U.S. patent application number 14/731868 was filed with the patent office on 2015-12-10 for travel-related weighted cognitive personas and profiles.
This patent application is currently assigned to COGNITE, INC.. The applicant listed for this patent is Cognite, Inc.. Invention is credited to John N. Faith, Kyle W. Kothe.
Application Number | 20150356441 14/731868 |
Document ID | / |
Family ID | 54769729 |
Filed Date | 2015-12-10 |
United States Patent
Application |
20150356441 |
Kind Code |
A1 |
Faith; John N. ; et
al. |
December 10, 2015 |
Travel-Related Weighted Cognitive Personas and Profiles
Abstract
A method, system and computer-usable medium for performing
cognitive computing operations 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
for incorporation into a cognitive graph; defining a travel-related
cognitive persona within the cognitive graph, the travel-related
cognitive persona corresponding to an archetype user model, the
travel-related cognitive persona comprising a set of nodes in the
cognitive graph, links among the set of nodes being weighted to
provide a weighted cognitive graph; associating a user with the
travel-related cognitive persona; and, performing a cognitive
computing operation based upon the travel-related cognitive persona
associated with the user.
Inventors: |
Faith; John N.; (Austin,
TX) ; Kothe; Kyle W.; (Austin, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cognite, Inc. |
Austin |
TX |
US |
|
|
Assignee: |
COGNITE, INC.
Austin
TX
|
Family ID: |
54769729 |
Appl. No.: |
14/731868 |
Filed: |
June 5, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62009626 |
Jun 9, 2014 |
|
|
|
Current U.S.
Class: |
706/55 |
Current CPC
Class: |
H04W 4/025 20130101;
G06F 16/24568 20190101; G06F 16/2465 20190101; G06F 16/972
20190101; G06F 16/955 20190101; G06N 5/022 20130101; G06F 16/287
20190101; G06F 16/9024 20190101 |
International
Class: |
G06N 5/04 20060101
G06N005/04; G06N 99/00 20060101 G06N099/00 |
Claims
1. A computer-implementable method for performing cognitive
computing operations 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 for
incorporation into a cognitive graph; defining a travel-related
cognitive persona within the cognitive graph, the travel-related
cognitive persona corresponding to an archetype user model, the
travel-related cognitive persona comprising a set of nodes in the
cognitive graph, links among the set of nodes being weighted to
provide a weighted cognitive graph; associating a user with the
travel-related cognitive persona; and, performing a cognitive
computing operation based upon the travel-related cognitive persona
associated with the user.
2. The method of claim 1, wherein: the travel-related cognitive
persona represents a set of attributes, each of the set of
attributes corresponding to a node of the set of nodes; and, an
amount of weighting between nodes of the set of nodes corresponds
to a degree of relevance between the persona and the
attributes.
3. The method of claim 2, wherein: the set of attributes comprise
at least one of demographic attributes, geographic attributes,
psychographic attributes, and behavioristic attributes.
4. The method of claim 1, wherein: a link between a first node of
the set of nodes and a second node of the set of nodes is
represented by an attribute weight, the attribute weight indicating
a degree of relevance between the attributes corresponding to the
first node and the second node.
5. The method of claim 1, further comprising: receiving feedback
from the user relating to the travel-related cognitive persona
associated with the user; and, revising weighting of the links
among the set of nodes of the weighted cognitive graph based upon
the feedback.
6. The method of claim 5, further comprising: using the revised
weighting to generate a second travel-related cognitive persona.
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 using travel-related
weighted cognitive personas and profiles in the performance of
travel-related cognitive inference and learning operations.
[0004] 2. Description of the Related Art
[0005] In general, "big data" refers to a collection of datasets so
large and complex that they become difficult to process using
typical database management tools and traditional data processing
approaches. These datasets can originate from a wide variety of
sources, including computer systems, mobile devices, credit card
transactions, television broadcasts, and medical equipment, as well
as infrastructures associated with cities, sensor-equipped
buildings and factories, and transportation systems. Challenges
commonly associated with big data, which may be a combination of
structured, unstructured, and semi-structured data, include its
capture, curation, storage, search, sharing, analysis and
visualization. In combination, these challenges make it difficult
to efficiently process large quantities of data within tolerable
time intervals.
[0006] Nonetheless, big data analytics hold the promise of
extracting insights by uncovering difficult-to-discover patterns
and connections, as well as providing assistance in making complex
decisions by analyzing different and potentially conflicting
options. As such, individuals and organizations alike can be
provided new opportunities to innovate, compete, and capture
value.
[0007] One aspect of big data is "dark data," which generally
refers to data that is either not collected, neglected, or
underutilized. Examples of data that is not currently being
collected includes location data prior to the emergence of
companies such as Foursquare or social data prior to the advent
companies such as Facebook. An example of data that is being
collected, but is difficult to access at the right time and place,
includes data associated with the side effects of certain spider
bites while on a camping trip. As another example, data that is
collected and available, but has not yet been productized of fully
utilized, may include disease insights from population-wide
healthcare records and social media feeds. As a result, a case can
be made that dark data may in fact be of higher value than big data
in general, especially as it can likely provide actionable insights
when it is combined with readily-available data.
SUMMARY OF THE INVENTION
[0008] A method, system and computer-usable medium are disclosed
for cognitive inference and learning operations.
[0009] In one embodiment, the invention relates to a method for
performing cognitive computing operations 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 for incorporation into a cognitive graph; defining a
travel-related cognitive persona within the cognitive graph, the
travel-related cognitive persona corresponding to an archetype user
model, the travel-related cognitive persona comprising a set of
nodes in the cognitive graph, links among the set of nodes being
weighted to provide a weighted cognitive graph; associating a user
with the travel-related cognitive persona; and, performing a
cognitive computing operation based upon the travel-related
cognitive persona associated with the user.
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 travel-related cognitive persona defined by
a first set of nodes in a cognitive graph;
[0019] FIG. 9 depicts a travel-related 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 travel-related cognitive persona defined
by a first set of nodes in a weighted cognitive graph;
[0021] FIG. 11 depicts a travel-related 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 travel-related cognitive personas and cognitive
profiles to generate travel-related composite cognitive insights;
and
[0023] FIGS. 13a and 13b are a generalized flowchart of
travel-related cognitive persona and cognitive profile operations
performed in the generation of travel-related composite cognitive
insights.
DETAILED DESCRIPTION
[0024] 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.
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] 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.
[0030] 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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.
[0038] 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.
[0039] 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.
[0040] 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."
[0041] 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.
[0042] 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.
[0043] 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.
[0044] 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.
[0045] 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.
[0046] 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.
[0047] 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.
[0048] 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.
[0049] 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 Cl, 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.
[0050] 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.
[0051] 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.
[0052] 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.
[0053] 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).
[0054] 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.
[0055] 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.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] In various embodiments, the graph query engine 326 is
implemented to receive and process queries such that they can be
bridged into a cognitive graph, as described in greater detail
herein, through the use of a bridging agent. In these embodiments,
the graph query engine 326 may include a query 426 component, a
translate 427 component, a bridge 428 component, and one or more
bridging agents 429.
[0088] 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.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] 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.
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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.
[0105] 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.
[0106] 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..
[0107] 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.
[0108] 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.
[0109] 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.
[0110] 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.
[0111] 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.
[0112] 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.
[0113] 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.
[0114] 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.
[0115] 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).
[0116] 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.
[0117] 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.
[0118] 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.
[0119] 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.
[0120] 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.
[0121] 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.
[0122] 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.
[0123] 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.
[0124] 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.
[0125] 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.
[0126] 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.
[0127] 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.
[0128] 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.
[0129] 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.
[0130] FIG. 8 depicts a travel-related cognitive persona defined in
accordance with an embodiment of the invention by a first set of
nodes in a travel-related cognitive graph. As used herein, a
travel-related cognitive graph broadly refers to a representation
of travel-related 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 travel-related data and
knowledge to be shared and reused across user, application,
organization, and community boundaries.
[0131] As likewise used herein, a travel-related cognitive persona
broadly refers to an archetype user model that represents a common
set of travel-related attributes associated with a hypothesized
group of users. In various embodiments, the common set of
travel-related attributes may be described through the use of
demographic, geographic, psychographic, behavioristic,
travel-oriented, 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 travel-related cognitive
persona's typical living and working locations (e.g., rural,
semi-rural, suburban, urban, etc.), characteristics associated with
individual locations (e.g., parochial, cosmopolitan, population
density, etc.), and possible travel destinations.
[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 travel-related product or using a travel-related service,
associated usage rates, perceived benefits, and so forth.
[0133] Likewise, the travel-oriented information may include
information related to various types of travel (e.g., budget,
luxury, adventure, etc.), purpose of travel (e.g., business,
pleasure, family vacation, etc.), and type of cuisine (e.g., fast
food, gourmet, local, etc.). The travel-oriented information may
likewise include information related to current location (e.g., a
particular address or geographic coordinates), destination (e.g.,
city, state, region, country, etc.), method of transportation
(e.g., plane, train, automobile, boat, etc.), and venues (e.g.,
restaurant, theater, museum, etc.) Other travel-oriented
information may include information related to activities (e.g.,
snow skiing, hiking, etc.), performances (e.g., concert, play,
etc.), seasons or dates (e.g., spring break, a scheduled
professional conference, etc.), and times (e.g., morning,
afternoon, evening, a particular time, etc.). Skilled practitioners
of the art will recognize that many such travel-related attributes
are possible and the foregoing is not intended to limit the spirit,
scope or intent of the invention.
[0134] In various embodiments, one or more travel-related cognitive
personas may be associated with a predetermined user, such as a
traveler. In certain embodiments, a predetermined travel-related
cognitive persona is selected and then used by a cognitive
inference and learning system (CILS) to generate one or more
travel-related composite cognitive insights as described in greater
detail herein. In these embodiments, the travel-related composite
cognitive insights that are generated for a user as a result of
using a first travel-related cognitive persona may be different
than the travel-related composite cognitive insights that are
generated as a result of using a second travel-related cognitive
persona. In various embodiments, provision of the travel-related
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 travel-related cognitive persona. In another embodiment, the
feedback information is used to create a new travel-related
cognitive persona. In yet another embodiment, the feedback
information is used to create one or more associated travel-related
cognitive personas, which inherit a common set of attributes from a
source travel-related cognitive persona. In one embodiment, the
feedback information is used to create a new travel-related
cognitive persona that combines attributes from two or more source
travel-related cognitive personas. In another embodiment, the
feedback information is used to create a travel-related cognitive
profile, described in greater detail herein, based upon the
travel-related 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.
[0135] In this embodiment, a travel-related cognitive persona 802
is defined by travel-related attributes TRA.sub.1 804, TRA.sub.2
806, TRA.sub.3 808, TRA.sub.4 810, TRA.sub.5 812, TRA.sub.6 814,
TRA.sub.7 816, which are respectively associated with a set of
corresponding nodes in a travel-related cognitive graph 800. As
shown in FIG. 8, the travel-related cognitive persona 802 is
associated with travel-related attributes TRA.sub.1 804 and
TRA.sub.4 810, which are in turn respectively associated with
travel-related attributes TRA.sub.2 806, TRA.sub.3 808, TRA.sub.5
812, and TRA.sub.6 814. Likewise, travel-related attributes
TRA.sub.1 804 and TRA.sub.4 810 are associated with each other as
well as with travel-related attribute TRA.sub.7 816.
[0136] As an example, the travel-related cognitive persona 802 may
represent a teacher of history who also has an interest in regional
cuisines. In this example, travel-related attribute TRA.sub.1 804
may be a demographic attribute representing the profession of
teaching history, while travel-related attribute TRA.sub.4 810 may
be a psychographic attribute associated with an interest in
regional cuisines. To continue the example, demographic
travel-related attributes TRA.sub.2 806 and TRA.sub.3 808 may
respectively be associated with teaching American and Texas
history, while psychographic travel-related attributes TRA.sub.5
812 and TRA.sub.6 814 may respectively associated with an interest
in Southern and Mexican cuisines. Likewise, travel-related
attribute TRA.sub.7 816 may be associated with historical regional
cuisines, which relates to both teaching history and an interest in
regional cuisines. In certain embodiments, a travel-related
attribute may be associated with two or more classes of
travel-related attributes. For example, travel-related attribute
TRA.sub.7 816 may be a demographic attribute, a psychographic
attribute, or both. In various embodiments, the travel-related
cognitive persona 802 may be defined by additional travel-related
attributes than those shown in FIG. 8. In certain embodiments, the
travel-related cognitive persona 802 may be defined by fewer
travel-related attributes than those shown in FIG. 8.
[0137] FIG. 9 depicts a travel-related 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 travel-related cognitive
profile refers to an instance of a travel-related cognitive persona
that references personal data associated with a predetermined user,
such as a traveler. In various embodiments, the personal data may
include the user's name, address, 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 travel-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 travel-related
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.
[0138] In this embodiment, a cognitive profile 902 is defined by
the addition of travel-related attributes TRA.sub.8 918, TRA.sub.9
920, TRA.sub.10 922, TRA.sub.11 924 to attributes TRA.sub.1 804,
TRA.sub.2 806, TRA.sub.3 808, TRA.sub.4 810, TRA.sub.5 812,
TRA.sub.6 814, TRA.sub.7 816, all of which are respectively
associated with a set of corresponding nodes in a travel-related
cognitive graph 900. As shown in FIG. 9, the travel-related
cognitive profile 902 is associated with travel-related attributes
TRA.sub.1 804 and TRA.sub.4 810, which are in turn respectively
associated with attributes TRA.sub.2 806, TRA.sub.3 808, TRA.sub.5
812, and TRa.sub.6 814. Likewise, travel-related attributes
TRA.sub.1 804 and TRA.sub.4 810 are associated with each other as
well as with attribute TRA.sub.7 816. As likewise shown in FIG. 9,
travel-related attribute TRA.sub.7 816 is associated with
travel-related attributes TRA.sub.9 920 and TRA.sub.11 924, both of
which are associated with travel-related attribute TRA.sub.10 922.
Likewise, travel-related attribute TRA.sub.11 924 is associated
with travel-related attribute TRA.sub.3 808, while travel-related
attribute TRA.sub.8 918 is associated with travel-related
attributes TRA.sub.6 814, TRA.sub.9 920 and TRA.sub.11 924.
[0139] To continue the example described in the descriptive text
associated with FIG. 8, psychographic travel-related attributes
TRA.sub.8 918, TRA.sub.9 920, and TRA.sub.10 922 may respectively
be associated with the Tex-Mex cuisine, tamales, and enchiladas.
Likewise, travel-related attribute TRA.sub.11 924 may be a
travel-related attribute such as a venue, a location travel-related
attribute, or both as it is associated with travel-related
attribute TRA.sub.7 816, which may also be a demographic
travel-related attribute, a psychographic travel-related attribute,
or both. For example, TRA.sub.11 924 may be related to a restaurant
located in San Antonio, Tex. that serves historical Tex-Mex
cuisine, such as enchiladas. In various embodiments, the
travel-related cognitive profile 902 may be defined by additional
travel-related attributes than those shown in FIG. 9. In certain
embodiments, the travel-related cognitive profile 902 may be
defined by fewer travel-related attributes than those shown in FIG.
9.
[0140] FIG. 10 depicts a travel-related 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
travel-related cognitive persona 1002 is defined by travel-related
attributes TRA.sub.1 804, TRA.sub.2 806, TRA.sub.3 808, TRA.sub.4
810, TRA.sub.5 812, TRA.sub.6 814, TRA.sub.7 816, which are
respectively associated with a set of corresponding nodes in a
travel-related 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 travel-related
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 travel-related attributes, while a lower
numeric value (e.g., `0.5`) may indicate a lower degree of
relevance.
[0141] As shown in FIG. 10, the degree of relevance between the
travel-related persona 1002 and travel-related attributes TRA.sub.1
804 and TRA.sub.4 810 is respectively indicated by attribute
weights AW.sub.1 1032 and AW.sub.4 1038. Likewise, the degree of
relevance between travel-related attribute TRA.sub.1 804 and
travel-related attributes TRA.sub.2 806 and TRA.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 travel-related attribute A.sub.4 810 and travel-related
attributes TRA.sub.5 812 and TRA.sub.6 814 is respectively
indicated by attribute weights AW.sub.5 1040 and AW.sub.6 1042.
Likewise, the degree of relevance between travel-related attributes
TRA.sub.1 804 and TRA.sub.4 810 is represented by attribute weight
AW.sub.7 1044, while the degree of relevance between travel-related
attribute TRA.sub.7 816 and travel-related attributes TRA.sub.1 804
and TRA.sub.4 810 is respectively represented by attribute weights
AW.sub.8 1046 and AW.sub.9 1048.
[0142] 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 travel-related 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 travel-related cognitive persona. In another
embodiment, the changed numeric values associated with the
predetermined attribute weights may be used to generate a new
travel-related cognitive persona. In yet another embodiment, the
changed numeric values associated with the predetermined attribute
weights may be used to generate a travel-related cognitive
profile.
[0143] FIG. 11 depicts a travel-related 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 travel-related cognitive profile 1102 is
defined by the addition of travel-related attributes TRA.sub.8 918,
TRA.sub.9 920, TRA.sub.10 922, TRA.sub.11 924 to travel-related
attributes TRAA.sub.1 804, TRA.sub.2 806, TRA.sub.3 808, TRA.sub.4
810, TRA.sub.5 812, TRA.sub.6 814, TRA.sub.7 816, all of which are
respectively associated with a set of corresponding nodes in a
travel-related weighted cognitive graph 1100. As shown in FIG. 11,
the travel-related cognitive profile 1102 is associated with
travel-related attributes TRA.sub.1 804 and TRA.sub.4 810, which
are in turn respectively associated with travel-related attributes
TRA.sub.2 806, TRA.sub.3 808, TRA.sub.5 812, and TRA.sub.6 814.
Likewise, travel-related attributes TRA.sub.1 804 and TRA.sub.4 810
are associated with each other as well as with travel-related
attribute TRA.sub.7 816. As likewise shown in FIG. 11,
travel-related attribute TRA.sub.7 816 is associated with
travel-related attributes TRA.sub.9 920 and TRA.sub.11 924, both of
which are associated with travel-related attribute TRA.sub.10 922.
Likewise, travel-related attribute TRA.sub.11 924 is associated
with travel-related attribute TRA.sub.3 808, while travel-related
attribute TRA.sub.8 918 is associated with travel-related
attributes TRA.sub.6 814, TRA.sub.9 920 and TRA.sub.11 924.
[0144] As shown in FIG. 11, the degree of relevance between
travel-related attributes TRA.sub.6 814 and TRA.sub.8 918 is
represented by attribute weight AW.sub.10 1150, while the degree of
relevance between travel-related attribute TRA.sub.8 918 and
travel-related attributes TRA.sub.9 920 and TRA.sub.10 922 is
respectively indicated by attribute weights AW.sub.11 1152 and
AW.sub.12 1154. Likewise, the degree of relevance between
travel-related attribute TRA.sub.10 922 and travel-related
attributes TRA.sub.9 920 and TRA.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
travel-related attribute TRA.sub.M 816 and travel-related
attributes TRA.sub.9 920 and TRA.sub.11 924 is respectively
indicated by attribute weights AW.sub.14 1158 and AW.sub.16 1162,
while the degree of relevance between travel-related attributes
TRA.sub.11 924 and TRA.sub.3 808 is represented by attribute weight
AW.sub.17 1164.
[0145] In various embodiments, the numeric value associated with
predetermined attribute weights may change as a result of the
performance of travel-related 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
travel-related cognitive profile. In another embodiment, the
changed numeric values associated with the predetermined attribute
weights may be used to generate a new travel-related cognitive
profile.
[0146] FIGS. 12a and 12b are a simplified process flow diagram
showing the use of travel-related cognitive personas and cognitive
profiles implemented in accordance with an embodiment of the
invention to generate travel-related composite cognitive insights.
As used herein, a travel-related composite cognitive insight
broadly refers to a set of travel-related 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 a travel-related cognitive graph 1282, as their data source
to respectively generate individual travel-related cognitive
insights. As used herein, a travel-related cognitive graph 1282
broadly refers to a representation of travel-related 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. In certain embodiments, different
cognitive applications 304 may interact with different
travel-related cognitive graphs 1282 to generate individual
travel-related cognitive insights for a user, such as a traveler.
In various embodiments, the resulting individual travel-related
cognitive insights are then composed to generate a set of
travel-related composite cognitive insights, which in turn is
provided to a user in the form of a cognitive insight summary
1248.
[0147] 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
travel-related 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 travel-related composite cognitive insight related to a
particular user of a particular device, at a particular location,
at a particular time, for a particular purpose.
[0148] In certain embodiments, the insight agents are selected for
orchestration as a result of receiving direct or indirect input
data 1242 from a user. In various embodiments, the direct user
input data 1242 may be a natural language inquiry. In certain
embodiments, the indirect user input data 1242 may include the
location of a user's device or the purpose for which it is being
used. As an example, the Geographical Positioning System (GPS)
coordinates of the location of a user's mobile device may be
received as indirect user input data 1242. As another example, a
user may be using the integrated camera of their mobile device to
take a photograph of a location, such as a restaurant, or an item,
such as a food product. In certain embodiments, the direct or
indirect user input data 1242 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.
[0149] In various embodiments, travel-related 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.
[0150] In various embodiments, the process 1208 component is
implemented to perform various travel-related 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.
[0151] 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.
[0152] 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 a travel-related 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 travel-related cognitive graph
1282.
[0153] The process 1208 component is likewise implemented in
various embodiments to perform bridging 1246 operations, described
in greater detail herein, to access the travel-related 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 travel-related cognitive
graph 1282 is accessed by the process 1208 component during the
learning 1236 phase of the travel-related composite cognitive
insight generation operations.
[0154] In various embodiments, a cognitive application 304 is
implemented to receive input data 1242 associated with an
individual user or a group of users. In these embodiments, the
input data 1242 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 1242 may
include contextual data, described in greater detail herein. Once
it is received, the input data 1242 is then submitted 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 input data 1242 to
generate a graph query 1244, as described in greater detail herein.
The graph query 1244 is then used to query the travel-related
cognitive graph 1282, which results in the generation of one or
more travel-related 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
travel-related cognitive graph 1282 to generate the one or more
travel-related composite cognitive insights.
[0155] In various embodiments, the graph query 1244 results in the
selection of a predetermined travel-related 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 travel-related cognitive personas stored
in the repository of cognitive 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 travel-related cognitive graph
1282.
[0156] 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 travel-related cognitive persona to
generate one or more travel-related composite cognitive insights
for a user. In various embodiments, the travel-related 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 travel-related composite cognitive insights that are generated
as a result of using a second set of contextual information.
[0157] As an example, a user may have two associated travel-related
cognitive personas, "business traveler" and "vacation traveler,"
which are respectively selected according to two sets of contextual
information. In this example, the "business traveler"
travel-related cognitive persona may be selected according to a
first set of contextual information associated with the user
performing travel planning activities in their office during
business hours, with the objective of finding the best price for a
particular hotel in a predetermined city on a specified weekday.
Conversely, the "vacation traveler" travel-related cognitive
persona may be selected according to a second set of contextual
information associated with the user performing cognitive travel
planning activities in their home over a weekend, with the
objective of finding a family-oriented resort with predetermined
amenities in a particular geographic region for a specified range
of dates. As a result, the travel-related composite cognitive
insights generated as a result of combining the first
travel-related cognitive persona with the first set of contextual
information will likely be different than the travel-related
composite cognitive insights generated as a result of combining the
second travel-related cognitive persona with the second set of
contextual information.
[0158] In various embodiments, the graph query 1244 results in the
selection of a predetermined travel-related 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, a set of contextual information
associated with a user is used to select the travel-related
cognitive profile from the repository of cognitive profiles `1`
through `n` 1274. In various embodiments, one or more
travel-related cognitive profiles may be associated with a
predetermined user. In these embodiments, a predetermined
travel-related cognitive profile is selected and then used by a
CILS to generate one or more travel-related composite cognitive
insights for the user as described in greater detail herein. In
certain of these embodiments, the selected travel-related cognitive
profile provides a basis for adaptive changes to the CILS, and by
extension, the travel-related composite cognitive insights it
generates.
[0159] In various embodiments, provision of the travel-related
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
travel-related cognitive persona. In another embodiment, the
feedback 1262 information is used to revise or modify the
travel-related cognitive profile associated with a user. In yet
another embodiment, the feedback 1262 information is used to create
a new travel-related 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 travel-related cognitive profiles,
which inherit a common set of attributes from a source
travel-related cognitive profile. In another embodiment, the
feedback 1262 information is used to create a new travel-related
cognitive profile that combines attributes from two or more source
travel-related 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.
[0160] In various embodiments, a travel-related cognitive profile
associated with a user may be either static or dynamic. As used
herein, a static travel-related cognitive profile refers to a
travel-related 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 and
various travel awards program identifiers which change
infrequently.
[0161] As likewise used herein, a dynamic travel-related cognitive
profile refers to a travel-related cognitive profile that contains
information associated with a user that changes on a dynamic basis.
For example, a user's travel-related 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 travel-related composite cognitive
insights to the user. In these embodiments, the user's interactions
with the CILS, and the resulting travel-related composite cognitive
insights that are generated, are used to update the dynamic
travel-related cognitive profile on an ongoing basis to provide an
up-to-date representation of the user in the context of the
travel-related cognitive profile used to generate the
travel-related composite cognitive insights.
[0162] In various embodiments, a travel-related 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 travel-related cognitive profile to
generate one or more travel-related composite cognitive insights
for the user. In these embodiments, the travel-related 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 travel-related composite cognitive
insights that are generated as a result of using a second set of
contextual information.
[0163] As an example, a user may have two associated travel-related
cognitive profiles, "runner" and "foodie," which are respectively
selected according to two sets of contextual information. In this
example, the "runner" travel-related 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 travel-related 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.
[0164] Conversely, the "foodie" travel-related cognitive profile
may be selected according to a second set of contextual information
associated with the user traveling to a destination city and
expressing an interest in trying a restaurant known for serving
innovative cuisine. To further continue this example, the user's
"foodie" travel-related 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 travel-related
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 an innovative
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
travel-related composite cognitive insights generated as a result
of combining the first travel-related cognitive profile with the
first set of contextual information will likely be different than
the travel-related composite cognitive insights generated as a
result of combining the second travel-related cognitive profile
with the second set of contextual information.
[0165] In various embodiments, a user's travel-related 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.
[0166] 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 travel-related 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 travel-related 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 travel-related
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.
[0167] In various embodiments, a set of contextually-related
interactions between a cognitive application 304 and the
travel-related cognitive graph 1282 are represented as a
corresponding set of nodes in a predetermined travel-related
cognitive session graph, which is then stored in a repository of
cognitive session graphs `1` through `n` 1252. As used herein, a
travel-related cognitive session graph broadly refers to a
cognitive graph whose nodes are associated with a travel-related
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. As likewise
used herein, a travel-related cognitive session broadly refers to a
cognitive session related to various aspects of travel, as
described in greater detail herein.
[0168] As an example, the travel-related 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 airline or chain of hotels
when making travel arrangements. A record of each query regarding
that airline or chain of hotels, or their selection, is iteratively
stored in a predetermined travel-related cognitive session graph
that is associated with the user and stored in a repository of
cognitive session graphs `1` through `n` 1252. As a result, the
preference of that airline or chain of hotels is ranked higher, and
is presented in response to contextually-related queries, even when
the preferred brand of airline or chain of hotels are 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 travel-related
cognitive session graph that is associated with the user and stored
in a repository of cognitive session graphs `1` through `n` 1252,
regardless of when each query is made.
[0169] As another example, a user queries a cognitive application
304 during business hours to locate an upscale restaurant located
close their headquarters office while on business travel. As a
result, a first travel-related cognitive session graph stored in a
repository of cognitive session graphs `1` through `n` 1252 is
associated with the user's query, which results in the provision of
travel-related composite cognitive insights related to restaurants
close to the user's headquarters office that are suitable for
business meetings. To continue the example, the same user queries
the same cognitive application 304 during the weekend to locate a
family-friendly, casual restaurant located close to their hotel
while on vacation travel. As a result, a second travel-related
cognitive session graph stored in a repository of cognitive session
graphs `1` through `n` 1252 is associated with the user's query,
which results in the provision of travel-related composite
cognitive insights related to restaurants suitable for family
meals. In these examples, the first and second travel-related
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 travel-related composite cognitive
insights.
[0170] As yet another example, a group of sales engineers travel
extensively to conduct product demonstrations at a sales prospect's
location as part of their responsibilities. In this example, the
product demonstrations and the group of sales engineers are
collectively associated with a predetermined travel-related
cognitive session graph stored in a repository of cognitive session
graphs `1` through `n` 1252. To continue the example, individual
sales engineers may submit queries related to the scheduling
product demonstrations at a particular sales prospect's location to
a cognitive application 304, such as a travel booking application.
In response, a predetermined travel-related cognitive session graph
stored in a repository of cognitive session graphs `1` through `n`
1252 is used, along with the universal knowledge repository 880 and
travel-related cognitive graph 1282, to generate individual or
composite cognitive insights to provide travel booking information
that will result in lowering the cost of travel to a given sales
prospect's location. In this example, the cognitive application 304
may be queried by the individual sales engineers at different times
during some predetermined time interval, yet the same
travel-related cognitive session graph stored in a repository of
cognitive session graphs `1` through `n` 1252 is used to generate
composite cognitive insights related to lowering the cost of their
travel to a particular sales prospect's location.
[0171] In various embodiments, each travel-related cognitive
session graph associated with a user and stored in a repository of
cognitive 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 travel-related cognitive graph 1282. In certain
embodiments, each individual travel-related cognitive session graph
that is associated with the user and stored in a repository of
cognitive session graphs `1` through `n` 1252 introduces edges that
are not already present in the travel-related cognitive graph 1282.
More specifically, each of the travel-related cognitive session
graphs that is associated with the user and stored in a repository
of cognitive session graphs `1` through `n` 1252 establishes
various relationships that the travel-related cognitive graph 1282
does not already have.
[0172] In various embodiments, individual travel-related cognitive
profiles in the repository of profiles `1` through `n` 1274 are
respectively stored as travel-related cognitive session graphs in
the repository of cognitive session graphs 1252. In these
embodiments, predetermined nodes within each of the individual
travel-related cognitive profiles are linked 1254 to predetermined
nodes within corresponding travel-related cognitive session graphs
stored in the repository of cognitive session graphs `1` through
`n` 1254. In certain embodiments, individual nodes within each of
the travel-related cognitive profiles are likewise linked 1254 to
corresponding nodes within various travel-related cognitive
personas stored in the repository of cognitive personas `1` through
`n` 1272.
[0173] In various embodiments, individual graph queries 1244
associated with a predetermined travel-related cognitive session
graph stored in a repository of cognitive session graphs `1`
through `n` 1252 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.
[0174] For example, a user may be a young, upper middle-class,
urban-oriented person that typically enjoys eating at trendy
restaurants that are in walking distance of where they are staying
while on vacation. As a result, the user may be interested in
knowing about new or popular restaurants that are in walking
distance of the hotel they are staying at. 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 travel-related composite
insights may be provided as a ranked list of candidate restaurants
that match the user's preferences.
[0175] In various embodiments, the process 1208 component is
implemented to provide these travel-related composite cognitive
insights to the deliver 1210 component, which in turn is
implemented to deliver the travel-related 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.
[0176] To continue the preceding example, the user may have
received a list of candidate restaurants that may be suitable
venues. However, the user 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
travel-related cognitive session graph stored in a repository of
cognitive session graphs `1` through `n` 1252 that is associated
with the user and their original query.
[0177] 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 travel-related composite cognitive insights. In certain
of these embodiments, feedback 1262 received from the user is
stored in a predetermined travel-related cognitive session graph
that is associated with the user and stored in a repository of
cognitive session graphs `1` through `n` 1252, which is then used
to provide more accurate travel-related composite cognitive
insights in response to subsequent contextually-relevant queries
from the user.
[0178] As an example, travel-related 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
travel-related cognitive session graph that is associated with the
user and stored in a repository of cognitive 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 travel-related cognitive
session graph that is associated with the user and stored in a
repository of cognitive 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.
[0179] In various embodiments, the travel-related 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 in the form of one or more
insight streams 1264 to an associated travel-related cognitive
session graph stored in a repository of cognitive session graphs
`1` through `n` 1252. 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
travel-related composite cognitive insights and related feedback
1262, the location of the user, and the device used by the
user.
[0180] As an example, a query related to upcoming activities that
is received at 10:00 AM on a Saturday morning from a user's hotel
while on vacation may return travel-related composite cognitive
insights related to entertainment performances scheduled for the
weekend that are close to the hotel. Conversely, the same query
received at the same time on a Monday morning from a user's hotel
while on business travel may return travel-related composite
cognitive insights related to business (e.g., sponsored receptions
at an industry conference), scheduled during the work week. In
various embodiments, the information contained in the insight
streams 1264 is used to rank the travel-related composite cognitive
insights provided in the cognitive insight summary 1248. In certain
embodiments, the travel-related 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.
[0181] FIGS. 13a and 13b are a generalized flowchart of
travel-related cognitive persona and cognitive profile operations
performed in accordance with an embodiment of the invention to
generate travel-related composite cognitive insights. In this
embodiment, travel-related cognitive persona and profile operations
are begun in step 1302, followed by a cognitive application
requesting an Application Programming Interface (API) key from a
cognitive platform, described in greater detail herein, in step
1304. The method by which the API key is requested, generated and
provided to the cognitive application is a matter of design choice.
A cognitive session token is then issued to the cognitive
application, which uses it in step 1306 to establish a
travel-related composite cognitive session.
[0182] In various embodiments, the cognitive session may also
include the receipt of feedback from the user, likewise described
in greater detail herein. In one embodiment, the cognitive session
token is used to establish a travel-related composite cognitive
insight session that generates a new travel-related cognitive
session graph. In another embodiment, the cognitive session token
is used to establish a travel-related composite cognitive insight
session that appends travel-related composite cognitive insights
and user feedback to an existing travel-related cognitive session
graph associated with the user.
[0183] In various embodiments, the cognitive session token enables
the cognitive application to interact with a travel-related
cognitive session graph associated with the cognitive session
token. In these embodiments, the travel-related composite cognitive
insight session is perpetuated. For example, a given travel-related
composite cognitive insight session may last months or even years.
In certain embodiments, the cognitive session token expires after a
predetermined period of time. In these embodiments, the cognitive
session token is no longer valid once the predetermined period of
time expires. The method by which the period of time is determined,
and monitored, is a matter of design choice.
[0184] Direct and indirect user input data, as described in greater
detail herein, is received in step 1308, followed by a
determination being made in step 1310 whether or not the user is
identified. As an example, the identity of the user may be
determined from the direct and indirect user input data received in
step 1308. If it is determined in step 1310 that the user has been
identified, then a determination is made in step 1312 whether a
relevant travel-related cognitive profile exists for the user. If
not, or if the user was not identified in step 1310, then a
relevant travel-related cognitive profile is selected for the user
in step 1316. Otherwise, a relevant travel-related cognitive
profile is retrieved for the user in step 1314.
[0185] Thereafter, or once a relevant travel-related cognitive
persona has been selected for the user in step 1316, the direct and
indirect user input data is used with the selected travel-related
cognitive persona or retrieved travel-related cognitive profile to
generate and present contextually-relevant travel-related composite
cognitive insights to the user in step 1318. In various
embodiments, travel-related composite cognitive insights presented
to the user are stored in the travel-related cognitive session
graph associated with the cognitive session token. A determination
is then made in step 1320 whether a travel-related cognitive
profile is currently in use. If so then it is updated, as described
in greater detail herein, in step 1322. Thereafter, or if it is
determined in step 1320 that a travel-related cognitive profile is
not currently in use, a determination is then made in step 1324
whether to convert the travel-related cognitive persona currently
in use to a travel-related cognitive profile. If so, then the
travel-related cognitive persona currently in use is converted to a
travel-related cognitive profile in step 1326. The method by which
the travel-related cognitive persona is converted to a
travel-related cognitive profile is a matter of design choice.
[0186] However, if it was determined in step 1324 to not convert
the travel-related cognitive persona currently in use to a
travel-related cognitive profile, or once it has been converted in
step 1326, a determination is then made in step 1328 whether
feedback has been received from the user. If not, then a
determination is made in step 1340 whether to end travel-related
composite cognitive insights and feedback operations. If not, then
a determination is made in step 1342 whether the cognitive session
token for the target session has expired. If not, then the process
is continued, proceeding with step 1310. Otherwise, the process is
continued, proceeding with step 1304. However, if it is determined
in step 1324 not to end travel-related composite cognitive insights
and feedback operations, then travel-related composite cognitive
insights and feedback operations are ended in step 1344.
[0187] However, if it was determined in step 1328 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 1330. A determination is then made in step 1332
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 1340.
Otherwise, the cognitive platform uses the provided feedback in
step 1334 to generate contextually-relevant questions. Then, in
step 1336, the cognitive platform provides the
contextually-relevant questions, along with additional
travel-related composite insights, to the cognitive application. In
turn, the cognitive application provides the contextually-relevant
questions and additional travel-related composite cognitive
insights to the user in step 1334 and the process is continued,
proceeding with step 1320.
[0188] Although the present invention has been described in detail,
it should be understood that various changes, substitutions and
alterations can be made hereto without departing from the spirit
and scope of the invention as defined by the appended claims.
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