U.S. patent application number 15/960602 was filed with the patent office on 2019-01-24 for mining procedure dialogs from source content.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Qi Cheng Li, Li Jun Mei, Jian Wang, Yi Peng Yu, Xin Zhou.
Application Number | 20190026347 15/960602 |
Document ID | / |
Family ID | 62948549 |
Filed Date | 2019-01-24 |
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United States Patent
Application |
20190026347 |
Kind Code |
A1 |
Li; Qi Cheng ; et
al. |
January 24, 2019 |
MINING PROCEDURE DIALOGS FROM SOURCE CONTENT
Abstract
An embodiment of the invention may include a method, computer
program product and computer system for human-machine
communication. The method, computer program product and computer
system may include a computing device that maps linguistic data of
source content to a vector. The computing device may cluster the
linguistic data of source content. The computing device may
determine a plurality of segments based on the mapped linguistic
data and the clustered linguistic data. The computing device may
transform a segment of the plurality of segments into
representative data, the representative data is a function of the
remaining plurality of segments.
Inventors: |
Li; Qi Cheng; (Beijing,
CN) ; Mei; Li Jun; (Beijing, CN) ; Wang;
Jian; (Beijing, CN) ; Yu; Yi Peng; (Beijing,
CN) ; Zhou; Xin; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Family ID: |
62948549 |
Appl. No.: |
15/960602 |
Filed: |
April 24, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15841421 |
Dec 14, 2017 |
10037362 |
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15960602 |
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15657654 |
Jul 24, 2017 |
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15841421 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/2465 20190101;
G06K 9/6269 20130101; G06F 40/289 20200101; G06F 17/16 20130101;
G06F 40/30 20200101; G06F 16/172 20190101; G10L 15/1815 20130101;
G06F 16/9038 20190101; G06F 16/901 20190101; G06F 16/24575
20190101; G06F 16/2428 20190101; G06Q 10/06 20130101; G06F 16/90332
20190101; G06F 16/906 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06K 9/62 20060101 G06K009/62 |
Claims
1. A computer program product for multi-tier data storage, the
computer program product comprising: a computer-readable storage
medium having program instructions embodied therewith, wherein the
computer readable storage medium is not a transitory signal per se,
the program instructions executable by a computer to cause the
computer to perform a method, comprising: receiving source content,
the source content comprising linguistic data, from one or more
databases at a server communicating with the one or more databases
using a communication network; embedding the linguistic data of the
source content to a high-dimensional vector using a neural network;
clustering the sentences of the source content into a plurality of
sentence groups based on the embedded linguistic data; sequencing
the clustered sentence groups of the source content into a set of
sentence sequences, wherein each of the plurality of sentence
groups is represented by a single representative sentence;
transforming each sentence of the source content by replacing each
sentence with the single representative sentence of the sentence
group that each sentence is sequenced into; embedding each
representative sentence of the source content based on the sentence
that precedes and follows each sentence; mapping the relationships
between the embedded representative sentences of the source
content; identifying related representative sentences of the source
content; generating linguistic data blocks comprised of the related
representative sentences of the source content; identifying
similarities between the linguistic data blocks based on cohesions;
separating the linguistic data blocks into groups based on the
cohesions identified; and generating a procedure dialogue based on
the cohesions between data blocks of the same group.
Description
BACKGROUND
[0001] The present invention relates generally to a method, system,
and computer program for mining procedure dialogs from source
content containing linguistic data. More particularly, the present
invention relates to a method, system, and computer program for
embedding linguistic data of source content to identify and
generate procedure flows.
[0002] Data embedding is a process that maps segments of linguistic
data, e.g. words and/or sentences, to vectors of real numbers. Data
embedding enables the prediction of certain data segments based on
the data segments that surround that data segment based on the
relationships of those segments in a vector. Data embedding also
enables the prediction of a surrounding data segments based on a
single data segment based on the relationships of those segments in
a vector.
BRIEF SUMMARY
[0003] An embodiment of the invention may include a method,
computer program product and computer system for human-machine
communication. The method, computer program product and computer
system may include a computing device that maps linguistic data of
source content to a vector. The computing device may cluster the
linguistic data of source content. The computing device may
determine a plurality of segments based on the mapped linguistic
data and the clustered linguistic data. The computing device may
transform a segment of the plurality of segments into
representative data, the representative data is a function of the
remaining plurality of segments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1a illustrates a system for mining procedure dialogs
from source content, in accordance with an embodiment of the
invention;
[0005] FIG. 1b illustrates example operating modules of the
procedure dialog mining program of FIG. 1a;
[0006] FIG. 2 is a flowchart illustrating an example method of
procedure dialog mining, in accordance with an embodiment of the
invention;
[0007] FIG. 3 illustrates an example method of sentence block
formation by the procedure dialog mining program of FIGS. 1a-b, in
accordance with an embodiment of the invention;
[0008] FIG. 4 is a flowchart illustrating an example procedure flow
generated by the procedure dialog mining program of FIGS. 1a-b, in
accordance with an embodiment of the invention;
[0009] FIG. 5 is a flowchart illustrating an example procedure flow
dialogue generated by the procedure dialog mining program of FIGS.
1a-b, in accordance with an embodiment of the invention;
[0010] FIG. 6 is a flowchart illustrating interrelated procedure
flow topics generated by the procedure dialog mining program of
FIGS. 1a-b, in accordance with an embodiment of the invention;
[0011] FIG. 7 is a flowchart illustrating and example method of
procedure dialog mining, in accordance with an embodiment of the
invention;
[0012] FIG. 8 is a block diagram depicting the hardware components
of the procedure dialog mining system of FIGS. 1a-b, in accordance
with an embodiment of the invention;
[0013] FIG. 9 illustrated a cloud computing environment, in
accordance with an embodiment of the invention;
[0014] FIG. 10 illustrates a set of functional abstraction layers
provided by the cloud computing environment of FIG. 9, in
accordance with an embodiment of the invention.
DETAILED DESCRIPTION
[0015] Embodiments of the present invention will now be described
in detail with reference to the accompanying Figures.
[0016] FIG. 1a illustrates a procedure dialog mining system 100, in
accordance with an embodiment of the invention. In an example
embodiment, procedure dialog mining system 100 includes procedure
dialog mining server 120, content device 110, and user device 130
interconnected via network 140.
[0017] In the example embodiment, network 140 is the Internet,
representing a worldwide collection of networks and gateways to
support communications between devices connected to the Internet.
Network 140 may include, for example, wired, wireless or fiber
optic connections. In other embodiments, network 140 may be
implemented as an intranet, a local area network (LAN), or a wide
area network (WAN). In general, network 140 can be any combination
of connections and protocols that will support communications
between procedure dialog mining server 120, user device 130, and
content device 110.
[0018] Content device 110 may contain source content 112. Content
device 110 may be a desktop computer, a notebook, a laptop
computer, a tablet computer, a handheld device, a smart-phone, a
thin client, or any other electronic device or computing system
capable of storing audio, visual, or textual content and receiving
and sending that content to and from other computing devices such
as procedure dialog mining server 120 via network 140. In some
embodiments, content device 110 includes a collection of devices,
or data sources, in order to mine procedure dialogs. Content device
110 is described in more detail with reference to FIG. 8.
[0019] Source content 112 is a collection of linguistic data
located in files including, but not limited to, for example, audio,
visual and textual content. Source content 112 may be, for example,
call center records, technical documents, question and answer forum
data, and websites. Source content 112 is located on content device
110 that can be accessed through using network 140.
[0020] User device 130 may include user interface 132. User device
130 may be a desktop computer, a notebook, a laptop computer, a
tablet computer, a handheld device, a smart-phone, a cellular
phone, a landline phone, a thin client, or any other electronic
device, computing system, wired or wireless device capable of
receiving and sending content to and from other computing devices,
such as procedure dialog mining server 120, via network 140. User
device 130 is described in more detail with reference to FIG.
8.
[0021] User interface 132 includes components used to receive input
from a user and transmit the input to procedure dialog mining
program 124 residing on procedure dialog mining server 120, or
conversely to receive information from procedure dialog mining
program 124 and display the information to the user. In an example
embodiment, user interface 132 uses a combination of technologies
and devices, such as device drivers, to provide a platform to
enable users of user device 130 to interact with procedure dialog
mining program 124. In the example embodiment, user interface 132
receives input, such as textual input received from a physical
input device, such as a keyboard.
[0022] Procedure dialog mining server 120 includes procedure dialog
mining program 124 and procedure dialog mining database 122. In the
example embodiment, procedure dialog mining server 120 may be a
desktop computer, a notebook, a laptop computer, a tablet computer,
a thin client, or any other electronic device or computing system
capable of storing compiling and organizing audio, visual, or
textual content and receiving and sending that content to and from
other computing devices, such as user device 130 and content device
110, via network 140. Procedure dialog mining server 120 is
described in more detail with reference to FIG. 7.
[0023] Procedure dialog mining database 122 is a collection of
procedure knowledge graphs, obtained from processing source content
112 by procedure dialog mining program 124. The procedure knowledge
graphs may contain information concerning relationships or
cohesions between specific elements of source content 112 (topics,
sentences, words, etc.). Additionally, the data may contain
indexing information about the relationship or cohesion between the
specific elements.
[0024] Procedure dialog mining program 124 is a program capable of
building procedure dialog mining database 122 from source content
112, and retrieving specific content to be displayed by user
interface 132 based on input received on user interface 132.
[0025] FIG. 1b illustrates example modules of procedure dialog
mining program 124. In an example embodiment, procedure dialog
mining program 124 may include nine modules: data collection module
210, word embedding module 212, sentence embedding module 214,
sentence clustering module 216, sentence sequencing module 218,
contextual sentence module 220, sentence block module 222, cohesion
module 224, and procedure flow module 226.
[0026] Data collection module 210 receives source content 112 from
content device 110 for processing. In an alternative embodiment,
source content 112 may be collected by server 120 and stored in
database 122 and data collection module 210 receives source content
112 from database 122.
[0027] Word embedding module 212 processes the collected linguistic
data using word embedding techniques. Word embedding enables the
quantification and categorization of semantic similarities between
words based on their distributional properties in large samples of
linguistic data. Word embedding techniques involve mapping
linguistic segments, e.g., words and/or phrases, to vectors of real
numbers. Word embedding module 212 may use a variety of word
embedding methods and algorithms including, but not limited to,
word2vec and Global Vectors for Word Representation (GloVe).
[0028] Sentence embedding module 214 processes the collected
linguistic data using sentence embedding techniques. Sentence
embedding is similar to word embedding only using larger semantic
segments, e.g. sentences. Sentence embedding module 214 may use a
variety of sentence embedding methods and algorithms including, but
not limited to, methods and algorithms using recurrent neural
networks (RNNs) and/or long short term memory (LSTM).
[0029] Sentence clustering module 216 processes the embedded
linguistic data using sentence clustering techniques to group
(cluster) sentence level texts into logical groups (clusters).
Sentence clustering module 216 then generates a representative
sentence or cluster index for each cluster. For example, source
content 112 may contain the following sentences: 1) IBM.RTM., Corp.
(IBM is a registered trademark of International Business Machines,
Corp.) earned a record 8,088 U.S. patents in 2016; 2) IBM.RTM.,
Corp. has earned more patents than any other company for the past
24 years; 3) Fortune.RTM. (FORTUNE is a registered trademark of
Time, Inc.) magazine reports today on 2016 issued U.S. patents; and
4) Fortune.RTM. magazine interviews IBM.RTM. Chief Innovation
Officer about issued U.S. Patents. In the example, sentence
clustering module 216 breaks down the above sentences into two
clusters; sentences 1 and 2 would be clustered together as they
both pertain to the topic of IBM.RTM. Patents and sentences 3 and 4
would be clustered together as the both pertain to the topic of
Fortune Magazine Reporting. Sentence clustering module 216 would
then generate a representative sentence for each cluster such as
"IBM.RTM., Corp. earned more U.S. patents in last 24 years than any
other company" and "Fortune.RTM. magazine interviews IBM.RTM. Chief
Innovation Officer for report on 2016 issued U.S. patents".
Alternatively, sentence clustering module 216 may select a sentence
from the cluster of sentences to be the representative sentence of
the cluster. Sentence clustering module 216 may choose the
representative sentence from the cluster of sentences randomly or
based on a predetermined algorithm, such as which sentence maps the
closest to the other sentences of the cluster within a vector. In
another embodiment, sentence clustering module 216 may generate a
cluster index for each sentence cluster.
[0030] Sentence sequencing module 218 transforms source content 112
into a set of sentence sequences or a sequence of sentence
clustering indexes by replacing each sentence in source content 112
using the representative sentence of its cluster or using the
sentence clustering index. For example, source content 112 may
contain ten sentences clustered into five clusters of two sentences
based on word embedding, sentence embedding, and sentence
clustering. Sentence sequencing module 218 will transform source
content 112 into a sequence of the five clusters, with each cluster
being represented by a single sentence. Alternatively, sentence
sequencing module 218 will transform source content 112 into a
sequence of the five clusters, with each cluster being represented
by a sentence clustering index, e.g. S1, S2, S3, S4, and S5. Thus,
sentence sequencing module 218 may transform source content 112
from ten sentences into
S1.fwdarw.S2.fwdarw.S3.fwdarw.S4.fwdarw.55.
[0031] Contextual sentence module 220 processes the sequenced
linguistic data using contextual sentence embedding techniques.
Contextual sentence embedding uses methods and algorithms
including, but not limited to, contextual continuous
bag-of-sentences (cc-bos) and contextual skip-gram (c-sg) to
transform and represent sentences by their surrounding sentences,
creating representative, or contextual, sentences. Contextual
sentence embedding differs from the sentence embedding of sentence
embedding module 214. Contextual sentence embedding involves
mapping representations of particular sentences with a sentence
being represented its surrounding sentences. For example, the
second sentence cluster index (sS2) of source content 112 may be
represented using the values of the first, third and fourth
sentence cluster indexes (S2, S3, S4) of source content 112.
[0032] Sentence block module 222 processes the contextually
embedded linguistic data and identifies the relationships between
the sentences of source content 112 to create sentence blocks,
which map the relationship between the sentences based in their
contextual representation. For example, source content 112 may be a
dialogue between a customer and a service agent. The customer
states a problem in an initiating statement, which in turn may
cause the service agent to respond in various ways, which in turn
causes the customer to respond. In this example dialogue, there
would be three sentence blocks created, each starting with the
customer's initiating statement and ending with the customer's
response and having one possible service agent response in the
middle. Sentence block formation is described in more detail with
reference to FIG. 3.
[0033] Cohesion module 224 analyzes sentence blocks created from
all source content 112 and identifies similarities between the
sentence blocks. These similarities between sentence blocks are
referred to as cohesions. A threshold for determining similarity
between sentence blocks may be manually defined, such as the topic
of the sentence block. Further, cohesion module 224 may merge
sentence blocks together if the similarities between the two
sentence blocks exceed the manually set threshold.
[0034] Procedure flow module 226 produces procedure flows based on
the cohesions between the sentence blocks. Procedure flow formation
is described in more detail with reference to FIGS. 4-5.
[0035] FIG. 2 is a flowchart illustrating a method for mining
procedure dialogs from source content, in accordance with an
embodiment of the invention.
[0036] Referring to step 5310, data collection module 210 of
procedure dialog mining program 124 receives source content 112.
Furthermore, procedure dialog mining program 124 may receive source
content 112 using data collection module 210 from one or more
sources, or one or more devices. For example, procedure dialog
mining program 124 may receive source content from databases
containing call center records, technical documents, question and
answer forum data, and/or website news, etc.
[0037] Referring to step 5312, word embedding module 212 of
procedure dialog mining program 124 embeds the words of collected
source content 112. Procedure dialog mining program 124 may embed
the words of source content 112 by mapping the individual words to
vectors of real numbers. These word vectors of real numbers are
positioned within a vector space such that words that share common
contexts are near one another in the vector space. In an example
embodiment, procedure dialog mining program 124 may utilize a
neural network, such as, but not limited to, word2vec to embed the
words of source content 112. Word2vec may utilize either continuous
bag-of-words (CBOW) architecture model or continuous skip-gram
(C-SG) model architecture to embed the words of source content 112.
CBOW enables the prediction of a word based its surrounding words.
C-SG enables the prediction of surrounding words based on a single
word.
[0038] Referring to step S314, sentence embedding module 214 of
procedure dialog mining program 124 embeds the sentences of source
content 112. Sentence embedding uses similar mapping techniques as
word embedding using larger semantic elements of the source content
112, e.g. sentences. Thus, sentences of source content 112 would be
mapped to vectors of real numbers with those sentence vectors being
positioned within a vector space near other sentence vectors that
share similar contexts.
[0039] Referring to step S316, sentence clustering module 216 of
procedure dialog mining program 124 clusters the sentences of
source content 112. Sentence clustering is described in more detail
above with reference to sentence clustering module 216.
[0040] Referring to step S318, sentence sequencing module 218 of
procedure dialog mining program 124 transforms source content 112
into a set of sentence sequences or a sequence of sentence
clustering indexes. Sentence sequencing is described in more detail
above with reference to sentence sequencing module 218.
[0041] In an example embodiment steps S312, S314, S316, and S318
may occur simultaneously or in any order.
[0042] Referring to step S320, contextual sentence module 220 of
procedure dialog mining program 124 processes the sequenced and
embedded source content into contextually represented linguistic
data. Contextually represented linguistic data is linguistic data
that is represented by the surrounding linguistic data. For
example, source content 112 may have sentences S1, S2, and S3.
Sentence S2 may be contextually represented as a function of
sentences S1 and S3. Contextual sentence embedding is described in
more detail above with reference to contextual sentence module
220.
[0043] Referring to step S322, procedure dialog mining program 124
processes the contextualized linguistic data and identifies the
relationships between the sentences of source content 112 using
sentence block module 222. Sentence block module 222 creates
sentence blocks, which map the relationship between the sentences
based on the sentences' contextual representation. Sentence block
formation is described in more detail with reference to FIGS. 1b
and 3.
[0044] Referring to step S324, cohesion module 224 of procedure
dialog mining program 124 analyzes sentence blocks created from
source content 112 to identify similarities between the sentence
blocks. These similarities between sentence blocks are referred to
as cohesions. For example, procedure dialog mining program 124 may
identify all sentence blocks of a single call center conversation
that relate to printer hardware troubleshooting.
[0045] Referring to step S326, cohesion module 224 of procedure
dialog mining program 124 analyzes the cohesions between sentence
blocks of source content 112 to identify the relationships, such as
topic, within source content 112. For example, a single call center
conversation may cover various topics such as printer hardware
troubleshooting and wireless printing troubleshooting. Procedure
dialog mining program 124 will identify the cohesion boundaries,
e.g. all content relating to printer hardware versus all content
relating to wireless printing and separate the source content
conversation accordingly at step S328.
[0046] Referring to step S330, cohesion module 224 of procedure
dialog mining program 124 identifies the cohesions between all
sentence blocks of all source content 112, e.g. not just a single
call center conversation, but all call center conversations.
[0047] Referring to step S332, procedure dialog mining program 124
processes all related sentence blocks using procedure flow module
226 and generates a procedure flow. Procedure flow formation is
described in more detail with reference to FIGS. 4 and 5.
[0048] FIG. 3 illustrates sentence block formation using sentence
block module 222 of procedure dialog mining program 124, in
accordance with an embodiment of the invention. Sentence block
module 222 processes the contextually embedded linguistic data and
identifies the relationships between the sentences of source
content 112 to create sentence blocks. For example, source content
112 may be a dialogue between a customer and a service agent. The
contextually embedded linguistic data of this dialogue may be user
sentences 410 and agent sentences 412. User sentence 410a may cause
agent sentence 412a, 412b, and/or 412c, which then cause user
sentence 410c. Thus, sentence block module 222 transforms user
sentences 410 and agent sentences 412 into sentence blocks 414.
User sentences 410 and agent sentences 412 are represented in
sentence blocks 414 in their contextually embedded form.
[0049] FIG. 4 is a flowchart illustrating an example procedure flow
generated by procedure dialog mining program 124, in accordance
with an embodiment of the invention. Procedure flow module 226
processes the cohesions of sentence blocks to produce procedure
nodes and identifies all possible flows between the procedure
nodes.
[0050] In an example embodiment, the procedure nodes are questions
presented to a user and the overall procedure flow represents all
possible dialogue flows. For example, procedure node 510 is the
starting point of a procedure flow where the system asks a user to
indicate his/her problem. In the example embodiment, a user may
indicate one of two problem areas and the system proceeds
accordingly to either procedure node 512 or procedure node 520. The
system continues presenting a user with questions, processing the
user's responses, and proceeding along the procedure flow until a
solution is achieved at procedure node 522 or procedure node 524.
An example procedure flow dialogue following the format of FIG. 4
is illustrated in FIG. 5.
[0051] Referring to FIG. 5, procedure dialog mining program 124,
based on the procedure flow, determines if a user's problem is
hardware or software related. Procedure dialog mining program 124
proceeds through the procedure flow according to the user's
response to the first question. If the user indicates a hardware
problem, the next step determines the type of connection the user
has. Procedure dialog mining program 124 will then check the
connection and if the connection is not available, the program
opens a hardware ticket. If procedure dialog mining program 124 is
able to detect a connection, the program proceeds to ping the
intranet web server and if the ping fails the system opens a
software ticket. If procedure dialog mining program 124 determines
the ping was successful, the program closes. Returning to the
beginning of the procedure flow, if the user indicates a software
issue, procedure dialog mining program 124 proceeds directly to the
step of pinging the intranet web server and proceeds from there as
described above.
[0052] FIG. 6 is a flowchart illustrating interrelated procedure
flow topics. It can be appreciated that source content 112 may
contain, for example, a single customer service dialogue spanning
several different related or unrelated topics. Procedure dialog
mining program 124 using cohesion module 224 identifies cohesions
between all source content 112 and generates a procedure flow as
described above. Further, procedure dialog mining program 124 using
cohesion module 224 is able to map cohesions between different
topics of data.
[0053] Referring to FIG. 6, for example, procedure dialog mining
program 124 identifies three interrelated topics 602, 604, and 606
and maps the cohesions between the topics. Topic 602 may be broken
down into two subtopics 602a-b, topic 604 may be broken down into
three subtopics 604a-c, and topic 606 may be broken down into two
subtopics 606a-b. Procedure dialog mining program 124 may identify
relationships between the various topics and subtopics. In the
example, subtopic 602a may be flow to subtopic 602b or to subtopic
604a. Subtopic 604a may flow to subtopic 604b or to subtopic 606a.
Subtopic 604b may flow to subtopic 604c or to subtopic 606b.
Subtopic 606a may flow to subtopic 606b or to subtopic 604b.
Subtopic 606b may flow to subtopic 604c, which in turn may flow to
subtopic 602b.
[0054] FIG. 7 is a flowchart illustrating a method for receiving
user communications and selecting an appropriate procedure flow, in
accordance with an embodiment of the invention.
[0055] Referring to step 5650, server 120 receives user
communication from user device 130. User communication may be in
any form such as, but not limited to, a phone call, an email, a
website inquiry, etc. Server 120 analyzes and identifies the topic
of the user communication at step S652. Referring to step 5654,
server 120 selects the appropriate procedure flow, created by
procedure dialog mining program 124, stored in database 122 and
responds to the user communication at step 5656 based on the
identified procedure dialog.
[0056] FIG. 8 depicts a block diagram of components of procedure
dialog mining server 120, content device 110 and user device 130,
in accordance with an illustrative embodiment of the present
invention. It should be appreciated that FIG. 8 provides only an
illustration of one implementation and does not imply any
limitations with regard to the environments in which different
embodiments may be implemented. Many modifications to the depicted
environment may be made.
[0057] Procedure dialog mining server 120, content device 110 and
user device 130 may include communications fabric 702, which
provides communications between computer processor(s) 704, memory
706, persistent storage 708, communications unit 712, and
input/output (I/O) interface(s) 714. Communications fabric 702 can
be implemented with any architecture designed for passing data
and/or control information between processors (such as
microprocessors, communications and network processors, etc.),
system memory, peripheral devices, and any other hardware
components within a system. For example, communications fabric 702
can be implemented with one or more buses.
[0058] Memory 706 and persistent storage 708 are computer-readable
storage media. In this embodiment, memory 706 includes random
access memory (RAM) 716 and cache memory 718. In general, memory
706 can include any suitable volatile or non-volatile
computer-readable storage media.
[0059] The programs procedure dialog mining program 124 and
procedure dialog mining database 122 in procedure dialog mining
server 120; source content 112 in content device 110; and user
interface 132 stored in user device 130 are stored in persistent
storage 708 for execution by one or more of the respective computer
processors 704 via one or more memories of memory 706. In this
embodiment, persistent storage 708 includes a magnetic hard disk
drive. Alternatively, or in addition to a magnetic hard disk drive,
persistent storage 708 can include a solid state hard drive, a
semiconductor storage device, read-only memory (ROM), erasable
programmable read-only memory (EPROM), flash memory, or any other
computer-readable storage media that is capable of storing program
instructions or digital information.
[0060] The media used by persistent storage 708 may also be
removable. For example, a removable hard drive may be used for
persistent storage 708. Other examples include optical and magnetic
disks, thumb drives, and smart cards that are inserted into a drive
for transfer onto another computer-readable storage medium that is
also part of persistent storage 708.
[0061] Communications unit 712, in these examples, provides for
communications with other data processing systems or devices. In
these examples, communications unit 712 includes one or more
network interface cards. Communications unit 712 may provide
communications through the use of either or both physical and
wireless communications links. The programs procedure dialog mining
program 124 and procedure dialog mining database 122 in procedure
dialog mining server 120; source content 112 in content device 110;
and user interface 132 stored in user device 130 may be downloaded
to persistent storage 708 through communications unit 712.
[0062] I/O interface(s) 714 allows for input and output of data
with other devices that may be connected to procedure dialog mining
server 120, content device 110 and user device 130. For example,
I/O interface 714 may provide a connection to external devices 720
such as a keyboard, keypad, a touch screen, and/or some other
suitable input device. External devices 720 can also include
portable computer-readable storage media such as, for example,
thumb drives, portable optical or magnetic disks, and memory cards.
Software and data used to practice embodiments of the present
invention, e.g., the programs of procedure dialog mining program
124 and procedure dialog mining database 122 in procedure dialog
mining server 120; source content 112 in content device 110; and
user interface 132 stored in user device 130, can be stored on such
portable computer-readable storage media and can be loaded onto
persistent storage 708 via I/O interface(s) 714. I/O interface(s)
714 can also connect to a display 722.
[0063] Display 722 provides a mechanism to display data to a user
and may be, for example, a computer monitor.
[0064] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0065] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0066] Characteristics are as follows:
[0067] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0068] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0069] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0070] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0071] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported, providing
transparency for both the provider and consumer of the utilized
service.
[0072] Service Models are as follows:
[0073] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0074] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0075] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0076] Deployment Models are as follows:
[0077] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0078] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0079] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0080] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0081] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0082] Referring now to FIG. 9, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 includes one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 9 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0083] Referring now to FIG. 10, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 9) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 10 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0084] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0085] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0086] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 83 provides access to the cloud computing environment for
consumers and system administrators. Service level management 84
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0087] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and
procedure dialog mining 96.
[0088] The programs described herein are identified based upon the
application for which they are implemented in a specific embodiment
of the invention. However, it should be appreciated that any
particular program nomenclature herein is used merely for
convenience, and thus the invention should not be limited to use
solely in any specific application identified and/or implied by
such nomenclature.
[0089] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of code, which comprises one or more
executable instructions for implementing the specified logical
function(s). It should also be noted that, in some alternative
implementations, the functions noted in the block may occur out of
the order noted in the figures. For example, two blocks shown in
succession may, in fact, be executed substantially concurrently, or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality involved. It will also be noted
that each block of the block diagrams and/or flowchart
illustration, and combinations of blocks in the block diagrams
and/or flowchart illustration, can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
[0090] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0091] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0092] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0093] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0094] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0095] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0096] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0097] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0098] While steps of the disclosed method and components of the
disclosed systems and environments have been sequentially or
serially identified using numbers and letters, such numbering or
lettering is not an indication that such steps must be performed in
the order recited, and is merely provided to facilitate clear
referencing of the method's steps. Furthermore, steps of the method
may be performed in parallel to perform their described
functionality.
* * * * *