ROUTING CALLERS FROM A SET OF CALLERS IN AN OUT OF ORDER SEQUENCE

ROUTAGE D'APPELANTS A PARTIR D'UN ENSEMBLE D'APPELANTS DANS LE DESORDRE

English Abstract

Methods and systems are provided for routing callers to agents in a call- center routing environment. An exemplary method includes identifying caller date for at least one caller in a queue of callers, and skipping a caller at the front of the queue of callers for another caller based on the identified caller data. The caller data may include one or both demographic data and psychographic data. Skipping the caller may be further based on comparing caller data with agent data associated with an agent via a pattern matching algorithm such as a correlation algorithm, hi one example, if the caller at the front of the queue has been skipped a predetermined number of times the caller at the front is the next routed (and cannot be skipped again).

French Abstract

Des procédés et des systèmes sont décrits pour acheminer les appels vers les agents dans un environnement de routage de centre dappels. Un procédé exemplaire consiste en lidentification de données dappelant pour au moins un appelant dans une file dattente dappelants et le saut dun appelant à lavant de la file dattente dappelants pour un autre appelant en fonction des données dappelant définies. Les données dappelant peuvent comprendre soit des données démographiques, soit des données psychographiques, soit les deux. Le saut de lappelant peut en outre être basé sur la comparaison de données dappelant avec des données dagent associées à un agent par lintermédiaire dun algorithme de mise en correspondance de motif tel quun algorithme de corrélation. Par exemple, si lappelant à lavant de la file dattente a été sauté un nombre prédéterminé de fois, lappelant qui se trouve le premier dans la file dattente est le prochain dont lappel sera acheminé (et il est impossible dignorer de nouveau son appel).

Claims

CLAIMS

1. A method for handling contacts and agents in a contact center system
comprising:
automatically identifying, by at least one computer processor, a plurality of
contacts ordered chronologically in a queue for assignment to a plurality of
agents;
automatically identifying, by the at least one computer processor, information

about a first agent of the plurality of agents that becomes available for
assignment;
automatically assigning, by the at least one computer processor, a first out-
of-
order contact from the plurality of contacts to the first agent based on the
information about the
first agent and information about the first out-of-order contact, wherein the
first out-of-order
contact is a contact in the plurality of contacts that is assigned before at
least one other contact
of the plurality of contacts that is ordered chronologically ahead of the
first out-of-order contact
in the queue; and
automatically connecting, by the at least one computer processor, the first
out-
of-order contact to the first agent.
2. The method of claim 1, wherein the plurality of contacts are ordered
chronologically in the queue based on respective contact center system arrival
times of each
contact of the plurality of contacts.
3. The method of claim 1, wherein automatically assigning the first out-of-
order
contact increases an expected wait time of a first in-order contact of the
plurality of contacts.
4. The method of claim 1, further comprising automatically determining that
an
expected improvement to a contact interaction by automatically assigning the
first out-of-order
contact outweighs an expected cost by keeping a first in-order contact of the
plurality of
contacts on hold.
5. The method of claim 1, further comprising automatically searching the
plurality
of contacts for an assignment based on the information about the first agent.
6. The method of claim 1, wherein the first out-of-order contact is
automatically
connected using an outbound dialer of the contact center system.
7. The method of claim 1, further comprising automatically assigning a
second
out-of-order contact from the plurality of contacts to a second agent of the
plurality of agents
that becomes available for assignment to any of a remainder of the plurality
of contacts based
on information about the second agent.

36


8. A system for handling contacts and agents in a contact center system
comprising:
a processor communicatively coupled to the contact center system, wherein the
processor is configured to:
automatically identify a plurality of contacts ordered chronologically in a
queue
for assignment to a plurality of agents;
automatically identify information about a first agent of the plurality of
agents
that becomes available for assignment;
automatically assign a first out-of-order contact from the plurality of
contacts to
the first agent based on the information about the first agent and information
about the first
out-of-order contact, wherein the first out-of-order contact is a contact in
the plurality of
contacts that is assigned before at least one other contact of the plurality
of contacts that is
ordered chronologically ahead of the first out-of-order contact in the queue;
and
automatically connect the first out-of-order contact to the first agent.
9. The system of claim 8, wherein the plurality of contacts are ordered
chronologically in the queue based on respective contact center system arrival
times of each
contact of the plurality of contacts.
10. The system of claim 8, wherein automatically assigning the first out-of-
order
contact increases an expected wait time of a first in-order contact of the
plurality of contacts.
11. The system of claim 8, wherein the processor is further configured to
automatically determine that an expected improvement to a contact interaction
by
automatically assigning the first out-of-order contact outweighs an expected
cost by keeping a
first in-order contact of the plurality of contacts on hold.
12. The system of claim 8, wherein the processor is further configured to
automatically search the plurality of contacts for an assignment based on the
information about
the first agent.
13. The system of claim 8, wherein the first out-of-order contact is
automatically
connected using an outbound dialer of the contact center system.
14. The system of claim 8, wherein the processor is further configured to
automatically assign a second out-of-order contact from the plurality of
contacts to a second
agent of the plurality of agents that becomes available for assignment to any
of a remainder of
the plurality of contacts based on information about the second agent.

37


15. An article of manufacture for handling contacts and agents in a contact
center
system comprising:
a non-transitory processor readable medium; and
instructions stored on the medium;
wherein the instructions are configured to be readable from the medium by a
processor communicatively coupled to the contact center system and thereby
cause the
processor to operate so as to:
automatically identify a plurality of contacts ordered chronologically in a
queue
for assignment to a plurality of agents;
automatically identify information about a first agent of the plurality of
agents
that becomes available for assignment;
automatically assign a first out-of-order contact from the plurality of
contacts to
the first agent based on the information about the first agent and information
about the first
out-of-order contact, wherein the first out-of-order contact is a contact in
the plurality of
contacts that is assigned before at least one other contact of the plurality
of contacts that is
ordered chronologically ahead of the first out-of-order contact in the queue;
and
automatically connect the first out-of-order contact to the first agent.
16. The article of manufacture of claim 15, wherein the plurality of
contacts are
ordered chronologically in the queue based on respective contact center system
arrival times
of each contact of the plurality of contacts.
17. The article of manufacture of claim 15, wherein automatically assigning
the first
out-of-order contact increases an expected wait time of a first in-order
contact of the plurality
of contacts.
18. The article of manufacture of claim 15, wherein the processor is caused
to
operate further so as to automatically determine that an expected improvement
to a contact
interaction by automatically assigning the first out-of-order contact
outweighs an expected cost
by keeping a first in-order contact of the plurality of contacts on hold.
19. The article of manufacture of claim 15, wherein the processor is caused
to
operate further so as to automatically search the plurality of contacts for an
assignment based
on the information about the first agent.
20. The article of manufacture of claim 15, wherein the first out-of-order
contact is
automatically connected using an outbound dialer of the contact center system.

38

Description

ROUTING CALLERS FROM A SET OF CALLERS IN AN OUT OF ORDER SEQUENCE
BACKGROUND
1. Field:
[0002] The present invention relates to the field of routing phone
calls and other
telecommunications in a contact center system.
2. Related Art:
[0003] The typical contact center consists of a number of human
agents, with each
assigned to a telecommunication device, such as a phone or a computer for
conducting
email or Internet chat sessions, that is connected to a central switch. Using
these devices,
the agents are generally used to provide sales, customer service, or technical
support to
the customers or prospective customers of a contact center or a contact
center's clients.
[0004] Typically, a contact center or client will advertise to its
customers, prospective
customers, or other third parties a number of different contact cumbers or
addresses for a
particular service, such as for billing questions or for technical support.
The customers,
prospective customers, or third parties seeking a particular service will then
use this
contact information, and the incoming caller will be routed at one or more
routing points
to a human agent at a contact center who can provide the appropriate service.
Contact
centers that respond to such incoming contacts are typically referred to as
"inbound
contact centers,"
[0005] Similarly, a contact center can make outgoing contacts to
current or
prospective customers or third parties. Such contacts may be made to encourage
sales of
a product, provide technical support or billing information, survey consumer
preferences,
or to assist in collecting debts. Contact centers that make such outgoing
contacts are
referred to as "outbound contact centers."
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[0006] In both inbound contact centers and outbound contact
centers, the individuals
(such as customers, prospective customers, survey participants, or other third
parties) that
interact with contact center agents using a telecommunication device are
referred to in
this application as a "caller." The individuals acquired by the contact center
to interact
with callers are referred to in this application as an "agent"
[0007] Conventionally, a contact center operation includes a
switch system that
connects callers to agents. In an inbound contact center, these switches route
incoming
callers to a particular agent in a contact center, or, if multiple contact
centers are
deployed, to a particular contact center for further routing. In an outbound
contact center
employing telephone devices, dialers are typically employed in addition to a
switch
system. The dialer is used to automatically dial a phone number from a list of
phone
numbers, and to determine whether a live caller has been reached from the
phone number
called (as opposed to obtaining no answer, a busy signal, an error message, or
an
answering machine). When the dialer obtains a live caller, the switch system
routes the
caller to a particular agent in the contact center.
[0008] Routing technologies have accordingly been developed to
optimi7e the caller
experience. For example, U.S. Patent No. 7,236,584 describes a telephone
system for
equalizing caller waiting times across multiple telephone switches, regardless
of the
general variations in performance that may exist among those switches. Contact
routing
in an inbound contact center, however, is a process that is generally
structured to connect
callers to agents that have been idle for the longest period of time. In the
case of an
inbound caller where only one agent may be available, that agent is generally
selected for
the caller without further analysis. In another example, if there are eight
agents at a
contact center, and seven are occupied with contacts, the switch will
generally route the
inbound caller to the one agent that is available. If all eight agents are
occupied with
contacts, the switch will typically put the contact on hold and then route it
to the next
agent that becomes available. More generally, the contact center will set up a
queue of
incoming r!allers and preferentially route the longest-waiting callers to the
agents that
become available over time. Such a pattern of routing contacts to either the
first available
agent or the longest-waiting agent is referred to as "round-robin" contact
routing. In
round robin contact routing, eventual matches and connections between a caller
and an
agent are essentially random.
[0009] Some attempts have been made to improve upon these standard
yet essentially
random processes for connecting a caller to an agent. For example, U.S. Patent
No.
2
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7,209,549 describes a telephone routing system wherein an incoming caller's
language
preference is collected and used to route their telephone call to a particular
contact center
or agent that can provide service in that language. In this manner, language
preference is
the primary driver of matching and connecting a caller to an agent, although
once such a
preference has been made, callers are almost always routed in "round-robin"
fashion_
Other attempts have been made to alter the general round-robin system. For
example,
U.S. Patent No. 7,231,032 describes a telephone system wherein, the agents
themselves
each create personal routing rules for incoming callers, allowing each agent
to custorni7f.
the types of callers that are routed to them. These rules can include a list
of particular
callers the agent wants routed to them, such as callers that the agent has
interacted with
before. This system, however, is skewed towards the agent's preference and
does not
take into account the relative capabilities of the agents nor the individual
characteristics
of the callers and the agents themselves.
BRIEF SUMMARY
[0010] According to one example of the present invention, methods
and systems for a
call center include identifying caller data for a caller in a queue of
callers, and skipping
over a caller at the front or top of the queue (e.g., the next caller to be
routed based on the
queue order) by another caller of the set of callers based on the identified
caller data. The
caller data may include one or both of demographic data and psychographic
data.
Skipping the caller at the front of the queue may further be based on
comparing caller
data with agent data associated with an agent (e.g., an available agent). The
caller data
and agent data may be compared via a pattern matching algorithm such as
computer
model reflecting a predicted chance of a desired interaction. The best
matching caller in
queue may then skip the caller at the front of the queue and be routed to the
agent.
[0011] In one example, lithe caller at the front of the queue has
been skipped a
predetermined number of times (e.g., once, twice, or more) the caller is
routed to the next
available agent, and cannot be skipped again by another caller. For example,
the system
may only allow a caller at the front of the queue to be skipped a
predetermined number of
times.
[0012] The exemplary methods and systems may further include routing
the caller at
the front of the queue to the next available agent if a hold threshold for the
caller at the
front of the queue is exceeded. The hold threshold may include one or more of
a time
period, number of times the caller is skipped by other callers, or a multiple
of a predicted
hold time. The.hold threshold may be adjustable by a user.
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[0013] According to another example of the present invention,
methods and systems
for a call center include identifying caller data for a caller in a queue of
callers, and
jumping or moving the caller to a different position within the queue based on
the caller
data. The caller data may include one or both of demographic data and
psychographic
data. The caller can be jumped forward or backward in the queue relative to at
least one
other caller. Jumping the caller may further be based on comparing the caller
data with
agent data associated with an agent via a pattern matching algorithm such as a
correlation
algorithm.
[0014] In another example, methods and systems include routing a
caller from a
queue of callers out of order. In one example, a method includes identifying
caller data
for a caller of a plurality of callers in the queue, and routing the caller
from the queue out
of queue order based on the identified data. For example, a cnller that is not
at the top of
the queue may be routed from the queue based on the identified caller data,
out of order
with respect to the queue order. The caller may be routed to another queue of
callers, a
pool of callers, or an agent based on the identified caller data, where the
caller data may
include one or both of' demographic and psychographic data. The caller may be
routed
from the queue based on comparing the caller data with agent data associated
with an
agent via a pattern matching algorithm or computer model.
[00151 In another example, methods and systems include routing a
caller from a pool
of callers based on at least one caller data associated with the caller, where
a pool of
callers includes, e.g., a set of callers that are not chronologically ordered
and routed based
on a chronological order or hold time of the callers. The caller may be routed
from the
pool of callers to an agent, placed in another pool of callers, or placed in a
queue of
callers. The caller data may include demographic or psychographic data. The
caller may
be routed from the pool of callers based on comparing the railer data with
agent data
associated with an agent via a pattern matching algorithm or computer model.
[0016] In another example, methods and systems include pooling
incoming callers,
and causing a caller from the pool of callers to be routed. The caller may be
routed from
the pool of callers to an agent, placed in another pool of callers, or placed
in a queue of
callers. The caller may be routed based on identified caller data, which may
include
demographic or psychographic data. The caller may be routed from the pool of
callers
based on comparing the caller data with agent data associated with an agent
via a pattern
matching algorithm or computer model.
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[0017] In another example, methods and systems include identifying
caller data for a
caller of a set of callers, wherein the caller data comprises demographic or
physiographic
data, and causing a caller of the set of callers to be milted based on the
identified caller
data. The caller may be routed from the set of callers based on comparing the
caller data
with agent data associated with an agent via a pattern matching algorithm or
computer
model. The set of callers may include a queue of callers and the caller may be
routed to a
new position within the queue of callers, a different queue of callers, a pool
of callers, or
to an agent. Alternatively, the set of callers may include a pool of callers
and the caller
may be routed to a different pool of callers, a queue of callers, or to an
agent.
[0018] It is noted for comparison with the examples provided that
conventional
routing systems may include one or more queues (e.g., based on language,
preferred
account status, or the like), but are typically set-up to route and connect an
available agent
with the next caller in the queue. Further, it is noted that conventional
routing systems
typically determine up front, in a time-linear basis, whether a customer needs
a language
specific agent (e.g., Spanish) or is a preferred status customer, and then
assigns them into
an appropriate queue of callers on that basis. Conventional routing systems,
however, do
not pull callers from a queue out of order or jump callers within a queue.
Further,
conventional routing systems do not pool callers as described or match callers
from a pool
for routing to an agent as described.
[0019] In some examples, the methods and systems may further
include comparing
data associated with at least one of the callers to data associated with the
available agent.
In some examples, the caller data and agent data may be compared via a pattern
matching
algorithm and/or computer model for predicting the caller-agent pair having
the highest
probability of a desired outcome. In one example, a caller is routed from a
queue of
callers or a pool of callers based on a metric, e.g., a pattern matching
suitability score,
without relying solely or primarily on the caller's wait time or position
within a queue.
For instance, a caller may be connected with an agent before other callers in
the pool or
queue that have been waiting for a longer period of time based, at least in
part, on a
pattern matching algorithm.
[0020] In some examples, a hold threshold for one or more of the
callers in the pool
may be included as a factor, e.g., as a weighting factor used with other data
in the pattern
matching algorithm or trigger to route a caller. The hold threshold may
include a
predetermined time, a multiple of an average or expected hold time for the
caller when
the call arrives, the number of callers routed while they are on hold, e.g.,
how many times
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they have been "skipped" by other callers, and so on. For example, a caller
may be
assigned a hold threshold (e.g., seconds, minutes, or number of times they are
"skipped"),
which if exceeded, overrides the pattern matching algorithm, e.g., to prevent
a caller from
being held indefinitely. Further, each caller may be individually assigned a
hold
threshold, e.g., based on data associated with the caller, such as their
inclination to
generate revenue or preferred account status, or all callers may be given a
common hold
threshold.
[00211 In one example, a "cost" or "pain" function is applied to
callers in the queue or
pool to annlyze the varying chance of a successful interaction as callers wait
in the queue
or pool of callers. The pattern matching algorithm or computer model may use
the cost
function in mapping callers to agents. For instance, consider an example where
the best
matching agent for a caller might be occupied and have a 70% chance of
increased
revenue generation for a caller, but is not expected to be free soon (e.g., is
only a few
seconds into another call). The next best matching agent is free and has a 95%
chance of
increased revenue generation for the caller. The cost function may indicate
that the
system route the caller to the next best agent because the 70% chance of
increased
revenue generation for the caller will decrease over time, most likely below
95% by the
time the best agent is free.
[00221 In other examples, preferred callers (e.g., preferred account
members,
platinum/gold service levels, and so on) may be used to multiply a matching
score by
some "platinum" factor to accelerate connection time for such preferred
callers, or to
jump them within a queue of callers. In other examples, preferred callers may
by
included with different queues or pools for faster service.
[0023] Additionally, in one example, one or more hold thresholds may
be adjustable
and controlled by a user, e.g., in real-time via a displayed user interface.
For instance, a
user may be able to adjust the allowed hold time for a caller, or adjust the
weighting of a
cost function as used by the system. Furthermore, in some examples, the system
may
analyze and display an estimated effect on one or more output performance
variables of
the system in response to adjusting or setting a hold threshold. For instance,
increasing
the time a caller may be held may increase a certain output variable (e.g.,
revenue), but
decrease another output variable (e.g., customer satisfaction). Accordingly,
some
examples allow a user to adjust and view estimated performance effects based
on the hold
threshold(s).
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[0024] Various
performance based and/or pattern matching algorithms for matching
callers and agents based on available information regarding each may be
utilized. In
general, contact center routings are potentially improved or optimized by
routing contacts
such that callers are matched with and connected to particular agents in a
manner that
increases the chance of an interaction that is deemed beneficial to a contact
center
(referred to in this application as an "optimal interaction"). Examples of
optimal
interactions include increasing sales, decreasing the duration of the contact
(and hence the
cost to the contact center), providing for an acceptable level of customer
satisfaction, or
any other interaction that a contact center may seek to control or improve.
The exemplary
systems and methods can improve the chance of an optimal interaction by, in
general,
grading agents on an optimal interaction, and matching a graded agent with a
caller to
increase the chance of the optimal interaction. Once matched, the caller can
be connected
to the graded agent. In a more advanced embodiment, the systems and methods
can also
be used to increase the chance of an optimal interaction by matching a caller
to an agent
using a computer model derived from data describing demographic, geographic,
psychographic, past purchase behavior, personality characteristics (e.g., via
a Myers-
Brigg Type Indicator test or the like), time effects (e.g., data associated
with different
times of the day, week, month, etc.) or other relevant information about a
caller, together
with data describing demographic, geographic, psychographic, personality
characteristics,
time effects, or historical performance about an agent.
[00251 In another
example, exemplary systems and methods can be used to increase
the chances of an optimal interaction by combining agent grades (e.g., a grade
or rank of
the agent performance), agent demographic data, agent psychographic data, and
other
business-relevant data about the agent (individually or collectively referred
to in this
application as "agent data"), along with demographic, psychographic, and other
business-
relevant data about callers (individually or collectively referred to in this
application as
"caller data"). Agent and caller demographic data can comprise any of gender,
race, age,
education, accent, income, nationality, ethnicity, area code, zip code,
marital status, job
status, and credit score. Agent and caller psychographic data can comprise any
of
introversion, sociability, desire for financial success, and film and
television preferences.
(0026] Caller
demographic and psychographic data can be retrieved from available
databases by using the caller's contact information as an index. Available
databases
include, but are not limited to, those that are publicly available, those that
are
commercially available, or those created by a contact center or a contact
center client. In
7
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an outbound contact center environment, the caller's contact information is
known
beforehand. In an inbound contact center environment, the caller's contact
information
can be retrieved by examining the caller's CallerlD information or by
requesting this
information of the caller at the outset of the contact, such as through entry
of a caller
account number or other caller-identifying information. Other business-
relevant data
such as historic purchase behavior, current level of satisfaction as a
customer, or
volunteered level of interest in a product may also be retrieved from
available databases.
[00271 Once agent data and caller data have been collected, this
data may be passed
to a computational system. The computational system then, in turn, uses this
data in a
pattern matching algorithm to create a computer model that matches each agent
with each
caller and estimates the probable outcome of each matching along a number of
optimal
interactions, such as the generation of a sale, the duration of contact, or
the likelihood of
generating an interaction that a customer finds satisfying. As an example, the
systems
and methods may indicate that, by matching a caller to a female agent, the
matching will
increase the probability of a sale by 4 percent, reduce the duration of a
contact by 9
percent, and increase the satisfaction of the caller with the interaction by
12 percent.
Generally, the systems and methods will generate more complex predictions
spanning
multiple demographic and psychographic aspects of agents and callers.
Exemplary
systems and methods might conclude, for instance, that a caller if connected
to a single,
white, male, 25 year old, agent that has high speed internet in his home and
enjoys
comedic films will result in a 12 percent increase in the probability of a
sale, a 7 percent
increase in the duration of the contact, and a 2 percent decrease in the
caller's satisfaction
with the contact. In parallel, the exemplary systems and methods may also
determine that
the caller if connected to a married, black, female, 55 year old agent will
result in a 4
percent increase in the probability of a sale, a 9 percent decrease in the
duration of a
contact, and a 9 percent increase in the caller's satisfaction with the
contact.
[00281 Though this advanced embodiment preferably uses agent
grades,
demographic, psychographic, and other business-relevant data, along with
caller
demographic, psychographic, and other business-relevant data, other
embodiments can
eliminate one or more types or categories of caller or agent data to minimize
the
computing power or storage necessary to employ the exemplary methods and
systems.
100291 The pattern matching algorithm to be used in the exemplary
methods and
systems can comprise any correlation algorithm, such as a neural network
algorithm or a
genetic algorithm. To generally train or otherwise refine the algorithm,
actual contact
8
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results (as measured for an optimal interaction) are compared against the
actual agent and
caller data for each contact that occurred. The pattern matching algorithm can
then learn,
or improve its learning of, how matching certain callers with certain agents
will change
the chance of an optimal interaction. In this manner, the pattern matching
algorithm can
then be used to predict the chance of an optimal interaction in the context of
matching a
caller with a particular set of nailer data, with an agent of a particular set
of agent data.
Preferably, the pattern matching algorithm is periodically refined as more
actual data on
caller interactions becomes available to it, such as periodically training the
algorithm
every night after a contact center has finished operating for the day.
[00301 The pattern matching algorithm can be used to create a
computer model
reflecting the predicted chances of an optimal interaction for each agent and
caller
matching. Preferably, the computer model will comprise the predicted chances
for a set
of optimal interactions for every agent that is logged in to the contact
center as matched
against every available caller. Alternatively, the computer model can comprise
subsets of
these, or sets containing the aforementioned sets. For example, instead of
matching every
agent logged into the contact center with every available caller, the
exemplary methods
and systems can match every available agent with every available caller, or
even a
narrower subset of agents or callers. Likewise, the methods and systems can
match every
agent that ever worked on a particular campaign ¨ whether available or logged
in or not ¨
with every available caller. Similarly, the computer model can comprise
predicted
chances for one optimal interaction or a number of optimal interactions.
[0031] The computer model can also be further refined to comprise a
suitability score
for each matching of an agent and a caller. The suitability score can be
determined by
taking the chances of a set of optimal interactions as predicted by the
pattern matching
algorithm, and weighting those chances to place more or less emphasis on a
particular
optimal interaction as related to another optimal interaction. The suitability
score can
then be used in the exemplary methods and systems to determine which agents
should be
connected to which callers.
[00321 For example, it may be that the computer model indicates
that a caller match
with agent one will result in a high chance of a sale with but a high chance
of a long
contact, while a caller match with agent two will result in a low chance of a
sale but a
high chance of a short contact. If an optimal interaction for a sale is inure
heavily
weighted than an optimal interaction of low cost, then the suitability scores
for agent one
as compared to agent two will indicate that the caller should be connected to
agent one.
9
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If, on the other hand, an optimal interaction for a sale is less weighted than
an optimal
interaction for a low cost contact, the suitability score for agent two as
compared to agent
one will indicate that the caller should be connected to agent two.
[0033] Another aspect of the exemplary methods and system is that it
may develop
affinity databases by storing data, the databases comprising data on an
individual caller's
contact outcomes (referred to in this application as "caller affinity data"),
independent of
their demographic, psychographic, or other business-relevant information. Such
caller
affinity data can include the caller's purchase history, contact time history,
or customer
satisfaction history. These histories can be general, such as the caller's
general history for
purchasing products, average contact time with an agent, or average customer
satisfaction
ratings. These histories can also be agent specific, such as the caller's
purchase, contact
time, or customer satisfaction history when connected to a particular agent
The caller
affinity data can then be used to refine the matches that can be made using
the exemplary
methods and systems.
[0034] Another aspect of the exemplary methods and systems is that
it may develop
affinity databases that comprise revenue generation, cost, and customer
satisfaction
performance data of individual agents as matched with specific caller
demographic,
psycho graphic, or other business-relevant characteristics (referred to in
this application as
"agent affinity data"). An affinity database such as this may, for example,
result in the
exemplary methods and systems predicting that a specific agent performs best
in
interactions with callers of a similar age, and less well in interactions with
a caller of a
significantly older or younger age. Similarly this type of affinity database
may result in
the exemplary methods and systems predicting that an agent with certain agent
affinity
data handles callers originating from a particular geography much better than
the agent
handles callers from other geographies. As another example, exemplary methods
and
systems may predict that a particular agent performs well in circumstances in
which that
agent is connected to an irate caller.
[0035] Though affinity databases are preferably used in combination
with agent data
and caller data that pass through a pattern matching algorithm to generate
matches,
information stored in affinity databases can also be used independently of
agent data and
caller data such that the affinity information is the only information used to
generate
matches.
100361 Exemplary methods and systems can also comprise connection
rules to define
when or how to connect agents that are matched to a caller. The connection
rules can be
CA 3071165 2020-02-04

as simple as instructing the method or system to connect a caller according to
the best
match among all available agents with that particular caller. In this manner,
caller hold
time can be minimized. The connection rules can also be more involved, such as

instructing the method or system to connect a caller only when a minimum
threshold
match exists between an available agent and a caller, or to allow a defined
period of time
to search for a minimum matching or the best available matching at that time.
The
connection rules can also purposefully keep certain agents available while a
search takes
place for a potentially better match.
[0037] In another example, systems and methods include combining
multiple output
variables of a pattern matching algorithm (for matching callers and agents)
into a single
metric for use in controlling and managing the routing system. The pattern
matching
algorithm may include a neural network architecture, where the exemplary
methods and
systems combine outputs from multiple neural networks, one for each output
variable.
For example, the system and methods may determine a Z-score (e.g., a
dimensionless
standard score) for each of two or more variable outputs of a pattern matching
algorithm.
For example, the output variable may include or be associated with revenue
generation,
cost, customer satisfaction performance, first call resolution, cancellation
(e.g., later
cancelation of a sale due to buyer's remorse), or other variable outputs from
the pattern
matching algorithm of the system. A linear combination of the determined Z-
scores may
then be computed to provide a single score based on the multiple variables.
For instance,
a call routing center may combine two or more of the Z-scores for a desired
output of the
system (e.g., desiring to optimize some mix of the output variables or
deciding that one
variable is to be weighted more heavily than another variable). The linear
combination
and single score may then be used by the routing system for routing or
matching callers to
agents via the pattern matching algorithm, where, for example, the callers and
agents may
be matched in an attempt to maximize the output value or score of the
determined linear
combination of Z-scores for difference caller-agent pairs.
[00381 Further, in one example, the pattern matching algorithms and
Z-scores may be
influenced by the length of time a caller has been on hold, e.g., taking into
account a pain
threshold function of the caller. For instance, the probability of increased
revenue,
customer satisfaction, and so on may vary based on the wait time a caller is
held before
routing to an agent. For example, if a caller is held too long based on a hold
threshold or
cost function for caller wait time, the probability of a predicted outcome may
change
(e.g., after a certain time on hold the probability of a sale for the
particular caller may
11
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drop tremendously). As such, the system may route the caller to an otherwise
sub-
optimum agent match based on the linear combination of Z-scores and output
variables.
For example, the desired mix of output variables may be set to weight revenue
more than
cost or customer satisfaction, however, after a pain threshold is reached for
a particular
caller, the system may route that caller in a fashion more heavily weighting
customer
satisfaction.
[00391 According to another aspect of the exemplary systems and
methods described,
a visual computer interface and printable reports may be provided to the
contact center or
their clients to allow them to, in a real-time or a past performance basis,
monitor the
statistics of agent to caller matches, measure the optimal interactions that
are being
achieved versus the interactions predicted by the computer model, as well as
any other
measurements of real time or past performance using the methods described
herein. A
visual computer interface for changing the weighting on an optimal interaction
can also
be provided to the contact center or the contact center client, such that they
can, as
discussed herein, monitor or change the weightings in real time or at a
predetermined
time in the future.
[0040] Many of the techniques described here may be implemented in
hardware,
firmware, software, or a combination thereof. Preferably, the techniques are
implemented
in computer programs executing on programmable computers that each includes a
processor, a storage medium readable by the processor (including volatile and
nonvolatile
memory and/or storage elements), and suitable input and output devices.
Program code is
applied to data entered using an input device to perform the functions
described and to
generate output information. The output information is applied to one or more
output
devices. Moreover, each program is preferably implemented in a high level
procedural or
object-oriented programming language to communicate with a computer system.
However, the programs can be implemented in assembly or machine language, if
desired.
In any case, the language may be a compiled or interpreted language.
[0041] Each such computer program is preferably stored on a storage
medium or
device (e.g., CD-ROM, hard disk or magnetic diskette) that is readable by a
general or
special purpose programmable computer for configuring and operating the
computer
when the storage medium or device is read by the computer to perform the
procedures
described. The system also may be implemented as a computer-readable storage
medium,
configured with a computer program, where the storage medium so configured
causes a
computer to operate in a specific and predefined manner.
12
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BRIEF DESCRIPTION OF THE DRAWINGS
[0042] Figure 1 is a diagram reflecting the general setup of a
contact center
operation.
[0043] Figure 2 illustrates an exemplary contact center routing
system including a
pattern matching engine.
[0044] Figure 3 illustrates an exemplary routing system having a
mapping engine fur
routing callers based on performance and/or pattern matching algorithms
[0045] Figure 4 is a flowchart reflecting an example for matching a
caller from a
pool of callers to an agent using agent data and caller data.
[0046] Figure 5 is a flowchart reflecting an example for routing a
caller from a set of
callers.
[0047] Figure 6 is a flowchart reflecting an example for jumping a
caller within a
queue of callers.
[0048] Figure 7 is a flowchart reflecting an example for optimizing a
combination or
mix of multiple output variables of a pattern matching algorithm and computer
model.
[0049] Figure 8 is a flowchart reflecting another example for
optimizing a
combination or mix of multiple output variables of a pattern matching
algorithm and
computer model.
[0050] Figure 9 illustrates a typical computing system that may be
employed to
implement some or all processing functionality in certain embodiments of the
invention.
DETAILED DESCRIPTION OF THE INVENTION
[0051] The following description is presented to enable a person of
ordinary skill in
the art to make and use the invention, and is provided in the context of
particular
applications and their requirements. Various modifications to the embodiments
will be
readily apparent to those skilled in the art, and the generic principles
defined herein may
be applied to other embodiments and applications.
Moreover, in the following description, numerous details are set
forth for the purpose of explanation. However, one of ordinary skill in the
art will realize
that the invention might be practiced without the use of these specific
details. In other
instances, well-known structures and devices are shown in block diagram form
in order
not to obscure the description of the invention with unnecessary detail. Thus,
the present
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invention is not intended to be limited to the embodiments shown, but is to be
accorded
the widest scope consistent with the principles and features disclosed herein.
[0052] While the invention is described in terms of particular
examples and
illustrative figures, those of ordinary skill in the art will recognize that
the invention is not
limited to the examples or figures described. Those skilled in the art will
recognize that
the operations of the various embodiments may be implemented using hardware,
software, firmware, or combinations thereof, as appropriate. For example, some

processes can be carried out using processors or other digital circuitry under
the control of
software, firmware, or hard-wired logic. (The temi "logic" herein refers to
fixed
hardware, programmable logic and/or an appropriate combination thereof, as
would be
recognized by one skilled in the art to carry out the recited functions.)
Software and
firmware can be stored on computer-readnble storage media. Some other
processes can
be implemented using analog circuitry, as is well known to one of ordinary
skill in the art.
Additionally, memory or other storage, as well as communication components,
may be
employed in embodiments of the invention.
[0053] Initially, exemplary call routing systems and methods
utilizing pattera
matching algorithms and computer models are described for routing callers to
agents.
This description is followed by exemplary methods for routing callers from a
queue of
callers or a pool of callers, and exemplary systems and methods for optimizing
a mix of
multiple variable outcomes of the pattern matching algorithms and computer
models. For
example, systems and methods for combining various metrics associated with
multiple
variable outputs of the algorithms and combining them into a common metric for

matching callers to agents, routing callers from queues of callers or pools of
callers, or
jumping callers within a queue.
[00541 Figure 1 is a diagram reflecting the general setup of a
contact center operation
100. The network cloud 101 reflects a specific or regional telecommunications
network
designed to receive incoming callers or to support contacts made to outgoing
callers. The
network cloud 101 can comprise a single contact address, such as a telephone
number or
email address, or multiple contract addresses. The central router 102 reflects
contact
routing hardware and software designed to help route contacts among call
centers 103.
The central router 102 may not be needed where there is only a single contact
center
deployed. Where multiple contact centers are deployed, more routers may be
needed to
route contacts to another router for a specific contact center 103. At the
contact center
level 103, a contact center router 104 will route a contact to an agent 105
with an
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CA 3071165 2020-02-04

individual telephone or other telecommunications equipment 105. Typically,
there are
multiple agents 105 at a contact center 103, though there are certainly
embodiments
where only one agent 105 is at the contact center 103, in which case a contact
center
router 104 may prove to be unnecessary.
[0055] Figure 2 illustrates an exemplary contact center routing
system 200 (which
may be included with contact center router 104 of Figure 1). Broadly speaking,
routing
system 200 is operable to match callers and agents based, at least in part, on
agent
performance, pattern, matching algorithms or computer models based on caller
data and/or
agent data, and the like. Routing system 200 may include a communication
server 202
and a pattern matching engine 204 (referred to at times as "Satisfaction
Mapping" or
"SatMap") for receiving and matching incoming callers to agents.
[0056] The pattern matching engine 204 may operate in various manners
to match
callers to agents based on pattern matching algorithms and computer models,
which adapt
over time based on the performance or outcomes of previous caller-agent
matches. In one
example, the pattern matching engine 204 includes a neural network based
adoptive
pattern matching engine, described in greater detail below. Various other
exemplary
pattern matching and computer model systems and methods may be included with
content
routing system and/or pattern matching engine 204 are described in U.S. Serial
No.
= 12/021,251, entitled "Systems and Methods for Routing Callers to an Agent
in a Contact
Center," and filed January 28, 2008.
[0057] Routing system 200 may further include other components such
as collector
206 for collecting caller data of incoming callers, data regarding caller-
agent pairs,
outcomes of caller-agent pairs, agent data of agents, and the like. Further,
routing system
200 may include a reporting engine 208 for generating reports of performance
and
operation of the routing system 200. Various other servers, components, and
functionality are possible for inclusion with routing system 200. Further,
although shown
as a single hardware device, it will be appreciated that various components
may be
located remotely from each other (e.g., communication server 202 and routing
engine 204
need not be included with a common hardware/server system or included at a
common
location). Additionally, various other components and functionality may be
included
with routing system 200, but have been omitted here for clarity.
[0058] Figure 3 illustrates detail of exemplary routing engine 204.
Routing engine
204 includes a main mapping engine 304, which receives caller data and agent
data from
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databases 310 and 312. In some examples, routing engine 204 may route callers
based
solely or in part on performance data associated with agents. In other
examples, routing
engine 204 may make routing decisions based solely or in part on comparing
various
caller data and agent data, which may include, e.g., performance based data,
demographic
data, psychographic data, and other business-relevant data. Additionally,
affinity
databases (not shown) may be used and such information received by routing
engine 204
for making routing decisions.
[0059] In one example, routing engine 204 includes or is in
communication with one
or more neural network engines 306. Neural network engines 306 may receive
caller and
agent data directly or via routing engine 204 and operate to match and route
callers based
on pattern matching algorithms and computer models generated to increase the
changes of
desired outcomes. Further, as indicated in Figure 3, call history data
(including, e.g.,
caller-agent pair outcomes with respect to cost, revenue, customer
satisfaction, etc.) may
be used to retrain or modify the neural network engine 306.
[0060] Routing engine 204 further includes or is in communication
with hold
queue/pool logic 308. In one example, hold queue/pool logic 308 operates as a
queue for
a plurality of callers, for example, storing or accessing hold times, idle
times, and/or a
queue order of callers and agents, and operates with mapping engine 304 to map
callers to
agents based on queue order of the callers and/or agents. Mapping engine 304
may
operate, for example, to map callers based on a pattern matching algorithm,
e.g., as
included with neural network engine 306, or based on queue order, e.g., as
retrieved from
hold queue 308. Further, as described in greater detail below, hold queue/pool
logic 308
may operate with one or more of mapping engine 304 and neural network engine
306 to
pull callers from the queue out of the queue order for muting to an agent,
another queue,
or pool of caller. In another example, hold queue/pool logic 308 may operate
to pool
callers, where callers are pulled from the pool for routing to an agent,
another pool, or to a
queue of callers without respect to a hold time, idle time, or queue order
(e.g., there is no
ordered line of callers as in a queue).
[0061] The following are various exemplary methods in which the
pattern matching
engine may operate to route callers from a pool or queue of callers to an
available agent,
another queue or pool of callers, or to jump a caller within a queue of
callers. For
example, as described, the pattern matching algorithm may rate agents on
performance,
compare agent data and caller data and match per a pattern matching algorithm,
create
computer models to predict outcomes of agent-caller pairs, and the like. It
will be
16
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appreciated that a content router system may include software, hardware,
firmware, or
combinations thereof to implement the exemplary methods.
[0062] Figure 4 illustrates an exemplary method for routing a
caller within a call
center environment, e.g., by routing system 200. In one example, caller data
is
determined or identified for at least one caller of a set of callers at 402.
The caller data
may include demographic, psychographic, and other business-relevant data about
callers.
The set of callers may include any group of callers such as a queue of callers
or a pool of
callers (e.g., which may be stored or determined by hold queue/pool logic
308). The
caller may be routed from the set of callers at 404 based on the roller data
identified in
402 to an agent, another queue of callers, or to a pool of callers. For
instance, based on
the caller data alone or when compared to agent data via a pattern matching
algorithm,
computer model, or the like as discussed herein, the caller might be pulled
out of a queue
or pool of callers and routed to another queue or pool of callers. For
example, a caller
may be routed to a second queue of callers or a pool of callers, which may be
divided or
segmented based on particular caller data. Additionally, the caller might be
pulled from
the set of callers and muted to an available agent, e.g., based on the caller
data alone or
when compared to agent data via a pattern matching algorithm, computer model,
or the
like as discussed herein.
[0063] Figure 5 illustrates another exemplary method for routing a
caller within a call
center environment, e.g., by routing system 200. In this example, caller data
is
determined or identified for at least one caller of a queue of callers at 502,
for example, a
chronologically ordered queue of incoming callers, The caller data may include

demographic or psycho graphic data as described herein. The caller may then be
moved
or jumped at 504 within the queue of callers based on the caller data
identified in 502 to a
new position within the queue, e.g., jumping ahead (or back) of another caller
in the
queue order. For instance, based on the caller data alone or when compared to
agent data
via a pattern matching algorithm, computer model, or the like as discussed
herein, the
caller might be jumped in the queue ahead of other callers. The caller may be
routed to
an agent when having the highest priority queue position or otherwise pulled
and routed
out of queue order as described herein.
[0064] In another example, similar to that described above, a
caller at the front of a
queue of callers may be skipped by another caller based on the determined or
identified
caller data at 502. For example, as an agent becomes free, caller data of at
least one caller
in queue may be identified and the caller at the front of the queue skipped
(e.g., nulling
17
CA 3071165 2020-02-04

the callers routing priority) for another caller based on the identified
caller data. In one
example, the caller may be skipped based on identified caller data alone or
when
compared to agent data via a pattern matching algorithm, computer model, or
the like as
discussed herein. As described herein, the methods and systems may include a
predetermined maximum number of times a caller at the front of the queue may
be
skipped before being routed according to queue order. Further, as described
herein, the
callers may be routed to an agent, a pool of callers, or another queue of
callers.
[00651 Figures 6-9 describe various methods for using caller data
and/or agent data to
make routing decisions, e.g., evaluating caller data and making decision to
route callers to
agents, other queues or pools of callers, to jump a caller within a queue, and
so on.
Figure 6 illustrates an exemplary method for increasing the chances of an
optimal
interaction by combining agent grades (which may be determined from grading or

ranking agents on desired outcomes), agent demographic data, agent
psychographic data,
and/or other business-relevant data about the agent (individually or
collectively referred
to in this application as "agent data"), along with demographic,
psychographic, and/or
other business-relevant data about callers (individually or collectively
referred to in this
application as "caller data"). Agent and caller demographic data can comprise
any of:
gender, race, age, education, accent, income, nationality, ethnicity, area
code, zip code,
marital status, job status, and credit score. Agent and caller psychographic
data can
comprise any of introversion, sociability, desire for financial success, and
film and
television preferences.
[00661 In one example, a method for operating a contact center
includes determining
caller data associated with at least one caller of a set of callers (e.g., a
pool of callers on
hold), determining agent data associated with an agent or agents (e.g., an
available agent
or agents), comparing the agent data and the caller data (e.g., via a pattern
matching
algorithm), and matching one of the callers in the pool to the agent to
increase the chance
of an optimal interaction. In particular, at 602, caller data (such as a
caller demographic
or psychographic data) is identified or determined for at least one of a set
of callers. One
way of accomplighing this is by retrieving caller data from available
databases by using
the caller's contact information, as an index. Available databases include,
but are not
limited to, those that are publicly available, those that are commercially
available, or
those created by a contact center or a contact center client. In an outbound
contact center
environment, the caller's contact information is known beforehand. In an
inbound
contact center environment, the caller's contact information can be retrieved
by
18
CA 3071165 2020-02-04

examining the caller's CallerlD information or by requesting this information
of the caller
at the outset of the contact, such as through entry of a caller account number
or other
caller-identifying information. Other business-relevant data such as historic
purchase
behavior, current level of satisfaction as a customer, or volunteered level of
interest in a
product may also be retrieved from available databases.
[0067] It is typical for a queue of callers on hold to form at a
contact center. When a
queue has formed it is desirable to minimize the hold time of each caller in
order to
increase the chances of obtaining customer satisfaction and decreasing the
cost of the
contact, where cost can be, not only a function of the contact duration, but
also a function
of the chance that a caller will drop the contact if the wait is too long.
Accorrlingly, in
one example, after matching an agent with callers in a queue, for example, the
connection
rules can thus be configured to comprise an algorithm for queue jumping,
whereby a
favorable match of a caller on hold and an available agent will result in that
caller
"jumping" the queue by increasing the caller's connection priority so that the
caller is
passed to that agent first ahead of others in the chronologically listed
queue. The queue
jumping algorithm can be further configured to automatically implement a trade-
off
between the cost associated with keeping callers on hold against the benefit
in terms of
the chance of an optimal interaction taking place if the caller is jumped up
the queue, and
jumping callers up the queue to increase the overall chance of an optimal
interaction
taking place over time at an acceptable or minimum level of cost or chance of
customer
satisfaction. Callers can also be jumped up a queue if an affinity database
indicates that
an optimal interaction is particularly likely if the caller is matched with a
specific agent
that is already available. Additionally, callers can be pulled or routed from
the queue to
an agent, another queue, or a pool of callers as described herein.
[0068] At 604, agent data for one or more agents is identified or
determined, e.g., of
an available agent, One method of determining agent demographic or
psychographic data
can involve surveying agents at the time of their employment or periodically
throughout
their employment. Such a survey process can be manual, such as through a paper
or oral
survey, or automated with the survey being conducted over a computer system,
such as by
deployment over a web-browser. Though this advanced embodiment preferably uses

agent grades, demographic, psychographic, and other business-relevant data,
along with
caller demographic, psychographic, and other business-relevant data, other
embodiments
of the exemplary methods and systems can eliminate one or more types or
categories of
caller or agent data to minimize the computing power or storage necessary.
19
CA 3071165 2020-02-04

[0069] The agent data and caller data may then be compared at 606.
For instance, the
agent data and caller data can be passed to a computational system for
comparing caller
data to agent data for each agent-caller pair, i.e., the agent data is
compared in a pair-wise
fashion to each caller on hold. In one example, the comparison is achieved by
passing the
agent and caller data to a pattern matching algorithm to create a computer
model that
matches each caller with the agent and estimates the probable outcome of each
matching
along a number of optimal interactions, such as the generation of a sale, the
duration of
contact, or the likelihood of generating an interaction that a customer finds
satisfying.
[0070] Additionally, the amount of time a caller is on hold in the
pool of callers may
be considered. In one example, a "cost" or "pain" function is applied to
callers in the
pool to analyze the varying chance of a successful interaction as callers wait
in the pool.
The pattern matching algorithm or computes model may use the cost function in
mapping
callers to agents. For instance, consider an example where the best matching
agent for a
cAller might be occupied and have a 70% chance of increased revenue generation
for a
miler, but is not expected to be free soon (e.g., is only a few seconds into
another call).
The next best matching agent is free and has a 95% chance of increased revenue

generation for the caller. The cost function may indicate that the system
route the caller
to the next best agent because the 70% chance of increased revenue generation
for the
caller will decrease over time, most likely below 95% by the time the best
agent is free.
As such, the pattern matching algorithm or computer model may use the cost
function in
mapping callers to agents in addition to other caller and agent data.
[0071] Additionally, in one example, a hold threshold for one or
more of the callers in
the pool may be included as a factor, e.g., as a weighting factor used with
other data in the
pattern matching algorithm or trigger to route a caller. The hold threshold
may include a
predetermined time, a multiple of an average or expected hold time for the
caller when
the call arrives, the number of callers routed while they are on hold, e.g.,
how many times
they have been "skipped" by other callers, and so on. For example, a caller
may be
assigned a hold threshold (e.g., seconds, minutes, or number of times they are
"skipped"),
which if exceeded, overrides the pattern matching algorithm, e.g., to prevent
a caller from
being held indefinitely. Further, each ca liar may be individually assigned a
hold
threshold, e.g., based on data associated with the caller, such as their
inclination to
generate revenue or preferred account status, or all callers may be given a
common hold
threshold.
CA 3071165 2020-02-04

[0072) Exemplary pattern matching algorithms can include any
correlation algorithm,
such as a neural network algorithm or a genetic algorithm. In one example, a
resilient
backpropagation (RProp) algorithm may be used, as described by M. Riedmiller,
H.Braun: "A Direct Adaptive Method for Faster backpropagation Learning: The
RPROP
Algorithm," Proc. of the IEEE Intl. Con!. on Neural Networks 1993,
To generally train or otherwise refine the
algorithm, actual contact results (as measured for an optimal interaction) are
compared
against the actual agent and caller data for each contact that occurred. The
pattern
matching algorithm can then learn, or improve its learning ot how matching
certain
callers with certain agents will change the chance of an optimal interaction.
In this
manner, the pattern matching algorithm can then be used to predict the chance
of an
optimal interaction in the context of matching a caller with a particular set
of caller data,
with an agent of a particular set of agent data. Preferably, the pattern
matching algorithm
is periodically refined as more actual data on caller interactions becomes
available to it,
such as periodically training the algorithm every night after a contact center
has finished
operating for the day.
[0073] The pattern matching algorithm may create a computer model
reflecting the
predicted chances of an optimal interaction for each agent and caller
matching.
Preferably, the computer model will comprise the predicted chances for a set
of optimal
interactions for every agent that is logged in to the contact center as
matched against
every available caller. Alternatively, the computer model can comprise subsets
of these,
or sets containing the aforementioned sets. For example, instead of matching
every agent
logged into the contact center with every available caller, examples can match
every
available agent with every available caller, or even a narrower subset of
agents or callers.
Likewise, the present invention can match every agent that ever worked on a
particular
campaign ¨ whether available or logged in or not ¨ with every available
caller. Similarly,
the computer model can comprise predicted chances for one optimal interaction
or a
number of optimal interactions.
[0074] A computer model can also comprise a suitability score for
each matching of
an agent and a caller. The suitability score can be determined by taking the
chances of a
set of optimal interactions as predicted by the pattern matching algorithm,
and weighting
those chances to place more or less emphasis on a particular optimal
interaction as related
to another optimal interaction. The suitability score can then be used in the
exemplary
methods and systems to determine which agents should be connected to which
callers.
21
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[0075] Based on the pattern matching algorithm and/or computer
model, the method
further includes determining the caller having the best match to the agent at
908. As will
be understood, the best matching caller may depend on the pattern matching
algorithm,
computer model, and desired output variables and weightings selected by a
particular call
center. The determined best match caller is then routed to the agent at 910.
[0076] Caller data and agent data may further comprise affinity
data. As such,
exemplary methods and systems can also comprise affinity databases, the
databases
comprising data on an individual caller's contact outcomes (referred to in
this application
as "caller affinity data"), independent of their demographic, psychographic,
or other
business-relevant information. Such caller affinity data can include the
caller's purchase
history, contact time history, or customer satisfaction history. These
histories can be
general, such as the caller's general history for purchasing products, average
contact time
with an agent, or average customer satisfaction ratings. These histories can
also be agent
specific, such as the caller's purchase, contact time, or customer
satisfaction history when
connected to a particular agent.
[0077] The caller affinity data can then be used to refine the
matches that can be
made using the exemplary methods and systems. As an example, a certain caller
may be
identified by their caller Rffinity data as one highly likely to make a
purchase, because in
the last several instances in which the caller was contacted, the caller
elected to purchase
a product or service. This purchase history can then be used to appropriately
refine
matches such that the caller is preferentially matched with an agent deemed
suitable for
the caller to increase the chances of an optimal interaction. Using this
embodiment, a
contact center could preferentially match the caller with an agent who does
not have a
high grade for generating revenue or who would not otherwise be an acceptable
match,
because the chance of a sale is still likely given the caller's past purchase
behavior. This
strategy for matching would leave available other agents who could have
otherwise been
occupied with a contact interaction with the caller. Alternatively, the
contact center may
instead seek to guarantee that the caller is matched with an agent with a high
grade for
generating revenue, irrespective of what the matches generated using caller
data and
agent demographic or psychographic data may indicate.
[0078] A more advanced affinity database includes one in which a
caller's contact
outcomes are tacked across the various agent data. Such an analysis might
indicate, for
example, that the caller is most likely to be satisfied with a contact if they
are matched to
an agent of similar gender, race, age, or even with a specific agent. Using
this
22
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embodiment, a system or method could preferentially match a caller with a
specific agent
or type of agent that is known from the caller affinity data to have generated
an
acceptable optimal interaction.
[0079] Affinity databases can provide particularly actionable
information about a
caller when commercial, client, or publicly-available database sources may
lack
information about the caller. This database development can also be used to
further
enhance contact routing and agent-to-caller matching even in the event that
there is
available data on the caller, as it may drive the conclusion that the
individual caller's
contact outcomes may vary from what the commercial databases might imply. As
an
example, if a system or method were to rely solely on commercial databases in
order to
match a caller and agent, it may predict that the caller would be best matched
to an agent
of the same gender to achieve optimal customer satisfaction. However, by
including
affinity database information developed from prior interactions with the
caller, exemplary
methods and systems might more accurately predict that the caller would be
best matched
to an agent of the opposite gender to achieve optimal customer satisfaction.
[0080] Another aspect of the exemplary methods and system is that
it may develop
affinity databases that comprise revenue generation, cost, and customer
satisfaction
performance data of individual agents as matched with specific caller
demographic,
psychographie, or other business-relevant characteristics (referred to in this
application as
"agent affinity data"). An. affinity database such as this may, for example,
result in the
exemplary methods and systems predicting that a specific agent performs best
in
interactions with callers of a similar age, and less well in interactions with
a caller of a
significantly older or younger age. Similarly this type of affinity database
may result in
the examples predicting that an agent with certain agent Rifinity data handles
callers
originating from a particular geography much better than the agent handles
callers from
other geographies. As another example, the system or method may predict that a

particular agent performs well in circumstances in which that agent is
connected to an
irate caller.
[0081] Though affinity databases are preferably used in combination
with agent data
and caller data that pass through a pattern matching algorithm to generate
matches,
information stored in affinity databases can also be used independently of
agent data and
caller data such that the affinity information is the only information used to
generate
matches.
23
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[0082] The exemplary systems and methods may store data specific to
each routed
caller for subsequent analysis. For example, the systems and methods can store
data
generated in any computer model, including the chances for an optimal
interaction as
predicted by the computer model, such as the chances of sales, contact
durations,
customer satisfaction, or other parameters. Such a store may include actual
data for the
caller connection that was made, including the agent and caller data, whether
a sale
occurred, the duration of the contact, and the level of customer satisfaction.
Such a store
may also include actual data for the agent to caller matches that were made,
as well as
how, which, and when matches were considered pursuant to connection rules and
prior to
connection to a particular agent
[0083] Figure 7 illustrates an exemplary method for combining
multiple output
variables of a performance matching algorithm (for matching callers and
agents) into a
single metric for use in controlling and managing the routing system. The
exemplary
method includes determining a Z-score (e.g., a dimensionless standard score)
for each of
two or more variable outputs of the pattern matching algorithm at 702. The Z-
score, or
standard score, can be computed as follows:
z= (x¨u)/a
where x is the raw output of the pattern matching algorithm for a particular
output
variable, p, is the mean of the output variable, and a is the standard
deviation of the output
variable. A Z-score may be computed for any number of output variables of the
call
routing system (e.g., of the pattern matching algorithm used). Output
variables may
include or be associated with, for example, revenue generation, cost, customer

satisfaction, and the like.
[0084] The Z-scores are used at 704 to determine a Linear
combination of two or more
of the output variables, where the linear combination may be selected based on
a desired
mix or weighting of the output variables. For instance, a call center may
determine
customer satisfaction is the most important variable and weight revenue
generation and
cost less than customer satisfaction (e.g., assigning weighting fractions that
add up to 1).
The linear combination of the determined Z-scores may then be computed to
provide a
single score based on the multiple output variables and weighting factors. For
instance, a
call routing center may combine the Z-scores for a desired output of the
system (e.g.,
deciding that one variable is to be weighted more heavily than another
variable). The
linear combination may then be used by the routing system for routing or
matching callers
to agents via the pattern matching algorithm at 706. For example, the callers
and agents
24
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may be matched in an attempt to estimate or maximize the value or score of the

determined linear combination of Z-scores.
[00851 It should be noted that conventionally, for inbound call
centers, when many
callers are on hold and an agent becomes free the first caller in the queue
(e.g., that has
been held the longest) is routed to the free agent. As described herein,
however,
exemplary methods for routing callers includes pairing an available agent to
all callers
being held, and routing the best matching caller to the agent based on a
pattern matching
algorithm/computer model and desired output variables thereof. Figure 8
illustrates a
particular exemplary method for optimi7ing a combination or mix of multiple
output
variables of a pattern matching algorithm and/or computer model for the
particular
instance where multiple callers are on hold and one agent becomes free to
accept a caller.
The exemplary method includes determining a set of caller data from a sample
of callers
at 802. For example, the caller data may include caller data for all or some
of the callers
on hold, waiting for an agent, with the call center. The method further
includes
determining a set of agent data from an agent that becomes available to accept
a caller at
804, which may merely be accessed from known agent data.
[0086] The method further includes, for each possible agent-caller
pair, passing the
associated agent and caller data through a pattern matching algorithm/computer
model at
806. A Z-score may be determined for each agent-caller pair at 808, which are
based on
the pattern matching algorithm for each of the output variables (e.g., for
each neural
network output), as described in greater detail below. The highest scoring
agent-caller
pairing may then be connected, e.g., the best matching caller based on the Z-
scores is
routed.
[0087] A more detailed, but exemplary, pattern matching algorithm
and method for
combining multiple variable outputs thereof includes a neural network
algorithm or a
genetic algorithm. As described (e.g., with respect to Figure 6), a pattern
matching
algorithm such as a neural network,algorithm that can be trained or refined by
comparing
actual results against caller and agent data (e.g., comparing input and output
data) can
learn, or improve its learning of, how matching callers and agents changes the
chance of
an optimal interaction. The following includes an exemplary neural network
pattern
matching algorithm, followed by exemplary methods for scaling the output
scores and
combining the output scores into a composite score for determining caller-
agent pairings
for a desired outcome.
CA 3071165 2020-02-04

[0088] Initially, various terms of the exemplary pattern matching
algorithm are
defined to illustrate the operation. Let A={ai) (i = I,.. .,N) be the set of
currently logged
in agents in a queue which are available for matching to an incoming caller.
Note, these
agents may be in one physical call center or be distributed across several
call centers and
controlled by several Private Branch Exchanges (PBXs). Further, the set of
callers can be
denoted as:
C ={c]} .............................................. (1)
[00891 Each agent and caller has associated agent data and caller
data, e.g.,
demographic, psychographic information, etc. (in some cases caller data may
not be
available, e.g., when the caller's telephone number is either not available or
cannot be
found in an accessible database). Caller data and agent data can be denoted
respectively
as:
(i =1,...,N)(k=1,...,P)
(i =1,...,M)(k=1,...,Q) ............................. (2)
where there are P variables describing, for example, demographic and
psychographic
characteristics of the agents and Q variables describing these characteristics
of clients,
where P and Q are not necessarily equal.
[00901 There are also output variables, which describe certain
characteristics of the
call centres performance, which it is desired to optimi7e. The three most
commonly used
are Revenue, denoted R, cost, which usually is calculated as call handle time,
denoted
here 7', and Satisfaction, denoted S. In this illustrative example only these
three
exemplary output variables are considered, but it should be understood that
more
variables could be added or different variables substituted for revenue, cost,
and
satisfaction. For instance, other variables might include first call
resolution, cancellation
(e.g., later cancelation of a sale due to buyer's remorse), and the like.
[00911 An exemplary pattern matching algorithm or computer model
based on a
pattern matching algorithm may further include "levers", in this example three
levers, for
adjusting the degree to which each of the three output variables is optimized
in the pattern
matching algorithm when making agent-caller matches, These levers may be
denoted as:
LR,Lc 8t Ls (0 5 L,,, Lc, Ls 51) ...... (3)
where the three values are subject to the constraint:
LR+Lc+Ls=1 ........................................... (4)
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100921 In this particular example, for each output variable of the
pattern matching
algorithm, a resilient back-propagation (RPROP) neural network has been
trained. It will
be understood that a RPROP neural network is a learning heuristic for use in
neural
network architectures for providing an update mechanism based on past outcomes
to
improve the output of the algorithm over time. The resulting neural network
evaluation
functions, one each for Revenue, Cost, and Satisfaction, can be as follows:
1R JP 9 111
fc :11Z P Q 9 Ft
f3:111P+Q 9 (5)
Each of the evaluation functions takes a vector comprising the caller data and
agent data
(e.g., demographic, psychographic information, etc.) for one agent and one
caller and
maps it to a single real number, for example:
fR 043 jr.4, y_,
(6)
where the revenue neural network function is mapping the characteristics of
the i'th agent
and j'th caller to the single real number x.
[0093] The above described neural network pattern matching
algorithms may then be
used by an exemplary system to determine an optimal agent-caller pair from
available
agents and incoming callers. In one example, there are three types of
conditions under
which agent-caller pair decisions are made. They include:
i. Many agents are available and a caller calls in (Inbound) or
a call is to be
made to the next caller in lead list (Outbound).
Inbound calls are held and one agent is available.
Callers are held and more than one agent is available.
[00941 A call center typically will operate in condition ii (e.g.,
as described with
respect to Figures 4-6). The following examples are largely independent of the
above
described conditions, however, the most general case iii will be assumed. For
instance,
suppose at some instant that three agents are available:
A. = {a:} (1=1,2,3)
.................................................. (7)
27
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where the free agents are a subset of c,, c, the logged in agent pool: A' c
A=
Further, suppose that two callers are queued. This simple example provides
that there are
six (3 x 2 = 9) possible agent-caller pairings:
al <4 C1
al c2
a; + Cl
a2 <-
4401
a, o c2 ........................................... (8)
The exemplary pattern matching algorithm operates on these six possible
pairings to
determine the optimal matching output of the six possibilities given the three
lever
settings LR, Lc & Ls, which may be set by the contact routing center for a
desired output
performance.
[0095] In one example, the first step is to evaluate the six
possible pairings through
the revenue, cost, and satisfaction neural network algorithms. The system
looks up the
agent data and caller data (e.g., agents' and clients' demographic and
psychographic data)
to form six vectors of length P + Q and applies the neural network function to
each to
generate six real numbers. Taking revenue as the example, the system computes:
fR ji=-=,-/: y
fR (/a/,t,
rzi
fR y 6.1
................................................... (9)
where ru denotes the revenue neural network's output for the pairing of the
i'th agent
/A.
with the j'th caller (note, the notation here is such that at '1 refers to the
demographic and
psychographic information for agent a;'). In the same manner sets of six
numbers can be
calculated, call them c,j and sy being the outputs of the cost and
satisfaction neural
network functions respectively for the six agent-caller pairings.
28
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[0096] The outputs of the neural networks are on a somewhat
arbitrary scale, so to
compare them with each other they can be resealed to a common metric. To this
end a
large number of random pairings between the logged in agents (A) and callers
is formed
(e.g., using callers and agents beyond the six described above). For example,
call center
data for the particular queue under consideration from the previous day can be
used to
form a sample of hundreds, thousands, or more and random matches between
agents and
callers. For each neural network (e.g., for revenue, cost, and satisfaction)
these random
pairings are evaluated and a mean and standard deviation of the resulting
distributions of
neural network outputs may be calculated. For instance, computing the six
quantities PR,
0-R, PC, at, Ps, crs, where PR and OR are the mean and standard deviation of
the revenue
neural network output's distribution and similarly for cost and satisfaction.
[0097] Using the mean and standard deviations, a Z-score for each
of revenue, cost,
and satisfaction may be computed for the six agent-caller pairings:
.7R 71,2 ¨ PR (i=1,2,3 j=1,2)
J 0.R
zC C1,2 ¨ PC (1=1,2,3 j=1,2)
Si
crc
(1=1,2,3 j=1,2)
crs ............................................... (10)
[0098] A call center may wish to optimize a combination of the
output variables, as
expressed by the lever settings, to determine agent-caller pairs. The
determined Z-scores
may be combined into a composite Z-score and used by the pattern matching
algorithm
for choosing an optimAl agent-raller pair. In one example, a linear
combination of the
neural network outputs is formed to result in one overall Z for each agent to
caller pairing
as follows:
Z11 =sx + Lc x Z,c,i + Ls x Z,s3
= LR X ZIR,2 + Lc x Z+ L5'<
LR x + Lc x 4+ Ls x 4
Lsx 4,2+ Lc x 42+ Ls x 4.2
Z33 = Rx Z3R3 + Lc x 4 + Ls x
Z3,2 = Ls x 4,2+ Lc x 4,2+ Ls x Z3S .2 ............ (11)
From this the system and method can find the i and j for which:
= Max ({Z })
................................................... (12)
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and match or route agent I with caller/ In this example, with two available
agents and
three queued callers, the system and method may then match and route the two
available
agents to two of the three queued callers, choosing the two agent-caller pairs
with the
highest summed Z-scores.
[0099] In one example, instead of choosing the agent-caller pairing
with the highest
combined Z-score in Equation 11, the method checks whether the highest Z in
Equation
11 exceeds a preset threshold Z-score and only assign the caller to the agent
when it does.
If the threshold is not exceeded by the Z-score's of any of the available
agent-caller
pairings, the system does not assign a call and waits until more agents and/or
callers
become available and a pairing does exceed the threshold.
[0100] It should be noted, and recoEni7Pd, that in practice the
three outcome variables
discussed (i.e., revenue, cost, and satisfaction) are typically not
independent For
instance, in many call center situations revenue and cost, e.g., as measured
by handle
time, are anticorrelated since agents who spend longest on calls tend to have
higher sales
rates. Therefore, in one example, the lever settings described may be
determined from a
model taking this into consideration, for example, a regression based model
from past
data, set-up to maximize a combination of the output variables accounting for
their
interactions.
[0101] Additionally, in some examples, the pattern matching
algorithms and Z-scores
may be influenced by a hold threshold for a caller, e.g., the length of time a
caller has
been on hold, which may include a pain threshold of the caller, e.g., via a
cost function.
For instance, the probability of increased revenue, customer satisfaction, and
so on may
vary based on the wait time a caller is held before routing to an agent. For
example, if a
caller is held too long based on a hold threshold or cost function for caller
wait time, the
probability of a predicted outcome may change (e.g., after one minute on hold
the
probability of a sale for the particular caller may drop tremendously). As
such, the
system may route the caller to an otherwise sub-optimum agent match based on
the linear
combination of Z-scores and output variables. For example, the desired output
may be to
maximize revenue, however, after a pain threshold is reached for a caller, the
system may
route the caller in a fashion more heavily weighting customer satisfaction.
[0102] In some instances, rAller data may be missing or
unavailable. For instance,
demographic and psychographic data may not be known for a caller, or it may be
that the
PBX fails to provide the telephone number for a caller. In such cases the
exemplary
CA 3071165 2020-02-04

pattern matching algorithm will not perform as well because the lc values will
be
unknown. In one example, the algorithm may compute ZR, Zc and Zs in equation
(10)
without reference to the client at all. For instance, for each agent in A the
system may
have historical performance data, that is the values of revenue, cost, and
satisfaction
associated with each call that agent has handled over a historical period
(e.g., a period of
days or more such as 30 days). For each agent in the pool a Z-score (one each
for
revenue, cost and satisfaction performance) may be computed as:
¨R
¨R ¨ H
¨ ______________________
sd (HR)
¨c I ac
ZI ¨sd (Hc) (i
- 115
I -
sd (115)
.................................................. (13)
where KR is the average historical revenue performance of agent i, and FR and
.7(10/R)
are the mean and standard deviation respectively of the historical
performances of all N
agents in the pool. In the case that a caller's data is missing, the pairings
with that caller
in Equation 11 have these Z values used.
[0103] Missing agent data will generally not occur as gathering
agent data is typically
under the control of the call routing center. In an instance where some or all
agent data is
missing, however, the agent can be assigned a Z=0 value, which may give the
best
estimate, in the absence of agent data, of fit as the average (since the mean
of Z values is
zero).
[0104] It is noted that the call routing center or its clients may
modify the linear
combination, e.g., change the mixing or weighting of desired output variables,
over time.
Further, the underlying Z-scores may be recomputed over time, resulting in
changes to
the linear combination and routing of callers. Optionally, the contact center
or its clients
may control the mix of output variables over the Internet or some another data
transfer
system. As an example, a client of the contact center could access the mix of
output
variables currently in use over an intenaet browser and modify these remotely.
Such a
modification may be set to take immediate effect and, immediately after such a

modification, subsequent caller routings occur in line with the newly
establishing
31
CA 3071165 2020-02-04

combination of Z-scores. An instance of such an example may arise in a case
where a
contact center client decides that the most important strategic priority in
their business at
present is the maximization of revenues. In such a case, the client would
remotely alter
the combination to favor the routing and matching of agents that would
generate the
greatest probability of a sale in a given contact. Subsequently the client may
take the
view that maximization of customer satisfaction is more important for their
business. In
this event, they can remotely alter the combination such that callers are
routed to agents
most likely to maximize their level of satisfaction. Alternatively, changes
may be set to
take effect at a subsequent time, for instance, commencing the following
morning.
[0105] According to another aspect of the exemplary systems and
methods described,
a visual computer interface and printable reports may be provided to the
contact center or
their clients to allow them to, in a real-time or a past performance basis,
monitor the
statistics of agent to caller matches, measure the optimal interactions that
are being
achieved versus the interactions predicted by the computer model, as well as
any other
measurements of real time or past performance using the methods described
herein. A
visual computer interface for changing the weighting on an optimal interaction
can also
be provided to the contact center or the contact center client, such that they
can, as
discussed herein, monitor or change the weightings or desired outcome
variables in real
time or at a predetermined time in the future.
[01061 Many of the techniques described here may be implemented in
hardware or
software, or a combination of the two. Preferably, the techniques are
implemented in
computer programs executing on programmable computers that each includes a
processor, a storage medium readable by the processor (including volatile and
nonvolatile
memory and/or storage elements), and suitable input and output devices.
Program code is
applied to data entered using an input device to perform the functions
described and to
generate output information. The output information is applied to one or more
output
devices. Moreover, each program is preferably implemented in a high level
procedural or
object-oriented programming language to communicate with a computer system.
However, the programs can be implemented in assembly or machine language, if
desired.
In any case, the language may be a compiled or interpreted language.
[01071 Each such computer program is preferably stored on a storage
medium or
device (e.g., CD-ROM, hard disk or magnetic diskette) that is readable by a
general or
special purpose programmable computer for configuring and operating the
computer
when the storage medium or device is read by the computer to perform the
procedures
32
CA 3071165 2020-02-04

described. The system also may be implemented as a computer-readable storage
medium,
configured with a computer program., where the storage medium so configured
causes a
computer to operate in a specific and predefined manner.
[0108] Figure 9 illustrates a typical computing system 900 that may
be employed to
implement processing functionality in embodiments of the invention. Computing
systems
of this type may be used in clients and servers, for example. Those skilled in
the relevant
art will also recognize how to implement the invention using other computer
systems or
architectures. Computing system 900 may represent, for example, a desktop,
laptop or
notebook computer, hand-held computing device (PDA, cell phone, palmtop,
etc.),
mainframe, server, client, or any other type of special or general purpose
computing
device as may be desirable or appropriate for a given application or
environment.
Computing system 900 can include one or more processors, such as a processor
904.
Processor 904 can be implemented using a general or special purpose processing
engine
such as, for example, a microprocessor, microcontroller or other control
logic. In this
example, processor 904 is connected to a bus 902 or other communication
medium.
[0109] Computing system 900 can also include a main memory 908,
such as random
access memory (RAM) or other dynamic memory, for storing information and
instructions to be executed by processor 904. Main memory 908 also may be used
for
storing temporary variables or other intermediate information during execution
of
instructions to be executed by processor 904. Computing system 900 may
likewise
include a read only memory ("ROM") or other static storage device coupled to
bus 902
for storing static information and instructions for processor 904.
[0110] The computing system 900 may also include information
storage system 910,
which may include, for example, a media drive 912 and a removable storage
interface
920. The media drive 912 may include a drive or other mechanism to support
fixed or
removable storage media, such as a hard disk drive, a floppy disk drive, a
magnetic tape
drive, an optical disk drive, a CD or DVD drive (R or RW), or other removable
or fixed
media drive. Storage media 918 may include, for example, a hard disk, floppy
disk,
magnetic tape, optical disk, CD or DVD, or other fixed or removable medium
that is read
by and written to by media drive 912. As these examples illustrate, the
storage media 918
may include a computer-readable storage medium having stored therein
particular
computer software or data.
[0111] In alternative embodiments, information storage system 910
may include other
similar components for allowing computer programs or other instructions or
data to be
33
CA 3071165 2020-02-04

loaded into computing system 900. Such components may include, for example, a
removable storage unit 922 and an interface 920, such as a program cartridge
and
cartridge interface, a removable memory (for example, a flash memory or other
removable memory module) and memory slot, and other removable storage units
922 and
interfaces 920 that allow software and data to be transferred from the
removable storage
unit 918 to computing system 900.
[0112] Computing system 900 can also include a communications
interface 924.
Communications interface 924 can be used to allow software and data to be
transferred
between computing system 900 and external devices. Examples of communications
interface 924 can include a modem, a network interface (such as an Ethernet or
other MC
card), a communications port (such as for example, a USB port), a PCMCIA slot
and
card, etc. Software and data transferred via communications interface 924 are
in the form
of signals which can be electronic, electromagnetic, optical or other signals
capable of
being received by communications interface 924. These signals are provided to
commtmications interface 924 via a channel 928. This channel 928 may carry
signals and
may be implemented using a wireless medium, wire or cable, fiber optics, or
other
communications medium. Some examples of a channel include a phone line, a
cellular
phone link, an RF link, a network interface, a local or wide area network, and
other
communications channels.
[0113] In this document, the terms "computer program product,"
"computer-readable
medium" and the like may be used generally to refer to physical, tangible
media such as,
for example, memory 908, storage media 918, or storage unit 922. These and
other forms
of computer-readable media may be involved in storing one or more instructions
for use
by processor 904, to cause the processor to perform specified operations. Such

instructions, generally referred to as "computer program code" (which may be
grouped in
the form of computer programs or other groupings), when executed, enable the
computing
system 900 to perform features or functions of embodiments of the present
invention.
Note that the code may directly cause the processor to perform specified
operations, be
compiled to do so, and/or be combined with other software, hardware, and/or
firmware
elements (e.g., libraries for performing standard functions) to do so.
[0114] In an embodiment where the elements are implemented using
software, the
software may be stored in a computer-readable medium and loaded into computing

system 900 using, for example, removable storage media 918, drive 912 or
communications interface 924. The control logic (in this example, software
instructions
34
CA 3071165 2020-02-04

or computer program code), when executed by the processor 904, causes the
processor
904 to perform the functions of the invention as described herein.
[0115] It will be appreciated that, for clarity purposes, the above
description has
described embodiments of the invention with reference to different functional
units and
processors. However, it will be apparent that any suitable distribution of
functionality
between different functional units, processors or domains may be used without
detracting
from the invention. For example, functionality illustrated to be performed by
separate
processors or controllers may be performed by the same processor or
controller. Hence,
references to specific functional units are only to be seen as references to
suitable means
for providing the described functionality, rather than indicative of a strict
logical or
physical structure or organization.
[01161 The above-described embodiments of the present invention are
merely meant
to be illustrative and not limiting. Various changes and modifications may be
made
without departing from the invention in its broader aspects.
CA 3071165 2020-02-04

Representative Drawing