U.S. patent application number 16/668259 was filed with the patent office on 2021-01-07 for system and method for querying multiple information sources using agent assist within a cloud-based contact center.
The applicant listed for this patent is Talkdesk, Inc.. Invention is credited to Jafar Adibi, Bruno Antunes, Joao Carmo, Marco Costa, Charanya Kannan, Tiago Paiva.
Application Number | 20210005207 16/668259 |
Document ID | / |
Family ID | |
Filed Date | 2021-01-07 |
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United States Patent
Application |
20210005207 |
Kind Code |
A1 |
Adibi; Jafar ; et
al. |
January 7, 2021 |
SYSTEM AND METHOD FOR QUERYING MULTIPLE INFORMATION SOURCES USING
AGENT ASSIST WITHIN A CLOUD-BASED CONTACT CENTER
Abstract
Methods to reduce agent effort and improve customer experience
quality through artificial intelligence. The Agent Assist tool
provides contact centers with an innovative tool designed to reduce
agent effort, improve quality and reduce costs by minimizing search
and data entry tasks The Agent Assist tool is natively built and
fully unified within the agent interface while keeping all data
internally protected from third-party sharing.
Inventors: |
Adibi; Jafar; (Los Angeles,
CA) ; Paiva; Tiago; (San Francisco, CA) ;
Kannan; Charanya; (Oakland, CA) ; Antunes; Bruno;
(Sao Silvestre, PT) ; Carmo; Joao; (Porto, PT)
; Costa; Marco; (Lisboa, PT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Talkdesk, Inc. |
San Francisco |
CA |
US |
|
|
Appl. No.: |
16/668259 |
Filed: |
October 30, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62870913 |
Jul 5, 2019 |
|
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Current U.S.
Class: |
1/1 |
International
Class: |
G10L 17/00 20060101
G10L017/00; G10L 17/06 20060101 G10L017/06; H04L 29/08 20060101
H04L029/08; G06F 16/9538 20060101 G06F016/9538; G06F 16/9535
20060101 G06F016/9535 |
Claims
1. A method, comprising: executing an automation infrastructure
within a cloud-based contact center that includes a communication
manager, speech-to-text converter, a natural language processor,
and an inference processor exposed by application programming
interfaces; and executing an agent assist functionality within the
automation infrastructure that performs operations comprising:
receiving a communication from a customer; presenting text
associated with the communication in a unified user interface;
automatically analyzing the communication to determine a subject of
the customer's communication; automatically querying an in a
plurality of information sources in real time formation source for
at least one response to the subject; and presenting the at least
one response from the plurality of information sources the unified
user interface.
2. The method of claim 1, further comprising: parsing the
communication for key terms; and automatically highlighting the key
terms in the unified user interface.
3. The method of claim 2, the analyzing further comprising:
inferring an intent of the customer using an intent inference
module; and determining the keywords in accordance with the
intent.
4. The method of claim 1, further comprising scrolling the unified
user interface to receive subsequent communication from the
customer and to present subsequent responses from the information
source.
5. The method of claim 1, further comprising ranking a plurality of
responses from the plurality of information sources in accordance
with a relevance of each of the plurality of responses.
6. The method of claim 1, further comprising displaying an
indication in the unified user interface that the communications
being analyzed.
7. The method of claim 1, further comprising presenting
communication controls in the unified user interface.
8. The method of claim 1, further comprising presenting information
about the customer.
9. The method of claim 8, wherein the information comprises a
customer phone number, or biometric information about the
customer.
10. The method of claim 1, further comprising providing an
indication as to a current speaker.
11. A cloud-based software platform comprising: one or more
computer processors; and one or more computer-readable mediums
storing instructions that, when executed by the one or more
computer processors, cause the cloud-based software platform to
perform operations comprising: executing an automation
infrastructure within a cloud-based contact center that includes a
communication manager, speech-to-text converter, a natural language
processor, and an inference processor exposed by application
programming interfaces; and executing an agent assist functionality
within the automation infrastructure that performs operations
comprising: receiving a communication from a customer; presenting
text associated with the communication in a unified user interface;
automatically analyzing the communication to determine a subject of
the customer's communication; automatically querying an in a
plurality of information sources in real time formation source for
at least one response to the subject; and presenting the at least
one response from the plurality of information sources the unified
user interface.
12. The cloud-based software platform of claim 11, further
comprising instructions to cause operations comprising: parsing the
communication for key terms; and automatically highlighting the key
terms in the unified user interface.
13. The cloud-based software platform of claim 12, the analyzing
further comprising instructions to cause operations comprising:
inferring an intent of the customer using an intent inference
module; and determining the keywords in accordance with the
intent.
14. The cloud-based software platform of claim 11, further
comprising instructions to cause operations comprising scrolling
the unified user interface to receive subsequent communication from
the customer and to present subsequent responses from the
information source.
15. The cloud-based software platform of claim 11, further
comprising instructions to cause operations comprising ranking a
plurality of responses from the plurality of information sources in
accordance with a relevance of each of the plurality of
responses.
16. The cloud-based software platform of claim 11, further
comprising instructions to cause operations comprising displaying
an indication in the unified user interface that the communications
being analyzed.
17. The cloud-based software platform of claim 11, further
comprising instructions to cause operations comprising presenting
communication controls in the unified user interface.
18. The cloud-based software platform of claim 11, further
comprising instructions to cause operations comprising presenting
information about the customer.
19. The cloud-based software platform of claim 18, wherein the
information comprises a customer phone number, or biometric
information about the customer.
20. The cloud-based software platform of claim 11, further
comprising instructions to cause operations comprising providing an
indication as to a current speaker.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority to U.S. Provisional Patent
Application No. 62/870,913, filed Jul. 5, 2019, entitled "SYSTEM
AND METHOD FOR AUTOMATION WITHIN A CLOUD-BASED CONTACT CENTER,"
which is incorporated herein by reference in its entirety.
BACKGROUND
[0002] Today, contact centers are primarily on-premise software
solutions. This requires an enterprise to make a substantial
investment in hardware, installation and regular maintenance of
such solutions. Using on-premise software, agents and supervisors
are stationed in an on-site call center. In addition, a dedicated
IT staff is required because on-site software may be too
complicated for supervisors and agents to handle on their own.
Another drawback of on-premise solutions is that such solutions
cannot be easily enhanced to include capabilities to that meet the
current demands of technology, such as automation. Thus, there is a
need for a solution to enhance the agent experience to enhance the
interactions with customers who interact with contact centers.
SUMMARY
[0003] Disclosed herein are systems and methods for providing a
cloud-based contact center solution providing agent automation
through the use of e.g., artificial intelligence and the like.
[0004] In accordance with an aspect, there is disclosed a method,
comprising executing an automation infrastructure within a
cloud-based contact center that includes a communication manager,
speech-to-text converter, a natural language processor, and an
inference processor exposed by application programming interfaces;
and executing an agent assist functionality within the automation
infrastructure that performs operations comprising: receiving a
communication from a customer; presenting text associated with the
communication in a unified user interface; automatically analyzing
the communication to determine a subject of the customer's
communication; automatically querying an in a plurality of
information sources in real time formation source for at least one
response to the subject; and presenting the at least one response
from the plurality of information sources the unified user
interface. In accordance with another aspect, a cloud-based
software platform is disclosed in which the example method above is
performed.
[0005] Other systems, methods, features and/or advantages will be
or may become apparent to one with skill in the art upon
examination of the following drawings and detailed description. It
is intended that all such additional systems, methods, features
and/or advantages be included within this description and be
protected by the accompanying claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] The components in the drawings are not necessarily to scale
relative to each other. Like reference numerals designate
corresponding parts throughout the several views.
[0007] FIG. 1 illustrates an example environment;
[0008] FIG. 2 illustrates example component that provide
automation, routing and/or omnichannel functionalities within the
context of the environment of FIG. 1;
[0009] FIG. 3 illustrates a high-level overview of interactions,
components and flow of Agent Assist in accordance with the present
disclosure;
[0010] FIG. 4 illustrates an example operational flow in accordance
with the present disclosure and provides additional details of the
high-level overview shown in FIG. 3;
[0011] FIGS. 5A, 5B and 5C illustrate an example unified interface
showing aspects of the operational flows of FIGS. 3 and 4;
[0012] FIG. 6 illustrates an operational flow to analyze a
conversation to create smart notes;
[0013] FIG. 7 illustrates an example smart notes user
interface;
[0014] FIG. 8 illustrates an operational flow to analyze a
conversation to pre-populate forms;
[0015] FIG. 9 illustrates an example automatic scheduling user
interface;
[0016] FIG. 10 illustrates an overview of the real-time analytics
aspect of Agent Assist;
[0017] FIG. 11 illustrates an example operational flow to classify
agent conversations;
[0018] FIG. 12 illustrates an example operational flow of
escalation assistance; and
[0019] FIG. 13 illustrates an example computing device.
DETAILED DESCRIPTION
[0020] Unless defined otherwise, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art. Methods and materials similar or
equivalent to those described herein can be used in the practice or
testing of the present disclosure. While implementations will be
described within a cloud-based contact center, it will become
evident to those skilled in the art that the implementations are
not limited thereto.
[0021] The present disclosure is generally directed to a
cloud-based contact center and, more particularly, methods and
systems for proving intelligent, automated services within a
cloud-based contact center. With the rise of cloud-based computing,
contact centers that take advantage of this infrastructure are able
to quickly add new features and channels. Cloud-based contact
centers improve the customer experience by leveraging application
programming interfaces (APIs) and software development kits (SDKs)
to allow the contact center to change in in response to an
enterprise's needs. For example, communications channels may be
easily added as the APIs and SDKs enable adding channels, such as
SMS/MMS, social media, web, etc. Cloud-based contact centers
provide a platform that enables frequent updates. Yet another
advantage of cloud-based contact centers is increased reliability,
as cloud-based contact centers may be strategically and
geographically distributed around the world to optimally route
calls to reduce latency and provide the highest quality experience.
As such, customers are connected to agents faster and more
efficiently.
[0022] Example Cloud-Based Contact Center Architecture
[0023] FIG. 1 is an example system architecture 100, and
illustrates example components, functional capabilities and
optional modules that may be included in a cloud-based contact
center infrastructure solution. Customers 110 interact with a
contact center 150 using voice, email, text, and web interfaces in
order to communicate with agent(s) 120 through a network 130 and
one or more channels 140. The agent(s) 120 may be remote from the
contact center 150 and handle communications with customers 110 on
behalf of an enterprise or other entity. The agent(s) 120 may
utilize devices, such as but not limited to, work stations, desktop
computers, laptops, telephones, a mobile smartphone and/or a
tablet. Similarly, customers 110 may communicate using a plurality
of devices, including but not limited to, a telephone, a mobile
smartphone, a tablet, a laptop, a desktop computer, or other. For
example, telephone communication may traverse networks such as a
public switched telephone networks (PSTN), Voice over Internet
Protocol (VoIP) telephony (via the Internet), a Wide Area Network
(WAN) or a Large Area Network. The network types are provided by
way of example and are not intended to limit types of networks used
for communications.
[0024] The contact center 150 may be cloud-based and distributed
over a plurality of locations. The contact center 150 may include
servers, databases, and other components. In particular, the
contact center 150 may include, but is not limited to, a routing
server, a SIP server, an outbound server, automated call
distribution (ACD), a computer telephony integration server (CTI),
an email server, an IM server, a social server, a SMS server, and
one or more databases for routing, historical information and
campaigns.
[0025] The routing server may serve as an adapter or interface
between the switch and the remainder of the routing, monitoring,
and other communication-handling components of the contact center.
The routing server may be configured to process PSTN calls, VoIP
calls, and the like. For example, the routing server may be
configured with the CTI server software for interfacing with the
switch/media gateway and contact center equipment. In other
examples, the routing server may include the SIP server for
processing SIP calls. The routing server may extract data about the
customer interaction such as the caller's telephone number (often
known as the automatic number identification (ANI) number), or the
customer's internet protocol (IP) address, or email address, and
communicate with other contact center components in processing the
interaction.
[0026] The ACD is used by inbound, outbound and blended contact
centers to manage the flow of interactions by routing and queuing
them to the most appropriate agent. Within the CTI, software
connects the ACD to a servicing application (e.g., customer
service, CRM, sales, collections, etc.), and looks up or records
information about the caller. CTI may display a customer's account
information on the agent desktop when an interaction is
delivered.
[0027] For inbound SIP messages, the routing server may use
statistical data from the statistics server and a routing database
to the route SIP request message. A response may be sent to the
media server directing it to route the interaction to a target
agent 120. The routing database may include: customer relationship
management (CRM) data; data pertaining to one or more social
networks (including, but not limited to network graphs capturing
social relationships within relevant social networks, or media
updates made by members of relevant social networks); agent skills
data; data extracted from third party data sources including
cloud-based data sources such as CRM; or any other data that may be
useful in making routing decisions.
[0028] Customers 110 may initiate inbound communications (e.g.,
telephony calls, emails, chats, video chats, social media posts,
etc.) to the contact center 150 via an end user device. End user
devices may be a communication device, such as, a telephone,
wireless phone, smart phone, personal computer, electronic tablet,
etc., to name some non-limiting examples. Customers 110 operating
the end user devices may initiate, manage, and respond to telephone
calls, emails, chats, text messaging, web-browsing sessions, and
other multi-media transactions. Agent(s) 120 and customers 110 may
communicate with each other and with other services over the
network 130. For example, a customer calling on telephone handset
may connect through the PSTN and terminate on a private branch
exchange (PBX). A video call originating from a tablet may connect
through the network 130 terminate on the media server. The channels
140 are coupled to the communications network 130 for receiving and
transmitting telephony calls between customers 110 and the contact
center 150. A media gateway may include a telephony switch or
communication switch for routing within the contact center. The
switch may be a hardware switching system or a soft switch
implemented via software. For example, the media gateway may
communicate with an automatic call distributor (ACD), a private
branch exchange (PBX), an IP-based software switch and/or other
switch to receive Internet-based interactions and/or telephone
network-based interactions from a customer 110 and route those
interactions to an agent 120. More detail of these interactions is
provided below.
[0029] As another example, a customer smartphone may connect via
the WAN and terminate on an interactive voice response
(IVR)/intelligent virtual agent (IVA) components. IVR are
self-service voice tools that automate the handling of incoming and
outgoing calls. Advanced IVRs use speech recognition technology to
enable customers 110 to interact with them by speaking instead of
pushing buttons on their phones. IVR applications may be used to
collect data, schedule callbacks and transfer calls to live agents.
IVA systems are more advanced and utilize artificial intelligence
(AI), machine learning (ML), advanced speech technologies (e.g.,
natural language understanding (NLU)/natural language processing
(NLP)/natural language generation (NLG)) to simulate live and
unstructured cognitive conversations for voice, text and digital
interactions. IVA systems may cover a variety of media channels in
addition to voice, including, but not limited to social media,
email, SMS/MMS, IM, etc. and they may communicate with their
counterpart's application (not shown) within the contact center
150. The IVA system may be configured with a script for querying
customers on their needs. The IVA system may ask an open-ended
questions such as, for example, "How can I help you?" and the
customer 110 may speak or otherwise enter a reason for contacting
the contact center 150. The customer's response may then be used by
a routing server to route the call or communication to an
appropriate contact center resource.
[0030] In response, the routing server may find an appropriate
agent 120 or automated resource to which an inbound customer
communication is to be routed, for example, based on a routing
strategy employed by the routing server, and further based on
information about agent availability, skills, and other routing
parameters provided, for example, by the statistics server. The
routing server may query one or more databases, such as a customer
database, which stores information about existing clients, such as
contact information, service level agreement requirements, nature
of previous customer contacts and actions taken by contact center
to resolve any customer issues, etc. The routing server may query
the customer information from the customer database via an ANI or
any other information collected by the IVA system.
[0031] Once an appropriate agent and/or automated resource is
identified as being available to handle a communication, a
connection may be made between the customer 110 and an agent device
of the identified agent 120 and/or the automate resource. Collected
information about the customer and/or the customer's historical
information may also be provided to the agent device for aiding the
agent in better servicing the communication. In this regard, each
agent device may include a telephone adapted for regular telephone
calls, VoIP calls, etc. The agent device may also include a
computer for communicating with one or more servers of the contact
center and performing data processing associated with contact
center operations, and for interfacing with customers via voice and
other multimedia communication mechanisms.
[0032] The contact center 150 may also include a multimedia/social
media server for engaging in media interactions other than voice
interactions with the end user devices and/or other web servers
160. The media interactions may be related, for example, to email,
vmail (voice mail through email), chat, video, text-messaging, web,
social media, co-browsing, etc. In this regard, the
multimedia/social media server may take the form of any IP router
conventional in the art with specialized hardware and software for
receiving, processing, and forwarding multi-media events.
[0033] The web servers 160 may include, for example, social media
sites, such as, Facebook, Twitter, lnstagram, etc. In this regard,
the web servers 160 may be provided by third parties and/or
maintained outside of the contact center 160 that communicate with
the contact center 150 over the network 130. The web servers 160
may also provide web pages for the enterprise that is being
supported by the contact center 150. End users may browse the web
pages and get information about the enterprise's products and
services. The web pages may also provide a mechanism for contacting
the contact center, via, for example, web chat, voice call, email,
WebRTC, etc.
[0034] The integration of real-time and nonreal-time communication
services may be performed by unified communications (UC)/presence
sever. Real-time communication services include Internet Protocol
(IP) telephony, call control, instant messaging (IM)/chat, presence
information, real-time video and data sharing. Non-real-time
applications include voicemail, email, SMS and fax services. The
communications services are delivered over a variety of
communications devices, including IP phones, personal computers
(PCs), smartphones and tablets. Presence provides real-time status
information about the availability of each person in the network,
as well as their preferred method of communication (e.g., phone,
email, chat and video).
[0035] Recording applications may be used to capture and play back
audio and screen interactions between customers and agents.
Recording systems should capture everything that happens during
interactions and what agents do on their desktops. Surveying tools
may provide the ability to create and deploy post-interaction
customer feedback surveys in voice and digital channels. Typically,
the IVR/IVA development environment is leveraged for survey
development and deployment rules. Reporting/dashboards are tools
used to track and manage the performance of agents, teams,
departments, systems and processes within the contact center.
[0036] Automation
[0037] As shown in FIG. 1, automated services may enhance the
operation of the contact center 150. In one aspect, the automated
services may be implemented as an application running on a mobile
device of a customer 110, one or more cloud computing devices
(generally labeled automation servers 170 connected to the end user
device over the network 130), one or more servers running in the
contact center 150 (e.g., automation infrastructure 200), or
combinations thereof.
[0038] With respect to the cloud-based contact center, FIG. 2
illustrates an example automation infrastructure 200 implemented
within the cloud-based contact center 150. The automation
infrastructure 200 may automatically collect information from a
customer 110 user through, e.g., a user interface/voice interface
202, where the collection of information may not require the
involvement of a live agent. The user input may be provided as free
speech or text (e.g., unstructured, natural language input). This
information may be used by the automation infrastructure 200 for
routing the customer 110 to an agent 120, to automated resources in
the contact center 150, as well as gathering information from other
sources to be provided to the agent 120. In operation, the
automation infrastructure 200 may parse the natural language user
input using a natural language processing module 210 to infer the
customer's intent using an intent inference module 212 in order to
classify the intent. Where the user input is provided as speech,
the speech is transcribed into text by a speech-to-text system 206
(e.g., a large vocabulary continuous speech recognition or LVCSR
system) as part of the parsing by the natural language processing
module 210. The communication manager 204 monitors user inputs and
presents notifications within the user interface/voice interface
202. Responses by the automation infrastructure 200 to the customer
110 may be provided as speech using the text-to-speech system
208.
[0039] The intent inference module automatically infers the
customer's 110 intent from the text of the user input using
artificial intelligence or machine learning techniques. These
artificial intelligence techniques may include, for example,
identifying one or more keywords from the user input and searching
a database of potential intents (e.g., call reasons) corresponding
to the given keywords. The database of potential intents and the
keywords corresponding to the intents may be automatically mined
from a collection of historical interaction recordings, in which a
customer may provide a statement of the issue, and in which the
intent is explicitly encoded by an agent.
[0040] Some aspects of the present disclosure relate to
automatically navigating an IVR system of a contact center on
behalf of a user using, for example, the loaded script. In some
implementations of the present disclosure, the script includes a
set of fields (or parameters) of data that are expected to be
required by the contact center in order to resolve the issue
specified by the customer's 110 intent. In some implementations of
the present disclosure, some of the fields of data are
automatically loaded from a stored user profile. These stored
fields may include, for example, the customer's 110 full name,
address, customer account numbers, authentication information
(e.g., answers to security questions) and the like.
[0041] Some aspects of the present disclosure relate to the
automatic authentication of the customer 110 with the provider. For
example, in some implementations of the present disclosure, the
user profile may include authentication information that would
typically be requested of users accessing customer support systems
such as usernames, account identifying information, personal
identification information (e.g., a social security number), and/or
answers to security questions. As additional examples, the
automation infrastructure 200 may have access to text messages
and/or email messages sent to the customer's 110 account on the end
user device in order to access one-time passwords sent to the
customer 110, and/or may have access to a one-time password (OTP)
generator stored locally on the end user device. Accordingly,
implementations of the present disclosure may be capable of
automatically authenticating the customer 110 with the contact
center prior to an interaction.
[0042] In some implementations of the present disclosure an
application programming interface (API) is used to interact with
the provider directly. The provider may define a protocol for
making commonplace requests to their systems. This API may be
implemented over a variety of standard protocols such as Simple
Object Access Protocol (SOAP) using Extensible Markup Language
(XML), a Representational State Transfer (REST) API with messages
formatted using XML or JavaScript Object Notation (JSON), and the
like. Accordingly, a customer experience automation system 200
according to one implementation of the present disclosure
automatically generates a formatted message in accordance with an
API define by the provider, where the message contains the
information specified by the script in appropriate portions of the
formatted message.
[0043] Some aspects of the present disclosure relate to systems and
methods for automating and augmenting aspects of an interaction
between the customer 110 and a live agent of the contact center. In
an implementation, once a interaction, such as through a phone
call, has been initiated with the agent 120, metadata regarding the
conversation is displayed to the customer 110 and/or agent 120 in
the UI throughout the interaction. Information, such as call
metadata, may be presented to the customer 110 through the UI 205
on the customer's 110 mobile device 105. Examples of such
information might include, but not be limited to, the provider,
department call reason, agent name, and a photo of the agent.
[0044] According to some aspects of implementations of the present
disclosure, both the customer 110 and the agent 120 can share
relevant content with each other through the application (e.g., the
application running on the end user device). The agent may share
their screen with the customer 110 or push relevant material to the
customer 110.
[0045] In yet another implementation, the automation infrastructure
200 may also "listen" in on the conversation and automatically push
relevant content from a knowledge base to the customer 110 and/or
agent 120. For example, the application may use a real-time
transcription of the customer's input (e.g., speech) to query a
knowledgebase to provide a solution to the agent 120. The agent may
share a document describing the solution with the customer 110. The
application may include several layers of intelligence where it
gathers customer intelligence to learn everything it can about why
the customer 110 is calling. Next, it may perform conversation
intelligence, which is extracting more context about the customer's
intent. Next, it may perform interaction intelligence to pull
information from other sources about customer 100. The automation
infrastructure 200 may also perform contact center intelligence to
implement WFM/WFO features of the contact center 150.
[0046] Agent Assist Overview
[0047] Thus, in the context of FIGS. 1-2, the present disclosure
provides improvements by providing an innovative tool to reduce
agent effort and improve customer experience quality through
artificial intelligence (referred to herein as "Agent Assist").
Agent Assist is an innovative tool used within e.g., contact
centers, designed to reduce agent effort, improve quality and
reduce costs by minimizing search and data entry tasks Agent Assist
is fully unified within the agent interface while keeping all data
internally protected from third-party sharing. Agent Assist improve
quality and reduce costs by minimizing search and data entry tasks
through the use of AI capabilities. Agent Assist simplifies agent
effort and improves Customer Satisfaction/Net Promoter Score
CSAT/NPS.
[0048] Agent Assist is powered by artificial intelligence (Al) to
provide real-time guidance for frontline employees to respond to
customer needs quickly and accurately. For example, as a customer
110 states a need, agents 120 are provided answers or supporting
information immediately to expedite the conversation and simplify
tasks. Agent Assist determines why customers are calling and what
their intent is. Similarly, IVR assist makes recommendations to a
supervisor to optimize IVR for a better customer experience, for
example, Agent Assist helps optimize IVR questions to match
customers' reasons for calling and what their intent is.
[0049] By leveraging automated assistance and reducing
agent-supervisor ad-hoc interactions, Agent Assist gives
supervisors more time to focus on workforce engagement activities.
Agent Assist reduces manual supervision and assistance. Agent
Assist improves agent proficiency and accuracy. Agent Assist
reduces short and long term training efforts through real-time
error identification, eliminates busy work with smart note
technology (the ability to systematically recognize and enter all
key aspects of an interaction into the conversation notes); and
improved handle time with in-app automations.
[0050] With reference to FIG. 3, there is illustrated a high-level
overview of interactions, components and flow of Agent Assist in
accordance with the present disclosure. In operation, a customer
110 will contact the cloud-based contact center 150 through one or
more of the channels 140. as shown in FIG. 1. The agent 120 to whom
the customer 110 is routed may listen to the customer 110 while the
same time the Agent Assist functionality pulls information using a
knowledge graph engine 308. The knowledge graph engine 312 gathers
information from from one or more of a knowledgebase 302, a
customer relationship management (CRM) platform/a customer service
management (CSM) platform 304, and/or conversational transcripts
306 of other agent conversations to provide contextually relevant
information to the agent. Additionally, information captured within
the agent interface (see, FIGS. 5A-5C, 7 and 13) can be
automatically added to account profiles or work item tickets,
within the CRM, without any additional agent effort. Agent Assist
is an intelligent advisory tool which supplies data-driven
real-time recommendations, next best actions and automations to aid
agents in customer interactions and guide them to quality and
outcome excellency. This may include making recommendations based
on interactions, discussions and monitored KPIs. Agent Assist helps
match agent skill to the reasons why customers are calling. In
addition, information may be provided to the agent from third-party
sources via the web servers 160 (e.g., knowledge bases of product
manufacturers) or social media platforms.
[0051] With reference to FIG. 4, there is illustrated an example
operational flow 400 in accordance with the present disclosure, and
provides additional details of the high-level overview shown in
FIG. 4. At 402, the process begins wherein the system listens the
customer and agent voices as they speak (S. 404). For example, the
automation infrastructure 200 may process the customer speech, as
described with regard to FIG. 2. At 406, the agent voice is
separated from the customer voice into their own respective
channels. Once separated, at 408, unsupervised methods may be used
to automatically perform one or more of the following non-limiting
processes: apply biometrics to authenticate the caller/customer,
predict a caller gender, predict a caller age category, predict a
caller accent, and/or predict caller other demographics. Optionally
or alternatively, if speaker separation is not performed at 406,
then the system may distinguish between the customer and the agent
by analyzing time that either the agent or the customer talks or
listens, identify signature of agent voice or user voice, or apply
non-supervised methods to separate user and agent voice in
real-time.
[0052] The operational flow continues at 410, wherein the customer
voice and/or agent voice may be analyzed before transcription to
extract one or more of the following non-limiting features: [0053]
Pain [0054] Agony [0055] Empathy [0056] Being sarcastic [0057]
Speech speed [0058] Tone [0059] Frustration [0060] Enthusiasm
[0061] Interest [0062] Engagement
[0063] Understanding these features helps the agent 120 better
understand the customer 110. The agent 120 will be better able to
understand the customer's problem or issues so a resolution can be
more easily achieved.
[0064] At 412, the conversation between the agent and the customer
is transcribed in either real-time or post-call. This may be
performed by the speech-to-text component of the automation
infrastructure 200 and saved to a database. At 414, the agent voice
channel and the customer voice channel are separated. At 416, the
automation infrastructure 200 determines information about the
customer and agent, such as, intent, entities (e.g., names,
locations, times, etc.) sentiment, sentence phrases (e.g. verb,
noun, adjective, etc.). At 418, from the information determined at
416, Agent Assist provides useful insight to the agent 120. This
information, as shown in FIG. 3, may be information retrieved from
the relevant CRM, the most relevant documents in the related
knowledge base, and/or a relevant conversation and interaction that
occurred in the past that was related to a similar topic or other
feature of the interaction between the agent and the customer.
Information pulled from the knowledgebase may be highlighted to the
agent in a display, such as shown in FIGS. 5A-5C, 7 and 13.
[0065] Thus, in accordance with the operational flow of FIG. 4,
Agent Assist provides real-time guidance for frontline employees to
respond to customer needs quickly and accurately. As a customer 110
states his or her need, agents 120 will be delivered answers or
supporting information immediately to expedite the conversation and
simplify agent effort. By delivering information from CRM 304 or
knowledgebase 302 to the agent 120 in milliseconds, agent handling
time will handle be reduced and customers will realize a time
savings and ultimately a reduction in effort to interact with
businesses.
[0066] FIGS. 5A-5C illustrate an example unified interface 500
showing aspects of the operational flows of FIGS. 3 and 4. In FIGS.
5A-5C, the agent 120 is speaking on behalf of a financial
institution. The agent 120 could be speaking on behalf of any
entity for which the cloud-based contact center 150 serves. As
shown in FIG. 5A, the customer 110 is calling to ask questions
about setting up a retirement plan. Because the context of the
conversation is understood by the automation infrastructure 200 to
be related to a financial institution, Agent Assist identifies that
the term "retirement plan" is meaningful and highlights it to the
agent. As shown in FIG. 5B, Agent Assist provides a prompt 502
indicating to the agent 120 that there are many different types of
retirement plans that the customer 110 can choose from. A button or
other control 504 is provided such that the agent 120 can click a
link to see more information. The link to the information may
provide text, audio, video, messages, tweets, posts, etc. to the
agent 120. Agent Assist provides a segment and/or snippet in the
text that is relevant to the customer's needs. In other
implementations, Agent Assist provides a relevant interaction in
the past (e.g., a similar call with a similar issue that agent 120
was able to address, etc.) or provide cross channel information
(e.g., find a most relevant e-mail for a call, etc.). As shown in
FIG. 5C, Agent Assist may provide an option 506 to schedule a
meeting or call between the customer 110 and a financial planner
(i.e., a person with additional knowledge within the entity who may
satisfy the customer's request to the agent 120). Additional
details of the scheduling operation are described below with
reference to FIG. 8.
[0067] Smart Notes
[0068] FIGS. 6 and 7 provide details about the smart notes feature
of Agent Assist. The smart notes feature may be used by the agent
120 to summarize a conversation with the customer 120, extract
relevant portions of the interaction, etc. Important items in the
smart notes may be highlighted using bold fonts or other. The
process begins at 602 where operations 404-414 are performed. These
may be performed in parallel with the other features described
above. At 604, information is extracted from the transcript and
populated into the smart notes. As shown in FIG. 7, a call notes
user interface 702 is provided to the agent 120 with information
from the call with the customer 110 pre-populated in an input field
704. For example, in the context of a retailer, the phrases "status
of my last order" and "place a new order" may be determined to be
relevant information by the automation infrastructure 200, and is
populated into the call notes input field 704. At 606, important
terms may be highlighted. At 608, the process ends. As shown in
FIG. 7, the call notes user interface 702 may provide an option for
the user to edit and/or add notes.
[0069] In accordance with the operations performed in FIG. 6, Agent
Assist may analyze the conversation between the agent 120 and the
customer 110 to create smart notes. This conversation could be a
phone call, a text message, chat or video call, etc. Smart notes
extracts the most relevant information from this conversation. For
instance after a conversation, Agent Assist may determine that the
discussion between the agent and the customer was about "canceling
an old order " and " placing a new order." These would be extracted
as Smart Notes and provide to the agent, who has an option to
accept or modify the note, as show in FIG. 7. To achieve the above,
Agent Assist may separate the conversation between customer 110 and
agent 120 to find words and phrases that are common between agents
and customers, when a customer confirms a question, or when an
agent confirms what customer says. For instance, the agent 120 may
say, "Ok, so you would like to place a new order--correct?" In this
case, the Smart Note would be a summary of the call about placing a
new order.
[0070] Automatic Data Entry
[0071] In accordance with aspects of the disclosure, when Agent
Assist detects the participants in a conversation it may
automatically fill out any forms that pop-up after such
conversations. With reference to FIG. 8, the process begins at 802
where operations 404-414 are performed. These may be performed in
parallel with the other features described above. At 804,
information is extracted to populate forms. As shown in FIG. 9, in
response to the customer indicating that he or she is calling to
move forward on a job application, scheduling information may be
presented to the agent in a field 508. This information may
populate into user interface (902) field 904 together with
additional information in field 906 to schedule the call for an
interview with the appropriate person. In another example, if the
person says, "Hi my name is John? I like to return my iPhone 6," a
form may pop up with some of the information such as Name: John and
Phone: iPhone 6 prefilled into the form.
[0072] Such automated data entry includes but not limited to:
[0073] Date [0074] Time [0075] Day of the week [0076] First name
[0077] Last name [0078] Gender [0079] Address [0080] Object e.g.,
Samsung Galaxy [0081] Type of the Object--e.g. Galaxy S9 [0082]
Time of the day (e.g. morning, afternoon)
[0083] After the information is populated, the process ends at
806.
[0084] Real-Time Analytics and Error Detection
[0085] With reference to FIG. 10, Agent Assist may provide for
real-time analytics and error detection by monitoring a
conversation (i.e., a call, a text, an e-mail, video, chat, etc.)
between the customer 110 and agent 120 in real-time to detect the
following non-limiting categories:: [0086] Compliance--words that
should not say in the conversation. [0087] Competitors--if agent
says the name of competitors. [0088] A set of "do's and
don'ts"--words that agent should not say. [0089] If the agent is
angry, curse etc. [0090] If the agent is making fun of the caller.
[0091] If the agent talks too fast, too slow, or if there is a
delay between words. [0092] lithe agent shows empathy. [0093] lithe
agent violates any policy. [0094] lithe agent markets other
products. [0095] lithe agent talks about personal issues. [0096]
lithe agent is politically motivated. [0097] lithe agent promotes
violence.
[0098] The process monitors the agent in real-time and expands upon
the current state of the art, which is monitoring is at word level
to monitor the transcript of the conversation and look for certain
words or a variation of such words. For instance, if the agent is
talking about pricing, the system may look for words such as "our
pricing." "our price list," "do you want to know how much our
product is," etc. As another example, the agent may say "our
product is beating everybody else," which means the price is very
affordable. Other examples such as these are possible.
[0099] Artificial Intelligence (AI) Processing/Learning
[0100] In accordance with the present disclosure, a layer of deep
learning 1002 is applied to create a large set of all potential of
sentences and instances (natural language understanding 1004) where
the agent: [0101] Said X and meant A. [0102] Said Y and meant A.
[0103] Said Z but did not mean A. [0104] Said W and meant B.
[0105] This sets have several positive and negative examples around
concepts, such as "cursing," "being frustrated," "rude attitude,"
"too pushy for sale," "soft attitude," as well as word level
examples, such as "shut up." Deep learning 1002 does not need to
extract features, rather deep learning takes a set of sentences and
classes (class is positive/negative, good bad, cursing/not
cursing). Deep learning 1002 learns and builds a model out of all
of these examples. For example, audio files of conversations 1006
between agents 120 and customers 110 may be input to the deep
learning module 1002. Alternatively, transcribed words may be input
to the deep learning module 1002. Next, the system uses the learned
model to listen to any conversation in real time and to identify
the class such "cursing/not cursing." As soon as the system
identifies a class, and if it is negative or positive, it can do
the following: [0106] Send an alert to manager [0107] Make an
indicator red on the screen [0108] Send a note to the agent to be
reviewed in real-time or after the call [0109] Update some data
files for reporting and visualization.
[0110] As part of the above, the natural language understanding
1004 may be used for intent spotting 1008 to determine intent 1010,
which may be used for IVR analysis 1012 and/or agent performance
1014.
[0111] In this approach words are not important, rather the
combination of all of words, the order of words and al potential
variations of them have relevance. Deep learning 1002 considers all
of the potential signals that could describe and hint toward a
class. This approach is also language agnostic. It does not matter
what language agent or caller speaks as long as there are a set of
words and a set of classes, deep learning 1002 will learn and the
model can be applied to the same language. In addition to the
above, metadata may be added to every call, such as the time of the
call, the duration of the call, the number of times the agent
talked over the caller could be added to the data, etc.
[0112] Listening to Other Agents Conversation in Real-Time
[0113] As described above, Agent Assist may periodically perform
the following to classify conversations of other agents. With
reference to FIG. 11, the process begins at 1102. At 1104, a
feature vector of a conversation is created. Such feature vector(s)
includes but are not limited to: [0114] Time of the call [0115]
Duration of the call [0116] Topic of the call [0117] Frequency of
words in the customer transcription (e.g. Ticket 2, Delay 4, etc.)
[0118] Frequency of words in the agent transcription (e.g. rebook
3, etc.) [0119] Cluster conversations based on these features
[0120] At 1106, for the conversation happening in within a
predetermined period (e.g., one month), the following are
performed: [0121] Calculate the point wise mutual information
between all of the calls in one cluster [0122] Make a graph of all
calls in which the strength of the link is the weight of the point
wise mutual information.
[0123] At 1108, for the current file: [0124] Extract features
[0125] Find the cluster [0126] Calculate the point wise mutual
information [0127] Find the closest call to the current call [0128]
Show the content of the call to the agent.
[0129] At 1110, the process ends.
[0130] Learning Module
[0131] While the process 1100 analyzes calls, Agent Assist learns
and improves by analyzing user clicks. As relevant conversations
are presented to the agent (see, e.g., 306), if the agent clicks on
a conversation and spends time on it, then it means that the
conversation is relevant. Further, if the conversation is located,
e.g., third on the list, but the agent clicks on the first
conversation and moves forward, Agent Assist does not make any
assumptions about the conversation. Hence, the rank of the
conversation may be of importance depending on the agent's actions.
For the sake of simplicity, Agent Assist shows the top three
conversations to the agent. If some conversations ranked equally,
Agent Assist picks one based on heuristics, for instance any
conversation that has not been picked recently will be picked.
[0132] Escalation Assistance
[0133] With reference to FIG. 12, there is shown an example
operational flow of escalation assistance, which may occur when
agent cannot answer a customer question or when user is frustrated.
With escalation assistance, agent can transfer the call to his or
her supervisor, where the transfer will include a summary of the
call, along highlights of important notes. In this case, the
supervisor has insight into the context and reason for the
transfer, and the caller does not need to repeat the case over
again. The process begins at 1202 where operations 404-414 are
performed. These may be performed in parallel with the other
features described above. At 1204, information extracted is from
the transcript and populated into the smart notes with a call
summary. At 1206, notable items may be highlighted. At 1208, the
customer is transferred to the supervisor, where the supervisor is
fully briefed on the reasons for the transfer. At 1210, the process
ends.
[0134] Thus, the present disclosure described an Agent Assist tool
within a cloud-based contact center environment that is a
conversational guide that proactively delivers real-time
contextualized next best actions, in-app, to enhance the customer
and agent experience. Talkdesk Agent Assist uses AI to empower
agents with a personalized assistant that listens, learns and
provides intelligent recommendations in every conversation to help
resolve complex customer issues faster
[0135] General Purpose Computer Description
[0136] FIG. 13 shows an exemplary computing environment in which
example embodiments and aspects may be implemented. The computing
system environment is only one example of a suitable computing
environment and is not intended to suggest any limitation as to the
scope of use or functionality.
[0137] Numerous other general purpose or special purpose computing
system environments or configurations may be used. Examples of
well-known computing systems, environments, and/or configurations
that may be suitable for use include, but are not limited to,
personal computers, servers, handheld or laptop devices,
multiprocessor systems, microprocessor-based systems, network
personal computers (PCs), minicomputers, mainframe computers,
embedded systems, distributed computing environments that include
any of the above systems or devices, and the like.
[0138] Computer-executable instructions, such as program modules,
being executed by a computer may be used. Generally, program
modules include routines, programs, objects, components, data
structures, etc. that perform particular tasks or implement
particular abstract data types. Distributed computing environments
may be used where tasks are performed by remote processing devices
that are linked through a communications network or other data
transmission medium. In a distributed computing environment,
program modules and other data may be located in both local and
remote computer storage media including memory storage devices.
[0139] With reference to FIG. 13, an exemplary system for
implementing aspects described herein includes a computing device,
such as computing device 1300. In its most basic configuration,
computing device 1300 typically includes at least one processing
unit 1302 and memory 1304. Depending on the exact configuration and
type of computing device, memory 1304 may be volatile (such as
random access memory (RAM)), non-volatile (such as read-only memory
(ROM), flash memory, etc.), or some combination of the two. This
most basic configuration is illustrated in FIG. 13 by dashed line
1306.
[0140] Computing device 1300 may have additional
features/functionality. For example, computing device 1300 may
include additional storage (removable and/or non-removable)
including, but not limited to, magnetic or optical disks or tape.
Such additional storage is illustrated in FIG. 13 by removable
storage 1308 and non-removable storage 1310.
[0141] Computing device 1300 typically includes a variety of
tangible computer readable media. Computer readable media can be
any available tangible media that can be accessed by device 1300
and includes both volatile and non-volatile media, removable and
non-removable media.
[0142] Tangible computer storage media include volatile and
non-volatile, and removable and non-removable media implemented in
any method or technology for storage of information such as
computer readable instructions, data structures, program modules or
other data. Memory 1304, removable storage 1308, and non-removable
storage 1310 are all examples of computer storage media. Tangible
computer storage media include, but are not limited to, RAM, ROM,
electrically erasable program read-only memory (EEPROM), flash
memory or other memory technology, CD-ROM, digital versatile disks
(DVD) or other optical storage, magnetic cassettes, magnetic tape,
magnetic disk storage or other magnetic storage devices, or any
other medium which can be used to store the desired information and
which can be accessed by computing device 1300. Any such computer
storage media may be part of computing device 1300.
[0143] Computing device 1300 may contain communications
connection(s) 1312 that allow the device to communicate with other
devices. Computing device 1300 may also have input device(s) 1314
such as a keyboard, mouse, pen, voice input device, touch input
device, etc. Output device(s) 1316 such as a display, speakers,
printer, etc. may also be included. All these devices are well
known in the art and need not be discussed at length here.
[0144] It should be understood that the various techniques
described herein may be implemented in connection with hardware or
software or, where appropriate, with a combination of both. Thus,
the methods and apparatus of the presently disclosed subject
matter, or certain aspects or portions thereof, may take the form
of program code (i.e., instructions) embodied in tangible media,
such as floppy diskettes, CD-ROMs, hard drives, or any other
machine-readable storage medium wherein, when the program code is
loaded into and executed by a machine, such as a computer, the
machine becomes an apparatus for practicing the presently disclosed
subject matter. In the case of program code execution on
programmable computers, the computing device generally includes a
processor, a storage medium readable by the processor (including
volatile and non-volatile memory and/or storage elements), at least
one input device, and at least one output device. One or more
programs may implement or utilize the processes described in
connection with the presently disclosed subject matter, e.g.,
through the use of an application programming interface (API),
reusable controls, or the like. Such programs may be implemented in
a high level procedural or object-oriented programming language to
communicate with a computer system. However, the program(s) can be
implemented in assembly or machine language, if desired. In any
case, the language may be a compiled or interpreted language and it
may be combined with hardware implementations.
[0145] Although the subject matter has been described in language
specific to structural features and/or methodological acts, it is
to be understood that the subject matter defined in the appended
claims is not necessarily limited to the specific features or acts
described above. Rather, the specific features and acts described
above are disclosed as example forms of implementing the
claims.
* * * * *