U.S. patent application number 16/563943 was filed with the patent office on 2020-01-02 for cognitive routing of calls based on derived employee activity.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Hernan A. Cunico, Paul A.R. Frank, Martin G. Keen, Adam Smye-Rumsby.
Application Number | 20200007686 16/563943 |
Document ID | / |
Family ID | 68102023 |
Filed Date | 2020-01-02 |
United States Patent
Application |
20200007686 |
Kind Code |
A1 |
Keen; Martin G. ; et
al. |
January 2, 2020 |
COGNITIVE ROUTING OF CALLS BASED ON DERIVED EMPLOYEE ACTIVITY
Abstract
Cognitive routing of an incoming call includes analyzing
respective captured audio and video data related to each of a
plurality of agents of an enterprise, each agent associated with a
respective mobile device; and determining a respective current
activity in which each agent is engaged based on the agent's
related captured audio and video data. Such routing also includes
selecting one of the plurality of agents to receive an incoming
call based at least in part on the determined respective current
activity in which each agent is engaged; and routing an incoming
call to the mobile device associated with the selected one
agent.
Inventors: |
Keen; Martin G.; (Cary,
NC) ; Smye-Rumsby; Adam; (Reading, PA) ;
Cunico; Hernan A.; (Holly Springs, NC) ; Frank; Paul
A.R.; (Hamburg, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
68102023 |
Appl. No.: |
16/563943 |
Filed: |
September 9, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15995349 |
Jun 1, 2018 |
10440183 |
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16563943 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04M 3/5232 20130101;
H04M 2203/402 20130101; G06Q 10/063114 20130101; H04M 2201/50
20130101; H04M 3/548 20130101 |
International
Class: |
H04M 3/523 20060101
H04M003/523; G06Q 10/06 20060101 G06Q010/06 |
Claims
1-20. (canceled)
21. A computer-implemented method, comprising: analyzing respective
captured audio and video data related to each of a plurality of
agents respectively associated with different mobile devices;
determining, for each of the plurality of agents and based upon the
captured audio and video, a respective current activity being
engaged in; associating, to each of the determined current
activities of the plurality of agents, a score value indicative of
an appropriateness of interrupting the respective current activity;
selecting one of the plurality of agents to receive an incoming
call based upon the determined current activities of the plurality
of agents and the associated score values; and routing the incoming
call to a mobile device associated with the selected one agent.
22. The method of claim 21, wherein the selecting is based upon a
determined current emotional state of each agent.
23. The method of claim 22, wherein the determined current
emotional state of each agent is based on received biometric data
of each agent.
24. The method of claim 21, wherein the selecting is based upon a
determined current location of each agent.
25. The method of claim 21, wherein a status list of the determined
current activities is generated, and the status list is ranked
based on the associated score values.
26. The method of claim 25, wherein the selecting is based upon the
status list.
27. The method of claim 21, wherein the analyzing includes passive
listening analysis on the respective captured audio data.
28. A computer hardware device, comprising: a hardware processor
configured to initiate the following executable operations:
analyzing respective captured audio and video data related to each
of a plurality of agents respectively associated with different
mobile devices; determining, for each of the plurality of agents
and based upon the captured audio and video, a respective current
activity being engaged in; associating, to each of the determined
current activities of the plurality of agents, a score value
indicative of an appropriateness of interrupting the respective
current activity; selecting one of the plurality of agents to
receive an incoming call based upon the determined current
activities of the plurality of agents and the associated score
values; and routing the incoming call to a mobile device associated
with the selected one agent.
29. The system of claim 28, wherein the selecting is based upon a
determined current emotional state of each agent.
30. The system of claim 29, wherein the determined current
emotional state of each agent is based on received biometric data
of each agent.
31. The system of claim 28, wherein the selecting is based upon a
determined current location of each agent.
32. The system of claim 28, wherein a status list of the determined
current activities is generated, and the status list is ranked
based on the associated score values.
33. The system of claim 32, wherein the selecting is based upon the
status list.
34. The system of claim 28, wherein the analyzing includes passive
listening analysis on the respective captured audio data.
35. A computer program product, comprising: a computer readable
storage medium having program code stored thereon, the program code
executable by a data processing system to initiate the following
operations: analyzing respective captured audio and video data
related to each of a plurality of agents respectively associated
with different mobile devices; determining, for each of the
plurality of agents and based upon the captured audio and video, a
respective current activity being engaged in; associating, to each
of the determined current activities of the plurality of agents, a
score value indicative of an appropriateness of interrupting the
respective current activity; selecting one of the plurality of
agents to receive an incoming call based upon the determined
current activities of the plurality of agents and the associated
score values; and routing the incoming call to a mobile device
associated with the selected one agent.
36. The computer program product of claim 35, wherein the selecting
is based upon a determined current emotional state of each
agent.
37. The computer program product of claim 36, wherein the
determined current emotional state of each agent is based on
received biometric data of each agent.
38. The computer program product of claim 35, wherein the selecting
is based upon a determined current location of each agent.
39. The computer program product of claim 35, wherein a status list
of the determined current activities is generated, and the status
list is ranked based on the associated score values.
40. The computer program product of claim 39, wherein the selecting
is based upon the status list.
Description
BACKGROUND
[0001] The present invention relates to routing communication
sessions to one of a plurality of agents, and more specifically, to
deriving agents' current activities in order to select the agent to
which the communication session is routed.
[0002] With the proliferation of mobile phones, many enterprises
are replacing desk phones with a central phone system that routes
incoming calls directly to employee mobile devices. By the nature
of mobile devices, an on-shift employee could be involved in many
different types of activities when they receive an incoming phone
call. Many of these activities may make it inappropriate to answer
the call.
SUMMARY
[0003] A method includes analyzing, by a computer, respective
captured audio and video data related to each of a plurality of
agents of an enterprise, each agent associated with a respective
mobile device; determining, by the computer, a respective current
activity in which each agent is engaged based on the agent's
related captured audio and video data; selecting, by the computer,
one of the plurality of agents to receive an incoming call based at
least in part on the determined respective current activity in
which each agent is engaged; and routing, by the computer, an
incoming call to the mobile device associated with the selected one
agent.
[0004] A system includes a processor programmed to initiate
executable operations. In particular, the executable operations
include analyzing respective captured audio and video data related
to each of a plurality of agents of an enterprise, each agent
associated with a respective mobile device; determining a
respective current activity in which each agent is engaged based on
the agent's related captured audio and video data; selecting one of
the plurality of agents to receive an incoming call based at least
in part on the determined respective current activity in which each
agent is engaged; and routing an incoming call to the mobile device
associated with the selected one agent.
[0005] A computer program product includes a computer readable
storage medium having program code stored thereon. In particular,
the program code executable by a data processing system to initiate
operations including: analyzing, by the data processing system,
respective captured audio and video data related to each of a
plurality of agents of an enterprise, each agent associated with a
respective mobile device; determining, by the data processing
system, a respective current activity in which each agent is
engaged based on the agent's related captured audio and video data;
selecting, by the data processing system, one of the plurality of
agents to receive an incoming call based at least in part on the
determined respective current activity in which each agent is
engaged; and routing, by the data processing system, an incoming
call to the mobile device associated with the selected one
agent.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 is a block diagram illustrating an example of a
network data processing system in accordance with the principles of
the present disclosure.
[0007] FIGS. 2A-2D are flowcharts illustrating example methods of
providing cognitive call routing, in accordance with the principles
of the present disclosure.
[0008] FIG. 3 depicts a block diagram of a data processing system
in accordance with the present disclosure.
DETAILED DESCRIPTION
[0009] As defined herein, the term "responsive to" means responding
or reacting readily to an action or event. Thus, if a second action
is performed "responsive to" a first action, there is a causal
relationship between an occurrence of the first action and an
occurrence of the second action, and the term "responsive to"
indicates such causal relationship.
[0010] As defined herein, the term "computer readable storage
medium" means a storage medium that contains or stores program code
for use by or in connection with an instruction execution system,
apparatus, or device. As defined herein, a "computer readable
storage medium" is not a transitory, propagating signal per se.
[0011] As defined herein, the term "data processing system" means
one or more hardware systems configured to process data, each
hardware system including at least one processor programmed to
initiate executable operations and memory.
[0012] As defined herein, the term "processor" means at least one
hardware circuit (e.g., an integrated circuit) configured to carry
out instructions contained in program code. Examples of a processor
include, but are not limited to, a central processing unit (CPU),
an array processor, a vector processor, a digital signal processor
(DSP), a field-programmable gate array (FPGA), a programmable logic
array (PLA), an application specific integrated circuit (ASIC),
programmable logic circuitry, and a controller.
[0013] As defined herein, the term "automatically" means without
user intervention.
[0014] As defined herein, the term "user" means a person (i.e., a
human being). The terms "employee" and "agent" are used herein
interchangeably with the term "user".
[0015] With the proliferation of mobile phones, many enterprises
are replacing desk phones with a central phone system that routes
incoming calls directly to employee mobile devices. By the nature
of mobile devices, an on-shift employee could be involved in many
different types of activities when the enterprise receives an
incoming phone call. Many of these activities may make it
inappropriate to answer the call. As described herein, a system
and/or method can be implemented which derives the activity an
employee is engaged in and when an incoming call is received, the
call is routed to the mobile device of the employee that is in the
best situation to receive the phone call. The contemplated system
does more than merely check if the agent is presently on a call or
not. Rather a cognitive system is trained to recognize a number of
different activities that an agent may be engaged in so that
real-time audio and video data can be analyzed to classify the
agent as engaging in one of those recognizable activities. Based on
the deduced activity of each of a plurality of different agents,
one is selected to receive an incoming call.
[0016] FIG. 1 is a block diagram illustrating an example of a
network data processing system 120 that includes a communication
network 106. The communication network 106 is the medium used to
provide communications links between various devices and data
processing systems connected together within the computing
environment (or network data processing system, etc.) 120. The
communication network 106 may include connections, such as wire,
wireless communication links, or fiber optic cables. The
communication network 106 can be implemented as, or include, any of
a variety of different communication technologies such as a wide
area network (WAN), a local area network (LAN), a wireless network,
a mobile network, a Virtual Private Network (VPN), the Internet,
the Public Switched Telephone Network (PSTN), or similar
technologies.
[0017] One device in the network data processing system 120 is a
communications device 104 such as a telephone, mobile phone, IP
phone, tablet, computer, or other similar devices. An enterprise
107 can, for example, be a store, a warehouse, or other place of
employment. The enterprise 107 can have a plurality of agents that
are located within a physical environment of the enterprise. Each
agent can be associated with a respective mobile device 108 that
they can use to communicate with other communication devices. In
particular, an incoming call from the communications device 104 can
be routed to one of the mobile devices 108. Also, within the
enterprise 107 a number of sensors can be present which capture
data about the activities currently being performed within the
enterprise. Audio sensors 110 can be used to capture audio data at
different locations within the enterprise 107. In particular, the
audio sensors 110 can be a microphone within each of the mobile
devices 108. In this way, audio data can be collected and can be
directly associated with a particular agent because that agent is
known to be carrying the mobile device that captures a particular
stream of audio data. Video sensors 112 may also be present within
the enterprise 107. The video sensors 112 can include body cameras
or CCTV cameras, for example, or can include augmented-reality (AR)
glasses that have video capturing capabilities.
[0018] If one of the video sensors 112 is associated with a
particular agent of the enterprise (e.g., AR glasses), then the
captured video stream can be easily associated with that agent. If
one of the video sensors 112 is more general in nature (e.g., CCTV
cameras), then facial recognition algorithms or similar video
analysis can be used to identify one or more agents in a particular
stream of video data. Biometric sensors 116 such as, for example, a
smart watch can be used to capture current physiological data about
an agent of the enterprise. Location determining services 118 can
be utilized to determine where each agent is currently located
within the enterprise. This service can, for example, be tied to
the video sensors 112 so that the location of an agent is
determined by analysis of the video data. However, an alternative
is to use a location services feature already present within the
mobile devices 108. As mentioned above, when a particular mobile
device is known to be associated with a particular agent, then data
captured from sensors of the mobile device can be directly and
easily associated with that particular agent.
[0019] A cognitive system 102 can include a conventional automated
call distribution (ACD) 122 to accept incoming calls to the
enterprise 107 via the network 106. The ACD 122 can provide a
central number for the enterprise that callers can call to reach
one of the agents of the enterprise. As described below, the
cognitive system 102 also includes a cognitive call dispatcher
(CCD) 124 that selects which of the mobile devices 108 to route the
incoming call to. In an embodiment in which the cognitive system
102 does not communicate directly with the various sensors
described earlier, the enterprise 107 can include a communication
server 114 which collects the various sensor data and communicates
with the cognitive system 102.
[0020] The cognitive system 102 can also include pairing software
126 which allows one or more devices to be paired with the CCD 124.
Using technology such as BLUETOOTH, for example, a mobile device,
smart watch, AR glasses, etc. can be paired or linked with the CCD
124 so that the device can communicate with the CCD 124 and
transmit audio data, video data, biometric data, etc. to the CCD
124.
[0021] FIG. 2A provides a high level flowchart of providing
cognitive call routing in accordance with the principles of the
present disclosure. Referring to step 202 of FIG. 2A, the CCD
receives input from agents currently at the enterprise. For
example, the agents can be those currently on shift as an employee
of the enterprise. As described below, the agent can have one or
more mobile devices that are associated with the agent. That
association-related data can be stored in a database, for example,
that is accessible by the CCD. If the mobile device is a personal
device of the agent, then the association between the agent and the
device can be stored previously. In some alternatives, an agent can
be assigned a mobile device when coming on shift and the
association between the mobile device and the agent's identity is
then established and stored. The mobile device can then be paired
with the CCD to enable communication between the mobile device and
the CCD. Data such as audio and video data can then be transmitted
from the mobile device to the CCD which then derives that the data
is associated with the agent using that mobile device.
[0022] In step 204, the CCD analyzes the data being received from
the agents' mobile devices and derives the agents' current
activities. As explained below, the analysis can include visual
recognition, passive listening analysis, location analysis, and
biometric analysis. The CCD utilizes these different analysis
modalities to derive a current activity of each of the agents. In
step 206, the different activities are analyzed to determine which
of the agents is the most appropriate agent to handle an incoming
call.
[0023] FIG. 2B provides more details about step 202 from FIG. 2A.
In step 210, the mobile devices associated with an agent are paired
with the CCD. As mentioned above, this pairing can occur via
near-field communication (NFC) techniques such as BLUETOOTH for
example or can utilize other technologies such as Wi-Fi. As a
result of the pairing, the mobile devices and the CCD establish a
communication path between the two so that the CCD can receive
data, in step 212, from the mobile devices and recognize which
mobile device (and agent) the data relates to. The data can be a
variety of different information and can include the time of day,
the agents' work schedules, video data and audio from a physical
environment occupied by the agents, biometric-related data about
the agents' physiological states, and location information about
the location of the agents at the present time.
[0024] In general, the cognitive system is an artificial
intelligence system that classifies the data it receives through
analysis of that data. In the present disclosure, the data is
analyzed to determine an activity an agent is currently engaged in.
Taking the video analysis, for example, the cognitive system is
initially trained to recognize a limited number of "possible"
activities of an agent. The presence or absence of certain objects
in an image may be pertinent. The proximity of the agent to one or
more items in the environment may be pertinent. The cognitive
system can be trained with a variety of images to associate certain
data with one or more defined activities. The specific activities
which the cognitive system can recognize can vary based on the
enterprise. As an example, the enterprise could be a store which
has employees that can interact with customers in person, interact
with customers via a device, stock the storeroom, or take a break
in a lounge area. Thus, the cognitive system can be trained to
recognize the activity of climbing a ladder or the activity of
carrying boxes. The cognitive system can also be trained to
recognize the activity of talking with another person or the
activity of talking on the phone. Other example activities for this
example include stocking shelves, talking with a customer, talking
with another agent of the enterprise, talking on a mobile device,
using a mobile device or computer. In operation, the cognitive
system analyzes visual information (e.g., 3 seconds of a video
stream) to determine if the data indicates that an agent is engaged
in one of the activities the system was trained to recognize. If
so, then the agent's activity is derived to be that recognizable
activity. If not, then the agent is determined to be engaged in an
unrecognizable activity.
[0025] FIG. 2C provides more details about step 204 of FIG. 2A. In
step 220, the CCD analyzes video content to determine if it can
classify the agent's activity into one or more of the activities
that the CCD was trained to recognize. The video content can be a
small stream of video data (e.g., 3 seconds) and be collected
periodically (e.g., every minute). Thus, the CCD does not need to
determine an agent's current activity continuously but can assume,
for example, that if the agent is talking with a customer, then the
agent will likely be doing so for the next minute. However, the CCD
can include the capability to determine when the agent was first
classified as being engaged in the current activity and, thus, the
duration the agent has been engaged in the current activity can be
determined. Historical data can be collected about average
durations for various activities and this historical data can be
used to adjust the frequency of checking on the agent to determine
the agent's current activity. Also, the audio data surrounding an
agent can be used to adjust the frequency of checking on the agent
to determine the agent's current activity. Phrases such as "Hello",
"Please come back", etc. can be used to derive if a conversation is
just beginning or likely ending.
[0026] In addition to the visual recognition of activities, audio
data can be analyzed as well to determine an agent's current
activity. One particular technique utilizes passive listening
analysis. This technique employs predetermined keywords that an
enterprise defines based on what is appropriate for that enterprise
and the interactions its agents are likely to have. For example,
audio data of a conversation of the agent is not analyzed until a
keyword is recognized. Once a keyword is recognized, the next
portion of the conversation is captured and analyzed. Natural
language processing techniques can then be used to determine the
topic and/or content of the agent's conversation. Furthermore, the
words being used by an agent as well as the volume of the agent's
speech can be helpful in deriving the current activity of the
agent. For example, an agent that appears agitated in the video
data and is speaking louder than normal to another person may be
identified as an agent who likely should not be receiving an
incoming call in the near future.
[0027] In step 224, the biometric information can be used to help
identify the activity of the agent or the emotional state of the
agent. An agent's heart rate and respiration rate can indicate
their physical activity level and be used to help validate that the
agent is climbing a ladder or carrying heavy items. In addition,
however, the biometric data can be used to derive a person's
emotional state. Currently known techniques utilize a combination
of heart rate variability, movement analysis, and frequency of
speech as indicators of emotions the person is experiencing. For
example, the recognizable emotional states can be defined as
"angry", "relaxed", "happy", "worried", etc.
[0028] A present location of the agent can also be helpful in the
analysis performed by the CCD. The presence of the agent in the
lounge area starting in the last 30 second may indicate that the
agent is on break and should not receive an incoming call. The
presence of the agent in a stock room and visual recognition of the
agent carrying boxes may indicate that the agent should not be
interrupted to receive an incoming call. The presence of the agent
on the sales floor but talking with another employee may indicate
that the agent likely an appropriate agent to receive an incoming
call.
[0029] In step 226, the result of the individual analysis, or
classifier, steps are combined to derive the current activity in
which the agent is engaged. For example, visual recognition may
classify an agent as being in conversation with a customer, the
passive listening analysis detects the trigger word "buy" and
determines the customer and agent are talking about making a
purchase, and the emotional state analysis indicates the agent is
relaxed
[0030] FIG. 2D provides details about step 206 of FIG. 2A. In
configuring the CCD, an enterprise defines the recognizable
activities that an agent may be engaged in. Along with this
information, the enterprise defines how appropriate that activity
to be interrupted for an incoming call. Thus, the agents can be
ranked, according to their current activity, for receiving incoming
calls. Some activities, such as those related to safety concerns,
may be defined as "never-to-be-interrupted" while other activities
may be interrupted with an incoming call with certain activities
being more appropriate for interruption than others. Thus, the CCD
maintains a status of the agents presently on shift with regard to
what activity they are currently engaged in and when an incoming
call is received, in step 230, the CCD analyzes the status list, in
step 232, to select the agent that is most appropriate to interrupt
with the incoming call. In step 234, the CCD routes the incoming
call to the mobile device of the selected agent. In instances where
there are multiple agents appropriate to interrupt, the call can be
routed randomly to one of them. In an instance where no agents are
to be interrupted, the call may be routed to an automated system or
voicemail. In an instance where the selected agent does not answer
the incoming call within a predetermined time period, the CCD can
route the incoming call to the next most-appropriate agent on the
status list.
[0031] The routing of the incoming call can be adjusted by the
enterprise defining additional rules. For example, even if the
agent activity is appropriate to be interrupted, if the agent is
presently in a location that is extremely noisy, then the CCD can
prevent the call from being routed to that agent. Also, the
enterprise can define threshold values that are applied during
different parts of the shift for example. The threshold value
limits whether or not an incoming call is forwarded such that any
agent activity with a score or rank below the threshold value is
not interrupted.
[0032] As described above a cognitive system has been described
that can apply a plurality of different rules to attempt to
recognize and classify an activity of an agent that they are
current engaged in. A trained visual recognition system applies
rules to analyze video data to determine which of a number of
possible recognizable activities an agent is currently engaged in.
Facial recognition can be used to identify agents of the enterprise
as well as non-agents (e.g., customers) from the video data as
well. In addition, passive listening analysis is used to capture a
select portion of a conversation of the agent so that natural
language processing and similar techniques can be employed to
determine a context or meaning associated with the conversation.
Similarly, biometric data is collected about the agent's current
state and rules and analysis are applied to determine the agent's
activity level and emotional state, for example. The cognitive
system can then apply a second-level of rules and analysis to the
individual analysis steps just described. A combination of the
visually-derived activity, the content of the agent's conversation,
and the emotional state of the agent can be analyzed to derive a
complex definition of the agent's current activity. The cognitive
system can, for example, derive that the agent is calmly speaking
with a customer about making a purchase, or that the agent has just
started climbing a ladder in a back store room, or that the agent
is walking through a portion of the enterprise where loud machinery
prevents easily hearing phone calls. The present disclosure
contemplates any of a variety of functionally equivalent ways to
assign a predetermined score to each of the derived activities in
which the agent is engaged (e.g., 1-10, A-F, 1%-100%, etc.). The
scoring being indicative of how appropriate it would be to
interrupt the agent by routing an incoming phone call to the agent
based on the engaged activity. The predetermined score can be
defined by the enterprise. Each individual analysis result could be
assigned its own score and then combined to calculate an overall
score for the current activity. Some individual analysis values may
be weighted differently than others and some individual analysis
values may override all others if present (e.g., a loud location).
Lastly, the cognitive system, once a current activity is derived
for the agents, can create a status list of all the agents that
reflects their current activity and is ranked based on the
predetermined scores assigned to the particular activities. The
cognitive system can then route an incoming call to a mobile device
of the agent based on where that agent is relative to other agents
in the ranked status list.
[0033] Referring to FIG. 3, a block diagram of a data processing
system is depicted in accordance with the present disclosure. A
data processing system 400, such as may be utilized to implement
the hardware platform 102 or aspects thereof, e.g., as set out in
greater detail in FIG. 1, may comprise a symmetric multiprocessor
(SMP) system or other configuration including a plurality of
processors 402 connected to system bus 404. Alternatively, a single
processor 402 may be employed. Also connected to system bus 404 is
memory controller/cache 406, which provides an interface to local
memory 408. An I/O bridge 410 is connected to the system bus 404
and provides an interface to an I/O bus 412. The I/O bus may be
utilized to support one or more buses and corresponding devices
414, such as bus bridges, input output devices (I/O devices),
storage, network adapters, etc. Network adapters may also be
coupled to the system to enable the data processing system to
become coupled to other data processing systems or remote printers
or storage devices through intervening private or public
networks.
[0034] Also connected to the I/O bus may be devices such as a
graphics adapter 416, storage 418 and a computer usable storage
medium 420 having computer usable program code embodied thereon.
The computer usable program code may be executed to execute any
aspect of the present disclosure, for example, to implement aspect
of any of the methods, computer program products and/or system
components illustrated in FIG. 1-FIG. 2D. It should be appreciated
that the data processing system 400 can be implemented in the form
of any system including a processor and memory that is capable of
performing the functions and/or operations described within this
specification. For example, the data processing system 400 can be
implemented as a server, a plurality of communicatively linked
servers, a workstation, a desktop computer, a mobile computer, a
tablet computer, a laptop computer, a netbook computer, a smart
phone, a personal digital assistant, a set-top box, a gaming
device, a network appliance, and so on.
[0035] The data processing system 400, such as may also be utilized
to implement the cognitive system 102 or the cognitive call
dispatcher, or aspects thereof, e.g., as set out in greater detail
in FIG. 1.
[0036] While the disclosure concludes with claims defining novel
features, it is believed that the various features described herein
will be better understood from a consideration of the description
in conjunction with the drawings. The process(es), machine(s),
manufacture(s) and any variations thereof described within this
disclosure are provided for purposes of illustration. Any specific
structural and functional details described are not to be
interpreted as limiting, but merely as a basis for the claims and
as a representative basis for teaching one skilled in the art to
variously employ the features described in virtually any
appropriately detailed structure. Further, the terms and phrases
used within this disclosure are not intended to be limiting, but
rather to provide an understandable description of the features
described.
[0037] For purposes of simplicity and clarity of illustration,
elements shown in the figures have not necessarily been drawn to
scale. For example, the dimensions of some of the elements may be
exaggerated relative to other elements for clarity. Further, where
considered appropriate, reference numbers are repeated among the
figures to indicate corresponding, analogous, or like features.
[0038] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0039] 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.
[0040] 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.
[0041] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0042] 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.
[0043] 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.
[0044] 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.
[0045] The flowchart(s) and block diagram(s) 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(s) or block diagram(s)
may represent a module, segment, or portion of instructions, which
comprises one or more executable instructions for implementing the
specified logical function(s). In some alternative implementations,
the functions noted in the block may occur out of the order noted
in the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0046] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a," "an," and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "includes," "including," "comprises," and/or
"comprising," when used in this disclosure, specify the presence of
stated features, integers, steps, operations, elements, and/or
components, but do not preclude the presence or addition of one or
more other features, integers, steps, operations, elements,
components, and/or groups thereof.
[0047] Reference throughout this disclosure to "one embodiment,"
"an embodiment," "one arrangement," "an arrangement," "one aspect,"
"an aspect," or similar language means that a particular feature,
structure, or characteristic described in connection with the
embodiment is included in at least one embodiment described within
this disclosure. Thus, appearances of the phrases "one embodiment,"
"an embodiment," "one arrangement," "an arrangement," "one aspect,"
"an aspect," and similar language throughout this disclosure may,
but do not necessarily, all refer to the same embodiment.
[0048] The term "plurality," as used herein, is defined as two or
more than two. The term "another," as used herein, is defined as at
least a second or more. The term "coupled," as used herein, is
defined as connected, whether directly without any intervening
elements or indirectly with one or more intervening elements,
unless otherwise indicated. Two elements also can be coupled
mechanically, electrically, or communicatively linked through a
communication channel, pathway, network, or system. The term
"and/or" as used herein refers to and encompasses any and all
possible combinations of one or more of the associated listed
items. It will also be understood that, although the terms first,
second, etc. may be used herein to describe various elements, these
elements should not be limited by these terms, as these terms are
only used to distinguish one element from another unless stated
otherwise or the context indicates otherwise.
[0049] The term "if" may be construed to mean "when" or "upon" or
"in response to determining" or "in response to detecting,"
depending on the context. Similarly, the phrase "if it is
determined" or "if [a stated condition or event] is detected" may
be construed to mean "upon determining" or "in response to
determining" or "upon detecting [the stated condition or event]" or
"in response to detecting [the stated condition or event],"
depending on the context.
[0050] 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.
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