Note: Descriptions are shown in the official language in which they were submitted.
PROACTIVE KNOWLEDGE OFFERING SYSTEM AND METHOD
BACKGROUND
[0001] In the field of customer contact centers, it is desirable to
have customers
engage in self-service as much as possible, such as, for example, by browsing
an
enterprise's website and finding answers to questions that the customer may
have via
information provided by the website. This helps deflect calls to the contact
center
which uses up valuable agent time. Accordingly, it is desirable to provide
information
to customers as early as possible to allow customers to engage in such self-
service.
[0002] It is also desirable to extend other offers for engagement to the
customer,
but such offers should be paced based on availability of resources at the
contact
center as well as based on likelihood that the customers will accept the
offer.
BRIEF SUMMARY
[0003] In one embodiment, there is provided a method for proactively making
a
knowledge offer. A processor is configured to gather information on
interactions by a
user with resources provided by an enterprise having a customer contact
center. The
processor anticipates need of the user based on the gathered information and
generates a search query based on the anticipated need. The generating of the
query
includes receiving a first query and transforming the first query to a second
query.
Prior to the user expressly requesting knowledge relating to a particular
topic, the
processor proactively identifies and suggests the knowledge to the user based
on the
generated query. The proactively identifying and suggesting the knowledge
further
includes retrieving a classifier model for the knowledge, determining a
relevance score
based on the classifier model, wherein the relevance score is indicative of a
probability
that the search query is relevant to the knowledge, and outputting the
suggested
knowledge based on the relevance score; The processor receives feedback
relating
to the suggested knowledge updating based on the feedback, the relevance score
for
the suggested knowledge, and adding, by the processor, the search query as one
of
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a positive sample or a negative sample for the classifier model, based on the
feedback.
[0004] According to one embodiment, the resources include a website
provided
by the enterprise.
[0005] According to one embodiment, the interaction is a query typed by the
user.
[0006] According to one embodiment, the resources include agents of
the contact
center.
[0007] According to one embodiment, the interactions include past
interactions.
[0008] According to one embodiment, the anticipating the need of the
user
includes anticipating, by the processor, an express inquiry from the user.
[0009] According to one embodiment, the anticipating the need of the
user
includes anticipating user intent for a current interaction with one of the
resources
provided by the enterprise.
[0010] According to one embodiment, the search query includes text
retrieved
from the gathered information.
[0011] According to one embodiment, the information includes context
of the
interactions.
[0012] According to one embodiment, wherein the generating of the
query
includes identifying, by the processor, terms for describing the anticipated
need.
[0013] According to one embodiment, the processor is further configured to
conduct a first level search for a candidate set of knowledge documents based
on the
knowledge, and output a first score for each of the documents in the candidate
set of
knowledge documents. The processor is further configured to conduct a second
level
search of the candidate set of knowledge documents for relevant documents and
output a second score for each of the documents in the candidate set of
knowledge
documents, the second score for each of the documents is the relevance score.
The
processor generates a third score as a function of the first and second
scores, and
outputs one or more of the documents in the candidate set of knowledge
documents
based on the third score.
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[0014] According to one embodiment, the feedback is for a particular
document,
and the processor is further configured to train the classifier model for the
particular
document based on the feedback.
[0015] According to one embodiment, the feedback is an express
indication by
the user as to whether the suggested knowledge was helpful or not.
[0016] According to one embodiment, the feedback is implied from
actions by the
user.
[0016a] In another embodiment, there is provided a system for
proactively making
a knowledge offer. The system includes a processor and memory. The memory
includes instructions, that when executed by the processor, cause the
processor to
gather information on interactions by a user with resources provided by an
enterprise
having a customer contact center. The processor anticipates need of the user
based
on the gathered information. The processor generates a search query based on
the
anticipated need. The generating of the query includes receiving a first query
and
transforming the first query to a second query. Prior to the user expressly
requesting
knowledge relating to a particular topic, the processor proactively identifies
and
suggests the knowledge to the user based on the generated query. The
instructions
that cause the processor to proactively identify and suggest the knowledge
further
includes instructions that cause the processor to: retrieve a classifier model
for the
knowledge, determine a relevance score based on the classifier model, wherein
the
relevance score is indicative of a probability that the search query is
relevant to the
knowledge, and output the suggested knowledge based on the relevance score.
The
processor receives feedback relating to the suggested knowledge updates based
on
the feedback, the relevance score for the suggested knowledge, and add, by the
processor, the search query as one of a positive sample or a negative sample
for the
classifier model, based on the feedback.
[0017] These and other features, aspects and advantages will be more
fully
understood when considered with respect to the following detailed description
and
accompanying drawings.
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[0018] Embodiments described herein are directed to a system and
method for
making engagement offers. A processor monitors a navigation path of a user
navigating a website. The processor makes an offer for engagement with the
user on
a first web page reached by the user upon following the navigation path. The
processor monitors a reaction of the user to the offer for engagement, and
updates
tag data associated with a second web page encountered by the user while
traversing
the navigation path. The tag data is updated based on the reaction of the user
to the
offer for engagement.
[0019] According to one embodiment, the navigation path identifies a
sequence
of web pages visited by the user during a current web session.
[0020] According to one embodiment, the offer for engagement is an
offer for a
knowledge article.
[0021] According to one embodiment, the offer for engagement is an
offer for
interaction with a contact center agent.
[0022] According to one embodiment, the tag data includes information
relating
to user intent.
[0023] According to one embodiment, the tag data includes a distance
to the first
web page where the offer for engagement was made, and outcome of the offer for
engagement.
[0024] According to one embodiment, the processor is further configured to
detect a second user landing on the second web page, where the second user is
detected to have followed at least a portion of the navigation path. The
processor
calculates a confidence level that the second user will accept the offer for
engagement
on the second web page and makes the offer for engagement on the second web
page based on the calculated confidence level.
[0025] According to one embodiment, the confidence level is calculated
as a
function of a distance to the first web page.
[0026] According to one embodiment, the confidence level is calculated
based
on a likelihood that the second user will follow the navigation path.
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[0027] According to one embodiment, the confidence level is based on
identification of redirection to the website.
[0028] According to one embodiment, the offer for engagement is paced
based
on the calculated confidence level.
[0029] According to one embodiment, the pacing is based on availability of
agents to handle requests for interactions based on transmitted offers for
engagement.
[0030] According to one embodiment, the pacing includes transmitting,
by the
processor, more invitations for offers for engagement than what are expected
to be
accepted by users.
[0031] According to one embodiment, the navigation path is processed
for
determining user intent, and the offer for engagement is associated with the
determined user intent.
[0032] According to one embodiment, the processor is further
configured to
generate a query descriptive of the user intent, where the offer for
engagement is a
knowledge article related to the user intent.
[0033] These and other features, aspects and advantages will be more
fully
understood when considered with respect to the following detailed description
and
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWING
[0034] FIG. 1 is a block diagram of a proactive offer management
system
according to one embodiment;
[0035] FIG. 2 is a block diagram of a contact center system according
to one
embodiment;
[0036] FIG. 3 is a block diagram of a knowledge management system
according
to one embodiment;
[0037] FIG. 4 is a flow diagram of a process for proactively making
knowledge
offers according to one embodiment;
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[0038] FIG. 5 is a conceptual layout diagram of a step for
preprocessing an initial
query according to one exemplary embodiment;
[0039] FIG. 6A is a conceptual layout diagram of an exemplary FAQ
document
that may be stored as a knowledge article according to one embodiment;
[0040] FIG. 6B is a conceptual layout diagram of terms that are extracted
from a
knowledge document based on a preprocessing step which may be similar to the
preprocessing step conducted for input queries according to one embodiment;
[0041] FIGS. 7A-7B are screen shots of a web page providing access to
the
knowledge management system to a visitor of the website according to one
embodiment;
[0042] FIGS. 8A-86 are screen shots of an agent GUI that may be
invoked during
an interaction with a customer according to one embodiment;
[0043] FIG. 9 is a screen shot of a reporting screen depicting
performance of the
knowledge documents;
[0044] FIG. 10 is a layout diagram of exemplary intent tags that may be
maintained for a particular web page;
[0045] FIG. 11A is a block diagram of a computing device according to
an
embodiment;
[0046] FIG. 11B is a block diagram of a computing device according to
an
embodiment;
[0047] FIG. 11C is a block diagram of a computing device according to
an
embodiment;
[0048] FIG. 11D is a block diagram of a computing device according to
an
embodiment; and
[0049] FIG. 11E is a block diagram of a network environment including
several
computing devices according to an embodiment.
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DETAILED DESCRIPTION
[0050] Embodiments described herein are directed to a system and
method for
proactively making knowledge offers to users in response to perceived intent,
need,
or interest (collectively referred to as intent or need) of the users. The
users may be,
for example, customers of an enterprise browsing the enterprise's website for
information. The users may also be agents of a contact center supporting the
enterprise. The knowledge offer may be, for example, links to articles, blogs,
posts,
comments, and the like, identified as relevant in satisfying the perceived
need of the
user. Although links to knowledge is used as a main example throughout the
specification, embodiments also contemplate other mechanisms for presenting
knowledge to a user, such as, for example, orally, via text messages, via
email
messages, or via any other multimedia communication conventional in the art.
[0051] By proactively offering knowledge to a user, the user need not
waste time
and effort actively searching for the knowledge or contacting the contact
center to
receive the knowledge. In one example, a user seeking an answer on what to do
with
battery problems with his current phone may, while browsing the enterprise's
website
for an answer, automatically have the answer surfaced to him. Such proactive
knowledge offers allow users the opportunity for self-service and help deflect
calls to
the contact center. Also, the ability to quickly anticipate customer intent
and offer
knowledge in response to the anticipated intent allows problems to be
addressed in a
quicker manner. Quick resolution to problems helps to increase customer
satisfaction.
[0052] Embodiments described herein are also directed to determining a
user's
intent as the user navigates pages of an enterprise's website by comparing
similar
navigation paths followed by other users that exhibited similar intent.
Proactive
engagement offers/invitations may be made to the user based on the determined
intent. The proactive engagement invitations may be, for example, proactive
knowledge offers as discussed above that prompt or encourage users to click or
otherwise interact with the knowledge offers. The invitations may also be
other offers
for engagement such as, for example, offers to engage in an interaction with
an agent,
such as via chat, text, voice, or the like.
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[0053] Embodiments described herein may be exemplified via the
following two
use cases. For both use cases, the embodiments try to deduce user intent as
early
as possible and make a proactive offer in response to the deduced intent.
[0054] In a first use case, assume that while a user is filling an
email form
provided on the enterprise's website, the system analyzes content of the form
and
correlates the content with context surrounding the generating of the form.
The
context may relate to content of the visited website, how the user got there,
the user's
profile, engagement and interaction history while browsing the website, social
media
interactions by the user, current talk-of-the-town, news, public events, and
the like.
User intent may be deduced based on analysis of the form and context. A
confidence
level of the deduced intent may be computed for determining whether a matching
answer should be proactively offered to the user, such as, for example, prior
to the
user sending the email. If the computed confidence level satisfies a given
threshold,
a knowledge offer may be made to the user.
[0055] If the knowledge offer satisfies the customer's need, the user may
decide
to not send the email after all. This helps offload agents from manually
processing
the email and helps improve user experience by providing a faster response
than
would otherwise be provided after sending the email request. If the user sends
the
email anyway because suggested answer was not shown fast enough, the system
may be configured to use the identified answer in an automated response email
back
to the user, or as help for guiding the agent in drafting the response email.
[0056] If, however, the response was timely submitted, but the user
decides to
send the email anyway, an assumption may be made that the answer did not
satisfy
the user's need. This information may also be useful in formulating a response
back
to the user. For example, the system may, in this scenario, disable an
automated
email response that would otherwise insert the same unsatisfying answer back
to the
user, and instead, route the email to an agent for generating a manual
response. By
retrieving and viewing the interaction history of the user as well as
knowledge
information offered to the user, the agent may understand that the knowledge
information was timely displayed to the user and proceed accordingly.
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[0057] In a second use case, the system is configured to monitor a
user's
interaction with the enterprise's web site in order to determine user intent.
In this
regard, the system may be configured to monitor information such as, for
example,
searches conducted on the website, navigation paths taken while browsing the
website, attempts to contact the enterprise on a particular topic, and/or the
like. The
website interactions may be correlated to context information, such as the
context
information discussed above, to help determine user intent more accurately. If
the
system deduces the user's intent within a certain level of confidence, an
engagement
invitation is displayed for user selection. The engagement invitation may be,
for
example, invitation to click on a knowledge document. The engagement
invitation
may also be an invitation to interact with a live agent, such as, for example,
a pop-up
message asking, "do you want to chat/talk to an agent?" According to one
embodiment, the invitations are paced based on the level of confidence of the
deduced
intent. If the invitations are for live conversations with contact center
agents, the
invitations are also aligned with the ability of the contact center to
actually conduct the
conversations. That is, the invitations take into account the probability that
the users
will accept the invitations (referred to as hit rate logic). According to one
embodiment,
as soon as user intent is identified, the contact center prepares for an
anticipated
interaction, such as, for example, by making agent selection, retrieving
interaction
history for the user, determining a business strategy (matching offerings,
eligibility,
cross-/up-sell, etc.), and the like.
[0058] FIG. 1 is a block diagram of a proactive offer management
system
according to one embodiment. The system includes a web server 10 coupled to a
knowledge management system 12 and a contact center system 14 over a data
communications network 16. The data communications network 16 may a local area
network (LAN) (e.g., a private network such as a company Intranet, a
metropolitan
area network (MAN), or a wide area network (WAN) (e.g., the Internet), or a
combination thereof.
[0059] The web server 10 includes hardware and software for storing,
processing, and delivering web pages to requesting end user devices. The web
pages
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may be web pages of a website maintained on behalf of a business enterprise
which
my market products and services to consumers via the website. The web pages
may
be accessed via, for example, web browser software installed at the end user
devices.
The end users 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, support forum (whether
specific to a
certain product or service, or general in nature), voice call, email, web real
time
communication (WebRTC), or the like.
[0060] According to one embodiment, the web server 10 hosts a web
monitoring
module 18 configured to monitor behavior of visitors to the website. Such
behavior
may include, but is not limited to, material that is being browsed by the
visitors,
searches being conducted on the website, posts or other knowledge materials
that are
being accessed by the visitors, navigation path of the visitors, engagement
offers
provided to the visitors, outcome of the engagement offers, and/or the like.
The web
monitoring module 18 may also receive information on user actions from social
media
sites such as Facebook , Twitter , and the like.
[0061] The web monitoring module 18 may include tracking code that is
embedded in each web page of the website that is being monitored for visitor
activity.
The tracking code may be, for example, a JavaScript code. In other
embodiments
where Flash Web pages are provided, the tracking code may also be an Action
Script. According to one embodiment, the script defines what type of activity
is being
monitored, such as, for example, access of site features, navigation on web
pages
and between web pages, search activity, and any interaction with product ads,
price
comparison widgets, electronic shopping tools, electronic forms, and the like.
[0062] According to one embodiment, the enterprise for which the website is
maintained is also supported by a customer contact center. The customer
contact
center may serve the enterprise in performing the functions of sales and
service
relative to the products and services available through the enterprise and via
the
website. According to one exemplary embodiment, the contact center system 14
provides the necessary software and hardware (e.g., computers,
telecommunication
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equipment, and other infrastructure) that enables the contact center to
deliver services
to customers via telephone or other communication mechanisms. Such services
may
vary depending on the type of contact center, and may range from customer
service
to help desk, emergency response, telemarketing, order taking, and the like.
The
contact center system 14 may be located in-house to the enterprise, hosted in
equipment provided by a third-party service provider, or deployed in equipment
provided in a remote computing environment such as, for example, a private or
public
cloud environment.
[0063] According to one embodiment, the web server 10 and contact
center
system 14 provide interaction information and context surrounding the
interactions to
the knowledge management system 12. The knowledge management system 12 may
use this information to determine user intent and provide knowledge articles
or other
offers for engagement based on the determined intent. The knowledge management
system 12 may determine a user's intent by correlating user actions during a
given
session, accessing user's history, and considering other influential
parameters. The
knowledge management system 12 may also serve the knowledge in accordance with
the determined intent.
[0064] The context information may relate to a navigation path
followed by the
user as the user surfs the web pages provided by the web server, a history of
prior
engagements and interactions, user profile information, current news, social
media
postings, and/or the like. According to one embodiment, the knowledge
management
system proactively makes an offer for engagement based on deduced user intent,
preferably before the user makes an express request for the engagement. Of
course,
the offer for engagement may also be in response to an express request made by
the
user, such as, for example, an express search query submitted by the user, a
request
made during a chat session with a contact center agent, or an email Web form
submitted by the user.
[0065] According to one embodiment, the offer for engagement provided
by the
knowledge management system 12 may be an offer for chat, call back, or the
like.
The offer for engagement may also be an offer of a knowledge article
identified by the
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Date Recue/Date Received 2023-02-16
knowledge management system based on deduced need. In this regard, the
knowledge management system is coupled to a mass storage device 20 that
provides
a general and/or domain-specific knowledge base (also referred to as corpus)
for
indexing by the system 12. The knowledge documents forming the knowledge base
are dynamic by nature. That is, if the knowledge document becomes obsolete, it
is
replaced by updates.
[0066] According to one embodiment, the knowledge management system 12
also has access to different information sources 22 over the data
communication
network 16 that may be used to deduce/anticipate user intent and proactively
make
engagement offers relevant to the deduced intent. The information sources 22
may
include, but are not limited to, social media sites, news information sites,
traffic/weather sites, advertisement campaigns, event information sites,
reputation
management systems, and the like.
[0067] In the embodiment of FIG. 1, the web server 10, knowledge
management
system 12, and contact center system 14 are depicted as being separate
servers/systems. A person of skill in the art should recognize, however, that
the
functionality of one or more of the servers/systems may be integrated together
into a
single server/system or further subdivided into sub-systems/servers without
departing
from the spirit of the teachings herein. For example, the functionality of the
knowledge
management system 12 may be integrated into the monitoring module 18 of the
web
server 10, or integrated into the contact center system 14, as will be
appreciated by a
person of skill in the art.
[0068] FIG. 2 is a more detailed block diagram of the contact center
system 14
according to one embodiment. According to one exemplary embodiment, the system
14 includes a switch/media gateway 100 coupled to a communications network,
such
as, for example, a PSTN network or the communications network 16 of FIG. 1,
for
receiving and transmitting telephony calls between customers and the contact
center.
The switch/media gateway 100 may include a telephony switch configured to
function
as a central switch for agent level routing within the center. In this regard,
the switch
100 may include an automatic call distributor, a private branch exchange
(PBX), an
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IP-based software switch, and/or any other switch configured to receive
Internet-
sourced calls and/or telephone network-sourced calls. According to one
exemplary
embodiment, the switch is coupled to a call server 102 which may, for example,
serve
as an adapter or interface between the switch and the remainder of the
routing,
monitoring, and other call-handling components of the contact center.
[0069] The call server 102 may be configured to process PSTN calls,
VolP calls,
and the like. For example, the call server 102 may include a session
initiation protocol
(SIP) server for processing SIP calls. According to some exemplary
embodiments,
the call server 102 may, for example, 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 call.
[0070] According to one exemplary embodiment, the contact center
system 14
further includes an interactive media response (IMR) server 104, which may
also be
referred to as a self-help system, virtual assistant, or the like. The IMR
server 104
may be similar to an interactive voice response (IVR) server, except that the
IMR
server is not restricted to voice but may cover a variety of media channels
including
voice. Taking voice as an example, however, the IMR server may be configured
with
an IMR script for querying calling customers on their needs. For example, a
contact
center for a bank may tell callers, via the IMR script, to "press 1" if they
wish to get an
account balance. If this is the case, through continued interaction with the
IMR,
customers may complete service without needing to speak with an agent. The IMR
server 104 may also ask an open-ended question such as, for example, "How can
I
help you?" and the customer may speak or otherwise enter a reason for
contacting
the contact center.
[0071] The routing server 106 may be configured to take appropriate
action for
processing a call. For example, the routing server 106 may use data about the
call to
determine how the call should be routed. If the call is to be routed to a
contact center
agent, the routing server 106 may select an agent for routing the call based,
for
example, on a routing strategy employed by the routing server 106, and further
based
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on information about agent availability, skills, and other routing parameters
provided,
for example, by a statistics server 108.
[0072] In some embodiments, the routing server 106 may query a
customer
database, which stores information about existing clients, such as contact
information,
service level agreement (SLA) requirements, nature of previous customer
contacts
and actions taken by contact center to resolve any customer issues, and the
like. The
database may be, for example, Cassandra or any non-SQL database, and may be
stored in a mass storage device 30. The database may also be a SQL database
and
may be managed by any database management system such as, for example,
Oracle , IBM DB2TM, Microsoft SQL ServerTM, Microsoft AccessTM, PostgreSQL
,
MySQL , FoxPro , and SQLiteTM. The routing server 20 may query the customer
information from the customer database via an ANI or any other information
collected
by the IMR server 34.
[0073] Upon identification of an agent to whom to route the call, a
connection is
made between the caller and an agent device of an identified agent, such as,
for
example, the agent device 28 of FIG. 2. Received information about the caller
and/or
the caller's historical information may also be provided to the agent device
for aiding
the agent in better servicing the call. In this regard, the agent device 28
may include
a telephone adapted for regular telephone calls, VolP calls, and the like. The
agent
device 28 may also include a computer for communicating with one or more
servers
of the contact center system 14 and performing data processing associated with
contact center operations, and for interfacing with customers via voice and
other
multimedia communication mechanisms.
[0074] According to one embodiment, the agent device 28 is configured
with a
graphical user interface (GUI) for interfacing with the knowledge management
system
12. The agent GUI may provide a history of queries and answers provided by the
knowledge management server 12 for a particular customer with whom the agent
is
now interacting. The agent GUI may also allow an agent to submit queries for
receiving suggestions of knowledge documents that may be helpful to the agent
while
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interacting with the customer. The knowledge documents retrieved for use by
the
agent may also be based on deduced intent of the customer.
[0075] The suggested knowledge documents may include information that
are
approved for exposing to the customer, or information on what not to expose to
the
customer. For example, assuming that the deduced intent of the customer is
that he
wants to cancel service, and the interaction is transferred to a skilled agent
or
supervisor trying to preserve the business relationship, the retrieved
knowledge
document may be information on how to preserve the business relationship and
not
information to guide the customer in canceling the service. In another
example, the
knowledge document may be to provide guidance on how to respond to a customer
question (e.g., customer question about bending of smartphones sold by the
enterprise). Knowledge documents exposed to agents may also be subject of
confidentiality (e.g., only agents with certain confidentiality clearance may
have
access), or subject to authorization (e.g., certain agent might be authorized
to offer
.. particular services while others are not). In this regard, the knowledge
management
system may be configured to retrieve profile data of the agents prior to
suggesting
knowledge documents that have been tagged with confidentiality levels and/or
authorization levels.
[0076] The contact center system 14 further includes a reporting
server 114
configured to generate reports from data aggregated by the statistics server
108.
Such reports may include near real-time reports or historical reports
concerning the
state of resources, such as, for example, average waiting time, abandonment
rate,
agent occupancy, and the like. The reports may be generated automatically or
in
response to specific requests from a requestor (e.g., agent/administrator,
contact
center application, and/or the like).
[0077] The contact center system 14 may also include a
multimedia/social media
server 116 for engaging in media interactions other than voice interactions
with end
user devices and the web server 10. The media interactions may be related, for
example, to email, vmail (voice mail through email), chat, video, text-
messaging, web,
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social media (whether entirely within the domain of the enterprise or that
which is
monitored but is outside the proprietary enterprise domain), co-browsing, and
the like.
[0078] According to one exemplary embodiment, in addition to real-time
interactions, deferrable (also referred to as back-office or offline)
interactions/activities
may also be routed to the contact center agents. Such deferrable activities
may
include, for example, responding to emails, responding to letters, attending
training
seminars, or any other activity that does not entail real time communication
with a
customer. In this regard, an interaction (iXn) server 122 interacts with the
routing
server 106 for selecting an appropriate agent to handle the activity. Once
assigned to
an agent, the activity may be pushed to the agent, or may appear in the
agent's
workbin 120 as a task to be completed by the agent. The agent's workbin may be
implemented via any data structure conventional in the art, such as, for
example, a
linked list, array, and/or the like. The workbin may be maintained, for
example, in
buffer memory of the agent device 28.
[0079] According to one exemplary embodiment, the mass storage device(s)
110
may store one or more databases relating to agent data (e.g., agent profiles,
schedules, etc.), customer data (e.g., customer profiles), interaction data
(e.g., details
of each interaction with a customer, including reason for the interaction,
disposition
data, time on hold, handle time, etc.), and the like. According to one
embodiment,
some of the data (e.g., customer profile data) may be maintained in a customer
relations management (CRM) database hosted in the mass storage device 110 or
elsewhere. The mass storage device may take form of a hard disk or disk array
as is
conventional in the art.
[0080] According to one embodiment, the knowledge management system 12
is
a client of the mass storage device(s) 110 that receives information stored in
the mass
storage device(s), such as, for example, engagement and interaction history
data,
user profile data, and the like. Such data may be provided to the knowledge
management system 12 on a periodic basis and/or in response to a request for
such
data from the knowledge management system 12.
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Date Recue/Date Received 2023-02-16
I. Proactive Knowledge Offers
[0081]
FIG. 3 is a more detailed block diagram of the knowledge management
system 12 according to one embodiment. The system includes a preprocessing
module 200 coupled to a match scoring module 202 which in turn is coupled to a
query
relevance scoring module 204. According to one embodiment, the preprocessing
module 200 is configured to receive an initial query from, for example, the
web
monitoring module 18, and transform the query as needed to generate an updated
query. The initial query may be, for example, a natural language text that is
input by
a user on a web page provided by the web server. According to one embodiment,
the
query is submitted to the preprocessing module 200 even before the user has
taken
action to expressly submit the query. In this regard, when a threshold amount
of text
(which may be judged based on content) is entered by the user, an assumption
is
made that the user will eventually ask the question.
[0082] In
addition or in lieu of the text typed by the user, the initial query may also
include keywords generated by the web monitoring module 18 based on, for
example,
contextual data. Such contextual data may relate to content of the website
being
browsed by the user, the navigation path that led the user to the current web
page,
user identification information, and/or the like. According to one embodiment,
the
preprocessing module 200 may be configured to process the initial query to
identify
useful terms and expand on those terms to generate a list of topics that
relate to user
intent. According to one embodiment, the preprocessing module 200 may retrieve
additional context data from the contact center system 14 and/or other
information
sources 22 to expand the query based on the context data.
[0083]
The match scoring module 202 is configured to take the
updated/transformed query and match the query against a set of indexed
documents
208 stored, for example, in the mass storage device 20. Although the term
document
is used as a general term to refer to knowledge that is stored in the mass
storage
device, a person of skill in the art should recognize that knowledge may be
any type
of information to be conveyed to a user, such as, for example, articles,
answers to
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Date Recue/Date Received 2023-02-16
frequently asked questions (FAQs), blogs on expert sites, social media
postings, and
the like. According to one embodiment, the scoring module 202 may be
implemented
as a search engine that computes a degree of similarity between the updated
query
and various fields of documents stored in the mass storage device. The scoring
module 202 assigns a match score based on the computed degree of similarity.
The
documents are then ranked based on their match scores. The matching documents
output by the match scoring module may be bound at an upper limit preset by
the
knowledge management system 12.
[0084] The query relevance scoring module 204 is configured to take
the match
results from the match scoring module 202 and score each of the match results
against a trained document model that is maintained for the particular
document. In
this regard, the knowledge management system 12 maintains a binary classifier
model
in a model database 210, for each indexed document 208. There are benefits of
maintaining a per-document classifier, as opposed to a global classifier. For
example,
the per-document approach allows the classification problem to be modeled with
a
smaller and denser feature space. Also, the collision rate for incremental
learning is
generally smaller when there are many classifiers, and each is trained
quickly.
Documents may also be added and deleted from the knowledge base without
affecting
the classification of other documents already in the knowledge base.
[0085] The model is trained based on feedbacks received from users relating
to
relevancy of the document to the user's intent, after the document has been
suggested
to the user. If the document is tagged by a user as being relevant, the query
that
caused the document to be suggested becomes a positive sample for the
classifier.
If the document is tagged as not being relevant, the query becomes a negative
sample
for the classifier. The tagging may be explicit as well as implicit as
discussed in further
detail below. Over time, each of the per-document classifiers is trained with
positive
and negative samples from user feedbacks. Then, given a new query, each
classifier
may predict the chances of the new query being relevant to the document.
[0086] According to one embodiment, the knowledge management system 12
further includes a score combination module 206 that is configured to take,
for a
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Date Recue/Date Received 2023-02-16
candidate document, the match score output by the match scoring module 202 and
the relevance score output by the query relevance scoring module 204 and
generates
a combined score that is a function for the two scores. The combined score may
be
normalized to be within a fixed interval range like [0.0, 1.0]. According to
one
embodiment, the normalized score is treated as a value that reflects
confidence that
the knowledge document will satisfy the user's need. The documents are ranked
by
score and output as ranked results 212. According to one embodiment, documents
that have a confidence less than a preset confidence level are dropped from
the result
set. According to one embodiment, a knowledge lifecycle (if available for a
particular
.. document) is taken into account in determining whether a document should be
kept
or dropped from the result set. For example, if an answer is past its
knowledge
lifecycle and hence, obsolete, it is deleted from the result set. In some
embodiments,
the check for lifecycle information may also be performed by the match scoring
module
202 or query relevance scoring module.
[0087] FIG. 4 is a flow diagram of a process for proactively making
knowledge
offers according to one embodiment. In act 300, the preprocessing module 200
receives an initial query from the web monitoring module 18 or the agent
device 28.
The initial query may include text typed by the user in certain monitored
sections of a
web page or agent GUI. A monitored section may be a search box, fillable email
form,
chat box, or the like. After the user has typed a certain amount of text, the
web
monitoring module anticipates need of the user relating to the topic input so
far, and
submits the text as the initial query, even before the user selects an option
to expressly
submit the query. In another embodiment, the web monitoring module waits until
the
user submits a "search" command prior to submitting the initial query.
[0088] According to one embodiment, the web monitoring module 18 correlates
the content of the text with context surrounding the typing of the text and
submits the
context information as part of the initial query. In one embodiment, an
initial query
with just context data may be generated based on assumed user intent even if
the
user has not typed any text. In this regard, the web monitoring module 18 may
anticipates the user's need for knowledge based on interactions with the
website, and
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proactively generates and submits the initial query to allow early suggestions
of
knowledge that may be ultimately desired by the user.
[0089] The context information provided by the web monitoring module
18 may
relate to content of the visited website, navigation path, the user's profile,
and the like.
For example, if the web monitoring module 18 detects that a user is lingering
longer
than average on a particular portion of a web page, such as, for example, a
portion of
the web page displaying information on the newest iPhone to be released, the
web
monitoring module may deduce user intent that is related to the subject matter
displayed on the particular portion of the web page. For example, the web
monitoring
module may be configured to deduce that the user is interested in the new
iPhone .
According to one embodiment the web monitoring module 18 may retrieve and
transmit tag data associated with the particular portion of the web site as
the initial
query. Other context information may also be transmitted as part of the
initial query,
such as, for example, the amount of time in which the user was lingering,
information
on other pages visited by the user prior to lingering on the current page, the
navigation
path taken by the user to reach the current page, user identification
information (if the
user is logged in), and the like.
[0090] In act 302, the preprocessing module 200 transforms the initial
query into
an updated query with terms that are useful in discriminating user intent. The
processing of the initial query may involve, for example, token ization, Part-
Of-Speech
(POS) tagging, lemmatization, and/or stop word removal. As a person skill in
the art
will understand, POS tagging extracts terms that are nouns, verbs, or
adjectives, as
the relevant terms in the text which discriminate the user's true intention.
After the
relevant terms are identified, the preprocessing module 200 may be configured
to
expand each term with top-N similar words obtained, for example, from a
library of
synonyms accessible to the module. Of course, any other preprocessing
mechanism
conventional in the art may be used in addition or in lieu of the mechanisms
mentioned
herein, to generate an updated query that contain terms that identify user
intent.
[0091] According to one embodiment, as part of query processing and
expansion, the preprocessing module 200 may be configured to interact with the
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Date Recue/Date Received 2023-02-16
contact center system 14 and other information sources 22 to retrieve
information
relevant to user intent. For example, assume that the initial query sent by
the web
monitoring module relates to the new iPhone 6. The preprocessing module may,
upon
retrieving information on past interactions with the contact center system 14
or based
on analysis of posting on the user's social media site, understand that the
user has
been having problems with his Wi-Fi. The preprocessing module may anticipate
that
the user needs help in setting up the Wi-Fi for his new iPhone 6 and generate
an
updated/expanded query that includes the terms "iPhone 6," "Wi-Fi," and
"setup." The
terms "Wi-Fi" and "setup" may be retrieved, for example, from a summary of a
recent
interaction with the customer which is generated by the agent upon completion
of the
interaction and stored as part of the user's interaction history. In another
example,
information on past interaction history and/or user profile information may
help further
determine intent of an initial query as relating to sales or a complaint. For
example, if
the user information indicates that he does not own an iPhone 6, the initial
query may
be interpreted as the user being interested in purchasing the new iPhone 6, as
opposed to being a query for lodging a complaint about the new iPhone 6.
[0092] The updated query is transmitted to the match scoring module
202, in act
304, to search for a candidate set of knowledge documents that match the
transformed
query. According to one embodiment, each knowledge document includes a
question
and an answer to the question. Relevant terms found in the question-and-answer
portions are respectively stored in question-and-answer fields associated with
each
document. According to one embodiment, question and answer vectors are
generated based on respectively the question-and-answer fields by applying the
same
transform that is applied in act 302 to transform queries. According to one
embodiment, the document also stores in a feedback field, terms that are not
part of
the original document, but that are present in the query for which the
document was
tagged as relevant during user feedback. A feedback vector may also be
generated
in response.
[0093] According to one embodiment, the match scoring module computes
match scores for the documents in the mass storage device 20 by conducting a
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Date Recue/Date Received 2023-02-16
similarity search of the query over the question, answer, and feedback vectors
corresponding to the documents. According to one embodiment, one or more query
terms may be given a higher weight that other query terms for conducting the
similarity
search. For example, if a user is having problems with connecting a printer to
a
wireless router, using specific technical terms could indicate that the user
has
advanced knowledge on the subject, and already tried common troubleshooting
steps.
In this example, the technical terms could be given higher weight that some
other
terms of the query, to cause a more detailed knowledge document on the topic
to be
retrieved than a basic knowledge document on the same topic.
[0094] The matching documents are then ranked according to their match
scores. According to one embodiment, a ranking function such as, for example,
Okapi
BM25, may be used to rank matching documents according to their relevance to
the
updated query.
[0095] In act 306, the match scoring module 202 outputs, up to an
upper limit,
the matched set of documents along with their corresponding match scores.
[0096] In act 308, the query relevance scoring module 204 receives the
candidate set of matching documents from the matching scoring module 202 and
retrieves a classifier model for each document in the candidate set. According
to one
embodiment, online logistic regression (OLR), as will be understood by a
person of
skill in the art, is used as a per-document binary classifier. The per-
document
classification is based on feedback received from users after the document is
output
as suggested knowledge in response to a generated query. According to one
embodiment, the feedback is a binary feedback indicating a particular
suggested
document was useful or not. If the document is tagged as relevant, the query
relevance scoring module 204 (or some other classification module) adds the
query
that resulted in the document from being suggested, as a positive sample for
the
classifier for the document. Similarly, if the document is tagged as not being
relevant,
the query relevance scoring module 204 (or some other classification module)
adds
the query as a negative sample for the classifier for the document. In this
manner, the
per-document classifier provides the probability of the current query being
relevant for
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Date Recue/Date Received 2023-02-16
a document, given the past queries for which the document was found to be
relevant
or non-relevant.
[0097] According to one embodiment, for positive feedbacks only, any
new terms
of the query not already part of the document are added into the feedback
field for the
document and used for scoring matching documents by the match scoring module
202.
[0098] According to one embodiment, user feedbacks are provided
different
weights. Weights may be implemented by submitting repeated feedback in
different
volumes. In one embodiment, express feedbacks are given higher weight than
implied
feedbacks. Weights may also depend on the user providing the feedback. For
example, agent feedbacks may be given more weight than customer feedbacks.
[0099] According to one embodiment, implied feedbacks are assumed
based on
particular action or inaction by a user. For example, a negative feedback may
be
implied for unselected documents that are ranked higher than a document that
was
selected by the user. A positive feedback may be assumed for a document
selected
by a user if sufficient time was spent reviewing the document, the user
referenced the
document in a post, the user linked to the document, the user printed the
document,
the user emailed the document, and the like. In another example, if the user,
while
typing a query request or filling out an email form is presented with a
knowledge article,
the user's selection of a suggested knowledge article in conjunction with the
user
terminating the current session without expressly submitting the search query
or the
email form, may be interpreted as a positive feedback for the selected
article. On the
other hand, if the user, after receiving the suggested knowledge article
submits the
query anyway, or if the query was already submitted, the user initiates a new
query or
requests contact with a contact center agent, such actions may be interpreted
as
negative feedback for the suggested knowledge document.
[00100] According to one embodiment, feedbacks are weighted based on
reputation of the users providing the feedback. Such reputation data may be
retrieved
from reputation management systems such as Badgeville or BunchballTM.
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Date Recue/Date Received 2023-02-16
[00101] In embodiments where agents receive suggestions of knowledge
documents while interacting with a customer, customer sentiment/satisfaction
at the
end of the interaction may be used to implicitly determine relevance of the
document.
For example, a high (or low) customer satisfaction after using the document
may be
used to imply a positive (or negative) feedback for the document that was
used.
According to one embodiment, customer profile information may be used in
determining the weight of the feedback to be implied based on the customer
satisfaction information. Thus, an advanced approach correlates knowledge with
customer profile, rather than applying global metrics only.
[00102] In act 310, the query relevance scoring module outputs a relevance
score
for each candidate document based on retrieved classifier model for the
document.
In this regard, the query relevance scoring module 204 translates the updated
query
into a feature space for the retrieved model by vectorizing the updated query.
According to one embodiment, feature hashing, as will be understood by a
person of
skill in the art, is used for translating the updated query into the feature
space for the
classifier model. A relevance score is then assigned to the document for
indicating a
probability that the document will be relevant to the updated query based on
the
model.
[00103] In act 312, the score combination module 205 takes, for each
candidate
document, the match score output by the match scoring module 202 and the
relevance
score output by the query relevance scoring module 204 and generates a
combined
score that is a function for the two scores. The combined score may be
normalized to
be within a fixed interval range like [0.0, 1.0]. According to one embodiment,
the
normalized score is treated as a value that reflects confidence that the
knowledge
document is relevant to the deduced user intent.
[00104] In act 314, the documents are ranked by score and output as
ranked
results 212. In this regard, documents that have confidence (a combined score)
less
than a preset confidence level are dropped from the result set. According to
one
embodiment, links to the documents from the result set are displayed by the
web
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Date Recue/Date Received 2023-02-16
server (e.g., as a pop-up page) as suggested knowledge articles. The actual
text of
the documents may be viewed upon selection of the links.
[00105] According to one embodiment, the presentation of the suggested
knowledge may depend on feedback received from users, preference of the users,
and/or other user profile data. Different presentations may be available for a
particular
document where each presentation differs in the way that the document is
worded.
For example, one presentation option may present the document in a compact
manner
with simple words, while another presentation option may present the document
in a
verbose manner with technical terms. The type of presentation option to use
for a
particular document may depend on the popularity of the presentation option
(e.g.,
measured based on clicks for documents presented according to the presentation
option). The type of presentation option to use may also depend on the profile
of the
user, such as, for example, the education level of the user.
[00106] A person of skill in the art should appreciate that the two-
level search
algorithm discussed with respect to FIG. 4 may be used outside of proactive
engagement/knowledge offers. For example, the algorithm may apply to the
following
areas for interaction optimization:
agent routing ¨ where analysis of inbound interactions map to a query, and
models of agent skills, manually specified and/or learned, map to the document
representing the agent;
agent assistance ¨ where implicit search from interactions, or explicit
searches
from agents, drive search against a corpus of prior interactions, or knowledge
articles;
offer-recommendation ¨ where event and text-based interaction inputs
can be applied for offer selection.
[00107] In using the search algorithm for agent routing, the documents may
be
documents representing skills of an agent. The per-document classifier is thus
a
classifier of the agent skills for each agent. Feedback in this instance may
be based
on data relating to the handling of the interaction, such as, for example,
outcome of
the interaction, customer satisfaction, and the like.
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Date Recue/Date Received 2023-02-16
[00108] According to one embodiment, contact center infrastructure may
be used
for enterprise knowledge management. In this regard, an enterprise user
looking for
certain knowledge might be served by different output mechanisms that provide
the
knowledge, such as, for example, via explicit direct answer, list of related
documents,
list of employees with knowledge about the subject, references to related
enterprise
projects, list of external experts, references to related training courses,
video, and/or
the like. The user's inquiry may be submitted through various media,
including, for
example, e.g., web search portal, voice (calling a reserved knowledge number),
chat
, enterprise social (e.g., the enterprise's Twitter or Facebook page), email
(to
knowledge alias), and the like. A query is generated based on the inquiry as
described
above. A voice-to-text processing device may be invoked for inquiries that are
initially
provided as voice.
[00109] According to one embodiment, the knowledge management system
receiving the inquiry is configured to determine how to respond to the
inquiry, including
when to respond as well as the media channel to invoke. For example, the
knowledge
response may be immediate or deferred, through same media channel or different
one
(e.g., voice inquiry answered by email), and the like. In this regard, the
knowledge
management system, or some other server of the contact center system in
communication with the knowledge management system (e.g., iXn server 122), may
include an engine for intake, prioritization, routing, selection of
appropriate response
channel, and the like. Such determinations may be based, for example, on rules
that
are invoked, for example, based on information gathered on interactions
between the
user and resources of the contact center. For example, customer profile data,
business objectives, subject matter of the inquiry, medium for transmitting
the inquiry,
and the like, may be used for selecting the appropriate rules.
[00110] According to one embodiment, if a knowledge request is deemed
to
require a real-time response based on the invoked rule, information on
presence/availability of the agents to answer questions in real time may be
used for
selecting agents to which the request is to be routed. If the knowledge
request is
deemed to be fit for a deferred response, the iXn server 122 may be invoked
for
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Date Recue/Date Received 2023-02-16
categorization and prioritization of the knowledge request for generating a
deferred
task that is routed to an appropriate contact center agent.
[00111] FIG. 5 is a conceptual layout diagram of a step for
preprocessing an initial
query according to one exemplary embodiment. In this example, the initial
query is:
"Can I return a shirt I received as a gift?" After tokenization, part-of-
speech tagging,
and lemmatization, the updated query includes the terms "return, shirt,
receive, gift."
[00112] FIG. 6A is a conceptual layout diagram of an exemplary FAQ
document
that may be stored as a knowledge article in the mass storage device 20
according to
one embodiment. The document includes a preferred query 400 as well as an
answer
402 that is responsive to the query.
[00113] FIG. 6B is a conceptual layout diagram of terms that are
extracted from a
knowledge document based on a preprocessing step which may be similar to the
preprocessing step conducted for input queries. According to one embodiment,
the
terms are stored in question-and-answer fields. Although not shown in this
example,
a separate feedback field may also be maintained which includes terms
extracted from
queries to which the document was given a positive feedback.
[00114] FIGS. 7A-7B are screen shots of a web page providing access to
the
knowledge management system to a visitor of the website according to one
embodiment. As seen in FIG. 7A, the web page includes a search box 400 where a
user may enter a natural language query relating to a topic of interest.
According to
one embodiment, the user types the query and selects a search command 402 to
retrieve a set of most relevant knowledge documents 404a-404c selected based
on
the process described with respect to FIG. 4. The relevant knowledge documents
may also be proactively offered as the user types the query, but before the
user
actuates the search command 402, and/or based on interactions with the website
and
surrounding context information.
[00115] According to one embodiment, the knowledge documents are
displayed
according to the combined score given to each document, with the highest-
ranking
document appearing first. Selection of one of the suggested knowledge
documents
by, for example, selecting a "more" option 406, causes display of the full
answer to
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Date Recue/Date Received 2023-02-16
the query as depicted in FIG. 7B. According to one embodiment, the user may
provide
express feedback on whether the document was relevant (helpful) in satisfying
the
user's need by actuating a positive or negative feedback option 410, 412. The
user
may always escalate to an agent by transmitting a request for interaction by
actuating
an interaction option 414.
[00116] According to one embodiment, a user transmitting a request for
interaction
by actuating the interaction option 414 is routed to an agent of the contact
center. The
routing strategy that is invoked to route the interaction may take into
account the query
typed and/or submitted by the user prior to invoking the interaction option
414. For
example, the content of the query, after having undergone preprocessing, may
be
used to find an agent with skills that match the content. An agent who has
answered
the question previously may be identified as having skills that match the
query. Such
an agent may be selected for routing the interaction to the agent.
[00117] FIGS. 8A-8B are screen shots of an agent GUI that may be
invoked during
an interaction with a customer according to one embodiment. The agent GUI
includes
a history tab 420 for displaying a history of queries submitted by a user.
Selection of
a particular query 422 causes display of details pertaining to the query, such
as, for
example, a list of knowledge documents 424 that were suggested to the user in
response to the query. Such information helps the agent get a better
understanding
of the intent of the user in requesting the interaction with the agent.
[00118] According to one embodiment, the agent may also conduct
searches of
the knowledge base by selecting a search tab 426. The search tab causes
display of
a search interface that allows the user to select a particular knowledge base
428 that
he is interested in searching. An initial search term is typed in a search box
430, and
relevant knowledge documents are retrieved according to the process described
with
respect to FIG. 4. The documents are then displayed in a results area 432 in
an order
of relevance. Selection of a suggested knowledge document causes the full text
of
the document to be displayed in area 434. The agent may provide express
feedback
of the relevancy of the document to his query by actuating options 436, 438.
According to one embodiment, the agent uses the text of the knowledge document
to
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Date Recue/Date Received 2023-02-16
assist the customer with whom the agent is interacting. In this regard, the
agent
actuates a "copy content" option 440 to use the text of the document in, for
example,
a response email or chat response back to the customer.
[00119]
FIG. 9 is a screen shot of a reporting screen depicting performance of the
knowledge documents. The report may provide information as to how many
documents received feedback, what the feedback was like, and the like. A list
of
unanswered questions may also be tracked, and a task may be generated for a
knowledge worker to review and handle. For example, if the question did not
have an
answer, the knowledge worker may generate an answer and store it in the mass
storage device 20. If, however, a question is unanswered because the question
was
not recognized, the question may be added as an alternate question to a
corresponding question that is recognized.
Making Engagement Offers Based on Observed Navigation Path
[00120]
Embodiments described herein are also directed to determining user
intent as the user navigates pages of the enterprise's website for making
engagement
offers to the user. According to one embodiment, the web monitoring module
keeps
track of interactions with the website, including the navigation path of the
user as the
user browses the website. The web monitoring module further keeps track of
engagement offers made to the user during the current browsing session,
including
information on whether the offer for engagement was accepted, rejected, or
ignored.
[00121] By observing navigation behavior of users and results of engagement
offers made to those users, the web monitoring module may correlate the
navigation
behaviors to user intent. For example, the web monitoring module may deduce,
based
on observations made so far, that customers navigating a particular way (e.g.,
P7,
P11, P1, P23, P4, P71) where P7 is a starting web page and P71 is an ending
web
page, have an intent related to subject A; hence, have a high likelihood of
accepting
an engagement offer relating to subject A. If a user follows the observed
navigation
path but has not yet reached the end of the navigation path (e.g., has not yet
reached
P71), an engagement offer relating to subject A may still be made at a point
prior to
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P71 based on confidence that the customer is indeed interested in subject A.
Confidence may be based on various factors including the probability that the
user will
indeed continue on the path and other context data other than navigation path
such
as, for example, the range of topics associated with the particular page, user
profile
data, and the like. One benefit of deducing user intent as soon as possible to
make
engagement offers sooner than later is that it helps to provide earlier
satisfaction of
user need. If the offer for engagement is a knowledge document that may allow
the
user to engage in self-help, the early offer of such knowledge document may
help
deflect calls to the contact center. At the same time, it is desirable to
monitor the user
behavior long enough to predict his intent more precisely. Thus, according to
one
embodiment, a knowledge offer is not made unless there is a threshold level of
confidence that the user intent has been deduced.
[00122] According to one embodiment, in addition or in lieu of making
the
engagement offers, the web monitoring module 18 may interface with the contact
center system 14 to prepare for a likely engagement by making agent selection,
retrieving interaction history for the user, determining a business strategy
(matching
offerings, eligibility, cross-/up-sell, etc.), and the like.
[00123] According to one embodiment, one or more intent tags are
maintained for
each web page for calculating confidence of a user's intent. Unlike static
metadata
that is used to describe attributes of the web page, intent tags according to
embodiments described herein are dynamic and may be updated based on the
monitored user interactions. According to one embodiment, the intent tags may
be
maintained in association with the web page without, for example, modifying
the actual
web page itself.
[00124] FIG. 10 is a layout diagram of exemplary intent tags 500a-500c
(collectively referenced as 500) that may be maintained for a particular web
page.
According to one embodiment, the intent tags 500 may be associated with a
particular
intent topic (e.g., mortgage contract, buying a camera, etc.) and/or content
of the web
page. If a particular web page is associated with multiple topics, separate
intent tags
may be maintained for each topic.
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Date Recue/Date Received 2023-02-16
[00125] Each intent tag 500 may include values/weights for different
tag elements
such as, for example, navigation distance and engagement result. The
navigation
distance is a number of steps on a particular navigation path that have to be
taken in
order to reach a web page where engagement was offered. The engagement result
.. identifies whether the engagement at that page was accepted, rejected, or
ignored.
The intent tag may further identify a particular navigation path to the
current web page.
[00126] According to one embodiment, the web monitoring module 18
dynamically
updates the weights of the tag elements based on observations. For example,
assume that an observed navigation path for a particular web page is as
follows: (P7,
P11, P1, P23, P4, P71), where P7 is a page were observed browsing started, and
P71
is a last page where browsing ended. Assume that an engagement offer related
to a
particular user intent is made on P71, and the offer is accepted. The intent
tag for P71
for this observed navigation path is updated by the web monitoring module to
indicate
a navigation distance of 0 (since this is the page in which the engagement
offer was
made), and to indicate an engagement result of 1 (since the engagement offer
was
accepted). If P71 is associated with more than one topic, the true topic of
interest to
the user may be identified during an actual interaction with the user. The tag
for that
topic of interest is then modified with the navigation distance and engagement
result.
[00127] The tag for P4, the web page just prior to reaching P71, may
similarly be
updated to reflect a navigation distance of -1 and engagement result of 1. The
tag for
P4 may also include statistics reflecting likelihood of following the given
navigation
path based on observed history. Such likelihood may be used to alter the
confidence
of the user's intent.
[00128] The tags of other web pages P23, P1, P11, and P7 are updated in
a similar
manner.
[00129] When the system is in production long enough, the tag
(associated
meaning and aggregated value) may be used for assessing the likelihood of
correctly
determining the intent of a user at a particular web page. In this regard, the
aggregated values/weights of a particular tag may be used for determining
confidence
that an engagement offered on the page will be accepted by the user. For
example,
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Date Recue/Date Received 2023-02-16
a confidence value at each page may be calculated as a function of the weight
provided by the associated tag. In the above example, the weights decrease the
further the pages are on the navigation path from the page where the
engagement
offer was made and accepted. Thus, the weight of P23 is less than the weight
of P4.
Hence, according one embodiment, the confidence on page P23 for a particular
user-
intent is computed to be less than the confidence on page P4. According to one
embodiment, the confidence value may be modified based on other context
information as described herein.
[00130] According to one embodiment, the web engagement module 10
formulates rules for sending engagement offers. The rules may be based on
confidence computations based on the intent tags as described above. Other
rules
may also be formulated to work, for example, in conjunction with rules that
are based
on confidence computation. For example, the web monitoring module 18 may be
configured to detect "negative" web pages with the lowest hit rate. A rule may
thus be
created that prevents engagement offers to be made when a customer is located
on
one of these pages regardless of the confidence value. Similarly, the web
monitoring
module 18 may be configured to detect "positive" web pages with the highest
hit rate.
A rule may thus be created that causes the web monitoring module to send
engagement offers if a customer lands on such a web page, regardless of the
confidence value.
[00131] In another example, the web monitoring module may find
dependency of
the duration between opening a web page and positive/negative answer to an
engagement offer. A rule may be generated based on such learned dependency.
[00132] Other data may also be considered in generating rules and/or
computing
__ confidence values. For example, a customer's profile (if known) may be used
as a
filter for determining whether an offer for engagement will be made. For
example, the
customer's profile may indicate a likelihood that the user will continue on a
particular
navigation path. Running advertisements may impact weight given to a
particular
subject/topic. Past historical data such as, for example, the time of day, day
of week,
season, may also be used to determine confidence values.
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Date Recue/Date Received 2023-02-16
[00133] The origin of the navigation, if known, may be considered in
generating
rules and/or computing confidence values. For example, the user might start
the web
session from another site external to the website provided by the enterprise.
For
example, the user may start the web session from his home page where the user
may
be searching for something (product, service, enterprise brand, etc.) using a
preferred
search engine. Selecting a link from the search results may redirect the user
to the
enterprise's landing page. There could be different landing pages for
different topics,
or a general enterprise home page. In another example, the user may navigate
directly to a particular web page through a URL provided to the user in, for
example,
.. an email, or auto-generated when scanning a product QR code or the like.
The user
may also navigate directly to a particular web page by typing the URL.
Monitoring
kicks in when the user lands on a web page of the enterprise. Knowing the
user's
navigation origin when arriving at the enterprise's web page may be helpful in
deducing customer intent.
[00134] In another example, the enterprise may have a business relation
with
other commonly used web service companies, such as Google search, Amazon ,
eBay , Newegg , Yahoo! , or the like. The web service companies may display
the
enterprise advertisements, including contact links. This could be either a
direct
engagement dialog, or a link to the enterprise site with a specific landing
page. In the
latter case, monitoring of user behavior starts after arrival at the
enterprise landing
page. The web server may provide dedicated landing pages for all available
navigation possibilities, or the same landing pages for individual services
regardless
of where the user is navigating from. In any case, knowing how the user
arrived at
the landing site may help with intent determination and potentially faster
engagement.
[00135] According to one embodiment, time spent by a user on the website,
or a
particular web page may be considered in generating rules and/or computing
confidence values. An analogy may be made to providing assistance to a
customer
in a food store. If the customer knows exactly what to buy, he does it quickly
and
decisively (e.g., he knows to buy milk, bread, meat, vegetables, fruits, and
that's it).
In this case the customer is shopping in a do-not-disturb mode and may not
accept an
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Date Recue/Date Received 2023-02-16
assistance offer. However, if the customer stays at a particular product for
relatively
long time, it is much more likely that the assistance will be accepted. A same
approach
may be applied in the web engagement scenario. The longer the user stays on
the
web site or on particular page, the higher probability to accept an engagement
offer.
[00136] According to one embodiment, engagement offers are paced based on
the calculated confidence levels. In this regard, a number of engagement
offers that
are sent to users at a particular point in time may be a function of the
confidence level
and availability of agents. For example, if at a particular point in time, 5
customers
have navigated to a web page where the confidence level of deduced intent is
90%,
and there are only 5 agents available to handle the interaction, the web
engagement
module may be configured to avoid sending engagement offers other than
engagement offers to the 5 customers. In this case, there is no over-inviting
of
customers to prompt them to engage. If, however, there are 10 customers that
have
navigated to web pages where the confidence level is only 30%, additional
invitations
for engagement may be transmitted in addition to the invitations to the 10
customers.
For example, invitations may be extended to customers landing on web pages
with
even a lower confidence level to give such customers the opportunity to accept
the
invitations. The number of over-invitation may depend on the confidence level
associated with the other web pages.
[00137] Pacing may also be exercised in embodiments where the engagement
offers relate to knowledge articles. In this scenario, over-invitation may be
based on
the calculated confidence level. Since showing off-topic answers fast will be
likely
annoying and counter-productive, the over-invitations may be carried out for
situations
where confidence is above a certain threshold.
[00138] According to one embodiment, if the offer for engagement is a
knowledge
article, the identification of such knowledge article may be based on a query
that is
generated by the web monitoring module based on the monitored user
interaction.
The query may include, for example, the deduced topic/intent, along with
surrounding
context data as discussed above.
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[00139] Each of the various servers, controllers, switches, gateways,
engines,
and/or modules (collectively referred to as servers) in the afore-described
figures may
be a process or thread, running on one or more processors, in one or more
computing
devices 1500 (e.g., FIG. 11A, FIG. 11B), executing computer program
instructions and
interacting with other system components for performing the various
functionalities
described herein. The computer program instructions are stored in a memory
which
may be implemented in a computing device using a standard memory device, such
as, for example, a random-access memory (RAM). The computer program
instructions may also be stored in other non-transitory computer readable
media such
as, for example, a CD-ROM, flash drive, or the like. Also, a person of skill
in the art
should recognize that a computing device may be implemented via firmware
(e.g., an
application-specific integrated circuit), hardware, or a combination of
software,
firmware, and hardware. A person of skill in the art should also recognize
that the
functionality of various corn puting devices may be combined or integrated
into a single
computing device, or the functionality of a particular computing device may be
distributed across one or more other computing devices without departing from
the
scope of the exemplary embodiments described herein. A server may be a
software
module, which may also simply be referred to as a module. The set of modules
in the
contact center may include servers, and other modules.
[00140] The various servers may be located on a computing device on-site at
the
same physical location as the agents of the contact center or may be located
off-site
(or in the cloud) in a geographically different location, e.g., in a remote
data center,
connected to the contact center via a network such as the Internet. In
addition, some
of the servers may be located in a computing device on-site at the contact
center while
others may be located in a computing device off-site, or servers providing
redundant
functionality may be provided both via on-site and off-site computing devices
to
provide greater fault tolerance. In some embodiments, functionality provided
by
servers located on computing devices off-site may be accessed and provided
over a
virtual private network (VP N) as if such servers were on-site, or the
functionality may
be provided using a software as a service (SaaS) to provide functionality over
the
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Date Recue/Date Received 2023-02-16
Internet using various protocols, such as by exchanging data using encoded in
extensible markup language (XML) or JavaScript Object notation (JSON).
[00141] FIG. 11A and FIG. 11B depict block diagrams of a computing
device 1500
as may be employed in exemplary embodiments. Each computing device 1500
includes a central processing unit 1521 and a main memory unit 1522. As shown
in
FIG. 11A, the computing device 1500 may also include a storage device 1528, a
removable media interface 1516, a network interface 1518, an input/output
(I/O)
controller 1523, one or more display devices 1530c, a keyboard 1530a and a
pointing
device 1530b, such as a mouse. The storage device 1528 may include, without
limitation, storage for an operating system and software. As shown in FIG.
11B, each
computing device 1500 may also include additional optional elements, such as a
memory port 1503, a bridge 1570, one or more additional input/output devices
1530d,
1530e and a cache memory 1540 in communication with the central processing
unit
1521. The input/output devices 1530a, 1530b, 1530d, and 1530e may collectively
be
referred to herein using reference numeral 1530.
[00142] The central processing unit 1521 is any logic circuitry that
responds to,
and processes instructions fetched from the main memory unit 1522. It may be
implemented, for example, in an integrated circuit, in the form of a
microprocessor,
microcontroller, or graphics processing unit (GPU), or in a field-programmable
gate
array (FPGA) or application-specific integrated circuit (ASIC). The main
memory unit
1522 may be one or more memory chips capable of storing data and allowing any
storage location to be directly accessed by the central processing unit 1521.
As shown
in FIG. 11A, the central processing unit 1521 communicates with the main
memory
1522 via a system bus 1550. As shown in FIG. 11B, the central processing unit
1521
may also communicate directly with the main memory 1522 via a memory port
1503.
[00143] FIG. 11B depicts an embodiment in which the central processing
unit 1521
communicates directly with cache memory 1540 via a secondary bus, sometimes
referred to as a backside bus. In other embodiments, the central processing
unit 1521
communicates with the cache memory 1540 using the system bus 1550. The cache
memory 1540 typically has a faster response time than main memory 1522. As
shown
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Date Recue/Date Received 2023-02-16
in FIG. 11A, the central processing unit 1521 communicates with various I/O
devices
1530 via the local system bus 1550. Various buses may be used as the local
system
bus 1550, including a Video Electronics Standards Association (VESA) Local bus
(VLB), an Industry Standard Architecture (ISA) bus, an Extended Industry
Standard
Architecture (EISA) bus, a MicroChannel Architecture (MCA) bus, a Peripheral
Component Interconnect (PCI) bus, a PCI Extended (PCI-X) bus, a PCI-Express
bus,
or a NuBus. For embodiments in which an I/O device is a display device 1530c,
the
central processing unit 1521 may communicate with the display device 1530c
through
an Advanced Graphics Port (AGP). FIG. 11B depicts an embodiment of a computer
1500 in which the central processing unit 1521 communicates directly with I/O
device
1530e. FIG. 11B also depicts an embodiment in which local busses and direct
communication are mixed: the central processing unit 1521 communicates with
I/O
device 1530d using a local system bus 1550 while communicating with I/O device
1530e directly.
[00144] A wide variety of I/O devices 1530 may be present in the computing
device
1500. Input devices include one or more keyboards 1530a, mice, trackpads,
trackballs, microphones, and drawing tablets. Output devices include video
display
devices 1530c, speakers, and printers. An I/O controller 1523, as shown in
FIG. 11A,
may control the I/O devices. The I/O controller may control one or more I/O
devices
such as a keyboard 1530a and a pointing device 1530b, e.g., a mouse or optical
pen.
[00145] Referring again to FIG. 11A, the computing device 1500 may
support one
or more removable media interfaces 1516, such as a floppy disk drive, a CD-ROM
drive, a DVD-ROM drive, tape drives of various formats, a USB port, a Secure
Digital
or COMPACT FLASHIm memory card port, or any other device suitable for reading
data from read-only media, or for reading data from, or writing data to, read-
write
media. An I/O device 1530 may be a bridge between the system bus 1550 and a
removable media interface 1516.
[00146] The removable media interface 1516 may for example be used for
installing software and programs. The computing device 1500 may further
comprise a
storage device 1528, such as one or more hard disk drives or hard disk drive
arrays,
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Date Recue/Date Received 2023-02-16
for storing an operating system and other related software, and for storing
application
software programs. Optionally, a removable media interface 1516 may also be
used
as the storage device. For example, the operating system and the software may
be
run from a bootable medium, for example, a bootable CD.
[00147] In some embodiments, the computing device 1500 may comprise or be
connected to multiple display devices 1530c, which each may be of the same or
different type and/or form. As such, any of the I/O devices 1530 and/or the
I/O
controller 1523 may comprise any type and/or form of suitable hardware,
software, or
combination of hardware and software to support, enable or provide for the
connection
to, and use of, multiple display devices 1530c by the computing device 1500.
For
example, the computing device 1500 may include any type and/or form of video
adapter, video card, driver, and/or library to interface, communicate, connect
or
otherwise use the display devices 1530c. In one embodiment, a video adapter
may
comprise multiple connectors to interface to multiple display devices 1530c.
In other
embodiments, the computing device 1500 may include multiple video adapters,
with
each video adapter connected to one or more of the display devices 1530c. In
some
embodiments, any portion of the operating system of the computing device 1500
may
be configured for using multiple display devices 1530c. In other embodiments,
one or
more of the display devices 1530c may be provided by one or more other
computing
.. devices, connected, for example, to the computing device 1500 via a
network. These
embodiments may include any type of software designed and constructed to use
the
display device of another computing device as a second display device 1530c
for the
computing device 1500. One of ordinary skill in the art will recognize and
appreciate
the various ways and embodiments that a computing device 1500 may be
configured
to have multiple display devices 1530c.
[00148] A computing device 1500 of the sort depicted in FIG. 11A and
FIG. 11B
may operate under the control of an operating system, which controls
scheduling of
tasks and access to system resources. The computing device 1500 may be running
any operating system, any embedded operating system, any real-time operating
system, any open-source operating system, any proprietary operating system,
any
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Date Recue/Date Received 2023-02-16
operating systems for mobile computing devices, or any other operating system
capable of running on the computing device and performing the operations
described
herein.
[00149] The computing device 1500 may be any workstation, desktop
computer,
laptop or notebook computer, server machine, handheld computer, mobile
telephone
or other portable telecommunication device, media playing device, gaming
system,
mobile computing device, or any other type and/or form of computing,
telecommunications or media device that is capable of communication and that
has
sufficient processor power and memory capacity to perform the operations
described
herein. In some embodiments, the computing device 1500 may have different
processors, operating systems, and input devices consistent with the device.
[00150] In other embodiments the computing device 1500 is a mobile
device, such
as a Java-enabled cellular telephone or personal digital assistant (PDA), a
smart
phone, a digital audio player, or a portable media player. In some
embodiments, the
computing device 1500 comprises a combination of devices, such as a mobile
phone
combined with a digital audio player or portable media player.
[00151] As shown in FIG. 11C, the central processing unit 1521 may
comprise
multiple processors P1, P2, P3, P4, and may provide functionality for
simultaneous
execution of instructions or for simultaneous execution of one instruction on
more than
one piece of data. In some embodiments, the computing device 1500 may comprise
a parallel processor with one or more cores. In one of these embodiments, the
computing device 1500 is a shared memory parallel device, with multiple
processors
and/or multiple processor cores, accessing all available memory as a single
global
address space. In another of these embodiments, the computing device 1500 is a
distributed memory parallel device with multiple processors each accessing
local
memory only. In still another of these embodiments, the computing device 1500
has
both some memory which is shared and some memory which may only be accessed
by particular processors or subsets of processors. In still even another of
these
embodiments, the central processing unit 1521 comprises a multicore
microprocessor,
.. which combines two or more independent processors into a single package,
e.g., into
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Date Recue/Date Received 2023-02-16
a single integrated circuit (IC). In one exemplary embodiment, depicted in
FIG. 11D,
the computing device 1500 includes at least one central processing unit 1521
and at
least one graphics processing unit 1521'.
[00152] In some embodiments, a central processing unit 1521 provides
single
instruction, multiple data (SIMD) functionality, e.g., execution of a single
instruction
simultaneously on multiple pieces of data. In other embodiments, several
processors
in the central processing unit 1521 may provide functionality for execution of
multiple
instructions simultaneously on multiple pieces of data (MIMD). In still other
embodiments, the central processing unit 1521 may use any combination of SIMD
and
MIMD cores in a single device.
[00153] A computing device may be one of a plurality of machines
connected by
a network, or it may comprise a plurality of machines so connected. FIG. 11E
shows
an exemplary network environment. The network environment comprises one or
more
local machines 1502a, 1502b (also generally referred to as local machine(s)
1502,
client(s) 1502, client node(s) 1502, client machine(s) 1502, client
computer(s) 1502,
client device(s) 1502, endpoint(s) 1502, or endpoint node(s) 1502) in
communication
with one or more remote machines 1506a, 1506b, 1506c (also generally referred
to
as server machine(s) 1506 or remote machine(s) 1506) via one or more networks
1504. In some embodiments, a local machine 1502 has the capacity to function
as
both a client node seeking access to resources provided by a server machine
and as
a server machine providing access to hosted resources for other clients 1502a,
1502b.
Although only two clients 1502 and three server machines 1506 are illustrated
in FIG.
11E, there may, in general, be an arbitrary number of each. The network 1504
may
be a local-area network (LAN), e.g., a private network such as a company
Intranet, a
metropolitan area network (MAN), or a wide area network (WAN), such as the
Internet,
or another public network, or a combination thereof.
[00154] The computing device 1500 may include a network interface 1518
to
interface to the network 1504 through a variety of connections including, but
not limited
to, standard telephone lines, local-area network (LAN), or wide area network
(WAN)
links, broadband connections, wireless connections, or a combination of any or
all of
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Date Recue/Date Received 2023-02-16
the above. Connections may be established using a variety of communication
protocols. In one embodiment, the computing device 1500 communicates with
other
computing devices 1500 via any type and/or form of gateway or tunneling
protocol
such as Secure Socket Layer (SSL) or Transport Layer Security (TLS). The
network
interface 1518 may comprise a built-in network adapter, such as a network
interface
card, suitable for interfacing the computing device 1500 to any type of
network capable
of communication and performing the operations described herein. An I/O device
1530
may be a bridge between the system bus 1550 and an external communication bus.
[00155] According to one embodiment, the network environment of FIG.
11E may
be a virtual network environment where the various components of the network
are
virtualized. For example, the various machines 1502 may be virtual machines
implemented as a software-based computer running on a physical machine. The
virtual machines may share the same operating system. In other embodiments,
different operating system may be run on each virtual machine instance.
According
.. to one embodiment, a "hypervisor type of virtualization is implemented
where multiple
virtual machines run on the same host physical machine, each acting as if it
has its
own dedicated box. Of course, the virtual machines may also run-on different
host
physical machines.
[00156] Other types of virtualization is also contemplated, such as,
for example,
the network (e.g., via Software Defined Networking (SDN)). Functions, such as
functions of the session border controller and other types of functions, may
also be
virtualized, such as, for example, via Network Functions Virtualization (NFV).
[00157] It is the Applicant's intention to cover all such uses of the
teachings herein
and those changes and modifications which could be made to the embodiments
herein
chosen for the purpose of disclosure without departing from the spirit and
scope of the
teachings herein. Thus, the present embodiments should be considered in all
respects as illustrative and not restrictive.
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