U.S. patent application number 15/582096 was filed with the patent office on 2018-11-01 for assessing complexity of dialogs to streamline handling of service requests.
This patent application is currently assigned to international Business Machines Corporation. The applicant listed for this patent is international Business Machines Corporation. Invention is credited to Pavan Kapanipathi Bangalore, Qingzi Vera Liao, Biplav Srivastava.
Application Number | 20180314685 15/582096 |
Document ID | / |
Family ID | 63894957 |
Filed Date | 2018-11-01 |
United States Patent
Application |
20180314685 |
Kind Code |
A1 |
Srivastava; Biplav ; et
al. |
November 1, 2018 |
ASSESSING COMPLEXITY OF DIALOGS TO STREAMLINE HANDLING OF SERVICE
REQUESTS
Abstract
A dialogue complexity assessment method, system, and computer
program product for introducing the notion of dialogue complexity
to understand and compare dialogues in a repository, calculating
the dialogue complexity, use the dialogue complexity to understand
customer interactions in a variety of domains using public and
proprietary data, and demonstrate the dialogue complexity usage to
improve a service management operation.
Inventors: |
Srivastava; Biplav;
(YORKTOWN HEIGHTS, NY) ; Liao; Qingzi Vera;
(YORKTOWN HEIGHTS, NY) ; Bangalore; Pavan
Kapanipathi; (YORKTOWN HEIGHTS, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
international Business Machines Corporation |
Armonk |
NY |
US |
|
|
Assignee: |
international Business Machines
Corporation
|
Family ID: |
63894957 |
Appl. No.: |
15/582096 |
Filed: |
April 28, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 40/289 20200101;
G06F 40/35 20200101; G06F 40/284 20200101; G10L 15/01 20130101;
G06F 40/253 20200101; H04M 3/5175 20130101 |
International
Class: |
G06F 17/27 20060101
G06F017/27 |
Claims
1. A computer-implemented dialogue complexity assessment method,
the method comprising: calculating a complexity utilizing
domain-dependent terms and domain-independent terms of a dialogue
in stages by: computing a complexity of the dialogue at an
utterance level; computing a complexity of the dialogue at a turn
level; and computing a complexity of the dialogue based on the
complexity of the constituent turns and utterances, wherein the
complexity calculation distinguishes between any of a
domain-dependent term, a common language term, and a stop word in
the dialogue.
2. The computer-implemented method of claim 1, further comprising
determining a reason for the complexity of the dialogue, and
wherein the complexity is computed by computing the complexity of
the dialogue at the dialogue level based on the utterance level
complexity and the turn level complexity.
3. The computer-implemented method of claim 2, wherein the
determining further includes determining rules to explain a reason
for dialogue complexity, the rules including a language of the
dialogue, a domain of a conversation of the dialogue, and an
understandability of a content measured by their uniqueness to the
domain.
4. The computer-implemented method of claim 1, further comprising
managing a service handling based on the calculated complexity of
the dialogue.
5. The computer-implemented method of claim 4, wherein the managing
determines how a service request is handled at each turn based on
the calculated complexity of the dialogue.
6. The computer-implemented method of claim 1, further comprising
ranking a set of service handlers based on the calculated
complexity of the dialogue.
7. The computer-implemented method of claim 1, wherein the
complexity uses an N-gram structure.
8. (canceled)
9. The computer-implemented method of claim 1, wherein a customer
rating of an interaction is weighted with the calculated complexity
of the dialogue and a duration of the dialogue, and averaged over a
whole duration that an agent is to be evaluated during the dialogue
to determine an agent score for a service handler.
10. The computer-implemented method of claim 6, wherein the service
handlers are managed by at least one of: ranking a set of the
service handlers; assigning an agent to handle service requests;
ranking dialogues in a corpus based on the calculated complexity of
the dialogue; and improving an acquisition of a ground truth for a
dialogue system.
11. The computer-implemented method of claim 1, embodied in a
cloud-computing environment.
12. A computer program product for dialogue complexity assessment,
the computer program product comprising a computer readable storage
medium having program instructions embodied therewith, the program
instructions executable by a computer to cause the computer to
perform: calculating a complexity utilizing domain-dependent terms
and domain-independent terms of a dialogue in stages by: computing
a complexity of the dialogue at an utterance level; computing a
complexity of the dialogue at a turn level; and computing a
complexity of the dialogue based on the complexity of the
constituent turns and utterances, wherein the complexity
calculation distinguishes between any of a domain-dependent term, a
common language term, and a stop word in the dialogue.
13. The computer program product of claim 12, further comprising
determining a reason for the complexity of the dialogue, and
wherein the complexity is computed by computing the complexity of
the dialogue at the dialogue level based on the utterance level
complexity and the turn level complexity.
14. The computer program product of claim 13, wherein the
determining further includes determining rules to explain the
reason for dialogue complexity, the rules including a language of
the dialogue, a domain of a conversation of the dialogue, and an
understandability of a content measured by their uniqueness to
domain.
15. The computer program product of claim 12, further comprising
managing a service handling based on the calculated complexity of
the dialogue.
16. The computer program product of claim 15, wherein the managing
determines how a service request is handled at each turn based on
the calculated complexity of the dialogue.
17. The computer program product of claim 12, further comprising
ranking a set of service handlers based on the calculated
complexity of the dialogue.
18. The computer program product of claim 12, wherein the
complexity uses an N-gram structure.
19. A dialogue complexity assessment system, said system
comprising: a processor; and a memory, the memory storing
instructions to cause the processor to perform: calculating a
complexity utilizing domain-dependent terms and domain-independent
terms of a dialogue in stages by: computing a complexity of the
dialogue at an utterance level; computing a complexity of the
dialogue at a turn level; and computing a complexity of the
dialogue based on the complexity of the constituent turns and
utterances, wherein the complexity calculation distinguishes
between any of a domain-dependent term, a common language term, and
a stop word in the dialogue.
20. The system of claim 19, embodied in a cloud-computing
environment.
21. The computer-implemented method of claim 1, wherein the
utterance level, the turn level, and the complexity of the dialogue
are calculated and directly used as a dialogue corpus to
automatically learn the domain-dependent terms and the
domain-independent terms and uses them along with language terms
and keywords for the complexity calculation.
Description
BACKGROUND
[0001] The present invention relates generally to a dialogue
complexity assessment method, and more particularly, but not by way
of limitation, to a system, method, and computer program product
for determining complexity as a data-driven, context-independent
indicator to manage sets of dialogs and services operations.
[0002] Service industry thrives on engaged customers using a
company's offerings, and dialogs, whether written or spoken, is a
common form of such an interaction. Over time, organizations
collect a sizable volume of dialogue data that may be proprietary
or public depending on how customer service is provided.
[0003] As a customer calls up their service provider for a request,
their interaction may be routine or extraordinary. Recently, there
has been significant interest in the service management domain to
automatically analyze such interaction data to better understand
customer needs and ways to address them. For example, conventional
techniques have considered tracking high-level indicators such as
sentiments about how customer interactions are progressing in a
service center and enable managers to take pro-active actions.
[0004] Thus, there is a need in the art for a dialogue complexity
measure to characterize interactions with customers at the levels
of utterances, turns and overall dialogs using dialogue data from
online repositories as well as contact centers of service
providers.
SUMMARY
[0005] In an exemplary embodiment, the present invention can
provide a computer-implemented dialogue complexity assessment
method, the method including calculating staged measures of a
complexity of a dialogue by: computing the complexity of the
dialogue at an utterance level, computing the complexity of the
dialogue at a turn level, and using the two complexity measures to
compute the aggregate complexity of the dialogue. One or more other
exemplary embodiments include a computer program product and a
system.
[0006] Other details and embodiments of the invention will be
described below, so that the present contribution to the art can be
better appreciated. Nonetheless, the invention is not limited in
its application to such details, phraseology, terminology,
illustrations and/or arrangements set forth in the description or
shown in the drawings. Rather, the invention is capable of
embodiments in addition to those described and of being practiced
and carried out in various ways and should not be regarded as
limiting.
[0007] As such, those skilled in the art will appreciate that the
conception upon which this disclosure is based may readily be
utilized as a basis for the designing of other structures, methods
and systems for carrying out the several purposes of the present
invention. It is important, therefore, that the claims be regarded
as including such equivalent constructions insofar as they do not
depart from the spirit and scope of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] Aspects of the invention will be better understood from the
following detailed description of the exemplary embodiments of the
invention with reference to the drawings, in which:
[0009] FIG. 1 exemplarily shows a high-level flow chart for a
dialogue complexity assessment method 100 according to an
embodiment of the present invention;
[0010] FIG. 2 exemplarily depicts a distribution of turn complexity
in step 101 according to an embodiment of the present
invention;
[0011] FIG. 3 exemplarily depicts an adaptive system architecture
according to an embodiment of the present invention;
[0012] FIG. 4. exemplarily depicts ground truth acquisition
according to an embodiment of the present invention;
[0013] FIG. 5 depicts a cloud-computing node 10 according to an
embodiment of the present invention;
[0014] FIG. 6 depicts a cloud-computing environment 50 according to
an embodiment of the present invention; and
[0015] FIG. 7 depicts abstraction model layers according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0016] The invention will now be described with reference to FIGS.
1-7, in which like reference numerals refer to like parts
throughout. It is emphasized that, according to common practice,
the various features of the drawings are not necessarily to scale.
On the contrary, the dimensions of the various features can be
arbitrarily expanded or reduced for clarity.
[0017] By way of introduction of the example depicted in FIG. 1, an
embodiment of a dialogue complexity assessment method 100 according
to the present invention can include various steps for calculating
a complexity of a dialogue, determining a reason for the
complexity, and manage a service handling.
[0018] By way of introduction of the example depicted in FIG. 5,
one or more computers of a computer system 12 according to an
embodiment of the present invention can include a memory 28 having
instructions stored in a storage system to perform the steps of
FIG. 1.
[0019] Although one or more embodiments may be implemented in a
cloud environment 50 (see e.g., FIG. 6), it is nonetheless
understood that the present invention can be implemented outside of
the cloud environment.
[0020] With reference generally to FIGS. 1-4, dialogue is made up
of a series of turns, where each turn is a series of utterances by
one or more participants playing one or more roles. In the example
of customer support center, a user contacts a service center and
enters into a dialogue with a customer support agent. The
participant roles here are that of a customer and an agent, and the
roles inter-leave in every turn. On the other hand, in the example
of online support, a person may raise an issue on a public portal
and anyone may reply. The role of all participants here is that of
a portal user because the original user request may remain
unresolved. Each user utterance in such a case of single role to
define a new turn.
[0021] Referring now to FIG. 1, in step 101, staged measures of a
complexity of a dialogue are calculated. That is, the complexity of
the dialogue is calculated by computing the complexity at an
utterance level, at a turn level, and then at a dialogue level
based on the turn level and utterance level complexity.
[0022] The desiderata from a dialogue complexity measure are that
it can be automatically calculated, can be agnostic to
representation (e.g., attributes, values, entities) and yet be able
to incorporate them where available, can allow comparison of
dialogues, be easy to interpret source of complexity, be
compassable over dialogue structure to allow computation ease and
any relative weighing, and support boundary condition
properties.
[0023] The boundary conditions are complexity of an utterance with
less participants should be less than or equal to the same
utterance with more participants and complexity of an utterance
with less complex words should be less than or equal to the same
utterance with more complex words.
[0024] The complexity of the utterance level is computed where a
word phrase w.sub.i, denoted c(w.sub.i), is defined by following
terms calculated in the given order:
c ( w i ) = { 1 | w i .di-elect cons. DS 0.5 | w i .di-elect cons.
ES 0 | w i .di-elect cons. SWL ( 1 ) ##EQU00001##
[0025] where SWL represent the set of stop words, ES stand for the
set of English subset (common words), DS for domain specific
words/phrases and rest of the words are part of noise set NS. An
utterance U consists of word phrases w.sub.i such that
|U|=N.sub.U.sup.w=.SIGMA..sub.i.sup.|U|w.sub.i. The complexity of
an utterance, denoted c(U), is defined as:
c ( U ) = i = 1 U c ( w i ) U ( 2 ) ##EQU00002##
[0026] A turn is a collection of utterances where each role gets to
speak at least once. For a 2-role dialogue, a turn consists of two
utterances. Two equations (3) and (4) of turn complexity are
proposed. The first one is averaging utterances within a turn,
calculated by:
c ( T ) = i = 1 T c ( U i ) T ( 3 ) ##EQU00003##
[0027] where the number of utterances U.sub.i within the turn T is
denoted by |T|. Since turn complexity can be seen as a way to
reflect the complexity of interactions at the moment of the turn,
in an-other definition, dialogue acts tag to calculate a weighted
sum of utterance complexity. Dialog acts are tags that indicate the
communicative function of the utterance. For example, an utterance
may intend for requesting information, providing information, or
for social functions such as greetings or closing the dialogue.
Dialog act can be both manually or automatically tagged.
[0028] The weighted turn complexity is calculated by:
c ( T DA ) = i = 1 T c ( U i ) * w .alpha. ( U i ) T ( 4 )
##EQU00004##
[0029] where a function .alpha.(U.sub.i) is available to get the
dialogue act tag for utterance U.sub.i. Further, for each dialogue
act j, its weight is denoted by w.sub.j (in 0-1 range). The
utterance and turn complexity measures defined above look at the
content of interaction. To measure complexity at a dialogue level,
both the content and its structure are allowed to be considered.
Thus, two components are available in the calculation: average turn
complexity to reflect the content complexity, and the length of the
dialogue relative to the maximum length in the dialogue dataset of
that kind. The latter component can be seen as reflecting the
structural complexity (length) of the particular dialogue relative
to the maximum structural complexity (length) that the service
context allows. While the dialogue length is used as a simple
indicator, more sophisticated structural features can be
introduced. One can weigh these independent components to arrive at
the total dialogue complexity.
c D = c ( D ) = w 1 * i = 1 N ( t ) c ( T i ) N D T + w 2 * N D T N
D T max ( 5 ) ##EQU00005##
[0030] where the number of turns T.sub.i in the dialogue D by
|D|=N.sub.D.sup.T. Let N.sub.D.sup.T.sup.max be the maximum number
of turns per dialogue in the dataset S(D.sub.i.di-elect cons.S). If
w.sub.2=0 is used, content is only considered. However, in
embodiments of the invention, equal weight to both with
w1=w2=0.5.
[0031] Thereby, the overall dialogue complexity can be calculated
by aggregating turn complexities by, for example, using a weighted
sum of turn complexities, using a weighted decay based on estimated
length of dialogue, and using machine learning based methods such
as supervised learning using human-annotation of dialogue
complexity and predicting dialogue length, decay parameter,
etc.
[0032] Thus, the proposed metric is compositional, and uses
available dialogue content and structure. In traditional analysis
of dialogs from linguistics point of view, the focus is on
read-ability of dialogs. The disclosed complexity measure focuses
on word selection and the meaning(s) they may convey. One can
conceive more advanced metrics such as by estimating the ability of
a person to use the dialogue to perform a particular task better,
provided additional data is available conveying signals about
goodness of task accomplished.
[0033] It is noted that the complexity calculation makes
distinction between domain specific terms, common language
(English) terms and stop words, that do not convey significant
meaning. When analyzing utterances, the method can use single
words, and in other embodiments, multi-word phrases or more
generally, N-gram structure, i.e., a sliding window of
N-neighboring words.
[0034] Dialog data is not uniform and so is the exhibited
complexity. In FIG. 2, analysis on three dialogue datasets is
shown: Human Resources (HR), Restaurant booking and Ubuntu online
technical support. We see that dialogues for Ubuntu technical
support have lower average turn complexity. By examining random
sample of dialogues, the reason can be determined as speakers'
lower domain expertise in this case comparing to other more
familiar topics. Dialogues of a Human Resource agent has more
polarized distribution, with highest percentage at the low end of
complexity. By examining dialogues in with low complexity, the
reason can be determined as more frequent social chit-chat with the
Human Resource agent.
[0035] In step 102, a reason for the complexity of the dialogue is
determined. That is, rule-based interpretation of complexity
differences or changes for underlying reasons. Possible rules
include, for example, complexity due to language usage (i.e., high
average utterance complexity due to domain expertise), complexity
due to procedural structure (i.e., high average turn complexity due
to intensive information requests (instead of chit-chat)), and
complexity due to inherent domain difference (i.e., high average
dialogue complexity in a medical domain).
[0036] Thus, the reason for calculated values of dialogue
complexity is explainable using rules, where the rules cover
language; structure of dialogue in terms of constituent turns,
utterances and words; domain of conversation, and understandability
consideration like inference chain.
[0037] In step 103, a service handling is managed based on the
measured complexity of the dialogue and the reason for the
complexity of the dialogue. For example, choice of handlers, system
modules, and repository can be decided. That is, in step 103,
service handling flow is managed based on dialogue complexity,
performance of service agents can be assessed, and system
components can be selected based on complexity.
[0038] For example, a customer support center can have M agents. An
agent a.sub.j handles Na.sub.j dialogs in time Ta.sub.j. A function
.phi.(di) is given to find the customer's satisfaction (C-SAT) with
a dialogue d.sub.i and its complexity is measured by function
c(d.sub.i). The problem is to assess the performance of a support
center's agent, represented as .omega.(a.sub.j). Thus,
.omega.(a.sub.j) is defined by Equation 6. Here, the customer
rating of an interaction is weighted with its complexity and
duration, and averaged over the whole duration that an agent has to
be evaluated. The result is a number which will be between 0-1 if c
and C-SAT are in that range. Now even agents who work over
different time periods (Ti) and nature of dialogs can be
compared.
.omega. 3 ( a j ) = 1 T a j * ( i = 1 N a j c ( d i ) * .phi. ( d i
) * t i ) ( 6 ) ##EQU00006##
[0039] Thereby, step 103 can provide a technique to handle a
service request at each turn based on dialogue complexity, to rank
a set of service handlers based on dialogue complexity, to rank
dialogs based on dialogue complexity, and to enhance and improve
ground truth acquisition for a dialogue system.
[0040] For example, a handler model can be provided for a turn in
which an automated handler has a value of 1 with a cost of 0.7 and
a success rate of (if partial dialogue complexity <0.5), 0.9;
else 0.1 and a human has a value of 1, a cost of 1, and a success
rate of 1. In other words, the automated response has a high
success rate when the complexity of the dialogue is low. In step
103, the balance between the automated response and the human
response is computed to maximize the return function. In other
words, an optimization problem can be set up where Maximize
.SIGMA..sub.Expected remainder dialog turns i
Expect(Value.sub.i-Cost.sub.i).
[0041] FIG. 3 shows an application of dialogue complexity metric
for managing dialogs that a system has with a person. In this
example, the system has a number of alternative components at each
stage of processing: dialogue interface, dialogue understanding and
dialogue management. A computational dialogue system can determine
which modules to be used for a user or for a task based on the
complexity of dialogues a user is conducting. The estimation can be
made from either real-time incoming dialogues, or historical data
of the user or the task. When the dialogue complexity reaches
certain threshold, it can be determined that certain costly modules
should be used. When below the threshold, these modules may be
excluded to balance cost. The cost may include, but should not be
limited to, development cost and running cost. The more costly
modules may include, but should not be limited to, semantic parsing
in understanding user input, inference engine for dialogue
management, and mixed-initiative interaction in dialogue system
interface.
[0042] FIG. 4 shows another application of dialogue complexity
metric, this time to help a dialogue system built using machine
learning. Most such dialogue systems that utilize machine learning
techniques are supervised. An important task of supervision is
annotation of utterances to train a machine learning model
(conversation model). The annotation is performed by domain experts
by mapping/labeling utterances from users to appropriate responses.
However, selecting utterances to be labeled from a large pool of
unlabeled utterances is critical to build an accurate
conversational model. A prominent technique for selecting data
points and re-training the machine learning models, that includes
conversational models, is Active Learning. In such scenarios, in
one embodiment, the complexity measure in conjunction with other
features such as confidence (the probability of machine learning
classifier to classify the utterance) can be used to select
utterances from unlabeled set to be annotated. The selection
process will now have the ability to learn the range of complexity
scores for utterances to be labeled to improve the performance of
the conversation model.
[0043] Thus, the method 100 can directly use a dialogue corpus to
automatically learn (extract) domain specific terms and uses them
along with language (English) terms and keywords for complexity
calculation. This makes the calculation an automatic process
without need for input content markers. Also, the method 100 can
generate complexity scores at multiple levels of dialogue to
facilitate interpretations of complexity and enable correspondence
in system development and in addition to content-based calculation,
the invention can also calculate complexity based on machine
learning methods, by taking user utterance as input, assuming
labeled data is available. The invention uses rules to explain
reason for dialogue complexity, where the rules cover language of
dialogue, domain of conversation, and understandability of content
measured by their uniqueness to domain. Also, the invention can
measure complexity based on variance in volume of content, date and
time of content recording and its applicability, sentiment of
participants (human or agents).
[0044] The embodiment of the proposed method was implemented and
run on public dialogue datasets. The average calculated complexity
for them at different levels are shown below. It helps all
stakeholders take better decisions customized to characteristics of
the datasets.
TABLE-US-00001 M (utt.) M (turn) M (dialog) Ubuntu 0.767 0.767
0.407 Insurance 0.789 0.789 0.894 HR 0.801 0.803 0.423 Restaurant|
0.788 0.788 0.518
[0045] Exemplary Aspects, Using a Cloud Computing Environment
[0046] Although this detailed description includes an exemplary
embodiment of the present invention in a cloud computing
environment, it is to be understood that implementation of the
teachings recited herein are not limited to such 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.
[0047] 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.
[0048] Characteristics are as follows:
[0049] 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.
[0050] 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).
[0051] 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 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).
[0052] 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.
[0053] 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.
[0054] Service Models are as follows:
[0055] 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
circuits 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.
[0056] 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.
[0057] 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).
[0058] Deployment Models are as follows:
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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).
[0063] 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 comprising a network of interconnected nodes.
[0064] Referring now to FIG. 5, a schematic of an example of a
cloud computing node is shown. Cloud computing node 10 is only one
example of a suitable node and is not intended to suggest any
limitation as to the scope of use or functionality of embodiments
of the invention described herein. Regardless, cloud computing node
10 is capable of being implemented and/or performing any of the
functionality set forth herein.
[0065] Although cloud computing node 10 is depicted as a computer
system/server 12, it is understood to be operational with numerous
other general purpose or special purpose computing system
environments or configurations. Examples of well-known computing
systems, environments, and/or configurations that may be suitable
for use with computer system/server 12 include, but are not limited
to, personal computer systems, server computer systems, thin
clients, thick clients, hand-held or laptop circuits,
multiprocessor systems, microprocessor-based systems, set top
boxes, programmable consumer electronics, network PCs, minicomputer
systems, mainframe computer systems, and distributed cloud
computing environments that include any of the above systems or
circuits, and the like.
[0066] Computer system/server 12 may be described in the general
context of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are preformed by remote processing circuits that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
circuits.
[0067] Referring now to FIG. 5, a computer system/server 12 is
shown in the form of a general-purpose computing circuit. The
components of computer system/server 12 may include, but are not
limited to, one or more processors or processing units 16, a system
memory 28, and a bus 18 that couples various system components
including system memory 28 to processor 16.
[0068] Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0069] Computer system/server 12 typically includes a variety of
computer system readable media. Such media may be any available
media that is accessible by computer system/server 12, and it
includes both volatile and non-volatile media, removable and
non-removable media.
[0070] System memory 28 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
30 and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further described below, memory 28 may
include a computer program product storing one or program modules
42 comprising computer readable instructions configured to carry
out one or more features of the present invention.
[0071] Program/utility 40, having a set (at least one) of program
modules 42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may be
adapted for implementation in a networking environment. In some
embodiments, program modules 42 are adapted to generally carry out
one or more functions and/or methodologies of the present
invention.
[0072] Computer system/server 12 may also communicate with one or
more external devices 14 such as a keyboard, a pointing circuit,
other peripherals, such as display 24, etc., and one or more
components that facilitate interaction with computer system/server
12. Such communication can occur via Input/Output (I/O) interface
22, and/or any circuits (e.g., network card, modem, etc.) that
enable computer system/server 12 to communicate with one or more
other computing circuits. For example, computer system/server 12
can communicate with one or more networks such as a local area
network (LAN), a general wide area network (WAN), and/or a public
network (e.g., the Internet) via network adapter 20. As depicted,
network adapter 20 communicates with the other components of
computer system/server 12 via bus 18. It should be understood that
although not shown, other hardware and/or software components could
be used in conjunction with computer system/server 12. Examples,
include, but are not limited to: microcode, circuit drivers,
redundant processing units, external disk drive arrays, RAID
systems, tape drives, and data archival storage systems, etc.
[0073] Referring now to FIG. 6, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 10 with which local
computing circuits 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 circuit.
It is understood that the types of computing circuits 54A-N shown
in FIG. 6 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized circuit over any type of network and/or
network addressable connection (e.g., using a web browser)
[0074] Referring now to FIG. 7, an exemplary set of functional
abstraction layers provided by cloud computing environment 50 (FIG.
6) is shown. It should be understood in advance that the
components, layers, and functions shown in FIG. 7 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:
[0075] 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 circuits
65; and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0076] 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.
[0077] 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 comprise 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.
[0078] 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
dialogue complexity assessment method 100 in accordance with the
present invention.
[0079] 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.
[0080] 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 electronelectromagnetic 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.
[0081] 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.
[0082] 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 users
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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
[0088] Further, Applicant's intent is to encompass the equivalents
of all claim elements, and no amendment to any claim of the present
application should be construed as a disclaimer of any interest in
or right to an equivalent of any element or feature of the amended
claim.
* * * * *