurrence of an audio cue may indicate an association between a
certain portions of the annotated audio stream and different types
of content elements. For example, one audio cue (e.g., a "beep")
may represent an association with a non-commercial content element
while a different audio cue (e.g., a "chime") may represent an
association with a non-commercial offer content element. Skilled
practitioners of the art will recognize that many such embodiments
and examples are possible and the foregoing is not intended to
limit the spirit the spirit, scope or intent of the invention.
[0191] Referring now to FIGS. 13a and 13b, cognitive media content
management operations are initiated by a cognitive application 304
requesting an Application Programming Interface (API) key from a
cognitive platform 1210. The method by which the API key is
requested, generated and provided to the cognitive application 304
is a matter of design choice. A cognitive session token is then
issued to the cognitive application 304, which uses it establish a
cognitive media session, described in greater detail herein.
[0192] In various embodiments, the cognitive media session may
include the receipt of feedback 1262 from the user. In one
embodiment, the cognitive session token is used to establish a
cognitive media session that results in the generation of a new
cognitive session graph. In another embodiment, the cognitive
session token is used to establish a cognitive media session that
appends composite cognitive insights and user feedback 1262 to an
existing cognitive session graph associated with the user. In this
embodiment, the resulting or existing cognitive media session
graphs are stored in a repository of cognitive session graphs `1`
through `n` 1252.
[0193] In various embodiments, the cognitive session token enables
the cognitive application 304 to interact with a cognitive session
graph associated with the cognitive session token. In these
embodiments, the cognitive media session is perpetuated. For
example, a given cognitive media session may last hours, days,
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.
[0194] Direct and indirect user input data 1242, as described in
greater detail herein, is then received by the cognitive platform
1210 to determine whether or not the user of the cognitive
application 304 has been identified. If the user has been
identified, then a determination is made whether a relevant
cognitive profile exists for the user in the repository of
cognitive profiles `1` through `n` 1274. If so, then the relevant
profile is retrieved. Otherwise, a relevant cognitive persona is
selected for the user from the repository of cognitive personas `1`
through `n` 1272. Once a relevant cognitive profile has been
retrieved, or a relevant cognitive persona has been selected,
ongoing operations are performed to provide requested cognitive
media to the user.
[0195] If the user selects, as described in greater detail herein,
a subset of the requested cognitive media content, then a set of
related cognitive media content is selected and used to generate
one or more composite cognitive insights. In various embodiments,
direct and indirect user input data 1242 is used with the selected
cognitive persona or retrieved cognitive profile to generate the
composite cognitive insights, which in turn are provided to the
user in the form of a cognitive insight summary 1248. In certain
embodiments, the direct and indirect user input data 1242 includes
cognitive intent. As used herein, cognitive intent broadly refers
to an identified behavior pattern associated with a user, which is
used by a CILS to generate one or more composite cognitive
insights.
[0196] In various embodiments, composite cognitive insights
provided to the user are stored in the cognitive media session
graph associated with the cognitive session token. In certain of
these embodiments, the cognitive media session graph is in turn
stored in the repository of cognitive session graphs `1` through
`n` 1252. In various embodiments, cognitive evidence 1326 is
incorporated into the composite cognitive insights before they are
provided to the user in the form of a cognitive insight summary
1248. As used herein, cognitive evidence 1326 broadly refers to
corroborating evidence of the relevance of a composite cognitive
insight generated by a CILS.
[0197] Once the cognitive insight summary has been generated, it is
then provided to the cognitive application 304, where it is
formatted into a form suitable for the media being used in the
cognitive media session. In various embodiments, the formatting of
the cognitive insight summary 1248 may result in certain content
elements receiving more emphasis, priority or prominence than
others, as shown in formatted cognitive insight summaries `1` 1324
and `2` 1328 through `n` 1330. As an example, one non-commercial
(NC) content element is more prominent than the other in formatted
cognitive insight summary `1` 1324. Likewise, the advertising (AD)
content element is more prominent than the two non-commercial (NC)
content elements, the non-commercial offer (NO) content element,
and the commercial offer (CO) content element in formatted
cognitive insight summary `2` 1328.
[0198] In various embodiments, the formatting of the cognitive
insight summary 1254 may include the provision of cognitive
evidence 1326. In these embodiments, the method by which the
formatting is performed, and the prominence given to individual
cognitive media content elements and cognitive evidence 1326, is a
matter of design choice. Skilled practitioners of the art will
recognize that many such embodiments and examples are possible and
the foregoing is not intended to limit the spirit, scope or intent
of the invention.
[0199] Once the cognitive insight summary 1248 has been formatted
by the cognitive application 304 it is provided to the user, who in
turn may select a subset of the related cognitive media content
contained within the provided cognitive insight summary. If so,
then cognitive media content associated with the selected subset of
related cognitive media content is provided in response. In certain
embodiments, the user may interact with the associated cognitive
media content. If so, then the user's interaction with the
associated cognitive media content is monitored and collected as
user feedback 1262, described in greater detail herein. In various
embodiments, the resulting feedback 1262 received from the user is
used to generate contextually-relevant questions for provision to
the user. In certain embodiments, the resulting feedback 1262
received from the user is used to update the user's cognitive
profile if one is currently in use.
[0200] FIGS. 14a through 14c are a generalized flowchart of
cognitive media content management operations performed in
accordance with an embodiment of the invention to present composite
cognitive insights to a user. In this embodiment, cognitive media
content management operations are begun in step 1402, followed by a
cognitive application requesting an Application Programming
Interface (API) key from a cognitive platform, described in greater
detail herein, in step 1404. A cognitive session token is then
issued to the cognitive application, which uses it in step 1406 to
establish a cognitive media session, described in greater detail
herein.
[0201] Direct and indirect user input data, as described in greater
detail herein, is then received in step 1408, followed by a
determination being made in step 1410 to determine whether or not
the user has been identified. As an example, the identity of the
user may be determined from the direct and indirect user input data
received in step 1408. If it is determined in step 1410 that the
user has been identified, then a determination is made in step 1412
whether a relevant cognitive profile exists for the user. If not,
or if the user was not identified in step 1410, then a relevant
cognitive persona is selected for the user in step 1416. Otherwise,
a relevant cognitive profile is selected for the user in step 1414.
Thereafter, or once a relevant cognitive persona has been selected
for the user in step 1416, a request for predetermined cognitive
media content is received from the user in step 1418. Ongoing
operations are then performed in step 1420 to provide the requested
cognitive media content to the user.
[0202] A determination is then made in step 1422 whether the user
selects, as described in greater detail herein, a subset of the
requested cognitive media content. If so, then a set of related
cognitive media content is selected and used to generate one or
more composite cognitive insights in step 1424. A determination is
then made in step 1426 whether to provide cognitive evidence to the
user. If so, then associated cognitive evidence is generated and
incorporated into the previously-generated composite cognitive
insights in step 1428. Thereafter, or if it was determined in step
1426 to not provide cognitive evidence to the user, then a
cognitive insight summary is generated from the composite cognitive
insights in step 1430.
[0203] The resulting cognitive insight summary is then provided to
the cognitive application in step 1432, where it is formatted into
a form suitable for the media being used in the cognitive media
session. The resulting formatted cognitive insight summary is then
provided to the user by the cognitive application in step 1434. A
determination is then made in step 1436 whether the user selects a
subset of the related cognitive media content contained within the
provided cognitive insight summary. If so, then cognitive media
content associated with the selected subset of related cognitive
media content is provided to the user in step 1438. A determination
is then made in step 1440 whether the user interacts with the
associated cognitive media content. If so, then the user's
interaction with the associated cognitive media content is
monitored and collected as user feedback, described in greater
detail herein.
[0204] Thereafter, or if it was determined in step 1422 that the
user did not select a subset of requested cognitive media content,
or in step 1436 that the use user did not select a subset of
related cognitive media content, or in step 1440 that the user did
not interact with the associated cognitive media content, then a
determination is then made in step 1444 whether a cognitive profile
is currently in use. If so then it is updated, as described in
greater detail herein, in step 1446. Thereafter, or if it is
determined in step 1444 that a cognitive profile is not currently
in use, a determination is then made in step 1448 whether to
convert the cognitive persona currently in use to a cognitive
profile. If so, then the cognitive persona currently in use is
converted to a cognitive profile in step 1450. The method by which
the cognitive persona is converted to a cognitive profile is a
matter of design choice.
[0205] However, if it was determined in step 1448 to not convert
the cognitive persona currently in use to a cognitive profile, or
once it has been converted in step 1450, a determination is then
made in step 1452 whether feedback has been received from the user.
If not, then a determination is made in step 1464 whether to end
cognitive media content management operations. If not, then
cognitive media content management operations are ended in step
1468. Otherwise, a determination is made in step 1466 whether the
cognitive session token for the target session has expired. If not,
then the process is continued, proceeding with step 1410.
Otherwise, the process is continued, proceeding with step 1404.
[0206] However, if it was determined in step 1452 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 1454. A determination is then made in step 1456
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 1464.
Otherwise, the cognitive platform uses the provided feedback in
step 1458 to generate contextually-relevant questions. Then, in
step 1460, 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 1462
and the process is continued, proceeding with step 1420.
[0207] 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.
* * * * *