This disclosure generally relates to contact centers and, more particularly, to techniques for behavioral pairing in a contact center system.
A typical contact center algorithmically assigns contacts arriving at the contact center to agents available to handle those contacts. At times, the contact center may have agents available and waiting for assignment to inbound or outbound contacts (e.g., telephone calls, Internet chat sessions, email). At other times, the contact center may have contacts waiting in one or more queues for an agent to become available for assignment.
In some typical contact centers, contacts are assigned to agents ordered based on time of arrival, and agents receive contacts ordered based on the time when those agents became available. This strategy may be referred to as a “first-in, first-out”, “FIFO”, or “round-robin” strategy. In some contact centers, contacts or agents are assigned into different “skill groups” or “queues” prior to applying a FIFO assignment strategy within each such skill group or queue. These “skill queues” may also incorporate strategies for prioritizing individual contacts or agents within a baseline FIFO ordering. For example, a high-priority contact may be given a queue position ahead of other contacts who arrived at an earlier time, or a high-performing agent may be ordered ahead of other agents who have been waiting longer for their next call. Regardless of such variations in forming one or more queues of callers or one or more orderings of available agents, contact centers typically apply FIFO to the queues or other orderings. Once such a FIFO strategy has been established, assignment of contacts to agents is automatic, with the contact center assigning the first contact in the ordering to the next available agent, or assigning the first agent in the ordering to the next arriving contact. In the contact center industry, the process of contact and agent distribution among skill queues, prioritization and ordering within skill queues, and subsequent FIFO assignment of contacts to agents is typically managed by a system referred to as an “Automatic Call Distributor” (“ACD”).
Some contact centers may use a “performance-based routing” or “PBR” approach to ordering the queue of available agents or, occasionally, contacts. For example, when a contact arrives at a contact center with a plurality of available agents, the ordering of agents available for assignment to that contact would be headed by the highest-performing available agent (e.g., the available agent with the highest sales conversion rate, the highest customer satisfaction scores, the shortest average handle time, the highest performing agent for the particular contact profile, the highest customer retention rate, the lowest customer retention cost, the highest rate of first-call resolution). PBR ordering strategies attempt to maximize the expected outcome of each contact—agent interaction but do so typically without regard for utilizing agents in a contact center uniformly. Consequently, higher-performing agents may receive noticeably more contacts and feel overworked, while lower-performing agents may receive fewer contacts and idle longer, potentially reducing their opportunities for training and improvement as well as potentially reducing their compensation.
In view of the foregoing, it may be understood that there is a need for a system that both attempts to balance the utilization of agents while improving contact center performance beyond what FIFO strategies deliver.
Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for pairing in a contact center comprising ordering one or more contacts, ordering one or more agents, comparing, by at least one processor, a first difference in ordering between a first contact and a first agent in a first pair with a second difference in ordering between a second contact and a second agent in a second pair, and selecting, by the at least one processor, the first pair or the second pair for connection based on the comparing, wherein the first contact and the second contact may be different or the first agent and the second agent may be different.
In accordance with other aspects of this particular embodiment, selecting the first pair or the second pair based on the comparing may further comprise applying, by the at least one processor, a diagonal strategy to the orderings.
In accordance with other aspects of this particular embodiment, the ordering of one or more contacts or the ordering of one or more agents may be expressed as percentiles.
In accordance with other aspects of this particular embodiment, the ordering of one or more contacts or the ordering of one or more agents may be expressed as percentile ranges.
In accordance with other aspects of this particular embodiment, each of the one or more contacts or each of the one or more agents may be assigned a percentile within each contact or agent's respective percentile range.
In accordance with other aspects of this particular embodiment, an assigned percentile may be a midpoint of a percentile range.
In accordance with other aspects of this particular embodiment, an assigned percentile may be a random percentile of a percentile range.
In accordance with other aspects of this particular embodiment, the method may further comprise determining, by the at least one processor, a bandwidth for each contact type of the first and second contacts proportionate to a frequency at which contacts of each contact type become available for assignment.
In accordance with other aspects of this particular embodiment, the method may further comprise targeting, by the at least one processor, a balanced agent utilization.
In accordance with other aspects of this particular embodiment, targeting the balanced agent utilization may further comprise determining, by the at least one processor, proportional bandwidth for each of the one or more agents.
In accordance with other aspects of this particular embodiment, a selected agent of the selected pair may not be any of an agent lagging in a fairness metric, an agent rated highest in a performance metric, an agent rated highest in a performance metric for a particular contact type, an agent previously assigned to a contact of the selected pair, a sequentially labeled agent, or a randomly selected agent.
In accordance with other aspects of this particular embodiment, a selected contact of the selected pairing may not be any of a contact at a head of a queue in the contact center, a longest-waiting contact, a highest-priority contact, or a randomly selected contact.
In accordance with other aspects of this particular embodiment, the selected one of the first pair and the second pair may comprise a worse expected instant outcome than the other of the first pair and the second pair.
In accordance with other aspects of this particular embodiment, a higher-ordered agent may remain available for subsequent assignment to a similarly higher-ordered contact, or a higher-ordered contact may remain available for subsequent assignment to a similarly higher-ordered agent.
In another particular embodiment, the techniques may be realized as system for pairing in a contact center system comprising at least one processor, wherein the at least one processor may be configured to perform the above-described method.
In another particular embodiment, the techniques may be realized as an article of manufacture for pairing in a contact center system comprising a non-transitory processor readable medium and instructions stored on the medium, wherein the instructions may be configured to be readable from the medium by at least one processor and thereby may cause the at least one processor to operate so as to perform the above-described method.
The present disclosure will now be described in more detail with reference to particular embodiments thereof as shown in the accompanying drawings. While the present disclosure is described below with reference to particular embodiments, it should be understood that the present disclosure is not limited thereto. Those of ordinary skill in the art having access to the teachings herein will recognize additional implementations, modifications, and embodiments, as well as other fields of use, which are within the scope of the present disclosure as described herein, and with respect to which the present disclosure may be of significant utility.
In order to facilitate a fuller understanding of the present disclosure, reference is now made to the accompanying drawings, in which like elements are referenced with like numerals. These drawings should not be construed as limiting the present disclosure, but are intended to be illustrative only.
A typical contact center algorithmically assigns contacts arriving at the contact center to agents available to handle those contacts. At times, the contact center may be in an “L1 state” and have agents available and waiting for assignment to inbound or outbound contacts (e.g., telephone calls, Internet chat sessions, email). At other times, the contact center may be in an “L2 state” and have contacts waiting in one or more queues for an agent to become available for assignment. Such L2 queues could be inbound, outbound, or virtual queues. Contact center systems implement various strategies for assigning contacts to agents in both L1 and L2 states.
The present disclosure generally relates to contact center systems, traditionally referred to as “Automated Call Distribution” (“ACD”) systems. Typically, such an ACD process is subsequent to an initial “Skills-based Routing” (“SBR”) process that serves to allocate contacts and agents among skill queues within the contact center. Such skill queues may distinguish contacts and agents based on language capabilities, customer needs, or agent proficiency at a particular set of tasks.
The most common traditional assignment method within a queue is “First-In, First-Out” or “FIFO” assignment wherein the longest-waiting contact is assigned to the longest-waiting agent. Some contact centers implement “Performance-based Routing” (“PBR”) wherein the longest waiting contact is assigned to the highest performing available agent. Variations of both such assignment methods commonly exist. For example, FIFO may select the least utilized available agent rather than the longest waiting agent. More generally, FIFO may select an agent most lagging in a particular metric or metrics. FIFO may also order queues of contacts where higher priority contact types may be positioned in a queue ahead of lower priority contact types. Similarly, PBR may be modified such that agent performance rankings may be altered depending on the type of contact pending assignment (e.g., Bala et al., U.S. Pat. No. 7,798,876). PBR may also be modified to avoid an extreme unbalancing of agent utilization by setting limits on maximum or minimum agent utilization relative to peers.
Variations of FIFO typically target “fairness” inasmuch as they are designed to balance the allocation of contacts to agents over time. PBR adopts a different approach in which the allocation of contacts to agents is purposefully skewed to increase the utilization of higher-performing agents and reduce the utilization of lower-performing agents. PBR may do so despite potential negative impacts on morale and productivity over time resulting from fatigue in over-utilized agents and inadequate opportunity for training and compensation in under-utilized agents.
Other eclectic assignment strategies are uncommonly, if ever, practiced. For instance, contacts may be randomly assigned to agents, irrespective of time of arrival, agent performance, or other variables. Alternatively, contact centers may seek to assign contacts to agents with whom they have had a recent previous interaction. Additionally, agents in an L1 scenario may be selected sequentially based on a “labelling strategy” in which a recurrent algorithmic ordering of agent assignment is predefined (“Agent 1”, “Agent 2”, “Agent 3”, “Agent 1”, “Agent 2”, “Agent 3”, “Agent 1”, etc.).
In particular, the present disclosure refers to optimized strategies for assigning contacts to agents that improve upon traditional assignment methods. The present disclosure refers to such strategies as “Behavioral Pairing” or “BP” strategies. Behavioral Pairing targets balanced utilization of agents within queues (e.g., skill queues) while simultaneously improving overall contact center performance potentially beyond what FIFO or PBR methods will achieve in practice. This is a remarkable achievement inasmuch as BP acts on the same contacts and same agents as FIFO or PBR methods, approximately balancing the utilization of agents as FIFO provides, while improving overall contact center performance beyond what either FIFO or PBR provide in practice.
BP improves performance by assigning agent and contact pairs in a fashion that takes into consideration the assignment of potential subsequent agent and contact pairs such that when the benefits of all assignments are aggregated they may exceed those of FIFO and PBR strategies. In some cases, BP results in instant contact and agent pairings that may be the reverse of what FIFO or PBR would indicate. For example, in an instant case BP might select the shortest-waiting contact or the lowest-performing available agent. BP respects “posterity” inasmuch as the system allocates contacts to agents in a fashion that inherently forgoes what may be the highest-performing selection at the instant moment if such a decision increases the probability of better contact center performance over time.
In FIFO Strategy 110, all four possible outcomes are equally likely. For example, if a 60% Contact arrives with both the 30% Agent and the 50% Agent available, either agent might be selected with equal probability based on, for example, which agent has been waiting longer or has been utilized less. Similarly, if the 30% Agent comes available with both a 60% Contact and a 20% Contact in Queue 100, either contact may be selected with equal probability and equal priority based on, for example, which contact has been waiting longer (e.g., earlier time of arrival). Therefore, in FIFO Strategy 100, the overall expected sales output of Queue 100 would be (6%+10%+18%+30%)/4=16%.
In PBR Strategy 120, the 50% Agent is preferentially assigned contacts whenever the 50% Agent is available. Therefore, PBR Strategy 120 would achieve its highest overall expected sales output in the case where the 50% Agent is always available upon arrival of a contact. This peak expectation is (10%+30%)/2=20%. However, this peak expectation is unlikely to be achieved in practice. For example, contacts may arrive while the 50% Agent is engaged and the 30% Agent is available. In this instance, PBR Strategy 120 would assign the contact to the 30% Agent. Thus, PBR performance in practice will approximate the performance of FIFO Strategy 110 in proportion to the percentage of instances in which non-preferred assignments occur. In many cases, multiple contacts may be waiting in Queue 100 (L2 state), and there may not be an opportunity to preferentially select the 50% Agent. If Queue 100 were persistently in an L2 state, PBR Strategy 120 would be expected to perform at the same rate as FIFO Strategy 110. In fact, if half the time Queue 100 were in an L2 state, and a further quarter of the time 50% Agent was unavailable because 50% Agent had been preferentially selected, then PBR Strategy 120 would still offer no expected improvement over FIFO Strategy 110. In Queue 100, PBR Strategy 120 only offers significant performance benefit over FIFO Strategy 110 when Queue 100 is in an L1 state for an extended period and, within that L1 state, there exists choice between the 50% Agent and the 30% Agent. However, in this case Queue 100 may be “overstaffed” inasmuch as it would require significant idle labor for potentially minor benefit. Accordingly, in practice PBR may be ineffective at substantially improving performance over FIFO.
In BP Strategy 130, a 20% Contact is preferentially assigned to the 30% Agent, and a60% Contact is preferentially assigned to the 50% Agent. Therefore, the peak expectation of Queue 100 performance under BP Strategy 130 is (6%+30%)/2=18%. Importantly, this peak expectation does not erode like PBR Strategy 120 in an L2 state. Hypothetically, if there was an arbitrarily long queue of contacts in a persistent L2 state, BP Strategy 130 would in fact operate at peak expected performance because whenever the 30% Agent became available there would be a 60% Contact pending assignment, and whenever the 50% Agent became available there would be a 20% Contact pending assignment.
Even though there are only two agents maximally available in Queue 100, BP Strategy 130 may still outperform PBR Strategy 120 in an L1 state. For example, if the 50% Agent was occupied half of the time, PBR Strategy 120 would deliver no benefit as the other half of the time PBR Strategy 120 would be forced to select the 30% Agent. However, in an L1 state under BP Strategy 130, availability of a 20% Contact would trigger use of the lower-performing 30% Agent in the instant pairing, thereby preserving the higher-performing 50% Agent for subsequent assignment. Thereafter, in the next iteration, if a 60% Contact became available for assignment then the assignment of the preserved 50% Agent would result in delivering BP Strategy 130's expected peak overall performance of 18%. This should occur approximately half the time, resulting in a significant improvement over both FIFO and PBR assignment strategies. When a pairing is to be made, the available agents may be ordered, and the available contacts may be ordered. In an L1 state, in which only one contact is available for assignment to an agent, the ordering of the agent is trivial. Similarly, in an L2 state, in which only one agent is available for assignment to a contact, the ordering of the contact is trivial.
In
In
Similarly, the agents have been ordered and percentiled into a 0.25 Agent and a 0.75 Agent. Contacts are then positioned on a first axis (in this instance, the Y-axis, or the rows of a grid) in order from lowest percentile midpoint to highest, and agents are similarly positioned on a second axis (in this instance, the X-axis, or the columns of the grid). Under such a structure, the diagonal strategy of assigning pairings of contacts with agents with the closest percentile midpoints is an improved mechanism of structuring a BP strategy, in this case BP Strategy 330. Under BP Strategy 330, the expected peak performance of Queue 300 would be (12%+32%)/2=22%, which exceeds FIFO Strategy 310's expected performance of (12%+16%+24%+32%)/4=21%, and may potentially exceed PBR Strategy 320's expected peak performance of (16%+32%)/2=24%.
While
Queue 400 has three equally available agents: a “0.166 Agent”, a “0.500 Agent”, and a “0.833 Agent”. The 0.166 Agent occupies the midpoint of percentile range 0% to 33.3% and therefore is the lowest-ordered agent according to some metric, while correspondingly 0.833 Agent occupies the midpoint of percentile range 66.6% to 100% and is therefore the highest-ordered agent. Agent 0.500 is the middle-ordered agent occupying the percentile range of 33.3% to 66.6%.
Inefficient BP Strategy 410 would strictly extend the strategy in
Efficient BP Strategy 510 improves on Inefficient BP Strategy 410 by seeking to most closely approximate a diagonal strategy. However, unlike the simplified case of Queue 300 which by example was able to precisely align contact and agent percentiles, Efficient BP Strategy 510 returns to targeting a balanced utilization of agents by relaxing the assumption of a one-to-one correspondence between contact types and agents and instead establishing a correspondence between ranges of percentiles.
In some embodiments, each contact may be assigned a percentile within the percentile range of each contact's type. These percentiles may be assigned randomly. In this scenario, some of the lowest-ordered, highest-frequency contacts (0% to 48% Contacts) may be preferably assigned to the lowest-ordered 0.166 Agent, while others of the lowest-ordered, highest-frequency contacts may be preferably assigned to the middle-ordered 0.500 Agent. Similarly, some of the middle-ordered, middle-frequency contacts (48% to 82% Contacts) may be preferably assigned to the middle-ordered 0.500 Agent, while others may be preferably assigned to the highest-performing 0.833 Agent.
For example, if a 0% to 48% Contact arrives, it may receive a random percentile of 10% (0.10). Assuming all of the agents in Queue 500 are available for assignment, the diagonal strategy would preferably assign this 0% to 48% Contact to the 0.166 Agent. Conceptually, a contact assigned a percentile of 10% falls within the percentile range (or “bandwidth”) accorded to the 0.166 Agent occupying the percentile range of 0% to 33.3%.
The next contact to arrive may be another a 0% to 48% Contact. In this instance, the contact may receive a random percentile of 42% (0.42). Again, assuming all of the agents in queue 500 are available for assignment, the diagonal strategy would preferably assign this 0% to 48% Contact to the 0.500 Agent, which occupies the percentile range of 33.3% to 66.6%.
Under Efficient BP Strategy 510, each of the three agents is expected to receive approximately one-third of all contacts over time, so the lowest-ordered 0.166 Agent is no longer over-utilized relative to the other agents, and the highest-ordered 0.833 Agent is no longer under-utilized relative to the other agents, as under Inefficient BP Strategy 410 (
At block 610, a percentile (or n-tile, quantile, percentile range, bandwidth, or other type of “score” or range of scores, etc.) may be determined for each available contact. For situations in which contacts are waiting on hold in a queue, percentiles may be determined for each of the contacts waiting on hold in the queue. For situations in which contacts are not waiting on hold in a queue, a percentile may be assigned to the next contact to arrive at the contact center. The percentiles may be bounded by a range of percentiles defined for a particular type or group of contacts based on information about the contact. The percentile bounds or ranges may be based on a frequency distribution or other metric for the contact types. The percentile may be randomly assigned within the type's percentile range.
In some embodiments, percentiles may be ordered according to a particular metric or combination of metrics to be optimized in the contact center, and a contact determined to have a relatively high percentile may be considered to be a “higher-value” contact for the contact center inasmuch as these contacts are more likely to contribute to a higher overall performance in the contact center. For example, a relatively high-percentile contact may have a relatively high likelihood of making a purchase.
In some embodiments, a percentile may be determined for a contact at the time the contact arrives at the contact center. In other embodiments, a percentile may be determined for the contact at a later point in time, such as when the contact arrives at a particular skill queue or ACD system, or when a request for a pairing is made.
After a percentile has been determined for each contact available for pairing, behavioral pairing method 600 may proceed to block 620. In some embodiments, block 620 may be performed prior to, or simultaneously with, block 610.
At block 620, a percentile may be determined for each available agent. For situations in which agents are idle, waiting for contacts to arrive, percentiles may be determined for each of the idle agents. For situations in which agents for a queue are all busy, a percentile may be determined to the next agent to become available. The percentiles may be bounded by a range of percentiles (e.g., “bandwidth”) defined based on all of the agents assigned to a queue (e.g., a skill queue) or only the available agents assigned to a particular queue. In some embodiments, the bounds or ranges of percentiles may be based on a desired agent utilization (e.g., for fairness, efficiency, or performance).
In some embodiments, agent percentiles may be ordered according to a particular metric or combination of metrics to be optimized in the contact center, and an agent determined to have a relatively high percentile may be considered to be a higher-performing agent for the contact center. For example, a relatively high-percentile agent may have a relatively high likelihood of making a sale.
In some embodiments, an agent's percentile may be determined at the time the agent becomes available within the contact center. In other embodiments, a percentile may be determined at a later point in time, such as when a request for a pairing is made.
After a percentile has been determined for each available agent and contact, behavioral pairing method 600 may proceed to block 630.
At block 630, a pair of an available contact and an available agent may be determined based on the percentiles determined for each available contact at block 610 and for each available agent at block 620. In some embodiments, the pair may be determined according to a diagonal strategy, in which contacts and agents with more similar percentiles (or the most similar percentiles) may be selected for pairing. For example, a behavioral pairing module may select a contact-agent pairing with the smallest absolute difference between the contact's score and the agent's score.
In some situations, multiple agents may be idle when a contact arrives (an L1 state). Under BP, the newly available contact may be paired with a selected one of the available agents that has a score more similar to the contact's score than other available agents. In other situations, multiple contacts may be waiting in a queue when an agent becomes available (an L2 state). Under BP, the newly available agent may be paired with a selected one of the contacts waiting in the queue that has a percentile more similar to the agent's percentile than other contacts waiting in the queue.
In some situations, selecting a pairing based on similarity of scores may result in selecting an instant pairing that might not be the highest performing instant pairing, but rather increases the likelihood of better future pairings.
After a pairing has been determined at block 630, behavioral pairing method 600 may proceed to block 640. At block 640, modules within the contact center system may cause the contact and agent of the contact-agent pair to be connected with one another. For example, a behavioral pairing module may indicate that an ACD system or other routing device may distribute a particular contact to a particular agent.
After connecting the contact and agent at block 640, behavioral pairing method 600 may end. In some embodiments, behavioral pairing method 600 may return to block 630 for determining one or more additional pairings (not shown). In other embodiments, behavioral pairing method 600 may return to block 610 or block 620 to determine (or re-determine) percentiles for available contacts or agents (not shown).
As shown in
The central switch 710 may not be necessary if there is only one contact center, or if there is only one PBX/ACD routing component, in the contact center system 700. If more than one contact center is part of the contact center system 700, each contact center may include at least one contact center switch (e.g., contact center switches 720A and 720B). The contact center switches 720A and 720B may be communicatively coupled to the central switch 710.
Each contact center switch for each contact center may be communicatively coupled to a plurality (or “pool”) of agents. Each contact center switch may support a certain number of agents (or “seats”) to be logged in at one time. At any given time, a logged-in agent may be available and waiting to be connected to a contact, or the logged-in agent may be unavailable for any of a number of reasons, such as being connected to another contact, performing certain post-call functions such as logging information about the call, or taking a break.
In the example of
The contact center system 700 may also be communicatively coupled to an integrated service from, for example, a third party vendor. In the example of
Behavioral pairing module 740 may receive information from a switch (e.g., contact center switch 720A) about agents logged into the switch (e.g., agents 730A and 730B) and about incoming contacts via another switch (e.g., central switch 710) or, in some embodiments, from a network (e.g., the Internet or a telecommunications network) (not shown).
The behavioral pairing module 740 may process this information and to determine which contacts should be paired (e.g., matched, assigned, distributed, routed) with which agents. For example, multiple agents are available and waiting for connection to a contact (L1 state), and a contact arrives at the contact center via a network or central switch. As explained above, without the behavioral pairing module 740, a contact center switch will typically automatically distribute the new contact to whichever available agent has been waiting the longest amount of time for an agent under a “fair” FIFO strategy, or whichever available agent has been determined to be the highest-performing agent under a PBR strategy.
With a behavioral pairing module 740, contacts and agents may be given scores (e.g., percentiles or percentile ranges/bandwidths) according to a pairing model or other artificial intelligence data model, so that a contact may be matched, paired, or otherwise connected to a preferred agent.
In an L2 state, multiple contacts are available and waiting for connection to an agent, and an agent becomes available. These contacts may be queued in a contact center switch such as a PBX or ACD device (“PBX/ACD”). Without the behavioral pairing module 740, a contact center switch will typically connect the newly available agent to whichever contact has been waiting on hold in the queue for the longest amount of time as in a “fair” FIFO strategy or a PBR strategy when agent choice is not available. In some contact centers, priority queuing may also be incorporated, as previously explained.
With a behavioral pairing module 740 in an L2 scenario, as in the L1 state described above, contacts and agents may be given percentiles (or percentile ranges/bandwidths, etc.) according to, for example, a model, such as an artificial intelligence model, so that an agent coming available may be matched, paired, or otherwise connected to a preferred contact.
At this point it should be noted that behavioral pairing in a contact center system in accordance with the present disclosure as described above may involve the processing of input data and the generation of output data to some extent. This input data processing and output data generation may be implemented in hardware or software. For example, specific electronic components may be employed in a behavioral pairing module or similar or related circuitry for implementing the functions associated with behavioral pairing in a contact center system in accordance with the present disclosure as described above. Alternatively, one or more processors operating in accordance with instructions may implement the functions associated with behavioral pairing in a contact center system in accordance with the present disclosure as described above. If such is the case, it is within the scope of the present disclosure that such instructions may be stored on one or more non-transitory processor readable storage media (e.g., a magnetic disk or other storage medium), or transmitted to one or more processors via one or more signals embodied in one or more carrier waves.
The present disclosure is not to be limited in scope by the specific embodiments described herein. Indeed, other various embodiments of and modifications to the present disclosure, in addition to those described herein, will be apparent to those of ordinary skill in the art from the foregoing description and accompanying drawings. Thus, such other embodiments and modifications are intended to fall within the scope of the present disclosure. Further, although the present disclosure has been described herein in the context of at least one particular implementation in at least one particular environment for at least one particular purpose, those of ordinary skill in the art will recognize that its usefulness is not limited thereto and that the present disclosure may be beneficially implemented in any number of environments for any number of purposes. Accordingly, the claims set forth below should be construed in view of the full breadth and spirit of the present disclosure as described herein.
This application is a continuation of U.S. patent application Ser. No. 16/926,404, filed Jul. 10, 2020, which is a continuation of U.S. patent application Ser. No. 16/035,428, filed Jul. 13, 2018, now U.S. Pat. No. 10,721,357, issued Jul. 21, 2020, which is a continuation of U.S. patent application Ser. No. 15/000,797, filed Jan. 19, 2016, now U.S. Pat. No. 10,051,124, issued Aug. 14, 2018, which is a continuation of U.S. patent application Ser. No. 14/871,658, filed Sep. 30, 2015, now U.S. Pat. No. 9,300,802, issued Mar. 29, 2016, which is a continuation-in-part of U.S. patent application Ser. No. 12/021,251, filed Jan. 28, 2008, now U.S. Pat. No. 9,712,679, issued Jul. 18, 2017, and is a continuation-in-part of U.S. patent application Ser. No. 14/530,058, filed Oct. 31, 2014, now U.S. Pat. No. 9,277,055, issued Mar. 1, 2016, which is a continuation of U.S. patent application Ser. No. 13/843,724, filed Mar. 15, 2013, now U.S. Pat. No. 8,879,715, issued Nov. 4, 2014, which claims priority to U.S. Provisional Patent Application No. 61/615,788, filed Mar. 26, 2012, U.S. Provisional Patent Application No. 61/615,779, filed Mar. 26, 2012, and U.S. Provisional Patent Application No. 61/615,772, filed Mar. 26, 2012, each of which is hereby incorporated by reference in its entirety as if fully set forth herein.
Number | Name | Date | Kind |
---|---|---|---|
5155763 | Bigus et al. | Oct 1992 | A |
5206903 | Kohler et al. | Apr 1993 | A |
5311572 | Friedes | May 1994 | A |
5327490 | Cave | Jul 1994 | A |
5537470 | Lee | Jul 1996 | A |
5702253 | Bryce et al. | Dec 1997 | A |
5825869 | Brooks et al. | Oct 1998 | A |
5903641 | Tonisson | May 1999 | A |
5907601 | David et al. | May 1999 | A |
5926538 | Deryugin et al. | Jul 1999 | A |
6044355 | Crockett et al. | Mar 2000 | A |
6044368 | Powers | Mar 2000 | A |
6049603 | Schwartz et al. | Apr 2000 | A |
6052460 | Fisher et al. | Apr 2000 | A |
6064731 | Flockhart et al. | May 2000 | A |
6088444 | Walker et al. | Jul 2000 | A |
6163607 | Bogart et al. | Dec 2000 | A |
6192122 | Flockhart | Feb 2001 | B1 |
6222919 | Hollatz et al. | Apr 2001 | B1 |
6292555 | Okamoto | Sep 2001 | B1 |
6324282 | McIllwaine et al. | Nov 2001 | B1 |
6333979 | Bondi et al. | Dec 2001 | B1 |
6389132 | Price | May 2002 | B1 |
6389400 | Bushey et al. | May 2002 | B1 |
6408066 | Andruska et al. | Jun 2002 | B1 |
6411687 | Bohacek et al. | Jun 2002 | B1 |
6424709 | Doyle et al. | Jul 2002 | B1 |
6434230 | Gabriel | Aug 2002 | B1 |
6496580 | Chack | Dec 2002 | B1 |
6504920 | Okon et al. | Jan 2003 | B1 |
6519335 | Bushnell | Feb 2003 | B1 |
6526135 | Paxson | Feb 2003 | B1 |
6528135 | Egret et al. | Mar 2003 | B1 |
6535600 | Fisher et al. | Mar 2003 | B1 |
6535601 | Flockhart et al. | Mar 2003 | B1 |
6553113 | Dhir et al. | Apr 2003 | B1 |
6570980 | Baruch | May 2003 | B1 |
6587556 | Judkins et al. | Jul 2003 | B1 |
6603854 | Judkins et al. | Aug 2003 | B1 |
6639976 | Shellum et al. | Oct 2003 | B1 |
6661889 | Flockhart et al. | Dec 2003 | B1 |
6704410 | McFarlane et al. | Mar 2004 | B1 |
6707904 | Judkins et al. | Mar 2004 | B1 |
6714643 | Gargeya et al. | Mar 2004 | B1 |
6744878 | Komissarchik et al. | Jun 2004 | B1 |
6763104 | Judkins et al. | Jul 2004 | B1 |
6774932 | Ewing et al. | Aug 2004 | B1 |
6775378 | Villena et al. | Aug 2004 | B1 |
6798876 | Bala | Sep 2004 | B1 |
6829348 | Schroeder et al. | Dec 2004 | B1 |
6832203 | Villena et al. | Dec 2004 | B1 |
6859529 | Duncan et al. | Feb 2005 | B2 |
6895083 | Bers et al. | May 2005 | B1 |
6922466 | Peterson et al. | Jul 2005 | B1 |
6937715 | Delaney | Aug 2005 | B2 |
6956941 | Duncan et al. | Oct 2005 | B1 |
6970821 | Shambaugh et al. | Nov 2005 | B1 |
6978006 | Polcyn | Dec 2005 | B1 |
7023979 | Wu et al. | Apr 2006 | B1 |
7039166 | Peterson et al. | May 2006 | B1 |
7050566 | Becerra et al. | May 2006 | B2 |
7050567 | Jensen | May 2006 | B1 |
7062031 | Becerra et al. | Jun 2006 | B2 |
7068775 | Lee | Jun 2006 | B1 |
7092509 | Mears et al. | Aug 2006 | B1 |
7103172 | Brown et al. | Sep 2006 | B2 |
7158628 | McConnell et al. | Jan 2007 | B2 |
7184540 | Dezonno et al. | Feb 2007 | B2 |
7209549 | Reynolds et al. | Apr 2007 | B2 |
7231032 | Nevman et al. | Jun 2007 | B2 |
7231034 | Rikhy et al. | Jun 2007 | B1 |
7236584 | Torba | Jun 2007 | B2 |
7245716 | Brown et al. | Jul 2007 | B2 |
7245719 | Kawada et al. | Jul 2007 | B2 |
7266251 | Rowe | Sep 2007 | B2 |
7269253 | Wu et al. | Sep 2007 | B1 |
7353388 | Gilman et al. | Apr 2008 | B1 |
7372952 | Wu et al. | May 2008 | B1 |
7398224 | Cooper | Jul 2008 | B2 |
7593521 | Becerra et al. | Sep 2009 | B2 |
7676034 | Wu et al. | Mar 2010 | B1 |
7725339 | Aykin | May 2010 | B1 |
7734032 | Kiefhaber et al. | Jun 2010 | B1 |
7798876 | Mix | Sep 2010 | B2 |
7826597 | Berner et al. | Nov 2010 | B2 |
7864944 | Khouri et al. | Jan 2011 | B2 |
7899177 | Bruening et al. | Mar 2011 | B1 |
7916858 | Heller et al. | Mar 2011 | B1 |
7940917 | Lauridsen et al. | May 2011 | B2 |
7961866 | Boutcher et al. | Jun 2011 | B1 |
7995717 | Conway et al. | Aug 2011 | B2 |
8000989 | Kiefhaber et al. | Aug 2011 | B1 |
8010607 | McCormack et al. | Aug 2011 | B2 |
8094790 | Conway et al. | Jan 2012 | B2 |
8126133 | Everingham et al. | Feb 2012 | B1 |
8140441 | Cases et al. | Mar 2012 | B2 |
8175253 | Knott et al. | May 2012 | B2 |
8229102 | Knott et al. | Jul 2012 | B2 |
8249245 | Jay et al. | Aug 2012 | B2 |
8295471 | Spottiswoode et al. | Oct 2012 | B2 |
8300798 | Wu et al. | Oct 2012 | B1 |
8306212 | Arora | Nov 2012 | B2 |
8359219 | Chishti et al. | Jan 2013 | B2 |
8433597 | Chishti et al. | Apr 2013 | B2 |
8472611 | Chishti | Jun 2013 | B2 |
8565410 | Chishti et al. | Oct 2013 | B2 |
8634542 | Spottiswoode et al. | Jan 2014 | B2 |
8644490 | Stewart | Feb 2014 | B2 |
8670548 | Xie et al. | Mar 2014 | B2 |
8699694 | Chishti et al. | Apr 2014 | B2 |
8712821 | Spottiswoode | Apr 2014 | B2 |
8718271 | Spottiswoode | May 2014 | B2 |
8724797 | Chishti et al. | May 2014 | B2 |
8731178 | Chishti et al. | May 2014 | B2 |
8737595 | Chishti et al. | May 2014 | B2 |
8750488 | Spottiswoode et al. | Jun 2014 | B2 |
8761380 | Kohler et al. | Jun 2014 | B2 |
8781100 | Spottiswoode et al. | Jul 2014 | B2 |
8781106 | Afzal | Jul 2014 | B2 |
8792630 | Chishti et al. | Jul 2014 | B2 |
8824658 | Chishti | Sep 2014 | B2 |
8831203 | Chang et al. | Sep 2014 | B2 |
8831207 | Agarwal | Sep 2014 | B1 |
8855292 | Brunson | Oct 2014 | B1 |
8856869 | Brinskelle | Oct 2014 | B1 |
8879715 | Spottiswoode et al. | Nov 2014 | B2 |
8903079 | Xie et al. | Dec 2014 | B2 |
8913736 | Kohler et al. | Dec 2014 | B2 |
8929537 | Chishti et al. | Jan 2015 | B2 |
8938063 | Hackbarth et al. | Jan 2015 | B1 |
8995647 | Li et al. | Mar 2015 | B2 |
9020137 | Chishti et al. | Apr 2015 | B2 |
9025757 | Spottiswoode et al. | May 2015 | B2 |
9215323 | Chishti | Dec 2015 | B2 |
9277055 | Spottiswoode et al. | Mar 2016 | B2 |
9300802 | Chishti | Mar 2016 | B1 |
9426296 | Chishti et al. | Aug 2016 | B2 |
9712676 | Chishti | Jul 2017 | B1 |
9712679 | Chishti et al. | Jul 2017 | B2 |
9781269 | Chishti et al. | Oct 2017 | B2 |
9787841 | Chishti et al. | Oct 2017 | B2 |
9917949 | Chishti | Mar 2018 | B1 |
9930180 | Kan et al. | Mar 2018 | B1 |
9942405 | Kan et al. | Apr 2018 | B1 |
RE46986 | Chishti et al. | Aug 2018 | E |
10116800 | Kan et al. | Oct 2018 | B1 |
10135987 | Chishti et al. | Nov 2018 | B1 |
RE47201 | Chishti et al. | Jan 2019 | E |
10284727 | Kan et al. | May 2019 | B2 |
10404861 | Kan et al. | Sep 2019 | B2 |
20010032120 | Stuart et al. | Oct 2001 | A1 |
20020018554 | Jensen et al. | Feb 2002 | A1 |
20020046030 | Haritsa et al. | Apr 2002 | A1 |
20020059164 | Shtivelman | May 2002 | A1 |
20020082736 | Lech et al. | Jun 2002 | A1 |
20020110234 | Walker et al. | Aug 2002 | A1 |
20020111172 | DeWolf et al. | Aug 2002 | A1 |
20020131399 | Philonenko | Sep 2002 | A1 |
20020138285 | DeCotiis et al. | Sep 2002 | A1 |
20020143599 | Nourbakhsh et al. | Oct 2002 | A1 |
20020161765 | Kundrot et al. | Oct 2002 | A1 |
20020184069 | Kosiba et al. | Dec 2002 | A1 |
20020196845 | Richards et al. | Dec 2002 | A1 |
20030002653 | Uckun | Jan 2003 | A1 |
20030059029 | Mengshoel et al. | Mar 2003 | A1 |
20030081757 | Mengshoel et al. | May 2003 | A1 |
20030095652 | Mengshoel et al. | May 2003 | A1 |
20030169870 | Stanford | Sep 2003 | A1 |
20030174830 | Boyer et al. | Sep 2003 | A1 |
20030217016 | Pericle | Nov 2003 | A1 |
20040028211 | Culp et al. | Feb 2004 | A1 |
20040057416 | McCormack | Mar 2004 | A1 |
20040096050 | Das et al. | May 2004 | A1 |
20040098274 | Dezonno et al. | May 2004 | A1 |
20040101127 | Dezonno et al. | May 2004 | A1 |
20040109555 | Williams | Jun 2004 | A1 |
20040117299 | Algiene et al. | Jun 2004 | A1 |
20040133434 | Szlam et al. | Jul 2004 | A1 |
20040210475 | Starnes et al. | Oct 2004 | A1 |
20040230438 | Pasquale et al. | Nov 2004 | A1 |
20040267816 | Russek | Dec 2004 | A1 |
20050013428 | Walters | Jan 2005 | A1 |
20050043986 | McConnell | Feb 2005 | A1 |
20050047581 | Shaffer et al. | Mar 2005 | A1 |
20050047582 | Shaffer et al. | Mar 2005 | A1 |
20050071223 | Jain et al. | Mar 2005 | A1 |
20050129212 | Parker | Jun 2005 | A1 |
20050135593 | Becerra et al. | Jun 2005 | A1 |
20050135596 | Zhao | Jun 2005 | A1 |
20050187802 | Koeppel | Aug 2005 | A1 |
20050195960 | Shaffer et al. | Sep 2005 | A1 |
20050286709 | Horton et al. | Dec 2005 | A1 |
20060062374 | Gupta | Mar 2006 | A1 |
20060098803 | Bushey et al. | May 2006 | A1 |
20060110052 | Finlayson | May 2006 | A1 |
20060124113 | Roberts | Jun 2006 | A1 |
20060184040 | Keller et al. | Aug 2006 | A1 |
20060222164 | Contractor et al. | Oct 2006 | A1 |
20060233346 | McIlwaine et al. | Oct 2006 | A1 |
20060256955 | Laughlin et al. | Nov 2006 | A1 |
20060262918 | Karnalkar et al. | Nov 2006 | A1 |
20060262922 | Margulies et al. | Nov 2006 | A1 |
20070036323 | Travis | Feb 2007 | A1 |
20070071222 | Flockhart et al. | Mar 2007 | A1 |
20070116240 | Foley et al. | May 2007 | A1 |
20070121602 | Sin et al. | May 2007 | A1 |
20070121829 | Tai et al. | May 2007 | A1 |
20070136342 | Singhai et al. | Jun 2007 | A1 |
20070154007 | Bernhard | Jul 2007 | A1 |
20070174111 | Anderson et al. | Jul 2007 | A1 |
20070198322 | Bourne et al. | Aug 2007 | A1 |
20070211881 | Parker-Stephen | Sep 2007 | A1 |
20070219816 | Van Luchene et al. | Sep 2007 | A1 |
20070255611 | Mezo et al. | Nov 2007 | A1 |
20070274502 | Brown | Nov 2007 | A1 |
20080002823 | Fama et al. | Jan 2008 | A1 |
20080004933 | Gillespie | Jan 2008 | A1 |
20080008309 | Dezonno et al. | Jan 2008 | A1 |
20080046386 | Pieraccinii et al. | Feb 2008 | A1 |
20080065476 | Klein et al. | Mar 2008 | A1 |
20080095355 | Mahalaha et al. | Apr 2008 | A1 |
20080118052 | Houmaidi et al. | May 2008 | A1 |
20080144803 | Jaiswal et al. | Jun 2008 | A1 |
20080152122 | Idan et al. | Jun 2008 | A1 |
20080181389 | Bourne et al. | Jul 2008 | A1 |
20080199000 | Su et al. | Aug 2008 | A1 |
20080205611 | Jordan et al. | Aug 2008 | A1 |
20080267386 | Cooper | Oct 2008 | A1 |
20080273687 | Knott et al. | Nov 2008 | A1 |
20090043670 | Johansson et al. | Feb 2009 | A1 |
20090086933 | Patel et al. | Apr 2009 | A1 |
20090171729 | Anisimov | Jul 2009 | A1 |
20090190740 | Chishti et al. | Jul 2009 | A1 |
20090190743 | Spottiswoode | Jul 2009 | A1 |
20090190744 | Xie et al. | Jul 2009 | A1 |
20090190745 | Xie et al. | Jul 2009 | A1 |
20090190746 | Chishti et al. | Jul 2009 | A1 |
20090190747 | Spottiswoode | Jul 2009 | A1 |
20090190748 | Chishti et al. | Jul 2009 | A1 |
20090190749 | Xie et al. | Jul 2009 | A1 |
20090190750 | Xie et al. | Jul 2009 | A1 |
20090232294 | Xie et al. | Sep 2009 | A1 |
20090234710 | Belgaied Hassine et al. | Sep 2009 | A1 |
20090245493 | Chen et al. | Oct 2009 | A1 |
20090304172 | Becerra et al. | Dec 2009 | A1 |
20090305172 | Tanaka et al. | Dec 2009 | A1 |
20090318111 | Desai et al. | Dec 2009 | A1 |
20090323921 | Spottiswoode et al. | Dec 2009 | A1 |
20100020959 | Spottiswoode | Jan 2010 | A1 |
20100020961 | Spottiswoode | Jan 2010 | A1 |
20100054431 | Jaiswal et al. | Mar 2010 | A1 |
20100054452 | Afzal | Mar 2010 | A1 |
20100054453 | Stewart | Mar 2010 | A1 |
20100086120 | Brussat et al. | Apr 2010 | A1 |
20100111285 | Chishti | May 2010 | A1 |
20100111286 | Chishti | May 2010 | A1 |
20100111287 | Xie et al. | May 2010 | A1 |
20100111288 | Afzal et al. | May 2010 | A1 |
20100142689 | Hansen et al. | Jun 2010 | A1 |
20100142698 | Spottiswoode et al. | Jun 2010 | A1 |
20100158238 | Saushkin | Jun 2010 | A1 |
20100183138 | Spottiswoode et al. | Jul 2010 | A1 |
20110022357 | Vock et al. | Jan 2011 | A1 |
20110031112 | Birang et al. | Feb 2011 | A1 |
20110069821 | Korolev et al. | Mar 2011 | A1 |
20110125048 | Causevic et al. | May 2011 | A1 |
20120051536 | Chishti et al. | Mar 2012 | A1 |
20120051537 | Chishti et al. | Mar 2012 | A1 |
20120166235 | Klemm | Jun 2012 | A1 |
20120183131 | Kohler et al. | Jul 2012 | A1 |
20120224680 | Spottiswoode et al. | Sep 2012 | A1 |
20120278136 | Flockhart et al. | Nov 2012 | A1 |
20120300920 | Fagundes et al. | Nov 2012 | A1 |
20130003959 | Nishikawa et al. | Jan 2013 | A1 |
20130022194 | Flockhart et al. | Jan 2013 | A1 |
20130051545 | Ross et al. | Feb 2013 | A1 |
20130251137 | Chishti et al. | Sep 2013 | A1 |
20130287202 | Flockhart et al. | Oct 2013 | A1 |
20140044246 | Klemm et al. | Feb 2014 | A1 |
20140079210 | Kohler et al. | Mar 2014 | A1 |
20140119531 | Tuchman et al. | May 2014 | A1 |
20140119533 | Spottiswoode et al. | May 2014 | A1 |
20140341370 | Li et al. | Nov 2014 | A1 |
20150055772 | Klemm et al. | Feb 2015 | A1 |
20150281448 | Putra et al. | Oct 2015 | A1 |
20160080573 | Chishti | Mar 2016 | A1 |
20160323449 | Drotos et al. | Nov 2016 | A1 |
20170064080 | Chishti et al. | Mar 2017 | A1 |
20180159977 | Danson et al. | Jun 2018 | A1 |
Number | Date | Country |
---|---|---|
2008349500 | May 2014 | AU |
2009209317 | May 2014 | AU |
2009311534 | Aug 2014 | AU |
101087271 | Dec 2007 | CN |
101645987 | Feb 2010 | CN |
102164073 | Aug 2011 | CN |
102387265 | Mar 2012 | CN |
102572139 | Jul 2012 | CN |
102724296 | Oct 2012 | CN |
102890799 | Jan 2013 | CN |
103684874 | Mar 2014 | CN |
102301688 | May 2014 | CN |
102017591 | Nov 2014 | CN |
104243730 | Dec 2014 | CN |
102004061512 | Jun 2006 | DE |
0493292 | Jul 1992 | EP |
0949793 | Oct 1999 | EP |
1032188 | Aug 2000 | EP |
1335572 | Aug 2003 | EP |
11-098252 | Apr 1999 | JP |
2000-069168 | Mar 2000 | JP |
2000-078291 | Mar 2000 | JP |
2000-078292 | Mar 2000 | JP |
2000-092213 | Mar 2000 | JP |
2000-507420 | Jun 2000 | JP |
2000-236393 | Aug 2000 | JP |
2000-253154 | Sep 2000 | JP |
2001-292236 | Oct 2001 | JP |
2001-518753 | Oct 2001 | JP |
2002-297900 | Oct 2002 | JP |
3366565 | Jan 2003 | JP |
2003-187061 | Jul 2003 | JP |
2004-056517 | Feb 2004 | JP |
2004-227228 | Aug 2004 | JP |
2006-345132 | Dec 2006 | JP |
2007-324708 | Dec 2007 | JP |
2009-081627 | Apr 2009 | JP |
2011-511533 | Apr 2011 | JP |
2011-511536 | Apr 2011 | JP |
2012-075146 | Apr 2012 | JP |
5421928 | Feb 2014 | JP |
5631326 | Nov 2014 | JP |
5649575 | Jan 2015 | JP |
2015-514371 | May 2015 | JP |
316118 | Dec 2013 | MX |
322251 | Jul 2014 | MX |
587100 | Oct 2013 | NZ |
587101 | Oct 2013 | NZ |
591486 | Jan 2014 | NZ |
592781 | Mar 2014 | NZ |
1-2010-501704 | Feb 2014 | PH |
1-2010-501705 | Feb 2015 | PH |
WO-199917517 | Apr 1999 | WO |
WO-2001063894 | Aug 2001 | WO |
WO-2006124113 | Nov 2006 | WO |
WO-2009097018 | Aug 2009 | WO |
WO-2010053701 | May 2010 | WO |
WO-2011081514 | Jul 2011 | WO |
Entry |
---|
Afiniti, “Afiniti® Enterprise Behavioral Pairing™ Improves Contact Center Performance,” White Paper, retrieved online from URL: <http://www.afinitit,com/wp-content/uploads/2016/04/Afiniti_White-Paper_Web-Email.pdf> 11 pages (2016). |
Anonymous. (2006) “Performance Based Routing in Profit Call Centers,” The Decision Makers' Direct, located at www.decisioncraft.com, Issue Jun. 2002 (3 pages). |
Cleveland, William S., “Robust Locally Weighted Regression and Smoothing Scatterplots,” Journal of the American Statistical Association, vol. 74, No. 368, Dec. 1979, pp. 829-836 (8 pages). |
Gans, N. et al., “Telephone Call Centers: Tutorial, Review and Research Prospects,” Manufacturing & Service Operations Management, vol. 5, No. 2, 2003, pp. 79-141, (84 pages). |
International Preliminary Report on Patentability issued in connection with PCT Application No. PCT/US2009/066254 dated Jun. 14, 2011 (6 pages). |
International Search Report and Written Opinion issued by the European Patent Office as International Searching Authority for International Application No. PCT/IB2016/001762 dated Feb. 20, 2017 (15 pages) . |
International Search Report and Written Opinion issued by the European Patent Office as International Searching Authority for International Application No. PCT/IB2016/001776 dated Mar. 3, 2017 (16 pages). |
International Search Report and Written Opinion issued by the European Patent Office as International Searching Authority for International Application No. PCT/IB2017/000570 dated Jun. 30, 2017 (13 pages). |
International Search Report issued in connection with International Application No. PCT/US13/33268 dated May 31, 2013 (2 pages). |
International Search Report issued in connection with PCT Application No. PCT/US/2009/054352 dated Mar. 12, 2010, 5 pages. |
International Search Report issued in connection with PCT Application No. PCT/US2008/077042 dated Mar. 13, 2009 (3 pages). |
International Search Report issued in connection with PCT Application No. PCT/US2009/031611 dated Jun. 3, 2009 (5 pages). |
International Search Report issued in connection with PCT Application No. PCT/US2009/066254 dated Feb. 24, 2010 (4 pages). |
International Search Report issued in connection with PCT/US2009/061537 dated Jun. 7, 2010 (5 pages). |
International Search Report issued in connection with PCT/US2013/033261 dated Jun. 14, 2013 (3 pages). |
International Search Report issued in connection with PCT/US2013/33265 dated Jul. 9, 2013 (2 pages). |
Ioannis Ntzoufras “Bayesian Modeling Using Winbugs An Introduction”, Department of Statistices, Athens University of Economics and Business, Wiley-Interscience, A John Wiley & Sons, Inc., Publication, Chapters, Jan. 1, 2007, pp. 155-220 (67 pages). |
Koole, G. (2004). “Performance Analysis and Optimization in Customer Contact Centers,” Proceedings of the Quantitative Evaluation of Systems, First International Conference, Sep. 27-30, 2004 (4 pages). |
Koole, G. et al. (Mar. 6, 2006). “An Overview of Routing and Staffing Algorithms in Multi-Skill Customer Contact Centers,” Manuscript, 42 pages. |
Ntzoufras, “Bayesian Modeling Using Winbugs”. Wiley Interscience, Chapters, Normal Regression Models, Oct. 18, 2007, Redacted version, pp. 155-220 (67 pages). |
Press, W. H. and Rybicki, G. B., “Fast Algorithm for Spectral Analysis of Unevenly Sampled Data,” The Astrophysical Journal, vol. 338, Mar. 1, 1989, pp. 277-280 (4 pages). |
Riedmiller, M. et al. (1993). “A Direct Adaptive Method for Faster Back Propagation Learning: The RPROP Algorithm,” 1993 IEEE International Conference on Neural Networks, San Francisco, CA, Mar. 28-Apr. 1, 1993, 1:586-591. |
Stanley et al., “Improving call center operations using performance-based routing strategies,” Calif. Journal of Operations Management, 6(1), 24-32, Feb. 2008; retrieved from http://userwww.sfsu.edu/saltzman/Publist.html. |
Written Opinion of the International Searching Authority issued in connection with International Application No. PCT/US13/33268 dated May 31, 2013, 7 pages. |
Written Opinion of the International Searching Authority issued in connection with PCT Application No. PCT/US/2009/054352 dated Mar. 12, 2010, 5 pages. |
Written Opinion of the International Searching Authority issued in connection with PCT Application No. PCT/US2008/077042 dated Mar. 13, 2009, 6 pages. |
Written Opinion of the International Searching Authority issued in connection with PCT Application No. PCT/US2009/031611 dated Jun. 3, 2009, 7 pages. |
Written Opinion of the International Searching Authority issued in connection with PCT Application No. PCT/US2009/066254 dated Feb. 24, 2010, 5 pages. |
Written Opinion of the International Searching Authority issued in connection with PCT/US2009/061537 dated Jun. 7, 2010, 10 pages. |
Written Opinion of the International Searching Authority issued in connection with PCT/US2013/033261 dated Jun. 14, 2013, 7 pages. |
Written Opinion of the International Searching Authority issued in connection with PCT/US2013/33265 dated Jul. 9, 2013, 7 pages. |
Number | Date | Country | |
---|---|---|---|
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Number | Date | Country | |
---|---|---|---|
61615779 | Mar 2012 | US | |
61615772 | Mar 2012 | US | |
61615788 | Mar 2012 | US |
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