U.S. patent application number 15/623668 was filed with the patent office on 2018-12-20 for computerized system and method for automatically transforming and providing domain specific chatbot responses.
The applicant listed for this patent is OATH INC.. Invention is credited to Siddhartha BANERJEE, Prakhar BIYANI, Kostas TSIOUTSIOULIKLIS.
Application Number | 20180365212 15/623668 |
Document ID | / |
Family ID | 64657445 |
Filed Date | 2018-12-20 |
United States Patent
Application |
20180365212 |
Kind Code |
A1 |
BANERJEE; Siddhartha ; et
al. |
December 20, 2018 |
COMPUTERIZED SYSTEM AND METHOD FOR AUTOMATICALLY TRANSFORMING AND
PROVIDING DOMAIN SPECIFIC CHATBOT RESPONSES
Abstract
Disclosed are systems and methods for improving interactions
with and between computers in content searching, generating,
hosting and/or providing systems supported by or configured with
personal computing devices, servers and/or platforms. The
disclosure provides a computerized framework for automatically
generating chatbot responses to produce domain-specific responses
that mimic native styles unique to particular domains. The
disclosed systems and methods construct domain-specific word-graphs
based on account activity from specific domains and generate
word-patterns. New words obtained from the patterns in the graph
are introduced to transform the regular response. The graph is then
pruned using data-driven thresholds in order to avoid spurious
transformations, and paragraph vectors are also utilized to assign
relevance scores to generated patterns such that only the patterns
that are contextually similar to the original response
(generic/regular response) are used. As result, the regular chatbot
response is rewritten using an optimized set of patterns.
Inventors: |
BANERJEE; Siddhartha;
(Sunnyvale, CA) ; BIYANI; Prakhar; (Sunnyvale,
CA) ; TSIOUTSIOULIKLIS; Kostas; (Sunnyvale,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
OATH INC. |
New York |
NY |
US |
|
|
Family ID: |
64657445 |
Appl. No.: |
15/623668 |
Filed: |
June 15, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/36 20130101;
G06F 40/253 20200101; G06F 40/35 20200101; G06F 40/284 20200101;
H04L 51/02 20130101; G06F 40/205 20200101 |
International
Class: |
G06F 17/27 20060101
G06F017/27; H04L 29/08 20060101 H04L029/08; H04L 12/58 20060101
H04L012/58 |
Claims
1. A method comprising: receiving, at a computing device, a request
from a user, said request input by the user respective to a chatbot
provided by the computing device, said request comprising a
sequence of words; searching, via the computing device, a database
based on said request, said search comprising identifying, within
said database, a first response comprising a sequence of words;
tokenizing, via the computing device, said first response, said
tokenizing comprising identifying each identified word from said
tokenizing of the first response as a token and tagging each token;
creating, via the computing device, a set of bigrams based at least
in part upon the tokenized first response, said bigrams comprising
adjacent words in said first response; determining, via the
computing device, a set of word patterns between each bigram based
on a domain word-graph, said domain word-graph comprising nodes and
edges, wherein each node corresponds to a word and each edge is a
directional link between the nodes, said determination comprising
determining a path within said domain word-graph between the words
in each bigram, and identifying, based on said determined path, a
set of word patterns along the path for each bigram; computing, via
the computing device, scores for each word pattern in said set of
word patterns, said computed scores indicating a co-occurrences,
contextual similarity and linguistic quality between the words in
the bigrams and the word patterns; selecting, via the computing
device, a word pattern based on the computed scores; modifying, via
the computing device, the first response by inserting the word
pattern into said first response based on the bigram from which it
was identified, said modification causing the creation of a second
response; and communicating, via the computing device, said second
response to said user in response to said request, for display of
said communicated second response within a user interface (UI)
associated with the chatbot.
2. The method of claim 1, further comprising: searching a
repository of synonymous words based on said first response;
identifying, based on said search, a set of synonyms for each word
in the first response.
3. The method of claim 2, wherein said created set of bigrams
includes the identified set of synonyms, wherein said set of word
patterns determination is based on said bigram set that includes
the identified set of synonyms.
4. The method of claim 1, wherein said computation of said
co-occurrence score comprises: computing a co-occurrence score for
each pair of words in said word patterns; and computing an average
score based on each co-occurrence score.
5. The method of claim 4, wherein said computation of said
co-occurrence score corresponds to a computation of weights of each
edge between each pair, wherein said weight computation comprises:
W ( e ij ) = freq ( w i w j ) freq ( w i ) * freq ( w j ) ,
##EQU00005## wherein W(e.sub.ij) denotes a weight of edge e.sub.ij
between nodes i and j with corresponding tokens w.sub.i and
w.sub.2, respectively; wherein freq denotes the frequency; wherein
the numerator computes a frequency of co-occurrence of tokens
w.sub.i and w.sub.2; and wherein the denominator computes unigram
frequencies of w.sub.i and w.sub.2.
6. The method of claim 1, wherein said computation of said
contextual similarity comprises: computing a cosine similarity
between a paragraph vector of the first response and a paragraph
vector of each word pattern; ranking cosine similarities of each
word pattern, wherein said selected word pattern is within a
predetermined number of top ranked patterns.
7. The method of claim 1, wherein said computation of said
linguistic quality comprises: computing a score of linguistic
confidence based on at least one language trained model; and
determining, based on the linguistic confidence scores, a
probability for each word pattern.
8. The method of claim 1, further comprising: determining a
dependency parser confidence score which provides an indication of
overall grammaticality of each word pattern, said computation of
the dependency parser confidence score comprising: F = D ( k ) * y
k + i , j .di-elect cons. k , j = i + 1 I ( p ij q ) * Sim ( p ij q
) * LQ ( p ij q ) ) * p ij q , ##EQU00006## wherein D(k) is the
dependency parser confidence score for each word pattern; and
wherein a number of word patterns for said D(k) computation is
dependent on a number of words in the first response and a number
of patterns between each pair of words.
9. The method of claim 1, wherein said tagging is performed by the
computing device executing a part-of-speech (POS) tagger associated
with said domain.
10. The method of claim 1, further comprising: identifying a domain
corresponding to said request, wherein said domain word-graph is
associated with said identified domain, said domain related to a
type of personality of speech.
11. The method of claim 1, further comprising: determining a
context based on said second response; causing communication, over
the network, of said context to a third party content platform to
obtain a digital content item comprising third party digital
content associated with said context; receiving, over the network,
said digital content item; and causing display said digital content
item in association with the communication of said second
response.
12. (canceled)
13. A non-transitory computer-readable storage medium tangibly
encoded with computer-executable instructions, that when executed
by a processor associated with a computing device, performs a
method comprising: receiving, at the computing device, a request
from a user, said request input by the user respective to a chatbot
provided by the computing device, said request comprising a
sequence of words; searching, via the computing device, a database
based on said request, said search comprising identifying, within
said database, a first response comprising a sequence of words;
tokenizing, via the computing device, said first response, said
tokenizing comprising identifying each identified word from said
tokenizing of the first response as a token and tagging each token;
creating, via the computing device, a set of bigrams based at least
in part upon the tokenized first response, said bigrams comprising
adjacent words in said first response; determining, via the
computing device, a set of word patterns between each bigram based
on a domain word-graph, said domain word-graph comprising nodes and
edges, wherein each node corresponds to a word and each edge is a
directional link between the nodes, said determination comprising
determining a path within said domain word-graph between the words
in each bigram, and identifying, based on said determined path, a
set of word patterns along the path for each bigram; computing, via
the computing device, scores for each word pattern in said set of
word patterns, said computed scores indicating a co-occurrences,
contextual similarity and linguistic quality between the words in
the bigrams and the word patterns; selecting, via the computing
device, a word pattern based on the computed scores; modifying, via
the computing device, the first response by inserting the word
pattern into said first response based on the bigram from which it
was identified, said modification causing the creation of a second
response; and communicating, via the computing device, said second
response to said user in response to said request, for display of
said communicated second response within a user interface (UI)
associated with the chatbot.
14. The non-transitory computer-readable storage medium of claim
13, further comprising: searching a repository of synonymous words
based on said first response; identifying, based on said search, a
set of synonyms for each word in the first response, wherein said
created set of bigrams includes the identified set of synonyms,
wherein said set of word patterns determination is based on said
bigram set that includes the identified set of synonyms.
15. The non-transitory computer-readable storage medium of claim
13, wherein said computation of said co-occurrence score comprises:
computing a co-occurrence score for each pair of words in said word
patterns; and computing an average score based on each
co-occurrence score.
16. The non-transitory computer-readable storage medium of claim
15, wherein said computation of said co-occurrence score
corresponds to a computation of weights of each edge between each
pair, wherein said weight computation comprises: W ( e ij ) = freq
( w i w j ) freq ( w i ) * freq ( w j ) , ##EQU00007## wherein
W(e.sub.ij) denotes a weight of edge e.sub.ij between nodes i and j
with corresponding tokens w.sub.i and w.sub.2, respectively;
wherein freq denotes the frequency; wherein the numerator computes
a frequency of co-occurrence of tokens w.sub.i and w.sub.2; and
wherein the denominator computes unigram frequencies of w.sub.i and
w.sub.2.
17. The non-transitory computer-readable storage medium of claim
13, wherein said computation of said contextual similarity
comprises: computing a cosine similarity between a paragraph vector
of the first response and a paragraph vector of each word pattern;
ranking cosine similarities of each word pattern, wherein said
selected word pattern is within a predetermined number of top
ranked patterns.
18. The non-transitory computer-readable storage medium of claim
13, wherein said computation of said linguistic quality comprises:
computing a score of linguistic confidence based on at least one
language trained model; and determining, based on the linguistic
confidence scores, a probability for each word pattern.
19. The non-transitory computer-readable storage medium of claim
13, further comprising: determining a dependency parser confidence
score which provides an indication of overall grammaticality of
each word pattern, said computation of the dependency parser
confidence score comprising: F = D ( k ) * y k + i , j .di-elect
cons. k , j = i + 1 I ( p ij q ) * Sim ( p ij q ) * LQ ( p ij q ) )
* p ij q , ##EQU00008## wherein D(k) is the dependency parser
confidence score for each word pattern; and wherein a number of
word patterns for said D(k) computation is dependent on a number of
words in the first response and a number of patterns between each
pair of words.
20. A computing device comprising: a processor; a non-transitory
computer-readable storage medium for tangibly storing thereon
program logic for execution by the processor, the program logic
comprising: logic executed by the processor for receiving, at the
computing device, a request from a user, said request input by the
user respective to a chatbot provided by the computing device, said
request comprising a sequence of words; logic executed by the
processor for searching, via the computing device, a database based
on said request, said search comprising identifying, within said
database, a first response comprising a sequence of words; logic
executed by the processor for tokenizing, via the computing device,
said first response, said tokenizing comprising identifying each
identified word from said tokenizing of the first response as a
token and tagging each token; logic executed by the processor for
creating, via the computing device, a set of bigrams based at least
in part upon the tokenized first response, said bigrams comprising
adjacent words in said first response; logic executed by the
processor for determining, via the computing device, a set of word
patterns between each bigram based on a domain word-graph, said
domain word-graph comprising nodes and edges, wherein each node
corresponds to a word and each edge is a directional link between
the nodes, said determination comprising determining a path within
said domain word-graph between the words in each bigram, and
identifying, based on said determined path, a set of word patterns
along the path for each bigram; logic executed by the processor for
computing, via the computing device, scores for each word pattern
in said set of word patterns, said computed scores indicating a
co-occurrences, contextual similarity and linguistic quality
between the words in the bigrams and the word patterns; logic
executed by the processor for selecting, via the computing device,
a word pattern based on the computed scores; logic executed by the
processor for modifying, via the computing device, the first
response by inserting the word pattern into said first response
based on the bigram from which it was identified, said modification
causing the creation of a second response; and logic executed by
the processor for communicating, via the computing device, said
second response to said user in response to said request, for
display of said communicated second response within a user
interface (UI) associated with the chatbot.
Description
[0001] This application includes material that is subject to
copyright protection. The copyright owner has no objection to the
facsimile reproduction by anyone of the patent disclosure, as it
appears in the Patent and Trademark Office files or records, but
otherwise reserves all copyright rights whatsoever.
FIELD
[0002] The present disclosure relates generally to improving the
performance of content searching, generating, providing and/or
hosting computer devices, systems and/or platforms by modifying the
capabilities and providing non-native functionality to such
devices, systems and/or platforms for a novel and improved
framework for automatically generating and/or transforming chatbot
responses to produce domain-specific responses that mimic native
styles unique to particular domains.
SUMMARY
[0003] With the tremendous growth in the field of Artificial
Intelligence (AI), chatbots have become very popular tools on the
internet for users to interact with network platforms. Chatbots, as
understood by those of skill in the art, are computer programs that
execute to conduct conversations with users via auditory or textual
methods. Such programs are designed to convincingly simulate how a
human would behave as a conversational partner, thereby passing the
Turing test. Chatbots (also referred to as "chatterbots"
interchangeably) are typically used in dialog systems for various
practical purposes including, for example, customer service or
information acquisition. Chatbots can utilize sophisticated natural
language processing techniques or mechanisms, and also can scan for
keywords within an input, then pull a reply with the most matching
keywords, or the most similar wording pattern from a database.
Conventional chatbots are part of virtual assistants such as
Google.RTM. Assistant, and are accessed by many organizations
applications, websites and on instant messaging platforms, such as
Facebook Messenger.RTM..
[0004] As such, there is a growing interest in building end-to-end
conversational systems; however, recent development in AI and/or
machine learning technologies have fell short in enabling chatbots
to generate responses that mimic specific speaking styles of
personalities. For example, some existing systems simply attempt to
produce chatbot responses in order for them to model human
personas, which is restricted to general human-like behavior and
not specific persona styles.
[0005] The disclosed systems and methods provide a novel framework
that simultaneously provides for chatbot responses to embody
accurate answers to questions asked to the conversational agent
while also transforming these regular responses and/or generating
new responses that mimic styles specific to particular domains with
which users can relate and are acquainted. For example, a user
interested in fashion or entertainment would enjoy getting bot
responses resembling the speaking styles of fashionistas or
entertainers, respectively.
[0006] Accordingly, in one or more embodiments, a method is
disclosed for a novel, computerized framework for automatically
generating and/or transforming chatbot responses to produce
domain-specific responses that mimic native styles unique to
particular domains (e.g., communities of similar personalities such
as, for example, politicians, singers, and the like). The instant
disclosure provides for computerized techniques to construct
domain-specific word-graphs using tweets posted from Twitter.RTM.
accounts (and/or any other type of network accessible
platform/resource that enables learning/training of a system to
understand language styles) that belong to users from specific
domains, and use the graph to generate word-patterns. As discussed
in more detail below, new words (obtained from the patterns in the
graph) are introduced to transform the regular response.
[0007] In some embodiments, the graph can be pruned (e.g.,
filtered, parsed, scraped and the like, as discussed in more detail
below) using data-driven thresholds (e.g., such as co-occurrence,
contextual similarity and linguistic quality metrics) in order to
avoid spurious transformations. In some embodiments, paragraph (or
other types of grammatical identifiers) vectors are also utilized
to assign relevance scores to generate word patterns, such that
only the patterns that are contextually similar to the original
response (generic/regular response) are used. As result, only the
best, most optimized set of patterns are used to rewrite the
regular chatbot response.
[0008] In accordance with one or more embodiments, a non-transitory
computer-readable storage medium is provided, the non-transitory
computer-readable storage medium tangibly storing thereon, or
having tangibly encoded thereon, computer readable instructions
that when executed cause at least one processor to perform a method
for a novel and improved framework for automatically generating
and/or transforming chatbot responses to produce domain-specific
responses that mimic native styles unique to particular
domains.
[0009] In accordance with one or more embodiments, a system is
provided that comprises one or more computing devices configured to
provide functionality in accordance with such embodiments. In
accordance with one or more embodiments, functionality is embodied
in steps of a method performed by at least one computing device. In
accordance with one or more embodiments, program code (or program
logic) executed by a processor(s) of a computing device to
implement functionality in accordance with one or more such
embodiments is embodied in, by and/or on a non-transitory
computer-readable medium.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The foregoing and other objects, features, and advantages of
the disclosure will be apparent from the following description of
embodiments as illustrated in the accompanying drawings, in which
reference characters refer to the same parts throughout the various
views. The drawings are not necessarily to scale, emphasis instead
being placed upon illustrating principles of the disclosure:
[0011] FIG. 1 is a schematic diagram illustrating an example of a
network within which the systems and methods disclosed herein could
be implemented according to some embodiments of the present
disclosure;
[0012] FIG. 2 depicts is a schematic diagram illustrating an
example of client device in accordance with some embodiments of the
present disclosure;
[0013] FIG. 3 is a schematic block diagram illustrating components
of an exemplary system in accordance with embodiments of the
present disclosure;
[0014] FIGS. 4A-4B are flowcharts illustrating steps performed in
accordance with some embodiments of the present disclosure;
[0015] FIG. 5 illustrates non-limiting example embodiments of the
disclosed systems and methods in accordance with some embodiments
of the present disclosure;
[0016] FIG. 6 is a flowchart illustrating steps performed in
accordance with some embodiments of the present disclosure; and
[0017] FIG. 7 is a block diagram illustrating the architecture of
an exemplary hardware device in accordance with one or more
embodiments of the present disclosure.
DESCRIPTION OF EMBODIMENTS
[0018] The present disclosure will now be described more fully
hereinafter with reference to the accompanying drawings, which form
a part hereof, and which show, by way of illustration, certain
example embodiments. Subject matter may, however, be embodied in a
variety of different forms and, therefore, covered or claimed
subject matter is intended to be construed as not being limited to
any example embodiments set forth herein; example embodiments are
provided merely to be illustrative. Likewise, a reasonably broad
scope for claimed or covered subject matter is intended. Among
other things, for example, subject matter may be embodied as
methods, devices, components, or systems. Accordingly, embodiments
may, for example, take the form of hardware, software, firmware or
any combination thereof (other than software per se). The following
detailed description is, therefore, not intended to be taken in a
limiting sense.
[0019] Throughout the specification and claims, terms may have
nuanced meanings suggested or implied in context beyond an
explicitly stated meaning. Likewise, the phrase "in one embodiment"
as used herein does not necessarily refer to the same embodiment
and the phrase "in another embodiment" as used herein does not
necessarily refer to a different embodiment. It is intended, for
example, that claimed subject matter include combinations of
example embodiments in whole or in part.
[0020] In general, terminology may be understood at least in part
from usage in context. For example, terms, such as "and", "or", or
"and/or," as used herein may include a variety of meanings that may
depend at least in part upon the context in which such terms are
used. Typically, "or" if used to associate a list, such as A, B or
C, is intended to mean A, B, and C, here used in the inclusive
sense, as well as A, B or C, here used in the exclusive sense. In
addition, the term "one or more" as used herein, depending at least
in part upon context, may be used to describe any feature,
structure, or characteristic in a singular sense or may be used to
describe combinations of features, structures or characteristics in
a plural sense. Similarly, terms, such as "a," "an," or "the,"
again, may be understood to convey a singular usage or to convey a
plural usage, depending at least in part upon context. In addition,
the term "based on" may be understood as not necessarily intended
to convey an exclusive set of factors and may, instead, allow for
existence of additional factors not necessarily expressly
described, again, depending at least in part on context.
[0021] The present disclosure is described below with reference to
block diagrams and operational illustrations of methods and
devices. It is understood that each block of the block diagrams or
operational illustrations, and combinations of blocks in the block
diagrams or operational illustrations, can be implemented by means
of analog or digital hardware and computer program instructions.
These computer program instructions can be provided to a processor
of a general purpose computer to alter its function as detailed
herein, a special purpose computer, ASIC, or other programmable
data processing apparatus, such that the instructions, which
execute via the processor of the computer or other programmable
data processing apparatus, implement the functions/acts specified
in the block diagrams or operational block or blocks. In some
alternate implementations, the functions/acts noted in the blocks
can occur out of the order noted in the operational illustrations.
For example, two blocks shown in succession can in fact be executed
substantially concurrently or the blocks can sometimes be executed
in the reverse order, depending upon the functionality/acts
involved.
[0022] These computer program instructions can be provided to a
processor of: a general purpose computer to alter its function to a
special purpose; a special purpose computer; ASIC; or other
programmable digital data processing apparatus, such that the
instructions, which execute via the processor of the computer or
other programmable data processing apparatus, implement the
functions/acts specified in the block diagrams or operational block
or blocks, thereby transforming their functionality in accordance
with embodiments herein.
[0023] For the purposes of this disclosure a computer readable
medium (or computer-readable storage medium/media) stores computer
data, which data can include computer program code (or
computer-executable instructions) that is executable by a computer,
in machine readable form. By way of example, and not limitation, a
computer readable medium may comprise computer readable storage
media, for tangible or fixed storage of data, or communication
media for transient interpretation of code-containing signals.
Computer readable storage media, as used herein, refers to physical
or tangible storage (as opposed to signals) and includes without
limitation volatile and non-volatile, removable and non-removable
media implemented in any method or technology for the tangible
storage of information such as computer-readable instructions, data
structures, program modules or other data. Computer readable
storage media includes, but is not limited to, RAM, ROM, EPROM,
EEPROM, flash memory or other solid state memory technology,
CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic
tape, magnetic disk storage or other magnetic storage devices, or
any other physical or material medium which can be used to tangibly
store the desired information or data or instructions and which can
be accessed by a computer or processor.
[0024] For the purposes of this disclosure the term "server" should
be understood to refer to a service point which provides
processing, database, and communication facilities. By way of
example, and not limitation, the term "server" can refer to a
single, physical processor with associated communications and data
storage and database facilities, or it can refer to a networked or
clustered complex of processors and associated network and storage
devices, as well as operating software and one or more database
systems and application software that support the services provided
by the server. Servers may vary widely in configuration or
capabilities, but generally a server may include one or more
central processing units and memory. A server may also include one
or more mass storage devices, one or more power supplies, one or
more wired or wireless network interfaces, one or more input/output
interfaces, or one or more operating systems, such as Windows
Server, Mac OS X, Unix, Linux, FreeBSD, or the like.
[0025] For the purposes of this disclosure a "network" should be
understood to refer to a network that may couple devices so that
communications may be exchanged, such as between a server and a
client device or other types of devices, including between wireless
devices coupled via a wireless network, for example. A network may
also include mass storage, such as network attached storage (NAS),
a storage area network (SAN), or other forms of computer or machine
readable media, for example. A network may include the Internet,
one or more local area networks (LANs), one or more wide area
networks (WANs), wire-line type connections, wireless type
connections, cellular or any combination thereof. Likewise,
sub-networks, which may employ differing architectures or may be
compliant or compatible with differing protocols, may interoperate
within a larger network. Various types of devices may, for example,
be made available to provide an interoperable capability for
differing architectures or protocols. As one illustrative example,
a router may provide a link between otherwise separate and
independent LANs.
[0026] A communication link or channel may include, for example,
analog telephone lines, such as a twisted wire pair, a coaxial
cable, full or fractional digital lines including T1, T2, T3, or T4
type lines, Integrated Services Digital Networks (ISDNs), Digital
Subscriber Lines (DSLs), wireless links including satellite links,
or other communication links or channels, such as may be known to
those skilled in the art. Furthermore, a computing device or other
related electronic devices may be remotely coupled to a network,
such as via a wired or wireless line or link, for example.
[0027] For purposes of this disclosure, a "wireless network" should
be understood to couple client devices with a network. A wireless
network may employ stand-alone ad-hoc networks, mesh networks,
Wireless LAN (WLAN) networks, cellular networks, or the like. A
wireless network may further include a system of terminals,
gateways, routers, or the like coupled by wireless radio links, or
the like, which may move freely, randomly or organize themselves
arbitrarily, such that network topology may change, at times even
rapidly.
[0028] A wireless network may further employ a plurality of network
access technologies, including Wi-Fi, Long Term Evolution (LTE),
WLAN, Wireless Router (WR) mesh, or 2nd, 3rd, or 4th generation
(2G, 3G, 4G or 5G) cellular technology, or the like. Network access
technologies may enable wide area coverage for devices, such as
client devices with varying degrees of mobility, for example.
[0029] For example, a network may enable RF or wireless type
communication via one or more network access technologies, such as
Global System for Mobile communication (GSM), Universal Mobile
Telecommunications System (UMTS), General Packet Radio Services
(GPRS), Enhanced Data GSM Environment (EDGE), 3GPP Long Term
Evolution (LTE), LTE Advanced, Wideband Code Division Multiple
Access (WCDMA), Bluetooth, 802.11b/g/n, or the like. A wireless
network may include virtually any type of wireless communication
mechanism by which signals may be communicated between devices,
such as a client device or a computing device, between or within a
network, or the like.
[0030] A computing device may be capable of sending or receiving
signals, such as via a wired or wireless network, or may be capable
of processing or storing signals, such as in memory as physical
memory states, and may, therefore, operate as a server. Thus,
devices capable of operating as a server may include, as examples,
dedicated rack-mounted servers, desktop computers, laptop
computers, set top boxes, integrated devices combining various
features, such as two or more features of the foregoing devices, or
the like. Servers may vary widely in configuration or capabilities,
but generally a server may include one or more central processing
units and memory. A server may also include one or more mass
storage devices, one or more power supplies, one or more wired or
wireless network interfaces, one or more input/output interfaces,
or one or more operating systems, such as Windows Server, Mac OS X,
Unix, Linux, FreeBSD, or the like.
[0031] For purposes of this disclosure, a client (or consumer or
user) device may include a computing device capable of sending or
receiving signals, such as via a wired or a wireless network. A
client device may, for example, include a desktop computer or a
portable device, such as a cellular telephone, a smart phone, a
display pager, a radio frequency (RF) device, an infrared (IR)
device an Near Field Communication (NFC) device, a Personal Digital
Assistant (PDA), a handheld computer, a tablet computer, a phablet,
a laptop computer, a set top box, a wearable computer, smart watch,
an integrated or distributed device combining various features,
such as features of the forgoing devices, or the like.
[0032] A client device may vary in terms of capabilities or
features. Claimed subject matter is intended to cover a wide range
of potential variations. For example, a simple smart phone, phablet
or tablet may include a numeric keypad or a display of limited
functionality, such as a monochrome liquid crystal display (LCD)
for displaying text. In contrast, however, as another example, a
web-enabled client device may include a high-resolution screen, one
or more physical or virtual keyboards, mass storage, one or more
accelerometers, one or more gyroscopes, global positioning system
(GPS) or other location-identifying type capability, or a display
with a high degree of functionality, such as a touch-sensitive
color 2D or 3D display, for example.
[0033] A client device may include or may execute a variety of
operating systems, including a personal computer operating system,
such as a Windows, iOS or Linux, or a mobile operating system, such
as iOS, Android, or Windows Mobile, or the like.
[0034] A client device may include or may execute a variety of
possible applications, such as a client software application
enabling communication with other devices, such as communicating
one or more messages, such as via email, for example Yahoo! .RTM.
Mail, short message service (SMS), or multimedia message service
(MMS), for example Yahoo! Messenger.RTM., including via a network,
such as a social network, including, for example, Tumblr.RTM.,
Facebook.RTM., LinkedIn.RTM., Twitter.RTM., Flickr.RTM., or
Google+.RTM., Instagram.RTM., to provide only a few possible
examples. A client device may also include or execute an
application to communicate content, such as, for example, textual
content, multimedia content, or the like. A client device may also
include or execute an application to perform a variety of possible
tasks, such as browsing, searching, playing, streaming or
displaying various forms of content, including locally stored or
uploaded images and/or video, or games (such as fantasy sports
leagues). The foregoing is provided to illustrate that claimed
subject matter is intended to include a wide range of possible
features or capabilities.
[0035] As discussed herein, reference to an "advertisement" should
be understood to include, but not be limited to, digital media
content embodied as a media item that provides information provided
by another user, service, third party, entity, and the like. Such
digital ad content can include any type of known or to be known
media renderable by a computing device, including, but not limited
to, video, text, audio, images, and/or any other type of known or
to be known multi-media item or object. In some embodiments, the
digital ad content can be formatted as hyperlinked multi-media
content that provides deep-linking features and/or capabilities.
Therefore, while some content is referred to as an advertisement,
it is still a digital media item that is renderable by a computing
device, and such digital media item comprises content relaying
promotional content provided by a network associated party.
[0036] The principles described herein may be embodied in many
different forms. The present disclosure provides a novel,
computerized framework that automatically transforms chatbot
responses into domain-specific responses that mimic native styles
unique to particular domains. The disclosed systems and methods
addresses the problem of transforming factual chatbot responses
(referred to as regular response) to a modified response that is
compatible with a domain-specific communication style--for example,
speaking or language styles (or patterns) of politicians. The goal
of the disclosed systems and methods is to preserve the content of
the original response, but alter its style by replacing existing
word sequences in regular chatbot responses with determined
stylized words or word sequences, thereby mimicking domain-specific
styles.
[0037] By way of background, conventional conversational agents
have recently received major attention from researchers, especially
from the perspective of Natural Language Generation (NLG).
Receiving accurate responses from a chatbot is essential; however,
bots that have functionality for mimicking personas or specific
speaking styles are absent in the field. As evidenced from the
discussion herein, having such functionality can enable a website
or network location (e.g., application, service or platform) to
increase its capability of retaining its users, in that providing
users with accurate, stylized responses can address the needs of a
user's requests and entertainment value.
[0038] For example, one conventional attempt to mimic human-like
conversations is to use a persona-model using a deep neural-network
model. However, such system does not differentiate between speakers
from different domains and does not generate text conforming to
specific speaking styles. In contrast to these systems, the instant
disclosure does not generate responses in conversations, but
modifies regular responses to fit domain-specific styles, as
discussed in more detail below.
[0039] In another example, some systems' chat and virtual agents
select the best possible response from a list of pre-populated
responses and templates. And, some systems model responses based on
"five-factor personalities" in order to model characters; however,
such systems are focused solely on a response perceiving
personality traits of a character rather than persona-based content
generation.
[0040] In contrast to the above-mentioned approaches, the disclosed
systems and methods provide a novel computerized response
generation environment that is completely data-driven with the
purpose of transforming regular responses (e.g., original factual
responses as in conventional systems) by modifying them to include
stylized content specific to a particular domain without relying on
predefined responses or templates. Therefore, according to
embodiments of the instant disclosure, the disclosed systems and
methods provide a novel end-to-end conversational engine that
provides chatbot responses that accurately and factually answers
questions with responses that mimic styles specific to particular
domains.
[0041] For purposes of this disclosure, the discussed embodiments
will center-around two domains and styles--politics and
entertainment; however, it should not be construed as limiting, as
any type of domain/style can be utilized and/or leveraged in
producing the generated stylized responses discussed herein. For
example, such domains/styles can also include, but are not limited
to, fashion, business, regional (country, northeastern, Boston
accents/cadences, and the like), slang, elementary, adult, radio
hosts, singers, actors, sports figures, commentators, and the like,
or some combination thereof.
[0042] According to embodiments of the instant disclosure, as
discussed in more detail below, a user enters a query with a
chatbot asking for a particular piece of content/information. For
example, "What is the weather today?" In response to this query,
the disclosed systems and methods will retrieve the
accurate/factual response (e.g., "The weather in New York City is
72 degrees and Sunny.") However, instead of simply outputting this
response, as in conventional systems, the disclosed systems and
methods automatically transform the initial chatbot response to
produce domain-specific response that mimics a native style unique
to the particular domain from which the query was entered.
Therefore, since the user entered the query from a domain, for
example, associated with a news source (e.g., cnn.com, which
provides political analysis), the response can be stylized
according to a political undertone such that it includes the type
of rhetoric a politician would use when answering a question. For
example, as a politician would typically say, the chatbot response
can be modified to state "Good afternoon Sir, the weather today in
the Big Apple appears to be 72 degrees and Sunny, hope you enjoy
your day." Thus, the transformed response retains the factual
content but adds a distinctive style easily identifiable and
attributable to a specific domain.
[0043] As discussed in more detail below at least in relation to
FIG. 6, according to some embodiments, information associated with,
derived from, or otherwise identified from, during or as a result
of generated chatbot response, as discussed herein, can be used for
monetization purposes and targeted advertising when providing,
delivering or enabling devices access or output a response.
Providing targeted advertising to users associated with such
discovered content can lead to an increased click-through rate
(CTR) of such ads and/or an increase in the advertiser's return on
investment (ROI) for serving such content provided by third parties
(e.g., digital advertisement content provided by an advertiser,
where the advertiser can be a third party advertiser, or an entity
directly associated with or hosting the systems and methods
discussed herein).
[0044] Certain embodiments will now be described in greater detail
with reference to the figures. In general, with reference to FIG.
1, a system 100 in accordance with an embodiment of the present
disclosure is shown. FIG. 1 shows components of a general
environment in which the systems and methods discussed herein may
be practiced. Not all the components may be required to practice
the disclosure, and variations in the arrangement and type of the
components may be made without departing from the spirit or scope
of the disclosure. As shown, system 100 of FIG. 1 includes local
area networks ("LANs")/wide area networks ("WANs")--network 105,
wireless network 110, mobile devices (client devices) 102-104 and
client device 101. FIG. 1 additionally includes a variety of
servers, such as content server 106, application (or "App") server
108, search server 120 and advertising ("ad") server 130.
[0045] One embodiment of mobile devices 102-104 is described in
more detail below. Generally, however, mobile devices 102-104 may
include virtually any portable computing device capable of
receiving and sending a message over a network, such as network
105, wireless network 110, or the like. Mobile devices 102-104 may
also be described generally as client devices that are configured
to be portable. Thus, mobile devices 102-104 may include virtually
any portable computing device capable of connecting to another
computing device and receiving information. Such devices include
multi-touch and portable devices such as, cellular telephones,
smart phones, display pagers, radio frequency (RF) devices,
infrared (IR) devices, Personal Digital Assistants (PDAs), handheld
computers, laptop computers, wearable computers, smart watch,
tablet computers, phablets, integrated devices combining one or
more of the preceding devices, and the like. As such, mobile
devices 102-104 typically range widely in terms of capabilities and
features. For example, a cell phone may have a numeric keypad and a
few lines of monochrome LCD display on which only text may be
displayed. In another example, a web-enabled mobile device may have
a touch sensitive screen, a stylus, and an HD display in which both
text and graphics may be displayed.
[0046] A web-enabled mobile device may include a browser
application that is configured to receive and to send web pages,
web-based messages, and the like. The browser application may be
configured to receive and display graphics, text, multimedia, and
the like, employing virtually any web based language, including a
wireless application protocol messages (WAP), and the like. In one
embodiment, the browser application is enabled to employ Handheld
Device Markup Language (HDML), Wireless Markup Language (WML),
WMLScript, JavaScript, Standard Generalized Markup Language (SMGL),
HyperText Markup Language (HTML), Dynamic HyperText Markup Language
(DHTML), eXtensible Markup Language (XML), and the like, to display
and send a message.
[0047] Mobile devices 102-104 also may include at least one client
application that is configured to receive content from another
computing device. The client application may include a capability
to provide and receive textual content, graphical content, audio
content, and the like. The client application may further provide
information that identifies itself, including a type, capability,
name, and the like. In one embodiment, mobile devices 102-104 may
uniquely identify themselves through any of a variety of
mechanisms, including a phone number, Mobile Identification Number
(MIN), an electronic serial number (ESN), or other mobile device
identifier.
[0048] In some embodiments, mobile devices 102-104 may also
communicate with non-mobile client devices, such as client device
101, or the like. In one embodiment, such communications may
include sending and/or receiving messages, searching for, viewing
and/or sharing photographs, audio clips, video clips, or any of a
variety of other forms of communications. Client device 101 may
include virtually any computing device capable of communicating
over a network to send and receive information. The set of such
devices may include devices that typically connect using a wired or
wireless communications medium such as personal computers,
multiprocessor systems, microprocessor-based or programmable
consumer electronics, network PCs, or the like. Thus, client device
101 may also have differing capabilities for displaying navigable
views of information.
[0049] Client devices 101-104 computing device may be capable of
sending or receiving signals, such as via a wired or wireless
network, or may be capable of processing or storing signals, such
as in memory as physical memory states, and may, therefore, operate
as a server. Thus, devices capable of operating as a server may
include, as examples, dedicated rack-mounted servers, desktop
computers, laptop computers, set top boxes, integrated devices
combining various features, such as two or more features of the
foregoing devices, or the like.
[0050] Wireless network 110 is configured to couple mobile devices
102-104 and its components with network 105. Wireless network 110
may include any of a variety of wireless sub-networks that may
further overlay stand-alone ad-hoc networks, and the like, to
provide an infrastructure-oriented connection for mobile devices
102-104. Such sub-networks may include mesh networks, Wireless LAN
(WLAN) networks, cellular networks, and the like.
[0051] Network 105 is configured to couple content server 106,
application server 108, or the like, with other computing devices,
including, client device 101, and through wireless network 110 to
mobile devices 102-104. Network 105 is enabled to employ any form
of computer readable media for communicating information from one
electronic device to another. Also, network 105 can include the
Internet in addition to local area networks (LANs), wide area
networks (WANs), direct connections, such as through a universal
serial bus (USB) port, other forms of computer-readable media, or
any combination thereof. On an interconnected set of LANs,
including those based on differing architectures and protocols, a
router acts as a link between LANs, enabling messages to be sent
from one to another, and/or other computing devices.
[0052] Within the communications networks utilized or understood to
be applicable to the present disclosure, such networks will employ
various protocols that are used for communication over the network.
Signal packets communicated via a network, such as a network of
participating digital communication networks, may be compatible
with or compliant with one or more protocols. Signaling formats or
protocols employed may include, for example, TCP/IP, UDP, QUIC
(Quick UDP Internet Connection), DECnet, NetBEUI, IPX,
APPLETALK.TM., or the like. Versions of the Internet Protocol (IP)
may include IPv4 or IPv6. The Internet refers to a decentralized
global network of networks. The Internet includes local area
networks (LANs), wide area networks (WANs), wireless networks, or
long haul public networks that, for example, allow signal packets
to be communicated between LANs. Signal packets may be communicated
between nodes of a network, such as, for example, to one or more
sites employing a local network address. A signal packet may, for
example, be communicated over the Internet from a user site via an
access node coupled to the Internet. Likewise, a signal packet may
be forwarded via network nodes to a target site coupled to the
network via a network access node, for example. A signal packet
communicated via the Internet may, for example, be routed via a
path of gateways, servers, etc. that may route the signal packet in
accordance with a target address and availability of a network path
to the target address.
[0053] According to some embodiments, the present disclosure may
also be utilized within or accessible to an electronic social
networking site. A social network refers generally to an electronic
network of individuals, such as, but not limited to, acquaintances,
friends, family, colleagues, or co-workers, that are coupled via a
communications network or via a variety of sub-networks.
Potentially, additional relationships may subsequently be formed as
a result of social interaction via the communications network or
sub-networks. In some embodiments, multi-modal communications may
occur between members of the social network. Individuals within one
or more social networks may interact or communication with other
members of a social network via a variety of devices. Multi-modal
communication technologies refers to a set of technologies that
permit interoperable communication across multiple devices or
platforms, such as cell phones, smart phones, tablet computing
devices, phablets, personal computers, televisions, set-top boxes,
SMS/MMS, email, instant messenger clients, forums, social
networking sites, or the like.
[0054] In some embodiments, the disclosed networks 110 and/or 105
may comprise a content distribution network(s). A "content delivery
network" or "content distribution network" (CDN) generally refers
to a distributed content delivery system that comprises a
collection of computers or computing devices linked by a network or
networks. A CDN may employ software, systems, protocols or
techniques to facilitate various services, such as storage,
caching, communication of content, or streaming media or
applications. A CDN may also enable an entity to operate or manage
another's site infrastructure, in whole or in part.
[0055] The content server 106 may include a device that includes a
configuration to provide content via a network to another device. A
content server 106 may, for example, host a site, service or an
associated application, such as, an email or messaging platform
(e.g., Yahoo!.RTM. Mail), a social networking site, a photo sharing
site/service (e.g., Tumblr.RTM.), a search platform or site, or a
personal user site (such as a blog, vlog, online dating site, and
the like) and the like. A content server 106 may also host a
variety of other sites, including, but not limited to business
sites, educational sites, dictionary sites, encyclopedia sites,
wikis, financial sites, government sites, and the like. Devices
that may operate as content server 106 include personal computers
desktop computers, multiprocessor systems, microprocessor-based or
programmable consumer electronics, network PCs, servers, and the
like. Likewise, the search server 120 may include a device that
includes a configuration to provide content via a network to
another device.
[0056] In some embodiments, the content server 106 (and/or other
servers 108, 120 and 130, for example), can host the chatbot engine
300 discussed below that enables the content provided by such
server(s) to be transformed/generated according the disclosed
systems and methods discussed herein.
[0057] Content server 106 can further provide a variety of services
that include, but are not limited to, streaming and/or downloading
media services, search services, email services, photo services,
web services, social networking services, news services,
third-party services, audio services, video services, instant
messaging (IM) services, SMS services, MMS services, FTP services,
voice over IP (VOIP) services, or the like. Such services, for
example a mail application and/or email-platform, can be provided
via the application server 108, whereby a user is able to utilize
such service upon the user being authenticated, verified or
identified by the service. Examples of content may include videos,
text, audio, images, or the like, which may be processed in the
form of physical signals, such as electrical signals, for example,
or may be stored in memory, as physical states, for example.
[0058] In a similar manner as the content server 106, the search
server 120 may include a device that includes a configuration to
provide content via a network to another device. The search server
120 can, for example, host a site, service or an associated
application, such as, an search engine (e.g., Yahoo! .RTM. Search,
Bing.RTM., Google Search.RTM., and the like), a social networking
site, a photo sharing site/service (e.g., Tumblr.RTM.), and the
like. Additionally, the search server 120 can further provide a
variety of services similar to those outlined above for the content
server 106.
[0059] An ad server 130 comprises a server that stores online
advertisements for presentation to users. "Ad serving" refers to
methods used to place online advertisements on websites, in
applications, or other places where users are more likely to see
them, such as during an online session or during computing platform
use, for example. Various monetization techniques or models may be
used in connection with sponsored advertising, including
advertising associated with user. Such sponsored advertising
includes monetization techniques including sponsored search
advertising, non-sponsored search advertising, guaranteed and
non-guaranteed delivery advertising, ad networks/exchanges, ad
targeting, ad serving and ad analytics. Such systems can
incorporate near instantaneous auctions of ad placement
opportunities during web page creation, (in some cases in less than
500 milliseconds) with higher quality ad placement opportunities
resulting in higher revenues per ad. That is advertisers will pay
higher advertising rates when they believe their ads are being
placed in or along with highly relevant content that is being
presented to users. Reductions in the time needed to quantify a
high quality ad placement offers ad platforms competitive
advantages. Thus higher speeds and more relevant context detection
improve these technological fields.
[0060] For example, a process of buying or selling online
advertisements may involve a number of different entities,
including advertisers, publishers, agencies, networks, or
developers. To simplify this process, organization systems called
"ad exchanges" may associate advertisers or publishers, such as via
a platform to facilitate buying or selling of online advertisement
inventory from multiple ad networks. "Ad networks" refers to
aggregation of ad space supply from publishers, such as for
provision en masse to advertisers. For web portals like Yahoo!
.RTM., advertisements may be displayed on web pages or in apps
resulting from a user-defined search based at least in part upon
one or more search terms. Advertising may be beneficial to users,
advertisers or web portals if displayed advertisements are relevant
to interests of one or more users. Thus, a variety of techniques
have been developed to infer user interest, user intent or to
subsequently target relevant advertising to users. One approach to
presenting targeted advertisements includes employing demographic
characteristics (e.g., age, income, gender, occupation, etc.) for
predicting user behavior, such as by group. Advertisements may be
presented to users in a targeted audience based at least in part
upon predicted user behavior(s).
[0061] Another approach includes profile-type ad targeting. In this
approach, user profiles specific to a user may be generated to
model user behavior, for example, by tracking a user's path through
a web site or network of sites, and compiling a profile based at
least in part on pages or advertisements ultimately delivered. A
correlation may be identified, such as for user purchases, for
example. An identified correlation may be used to target potential
purchasers by targeting content or advertisements to particular
users. During presentation of advertisements, a presentation system
may collect descriptive content about types of advertisements
presented to users. A broad range of descriptive content may be
gathered, including content specific to an advertising presentation
system. Advertising analytics gathered may be transmitted to
locations remote to an advertising presentation system for storage
or for further evaluation. Where advertising analytics transmittal
is not immediately available, gathered advertising analytics may be
stored by an advertising presentation system until transmittal of
those advertising analytics becomes available.
[0062] Servers 106, 108, 120 and 130 may be capable of sending or
receiving signals, such as via a wired or wireless network, or may
be capable of processing or storing signals, such as in memory as
physical memory states. Devices capable of operating as a server
may include, as examples, dedicated rack-mounted servers, desktop
computers, laptop computers, set top boxes, integrated devices
combining various features, such as two or more features of the
foregoing devices, or the like. Servers may vary widely in
configuration or capabilities, but generally, a server may include
one or more central processing units and memory. A server may also
include one or more mass storage devices, one or more power
supplies, one or more wired or wireless network interfaces, one or
more input/output interfaces, or one or more operating systems,
such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or the
like.
[0063] In some embodiments, users are able to access services
provided by servers 106, 108, 120 and/or 130. This may include in a
non-limiting example, authentication servers, search servers, email
servers, social networking services servers, SMS servers, IM
servers, MMS servers, exchange servers, photo-sharing services
servers, and travel services servers, via the network 105 using
their various devices 101-104. In some embodiments, applications,
such as a search application (e.g., Yahoo! .RTM. Search), mail or
messaging application (e.g., Yahoo! .RTM. Mail, Yahoo! .RTM.
Messenger), a photo sharing/user-generated content (UGC)
application (e.g., Flickr.RTM., Tumblr.RTM., Instagram.RTM. and the
like), a streaming video application (e.g., Netflix.RTM.,
Hulu.RTM., iTunes.RTM., Amazon Prime.RTM., HBO Go.RTM., and the
like), blog, photo or social networking application (e.g.,
Facebook.RTM., Twitter.RTM. and the like), and the like, can be
hosted by the application server 108 (or content server 106, search
server 120 and the like).
[0064] Thus, the application server 108 can store various types of
applications and application related information including
application data and user profile information (e.g., identifying
and behavioral information associated with a user). It should also
be understood that content server 106 can also store various types
of data related to the content and services provided by content
server 106 in an associated database 107, as discussed in more
detail below. Embodiments exist where the network 105 is also
coupled with/connected to a Trusted Search Server (TSS) which can
be utilized to render content in accordance with the embodiments
discussed herein. Embodiments exist where the TSS functionality can
be embodied within servers 106, 108, 120 and/or 130.
[0065] Moreover, although FIG. 1 illustrates servers 106, 108, 120
and 130 as single computing devices, respectively, the disclosure
is not so limited. For example, one or more functions of servers
106, 108, 120 and/or 130 may be distributed across one or more
distinct computing devices. Moreover, in one embodiment, servers
106, 108, 120 and/or 130 may be integrated into a single computing
device, without departing from the scope of the present
disclosure.
[0066] FIG. 2 is a schematic diagram illustrating a client device
showing an example embodiment of a client device that may be used
within the present disclosure. Client device 200 may include many
more or less components than those shown in FIG. 2. However, the
components shown are sufficient to disclose an illustrative
embodiment for implementing the present disclosure. Client device
200 may represent, for example, client devices discussed above in
relation to FIG. 1.
[0067] As shown in the figure, Client device 200 includes a
processing unit (CPU) 222 in communication with a mass memory 230
via a bus 224. Client device 200 also includes a power supply 226,
one or more network interfaces 250, an audio interface 252, a
display 254, a keypad 256, an illuminator 258, an input/output
interface 260, a haptic interface 262, an optional global
positioning systems (GPS) receiver 264 and a camera(s) or other
optical, thermal or electromagnetic sensors 266. Device 200 can
include one camera/sensor 266, or a plurality of cameras/sensors
266, as understood by those of skill in the art. The positioning of
the camera(s)/sensor(s) 266 on device 200 can change per device 200
model, per device 200 capabilities, and the like, or some
combination thereof.
[0068] Power supply 226 provides power to Client device 200. A
rechargeable or non-rechargeable battery may be used to provide
power. The power may also be provided by an external power source,
such as an AC adapter or a powered docking cradle that supplements
and/or recharges a battery.
[0069] Client device 200 may optionally communicate with a base
station (not shown), or directly with another computing device.
Network interface 250 includes circuitry for coupling Client device
200 to one or more networks, and is constructed for use with one or
more communication protocols and technologies as discussed above.
Network interface 250 is sometimes known as a transceiver,
transceiving device, or network interface card (NIC).
[0070] Audio interface 252 is arranged to produce and receive audio
signals such as the sound of a human voice. For example, audio
interface 252 may be coupled to a speaker and microphone (not
shown) to enable telecommunication with others and/or generate an
audio acknowledgement for some action. Display 254 may be a liquid
crystal display (LCD), gas plasma, light emitting diode (LED), or
any other type of display used with a computing device. Display 254
may also include a touch sensitive screen arranged to receive input
from an object such as a stylus or a digit from a human hand.
[0071] Keypad 256 may comprise any input device arranged to receive
input from a user. For example, keypad 256 may include a push
button numeric dial, or a keyboard. Keypad 256 may also include
command buttons that are associated with selecting and sending
images. Illuminator 258 may provide a status indication and/or
provide light. Illuminator 258 may remain active for specific
periods of time or in response to events. For example, when
illuminator 258 is active, it may backlight the buttons on keypad
256 and stay on while the client device is powered. Also,
illuminator 258 may backlight these buttons in various patterns
when particular actions are performed, such as dialing another
client device. Illuminator 258 may also cause light sources
positioned within a transparent or translucent case of the client
device to illuminate in response to actions.
[0072] Client device 200 also comprises input/output interface 260
for communicating with external devices, such as a headset, or
other input or output devices not shown in FIG. 2. Input/output
interface 260 can utilize one or more communication technologies,
such as USB, infrared, Bluetooth.TM., or the like. Haptic interface
262 is arranged to provide tactile feedback to a user of the client
device. For example, the haptic interface may be employed to
vibrate client device 200 in a particular way when the Client
device 200 receives a communication from another user.
[0073] Optional GPS transceiver 264 can determine the physical
coordinates of Client device 200 on the surface of the Earth, which
typically outputs a location as latitude and longitude values. GPS
transceiver 264 can also employ other geo-positioning mechanisms,
including, but not limited to, triangulation, assisted GPS (AGPS),
E-OTD, CI, SAI, ETA, BSS or the like, to further determine the
physical location of Client device 200 on the surface of the Earth.
It is understood that under different conditions, GPS transceiver
264 can determine a physical location within millimeters for Client
device 200; and in other cases, the determined physical location
may be less precise, such as within a meter or significantly
greater distances. In one embodiment, however, Client device may
through other components, provide other information that may be
employed to determine a physical location of the device, including
for example, a MAC address, Internet Protocol (IP) address, or the
like.
[0074] Mass memory 230 includes a RAM 232, a ROM 234, and other
storage means. Mass memory 230 illustrates another example of
computer storage media for storage of information such as computer
readable instructions, data structures, program modules or other
data. Mass memory 230 stores a basic input/output system ("BIOS")
240 for controlling low-level operation of Client device 200. The
mass memory also stores an operating system 241 for controlling the
operation of Client device 200. It will be appreciated that this
component may include a general purpose operating system such as a
version of UNIX, or LINUX.TM., or a specialized client
communication operating system such as Windows Client.TM., or the
Symbian.RTM. operating system. The operating system may include, or
interface with a Java virtual machine module that enables control
of hardware components and/or operating system operations via Java
application programs.
[0075] Memory 230 further includes one or more data stores, which
can be utilized by Client device 200 to store, among other things,
applications 242 and/or other data. For example, data stores may be
employed to store information that describes various capabilities
of Client device 200. The information may then be provided to
another device based on any of a variety of events, including being
sent as part of a header during a communication, sent upon request,
or the like. At least a portion of the capability information may
also be stored on a disk drive or other storage medium (not shown)
within Client device 200.
[0076] Applications 242 may include computer executable
instructions which, when executed by Client device 200, transmit,
receive, and/or otherwise process audio, video, images, and enable
telecommunication with a server and/or another user of another
client device. Other examples of application programs or "apps" in
some embodiments include browsers, calendars, contact managers,
task managers, transcoders, photo management, database programs,
word processing programs, security applications, spreadsheet
programs, games, search programs, and so forth. Applications 242
may further include search client 245 that is configured to send,
to receive, and/or to otherwise process a search query and/or
search result using any known or to be known communication
protocols. Although a single search client 245 is illustrated it
should be clear that multiple search clients may be employed. For
example, one search client may be configured to enter a search
query message, where another search client manages search results,
and yet another search client is configured to manage serving
digital content (e.g., advertisements) or other forms of digital
data associated with, but not limited to, IMs, emails, and other
types of known messages, or the like.
[0077] Having described the components of the general architecture
employed within the disclosed systems and methods, the components'
general operation with respect to the disclosed systems and methods
will now be described below with reference to FIGS. 3-5.
[0078] FIG. 3 is a block diagram illustrating the components for
performing the systems and methods discussed herein. FIG. 3
includes a chatbot engine 300, network 315 and database 320. The
chatbot engine 300 can be a special purpose machine or processor
and could be hosted by a search server, application server, content
server, social networking server, web server, messaging server,
content provider, email service provider, ad server, user's
computing device, and the like, or any combination thereof.
[0079] According to some embodiments, chatbot engine 300 can be
embodied as a stand-alone application that executes on a user
device. In some embodiments, the chatbot engine 300 can function as
an application installed on the user's device, and in some
embodiments, such application can be a web-based application
accessed by the user device over a network. In some embodiments,
the chatbot engine 300 can be installed as an augmenting script,
program or application to another searching, messaging and/or media
content hosting/serving application, service or platform, such as,
for example, Yahoo! .RTM. Search, Yahoo! .RTM. Mail, Yahoo! .RTM.
Messenger, Flickr.RTM., Tumblr.RTM., Twitter.RTM., Instagram.RTM.,
SnapChat.RTM., Facebook.RTM., and the like.
[0080] The database 320 can be any type of database or memory, and
can be associated with a content server on a network (e.g., content
server 106, search server 120, ad server 130 or application server
108 from FIG. 1) or a user's device (e.g., device 101-104 or device
200 from FIGS. 1-2). Database 320 comprises a dataset of data and
metadata associated with local and/or network information related
to users, services, applications, content (e.g., images) and the
like. Such information can be stored and indexed in the database
320 independently and/or as a linked or associated dataset. As
discussed above, it should be understood that the data (and
metadata) in the database 320 can be any type of information and
type, whether known or to be known, without departing from the
scope of the present disclosure.
[0081] According to some embodiments, database 320 can store data
for users, i.e., user data. According to some embodiments, the
stored user data can include, but is not limited to, information
associated with a user's profile, user interests, user behavioral
information, user attributes, user preferences or settings, user
demographic information, user location information (i.e., past and
present location(s) of the user, and future locations of the user
(derived from a calendar or schedule of the user--e.g., planned
activities), user biographic information, and the like, or some
combination thereof. In some embodiments, the user data can also
include, for purposes rendering and/or displaying images, user
device information, including, but not limited to, device
identifying information, device capability information, voice/data
carrier information, Internet Protocol (IP) address, applications
installed or capable of being installed or executed on such device,
and/or any, or some combination thereof. It should be understood
that the data (and metadata) in the database 320 can be any type of
information related to a user, content, a device, an application, a
service provider, a content provider, whether known or to be known,
without departing from the scope of the present disclosure.
[0082] According to some embodiments, database 320 can comprise
information associated with content providers, such as, but not
limited to, content generating and hosting sites or providers that
enable users to search for content, message (e.g., send or receive
messages), upload, download, share, edit, comment or otherwise
avail users to media content (e.g., Yahoo! .RTM. Search, Yahoo!
.RTM. Mail, Flickr.RTM., Tumblr.RTM., Twitter.RTM., Instagram.RTM.,
SnapChat.RTM., Facebook.RTM., and the like). Such sites may also
enable users to search for and purchase products or services based
on information provided by those sites, such as, for example,
Amazon.RTM., EBay.RTM. and the like. In some embodiments, database
320 can comprise data and metadata associated with such content
information from one and/or an assortment of media hosting
sites.
[0083] In some embodiments, database 320 can comprise a lexicon of
one or more words, by way of non-limiting example, a vocabulary,
dictionary or catalogue of words/phrases (e.g., known or learned
word combinations). As discussed below, the lexicon can be compiled
based on social networking, search and mail activity of users on a
network. In some embodiments, the lexicon information housed within
the database 320 can be arranged in accordance with various known
or to be known models in order to preserve an efficient and
accurate retrieval of terms within the database.
[0084] According to some embodiments, the lexicon in database 320
comprises terms (i.e., words, phrases or paragraphs) arranged
according to how they were generated. For example, terms associated
with search queries can be organized according to when they were
entered by a user and/or which other search terms were associated
therewith. Terms associated with mail messages, for example, can be
organized in accordance with other terms in the same or similar
messages (e.g., group text within a single message, group text
associated with a message thread). Terms associated with social
networking activity can be organized in accordance with their
order, their topic and/or by which users and/or the domain they are
associated with, for example. In some embodiments, the words are
arranged according to known or to be known language models. The
words in the lexicon can be assigned a unique identifier, such as,
but not limited to, a number or value. It should be understood that
generally no two words (or phrases) in a lexicon are associated
with the same unique identifier. Thus, a unique identifier should
be unique to one word/phrase in the lexicon.
[0085] According to some embodiments, n-grams may be encoded using
such word identifiers. As understood by those of skill in the art,
an n-gram involves computational linguistics for a contiguous
sequence of n-items from a given sequence of text. Thus, in the
lexicon within database 320, the terms that are related to one
another (e.g., terms in a search query or terms in a message) can
be arranged according to a language model utilized for identifying
the next item in such a sequence. It should be understood that any
known or to be known arrangement or model (e.g., Markov model)
and/or algorithm can be used for arranging one or more words, and
identifying such one or more words in the database 320.
[0086] In some embodiments, database 320 can be specific to a user,
a network service or platform, or a global lexicon (such as a
generic or learned/trained lexicon). Thus, in some embodiments, the
lexicon of terms in database 320 may be ranked or ordered according
to the number of times a user or users has used a term during a
predetermined period. In some embodiments, the lexicon in database
320 can be based on a user's or users' behavior (e.g., past
activity--for example, words or phrases used in social network
messages at or above a threshold (frequency over a predetermined
time) which takes precedence over global lexical norms and
conventions). Therefore, in some embodiments, a determined
frequency for which a user uses a word or phrase online may be
utilized to organize how a lexicon stores or organizes
words/phrases.
[0087] In some embodiments, the information stored in database 320
can be represented as an n-dimensional vector (or feature vector)
for each stored term, where the information associated with the
words (or text or keywords) within each search and/or message
corresponds to a node(s) on the vector. Additionally, the
information in database 320 can comprise, but is not limited to,
social metrics associated with the information (e.g., popularity of
the content or product--a number of views, shares, favorites,
reviews or purchases), a title or comment(s) associated with the
information, tags, descriptions, quality of the content, recency of
the content's upload and/or share(s), and the like. Such factors
can be derived from information provided by the user, a service
provider (e.g., Yahoo! .RTM.), by the content/service providers
providing content information (e.g., Tumblr.RTM., Flickr.RTM., or
third party vendor sites), or by other third party services (e.g.,
Twitter.RTM., Facebook.RTM., Instagram.RTM., and the like, or third
party sites that enable users to purchase products from other
vendors, such as Amazon.RTM.), or some combination thereof. In some
embodiments, such additional factors can also be translated as
nodes on the n-dimensional vector for a respective search query,
search result and/or message.
[0088] As such, database 320 can store and index content
information in database 320 as linked set of data and metadata,
where the data and metadata relationship can be stored as the
n-dimensional vector discussed above. Such storage can be realized
through any known or to be known vector or array storage, including
but not limited to, a hash tree, queue, stack, VList, or any other
type of known or to be known dynamic memory allocation technique or
technology. While the discussion of some embodiments involves
vector analysis of content information, as discussed above, the
information can be analyzed, stored and indexed according to any
known or to be known computational analysis technique or algorithm,
such as, but not limited to, Word2Vec analysis, cluster analysis,
data mining, Bayesian network analysis, Hidden Markov models,
artificial neural network analysis, logical model and/or tree
analysis, and the like.
[0089] In some embodiments, database 320 can be a single database
housing information associated with one or more devices, users,
services and/or content providers, and in some embodiments,
database 320 can be configured as a linked set of data stores that
provides such information, as each datastore in the set is
associated with and/or unique to a specific user, device, service
and/or content provider.
[0090] As discussed above, with reference to FIG. 1, the network
315 can be any type of network such as, but not limited to, a
wireless network, a local area network (LAN), wide area network
(WAN), the Internet, or a combination thereof. The network 315
facilitates connectivity of the chatbot engine 300, and the
database of stored resources 320. Indeed, as illustrated in FIG. 3,
the chatbot engine 300 and database 320 can be directly connected
by any known or to be known method of connecting and/or enabling
communication between such devices and resources.
[0091] The principal processor, server, or combination of devices
that comprises hardware programmed in accordance with the special
purpose functions herein is referred to for convenience as chatbot
engine 300, and includes a query module 302, word-graph
construction module 304, transformation module 306 and output
module 308. It should be understood that the engine(s) and modules
discussed herein are non-exhaustive, as additional or fewer engines
and/or modules (or sub-modules) may be applicable to the
embodiments of the systems and methods discussed. The operations,
configurations and functionalities of each module, and their role
within embodiments of the present disclosure will be discussed
below.
[0092] Turning to FIGS. 4A-4B, Process 400 of FIG. 4A and Process
450 of FIG. 4B detail steps performed in accordance with some
embodiments of the present disclosure for generating the most
appropriate domain-specific response, as discussed herein. Process
400 of FIG. 4A details the steps in generating domain-specific
graphs corresponding to stylized lexicons that are utilized to
generate the domain-specific response, and Process 450 of FIG. 4B
details the steps performed by a computer(s) generating such
response.
[0093] As discussed herein with reference to Processes 400 and 450,
a regular chatbot response is determined, which is a response
without any stylized (or stylistic) elements added. As discussed
above, for purposes of explanation with regard to Process 400 and
450, the transformation of the regular response will be detailed
with respect to two domains--politics and entertainment. As
discussed in detail below, the transformation of the regular
response to the domain-specific, stylized response will retain the
factual content of the regular response, but add a distinctive
style such that a user can easily identify and attribute the
response to a specific domain.
[0094] By way of a non-limiting example, as illustrated in FIG. 5,
a regular chatbot response for an inquiry 502 related to the
weather, for example, "How is the weather", would typically involve
the following text, for example: "It is very hot today"--item 504.
The response 504 would be transformed into a domain-specific
response based on the steps outlined in the below disclosure
related to Processes 400 and 450.
[0095] For example, for an entertainment-type domain (e.g.,
fashionista domain, for example), the regular response 504 of "It
is very hot today" would be transformed into a response 506: "Brace
yourselves lovely people, it is kinda hot today! xoxo." The factual
nature that it is going to be hot today remains in the transformed
response 506; however, it has been stylized from the perspective of
how fashionistas would typically speak. For example, the words
"Brace yourselves lovely people" "kinda" and "xoxo" have been added
to the text of regular response 504, as has the punctuation "," and
"!"; and the "." at the end of the regular response 504 has been
changed. Also, the word "very" has been removed in view of the
addition of "kinda".
[0096] In another non-limiting example, for a political-type
domain, the regular response 504 of "It is very hot today" would be
transformed into a response 508: "Ladies and gentlemen, it appears
to be very hot today. Stay safe." The factual nature that it is
going to be hot today remains in the transformed response 508;
however, it has been stylized from the perspective of how
politicians would typically speak. For example, the words "Ladies
and gentlemen", "appears to be", "very" and "stay safe" have been
added to the text of regular response 504, as has the punctuation
"," and ".". Also, the word "is" from the regular response 504 has
been removed in view of the addition of "appears to be".
[0097] Thus, as illustrated in FIG. 5, these example embodiments
show how a regular response 504 is transformed according to two
different domains: entertainment domain (as related to the
transformed response 506) and political domain (as related to the
transformed response 508). The responses 506 and 508 have subtle
differences based on stylistic elements of words used, formality in
tone, and the like, derived from the learned styles of each domain,
as discussed in detail below.
[0098] For purposes of this disclosure, in order to learn,
understand or otherwise leverage the specific styles how
vocabularies and word patterns of particular domains, the chatbot
engine 300 relies on analysis and identified and/or extracted data
from Twitter.RTM. messages. While the discussion herein will focus
on messages from Twitter.RTM., it should not be construed as
limiting, as any type of network accessible platform/resource from
which language processing can occur can be utilized as a basis for
formulating the word-graphs, for example, but not limited to, other
social networking sites (e.g., Facebook.RTM.), email (e.g., Yahoo!
Mail.RTM.), blogs, articles, instant messaging platforms (e.g.,
Yahoo! Messenger.RTM., WhatsApp.RTM., and the like), web portals,
and the like, or some combination thereof.
[0099] It is understood by those of ordinary skill in the art that
Twitter.RTM. users constitute different types of personas, such as,
for example, politicians, singers, actors, sports persons, and the
like. Therefore, as discussed in more detail below, the chatbot
engine utilizes tweets as a data source to model domain-specific
styles. Identifying differences in vocabularies and word-usage
patterns across domains is critical in modeling domain
peculiarities and hence, differentiating between domains. For
examples, tweets from fashionistas contain informal language
("xoxo," and "ahhhhh," for example) and heavy usage of emoticons.
In contrast, tweets from politicians are more formal. By
introducing the peculiarities of a domain-specific style in a
response, and keeping its existing factual content intact, the
style of a specific domain can be mimicked when outputting a
chatbot response to an inquiring user. This intuition forms the
core of disclosed methodology, as discussed herein.
[0100] Process 400 details the computerized techniques for
constructing domain-specific word-graphs using tweets posted from
Twitter.RTM. accounts (and/or any other type of network accessible
platform/resource that enables learning/training of a system to
understand language styles) that belong to users from specific
domains, and using the graph to generate word-patterns.
[0101] According to some embodiments, as discussed below, the
chatbot engine 300 constructs a domain-specific word-graph using
tweets from Twitter handles (accounts) belonging to a domain. In
some embodiments, the word-graph for a domain is based upon a
multi-sentence compression approach where the nodes represent words
(along with part-of-speech (POS) tags) and the edges connect two
adjacent words. In the instant disclosure, this approach is
extended and improved upon by constructing a reliable graph by
ignoring edges and nodes which do not meet specific constraints. In
some embodiments, the infrequent edges in the set of tweets are
removed, as discussed in more detail below. Traversing the
word-graph from one node to another results in several paths which
form certain word-patterns. The chatbot engine 300 filters out
patterns containing nouns to prevent deviation from the actual
information, and restricts paths between pairs where the second
word is an auxiliary verb to avoid introducing irrelevant patterns.
Multiple scores are determined and assigned to each pattern (e.g.,
importance score, contextual similarity score and linguistic
quality score), as discussed below.
[0102] Then, the resultant word-graph for a domain is then utilized
as part of an Integer-Linear Programming (ILP) technique executed
by the chatbot engine 300 in order to select the most appropriate
patterns based on the above mentioned scores and rewrite the
regular response. This rewriting, or transformation of the regular
response is disclosed in relation to Process 450 which details the
computerized steps for responding to an inquiry with a transformed
response by leveraging the constructed word graphs from Process
400.
[0103] Turing first to FIG. 4A, Steps 402-420 of Process 400 are
performed by the word-graph construction module 304 of the chatbot
engine 300. Process 400 begins with Step 402 where a domain
representing a category of word usage patterns is identified. As
discussed above, such word usage patterns are related to ways or
manners in which individuals speak (e.g., their personalities)--for
example, politicians, sports reports, commentators, fashionistas,
people in the entertainment industry and the like. As a result of
Step 402, messages, and for purposes of this disclosure (as
discussed above), tweets from this domain are retrieved. For
example, at least a sample of 100 k tweets from the domain (e.g.,
100 k tweets from politicians during the 2016 Presidential
Campaign).
[0104] In Step 404, each retrieved message is parsed in order to
identify the content of each message. According to embodiments, for
example, each retrieved tweet is parsed, and the parsed data is
analyzed in order to identify the words (or identifiable character
strings) within each tweet.
[0105] In Step 406, each message and each message's content is
analyzed in order to determine a type of each message and the
content contained therein. The message type is determined because
duplicate messages are discarded from the retrieved set of
messages. For example, "retweets" are discarded because they are
duplicates of an original tweet. The content type of each message
is determined because messages can contain any type of content,
including, but not limited to, words, numbers, hashtags, or other
symbols and identifiers, uniform resource locators (URLs), images,
videos, and the like, and for purposes for tokenizing the content,
it may be required to identify the type of content, as discussed
below.
[0106] In Step 408, based on the identified message types and
content types contained therein, the content of each message is
tokenized and tagged. As understood by those of skill in the art,
tokenization is a natural language processing (NLP) process of
breaking a stream of text up into identified words, phrases,
symbols, or other meaningful elements and identifying them as
"tokens." In some embodiments, such tokenization can be performed
by any known or to be known algorithm, technology or mechanism for
tokenizing content, including, but not limited to, NLTK Treebank
tokenizer technology. Based on the content type determination, all
URLs, hashtags, numbers and other symbols or identifiers (e.g.,
Twitter.RTM. handles) are modified into standard tokens. The words,
or tokens as discussed herein, are then tagged with part-of-speech
(POS) tags. For example, each token can be assigned a tag using a
Twitter-specific POS tagger (or any other type of known or to be
known POS tagger, or domain-specific tagger).
[0107] In Step 410, a word-graph for each message is constructed
based on the combination of each token and its POS tag. According
to embodiments of the instant disclosure, the word-graph for each
message comprises nodes and edges between the nodes. The node,
which is a combination of a word and its POS tag, represents a
token and are iteratively added or mapped to the graph. An edge is
created between two nodes if the corresponding tokens are adjacent
in the original message (or tweet). In some embodiments, the
adjacency direction between the nodes is maintained by the graph
having directed edges (showing the order of words in the message).
In some embodiments, adjacency can be bidirectional such that the
edges are bidirectional between adjacent words.
[0108] In Step 412, the word-graphs for each message are mapped
according to their respective tokens and edges, and based on such
mapping, a bidirectional adjacency value is determined between
tokens across messages. According to embodiments of the instant
disclosure, such mapping is performed according to a predefined or
predetermined set of rules that dictate how tokens are added to a
graph. For example, in some embodiments, if there are no nodes with
the same corresponding word and POS tag as w, a new node is created
with token w. If there is only one node in the graph with the same
corresponding word and POS tag as w, then w is mapped to that
node.
[0109] According to some embodiments, when there are multiple nodes
with the same word and POS tag as w, w is assigned to the node
which has the highest contextual similarity with w. Contextual
similarity, as discussed in more detail below, is a value
representing a number of common words within a window of one word
on either side of the nodes and the current token (w) in a tweet.
If multiple nodes have the same contextual similarity with w, then
w is assigned randomly to one of those nodes. If contextual
similarity is zero for all the nodes, a new node with w is created
as a token. Such determinations of context of words and messages,
and their contextual similarity enable the chatbot engine 300 to
avoid spurious mappings of words to existing nodes.
[0110] In some embodiments, adjacency between two tokens across
tweets can be bidirectional; therefore, the chatbot engine 300 can
execute the following strategy to maintain the acyclic nature of
the graph. For example, assuming a tweet with the following
consecutive pair of written words (referred to as a bigram):
w.sub.1.sub._w.sub.2, where w.sub.1 and w.sub.2 are the two tokens
in the bigram. For this adjacency, there will be a directed edge
from node n.sub.1 to node n.sub.2, whose corresponding tokens are
w.sub.1 and w.sub.2, respectively. In embodiments where there is a
tweet having a reverse bigram, i.e., w.sub.2.sub._.sub.2 w.sub.1,
then to avoid forming a cycle between nodes n.sub.1 and n.sub.2,
w.sub.2 is mapped to n.sub.2 using the above mentioned criteria,
but w.sub.1 is not mapped to n.sub.1 even if the mapping criteria
are met. Either a new node for w.sub.1 is created or w.sub.1 is
assigned to another node (other than n.sub.1) depending upon
whether the mapping criteria are met or not.
[0111] In Step 414, a word-graph for the domain, or specific to the
domain, is constructed based on the mapping of each message's
word-graphs. By way of a non-limiting example, using the following
example tweets in the politics domain:
[0112] 1. "We will win in 2016 because we are going to create an
unprecedented grassroots movement."
[0113] 2. "The only way we can win is if enough people come
together to join our movement. So, are you in?"
[0114] As can be seen above, the tweets have common words such as
"win" and "movement". Merging the sentences along the words would
result in several new possible patterns between pairs of words. For
example: a pattern--"unprecedented grassroots movement. So, are you
in?" between "unprecedented" and "in", that did not exist in any of
the two tweets but is now generated as a result of fusion between
both the tweets. According to some embodiments, as discussed
herein, two dummy nodes (-start- and -end-) can be introduced to
map the beginning and end of all the tweets.
[0115] In Step 416, the constructed word-graph from Step 414 is
pruned based on analysis of the nodes and edges in the domain
word-graph according to determined (or predetermined) constraints.
One of ordinary skill in the art would understand that constructing
a domain word-graph using adjacency relations between the words in
all the messages from a domain results in a large number of edges.
Not all the edges are very frequent (satisfying an occurrence
threshold), and may contain grammatically incorrect sequences due
to the general informal style of tweets. As such, a significant
number of such edges are determined to be irrelevant and should be
removed. Therefore, in order to favor relevant and grammatically
correct word patterns, the chatbot engine 300 executes a pruning
function at both node and edge levels. According to some
embodiments, the nodes that have less than a predetermined number
of edges (e.g., a constraint indicating a minimum number of edges:
5 edges, including both outgoing and incoming edges) are removed
from the domain word-graph.
[0116] In Step 418, an edge weight is computed for each of the
remaining edges in the domain word-graph. In Step 420, a further
pruning step is performed based on the determined edge weights,
such that the edges with edge weights lower than a threshold value
(e.g., the t.sup.th percentile value) are removed from the domain
word-graph.
[0117] According to some embodiments, the edge weight W can be
computed as follows:
W ( e ij ) = freq ( w i w j ) freq ( w i ) * freq ( w j ) , ( Eq .
1 ) ##EQU00001##
[0118] where W(e.sub.ij) denotes the weight of edge e.sub.ij
between nodes i and j with corresponding tokens w.sub.i and
w.sub.2, respectively; and freq denotes the frequency. From Eq. 1,
the numerator computes the frequency of co-occurrence of tokens
w.sub.i and w.sub.2, and the denominator computes the unigram
frequencies of w.sub.i and w.sub.2.
[0119] As such, as a result of Steps 402-420 of Process 400, a
domain-specific word-graph is compiled that is ready to use on a
regular response in order to insert stylized words that enables the
transformed response to mimic a personality associated with the
domain, as discussed below in relation to Process 450 of FIG.
4B.
[0120] Turning now to Process 450 of FIG. 4B, Steps 452-454 are
performed by the query module 302 of the chatbot engine 200; Steps
454-472 are performed by the transformation module 306; and Step
474 is performed by the output module 308. Process 450 details the
steps for transforming a regular response from a chatbot by
introducing and inserting relevant word patterns between existing
words of the response without modifying its factual content. Such
transformation is modeled as an Integer-Linear Programming (ILP)
problem.
[0121] Process 450 begins with Step 452 where an input is received
from a user in relation to a chatbot that includes a query. For
example, a user can be viewing a webpage and in response to a
chatbot dialog box being displayed on the page, the user can enter
a query, which can include a string of characters. As understood by
those of skill in the art, the input can be any type of input,
including, but not limited to, a character string of text, numbers
or symbols, a URL(s), image content, video content, voice or audio
content, longitude and latitude coordinates, global positioning
system (GPS) data, and the like, or some combination thereof. Thus,
Step 402 involves the entering of the query and the input that
triggers a search to be performed for a chatbot regular response to
the query.
[0122] In Step 454, in response to the query, the chatbot engine
300 searches an associated database (or another resource location
on the internet) for a response to the query. Searching and
identification of the proper chatbot response can be performed by
any known or to be known chatbot, or chatbot executed technology,
such as, but not limited to, NLP, n-gram analysis, vector
translation and analysis, and the like, or some combination
thereof. Therefore, as a result to Step 454, the chatbot engine 300
determines, retrieves or otherwise identifies a regular response to
the query, which includes a string or sequence of words.
[0123] In Step 456, the chatbot engine 300 tokenizes the regular
response in order to identify the individual words included in the
regular response. According to some embodiments, such tokenization
is performed in a similar manner as discussed above in relation to
Step 408 of Process 400.
[0124] In Step 458, based on the identified words identified from
the tokenization of the regular response occurring in Step 456, a
search of a repository is performed in order to determine, retrieve
or otherwise identify a set of synonyms for each word in the
regular response. According to some embodiments, the number of
synonyms for each word can be capped at a preset limit so as not to
have an unequal number of synonyms for particular words.
[0125] In some embodiments, whether a synonym for a word is
identified is based on the type of word. That is, the chatbot
engine 300 can implement basic syntactic rules to improve
grammatical correctness of the generated word patterns in the final
output (of Process 450). For example, if a second or subsequent
word in a word pair (bigram) is an auxiliary verb (such as, for
example, "is" or "are"), then a synonym for such word may not be
identified, as no new word would be introduced between the pair.
Without such constraint, there is a high probability that several
irrelevant words could be introduced between the stopwords that
result in an incoherent output.
[0126] In Step 460, a set of bigrams is created based on the
combinations of words in the regular response and in the determined
set of synonyms. In some embodiments, the created bigrams, embodied
as an extended set of bigrams from the bigrams of the regular
response, are constructed such that the synonyms of a word are
connected. In some embodiments, the extended bigrams are
constructed such that all possible combinations of words are
realizable from the constructed bigram.
[0127] In Step 462, a domain related to the regular response (or
chatbot) is identified. For example, if the query was entered in a
domain related to political news, a political domain is identified.
In another example, the context of the regular response (and/or the
query) can be determined (e.g., by parsing the text of the
response/query and determining its topic) and then identifying a
domain related to such topic. As a result of Step 462, a
domain-specific word-graph is identified that is related to the
identified domain. Such domain-specific word-graph is the graph
constructed in Process 400, discussed above.
[0128] In Step 464, for each bigram constructed in Step 460, word
patterns are determined, obtained or otherwise identified between
the words of the bigram from the domain word-graph associated with
the identified domain. According to some embodiments, the
identification of the word patterns can be performed by traversing
the graph from one node to another in order to identify a path
between the words in the bigram, and identifying the words between
the bigram words on such path. For example, if a bigram includes
the words "hot, today", then the first step is to identify the word
"hot" in the domain word-graph, then traverse the graph until the
word "today" is located; then, identify the word pattern (or
sequence of words) that appear along that path. In some
embodiments, such word pattern identification can be performed
according to the mapping steps discussed above in relation to Step
412 discussed above.
[0129] In some embodiments, the maximum number of words in the
identified word pattern can be restricted to a value K in order to
prevent a major deviation from the original meaning of the regular
response. In such embodiments, should a word pattern (or sequence
or path) have more than K words, then it can be discarded.
[0130] In Step 466, the chatbot engine 300 then computes word
pattern scores for each obtained word pattern, and identifies a
subset of word patterns based on their scores.
[0131] By way of a non-limiting example, a regular response
contains the following ordered set of words w.sub.1, w.sub.2,
w.sub.3 . . . w.sub.m. Two consecutive words in the above set of
words are: w.sub.i and w.sub.j, where j=i+1 (which indicates that
the max pattern to be inserted within each word is 1). Each pattern
between the pair of words w.sub.1 and w is denoted by
p.sup.q.sub.ij. As discussed above, patterns are obtained using a
domain-specific word graph structure constructed using tweets that
are associated with the domain. Each pattern has several scores
associated with it including: importance, contextual similarity and
linguistic quality:
[0132] Importance (I(p.sup.q.sub.ij)): Informativeness or
importance of the pattern computed using average co-occurrence
scores between every pair of words from the domain-specific
word-graph. The value is obtained using Eq. 1 discussed above.
[0133] Contextual Similarity (Sim(p.sup.q.sub.ij)): Contextual
similarity is computed as the cosine similarity between the
paragraph vector of the regular response and the paragraph vector
of the pattern. Generated patterns should be contextually relevant
to the original regular response otherwise the final response may
be incoherent and vague. For example, when transforming the
response "bond market", it should include patterns that fit into
the context of the "financial bond sector" and not a "bond movie".
To obtain contextually relevant patterns, the chatbot engine 300
computes similarities between the original regular response and the
generated patterns from the domain word-graph. In some embodiments,
the regular response and the patterns can be represented as vector
representations using, for example, Paragraph2Vec, where the cosine
similarities between the regular response and each pattern are
computed. The patterns with higher cosine similarities are ranked
higher and the transformation of the regular response is only based
on the top n patterns (e.g., top 5 patterns).
[0134] Linguistic Quality (LQ(p.sup.q.sub.ij)): Indicator of
grammaticality that assigns a score of linguistic confidence to a
sequence of words using two language models trained, for example,
on news and Twitter corpora. Such language modelling scores more
probable sequences higher than the sequences that have lesser
chances of occurring in the dataset.
[0135] According to some embodiments, the chatbot engine 300 may
restrict deviation in sentiment in the modified response by
limiting only those patterns whose sentiment levels were low. In
other words, marked differences in opinions (or facts) between the
original response and rewritten response are to be avoided. In some
embodiments, patterns that had linguistic quality values between -2
to +2 (negative to positive) are used in order to ensure the same
factual nature of the response is maintained upon
transformation.
[0136] In Step 468, a dependency parser confidence score is
determined based at least in part on the computed pattern scores
(from Step 466), which accounts for overall grammaticality of the
word patterns. In some embodiments, the dependency parser
confidence score is computed using the Stanford Dependency Parser.
The dependency parser confidence score is computed using the
following equation:
F = D ( k ) * y k + i , j .di-elect cons. k , j = i + 1 I ( p ij q
) * Sim ( p ij q ) * LQ ( p ij q ) ) * p ij q , ( Eq . 2 )
##EQU00002##
[0137] where D(k) is the dependency parser confidence score for
sentence y.sub.k. The number of sentences depends on the number of
words in the regular response and also the number of patterns
between each pair of words. Hence, if there are a, b, c and d
number of patterns between all the word pairs respectively, a
maximum of a*b*c*d sentences can be constructed. Due to this
exponential nature, the number of patterns between pairs of words
is restricted using an approach only keeping the top N patterns as
retained. In some embodiments, for example, only the top two
patterns are used.
[0138] In Step 470, the word pattern(s) with the highest dependency
parser confidence score is selected, and such selection is
according to a determined constraint that ensures that the selected
pattern is the most relevant and topical sequence of words in
relation to the regular response. In some embodiments, the
following constraint is utilized:
.A-inverted.p.sub.ij.sup.q between w.sup.i and
w.sup.j,.SIGMA.p.sub.ij.sup.q=1, (Eq. 3).
[0139] For example, the sentences (y.sub.k) are a result of
combination of word patterns. Only one of the sentences can be
selected out of all the possible sentences. Therefore, only a
single combination of patterns between the first and the last word
is selected corresponding to the selected sentence and all other
combinations are discarded. To model this constraint, each y.sub.k
is represented as a combination of patterns .PI.i,j.di-elect cons.s
. . . e,q.di-elect cons.1 . . . max.sub.qp.sub.ij.sup.q, selecting
only one pattern at a time between consecutive words. Here, s and e
refer to two indices corresponding to "dummy" start and end words
respectively. In some embodiments, the introduction of dummy words
allows for introducing patterns at the beginning and end of the
response. For example, if there are two words in a sentence,
w.sub.1 and w.sub.2, the addition of the start and end dummy words
creates a sequence w.sub.s, w.sub.1, w.sub.2 and W.sub.e. The
patterns between the first two words would be represented as
p.sup.1.sub.s1, p.sup.2.sub.s1, etc. Therefore, for example, one of
the possible combinations of patterns between words would be
p.sup.1.sub.s1, p.sup.1.sub.12, p.sup.1.sub.23, p.sup.1.sub.3e,
where the first patterns between adjacent words are combined, which
represents y.sub.1, the first modified sentence. Each sentence is
represented as the product of the patterns that constructed it:
.A-inverted. k , .A-inverted. q .di-elect cons. [ 1 num_patterns ]
y k = i , j .di-elect cons. k , j = i + 1 p ij q , ( Eq . 4 ) .
##EQU00003##
[0140] The num_patterns in Eq. 4 refers to the total number of
patterns used to construct a sentence, which in the example above
is equal to 4. According to some embodiments, since each sentence
is represented as a product of several patterns, the chatbot engine
300 is creating non-linear equation. However, since ILP constraints
need to be linear, the non-linearity is converted to linearity
using simple transformations. As the variables are all binary, each
y.sub.k is transformed using the pattern variables and linear
constraints.
[0141] For example, having y.sub.1=p.sup.1.sub.s1, p.sup.1.sub.12,
p.sup.1.sub.23, p.sup.1.sub.3e, the non-linear constraint can be
rewritten as follows:
y.sub.1<=p.sub.s1.sup.1
y.sub.1<=p.sub.12.sup.1
y.sub.1<=p.sub.23.sup.1
y.sub.1<=p.sub.3e.sup.1
y.sub.1>=p.sub.s1.sup.1+p.sub.12.sup.1+p.sub.23.sup.1+p.sub.3e.sup.1--
(num_patterns-1), (Eq.5).
[0142] Eq. 5 constrains the sentence variable such that it is equal
to 1 if and only if all the associated patterns are equal to 1;
otherwise it is equal to 0. Thus, as in Step 470, in some
embodiments, only m the sentences/patterns can be selected (per
bigram), and therefore, it is added as a constraint to the ILP,
which is represented by the following equation:
k y k = 1 , ( Eq . 6 ) . ##EQU00004##
[0143] Solving the ILP along with the above mentioned constraints
generates the optimal patterns used to modify the regular response.
However, according to some embodiments, with the input sentence
from the regular response, the complexity of the system continues
to grow exponentially as the products of patterns can result in
multiple sentences. Therefore, in some embodiments, a novel
approach is applied by the chatbot engine 300 where only a
predetermined number of patterns are selected between each set of
words. This threshold value is a product of the three scores
assigned to the patterns to obtain a ranked list, and only the top
m patterns for the ILP formulation are chosen--where, for example,
m is set to 2. In some embodiments, the maximum length of the
pattern is set to a threshold value L (where L, for example, can be
set to 2).
[0144] Therefore, in Step 472, the patterns selected by ILP are
used to fill the gaps between adjacent words (or bigrams) and a
revised response is generated. That is, a top ranked pattern is
selected, as discussed above, and in Step 472, the selected pattern
is inserted into the regular response, thereby modifying the
response. This modified/transformed regular response is then output
to the user in response to the query received in Step 452. Step
474. This response is output to the user for display within a user
interface (UI) associated with the chatbot.
[0145] By way of another non-limiting example, based on the above
discussions of Process 450 leveraging the domain word-graph
constructed by Process 400, a regular response of "He is a loser"
will be transformed using the domain word-graph built using tweets
(e.g., from the Twitter.RTM. Firehose) from the entertainment
domain (for example, from tweets sent by users from the
entertainment industry). The following are the pairs of words
between which patterns would be introduced: (i) -start-, he (ii)
is, a (iii) a, loser (iv) loser, -end-. As discussed above, the
-start- and -end-tokens are dummy tokens used to mark the start and
end of the input. As a result, patterns can, in some embodiments,
also be introduced before the first word and after the last word in
the response. Given a particular domain word graph, example
patterns between each pair of words are as follows: "is literally",
"a total loser, loser !!! xoxoxo". Combining all the suggested
patterns, results in the following sequence: "He is literally a
total loser !!! xoxoxo." Here, the input sentence is significantly
transformed to reflect the casual writing style used in tweets from
users that belong to the entertainment domain.
[0146] FIG. 6 is a work flow example 600 for serving relevant
digital media content associated with or comprising advertisements
(e.g., digital advertisement content) based on the information
associated with a generated chatbot response, as discussed above in
relation to FIGS. 3-5. Such information, referred to as "chatbot
response information" for reference purposes only, can include, but
is not limited to, information associated with the query or
question asked by a user, information related to the requesting
user, the domain associated with the response, the new words added
to the response, the style added to the original response, and the
like, and/or some combination thereof.
[0147] As discussed above, reference to an "advertisement" should
be understood to include, but not be limited to, digital media
content that provides information provided by another user,
service, third party, entity, and the like. Such digital ad content
can include any type of known or to be known media renderable by a
computing device, including, but not limited to, video, text,
audio, images, and/or any other type of known or to be known
multi-media. In some embodiments, the digital ad content can be
formatted as hyperlinked multi-media content that provides
deep-linking features and/or capabilities. Therefore, while the
content is referred as an advertisement, it is still a digital
content item that is renderable by a computing device, and such
digital content item comprises digital content relaying proprietary
or promotional content provided by a network associated third
party.
[0148] In Step 602, chatbot response information is identified. As
discussed above, the chatbot response information can be based any
of the information from processes outlined above with respect to
FIGS. 3-5. For purposes of this disclosure, Process 600 will refer
to single chatbot response as the basis for serving a digital
advertisement(s); however, it should not be construed as limiting,
as any number of responses, queries and the like, as well as
programs used during the chatbot response generation/transformation
can form such basis, without departing from the scope of the
instant disclosure.
[0149] In Step 604, a context is determined based on the identified
chatbot response information. This context forms a basis for
serving advertisements related to the chatbot response information.
In some embodiments, the context can be based on a determined
category which the chatbot response information of Step 602
represents. For example, the chatbot response can include content
associated with a category corresponding to "fashion"; therefore,
the context identified in Step 604 can be related to "fashion" or
other "clothing trends" and can be leveraged in order to identify
digital ad content of interest (for example, a digital ad providing
a promotion for a discount at a local (to the user's geographic
location) department store), as discussed herein in relation to the
steps of Process 600. In some embodiments, the identification of
the context from Step 604 can occur before, during and/or after the
analysis detailed above with respect to Process 400 (and its
sub-parts), or some combination thereof.
[0150] In Step 606, the determined context is communicated (or
shared) with an advertisement platform comprising an advertisement
server 130 and ad database. Upon receipt of the context, the
advertisement server 130 performs (e.g., is caused to perform as
per instructions received from the device executing the chatbot
engine 300) a search for a relevant advertisement within the
associated ad database. The search for an advertisement is based at
least on the identified context.
[0151] In Step 608, the advertisement server 130 searches the ad
database for a digital advertisement(s) that matches the identified
context. In Step 610, an advertisement is selected (or retrieved)
based on the results of Step 608. In some embodiments, the selected
advertisement can be modified to conform to attributes or
capabilities of the page, interface, message, platform, application
or method upon which the advertisement will be displayed, and/or to
the application and/or device for which it will be displayed. In
some embodiments, the selected advertisement is shared or
communicated via the application the user is utilizing to search,
view and/or render the chatbot response. Step 612. In some
embodiments, the selected advertisement is displayed within a
portion of the interface or within an overlaying or pop-up
interface associated with the interface used to enter the query and
receive/output the chatbot response.
[0152] As shown in FIG. 7, internal architecture 700 of a computing
device(s), computing system, computing platform and the like
includes one or more processing units, processors, or processing
cores, (also referred to herein as CPUs) 712, which interface with
at least one computer bus 702. Also interfacing with computer bus
702 are computer-readable medium, or media, 706, network interface
714, memory 704, e.g., random access memory (RAM), run-time
transient memory, read only memory (ROM), media disk interface 708
and/or media disk drive interface 720 as an interface for a drive
that can read and/or write to media including removable media such
as floppy, CD-ROM, DVD, media, display interface 710 as interface
for a monitor or other display device, keyboard interface 716 as
interface for a keyboard, pointing device interface 718 as an
interface for a mouse or other pointing device, and miscellaneous
other interfaces 722 not shown individually, such as parallel and
serial port interfaces and a universal serial bus (USB)
interface.
[0153] Memory 704 interfaces with computer bus 702 so as to provide
information stored in memory 704 to CPU 712 during execution of
software programs such as an operating system, application
programs, device drivers, and software modules that comprise
program code, and/or computer executable process steps,
incorporating functionality described herein, e.g., one or more of
process flows described herein. CPU 712 first loads computer
executable process steps from storage, e.g., memory 704, computer
readable storage medium/media 706, removable media drive, and/or
other storage device. CPU 712 can then execute the stored process
steps in order to execute the loaded computer-executable process
steps. Stored data, e.g., data stored by a storage device, can be
accessed by CPU 712 during the execution of computer-executable
process steps.
[0154] Persistent storage, e.g., medium/media 706, can be used to
store an operating system and one or more application programs.
Persistent storage can also be used to store device drivers, such
as one or more of a digital camera driver, monitor driver, printer
driver, scanner driver, or other device drivers, web pages, content
files, playlists and other files. Persistent storage can further
include program modules and data files used to implement one or
more embodiments of the present disclosure, e.g., listing selection
module(s), targeting information collection module(s), and listing
notification module(s), the functionality and use of which in the
implementation of the present disclosure are discussed in detail
herein.
[0155] Network link 728 typically provides information
communication using transmission media through one or more networks
to other devices that use or process the information. For example,
network link 728 may provide a connection through local network 724
to a host computer 726 or to equipment operated by a Network or
Internet Service Provider (ISP) 730. ISP equipment in turn provides
data communication services through the public, worldwide
packet-switching communication network of networks now commonly
referred to as the Internet 732.
[0156] A computer called a server host 734 connected to the
Internet 732 hosts a process that provides a service in response to
information received over the Internet 732. For example, server
host 734 hosts a process that provides information representing
image and/or video data for presentation at display 710. It is
contemplated that the components of system 700 can be deployed in
various configurations within other computer systems, e.g., host
and server.
[0157] At least some embodiments of the present disclosure are
related to the use of computer system 700 for implementing some or
all of the techniques described herein. According to one
embodiment, those techniques are performed by computer system 700
in response to processing unit 712 executing one or more sequences
of one or more processor instructions contained in memory 704. Such
instructions, also called computer instructions, software and
program code, may be read into memory 704 from another
computer-readable medium 706 such as storage device or network
link. Execution of the sequences of instructions contained in
memory 704 causes processing unit 712 to perform one or more of the
method steps described herein. In alternative embodiments,
hardware, such as ASIC, may be used in place of or in combination
with software. Thus, embodiments of the present disclosure are not
limited to any specific combination of hardware and software,
unless otherwise explicitly stated herein.
[0158] The signals transmitted over network link and other networks
through communications interface, carry information to and from
computer system 700. Computer system 700 can send and receive
information, including program code, through the networks, among
others, through network link and communications interface. In an
example using the Internet, a server host transmits program code
for a particular application, requested by a message sent from
computer, through Internet, ISP equipment, local network and
communications interface. The received code may be executed by
processor 702 as it is received, or may be stored in memory 704 or
in storage device or other non-volatile storage for later
execution, or both.
[0159] For the purposes of this disclosure a module is a software,
hardware, or firmware (or combinations thereof) system, process or
functionality, or component thereof, that performs or facilitates
the processes, features, and/or functions described herein (with or
without human interaction or augmentation). A module can include
sub-modules. Software components of a module may be stored on a
computer readable medium for execution by a processor. Modules may
be integral to one or more servers, or be loaded and executed by
one or more servers. One or more modules may be grouped into an
engine or an application.
[0160] For the purposes of this disclosure the term "user",
"subscriber" "consumer" or "customer" should be understood to refer
to a user of an application or applications as described herein
and/or a consumer of data supplied by a data provider. By way of
example, and not limitation, the term "user" or "subscriber" can
refer to a person who receives data provided by the data or service
provider over the Internet in a browser session, or can refer to an
automated software application which receives the data and stores
or processes the data.
[0161] Those skilled in the art will recognize that the methods and
systems of the present disclosure may be implemented in many
manners and as such are not to be limited by the foregoing
exemplary embodiments and examples. In other words, functional
elements being performed by single or multiple components, in
various combinations of hardware and software or firmware, and
individual functions, may be distributed among software
applications at either the client level or server level or both. In
this regard, any number of the features of the different
embodiments described herein may be combined into single or
multiple embodiments, and alternate embodiments having fewer than,
or more than, all of the features described herein are
possible.
[0162] Functionality may also be, in whole or in part, distributed
among multiple components, in manners now known or to become known.
Thus, myriad software/hardware/firmware combinations are possible
in achieving the functions, features, interfaces and preferences
described herein. Moreover, the scope of the present disclosure
covers conventionally known manners for carrying out the described
features and functions and interfaces, as well as those variations
and modifications that may be made to the hardware or software or
firmware components described herein as would be understood by
those skilled in the art now and hereafter.
[0163] Furthermore, the embodiments of methods presented and
described as flowcharts in this disclosure are provided by way of
example in order to provide a more complete understanding of the
technology. The disclosed methods are not limited to the operations
and logical flow presented herein. Alternative embodiments are
contemplated in which the order of the various operations is
altered and in which sub-operations described as being part of a
larger operation are performed independently.
[0164] While various embodiments have been described for purposes
of this disclosure, such embodiments should not be deemed to limit
the teaching of this disclosure to those embodiments. Various
changes and modifications may be made to the elements and
operations described above to obtain a result that remains within
the scope of the systems and processes described in this
disclosure.
* * * * *