Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 07/01/2026.
Status of Claims
Claims 1,3-5,7,8,10-12, and 14-19 have been amended with the 07/01/2026 amendment.
Claims 2,6,9,13,20,22 have been canceled by Applicant.
Claims 1,3-5,7,8,10-12,14-19 and 21 are currently pending and have been rejected as follows.
Response to Amendments / Arguments
Applicant’s 07/01/2026 amendment necessitated new grounds of rejection in this action.
Response to Applicant’s rebuttal arguments on the prior art rejections
- independent Claims 1,8 -
Remarks 07/01/2026 p.9 ¶4-p.10 ¶2 argues Leggett; Ernest W. US 5185780 A hereinafter Leggett does not teach the weighted-average limitation of Claims 1,8 namely:
- “wherein predicting the predicted number of scheduling units comprises computing a weighted average of (i) a standard number of scheduling units associated with a standard configuration corresponding to the total number of agents and (ii) an average actual number of scheduling units for one or more existing sets of contact centers having a number of agents corresponding to the standard configuration, wherein a weight assigned to the average actual number of scheduling units in the weighted average is based on a quantity of the one or more existing sets of contact centers” at independent method Claim 1 and independent system Claim 8.
Then Remarks 07/01/2026 p. 23 last ¶ - p.24 ¶3 argues that Leggett is not:
- #1 “predicting a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents” because Leggett does not predict or determine how many management units should be created (Remarks 07/01/2026 p. 23 last ¶ - p.24 ¶1)
- # 2 “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions” because Leggett allocates work among existing units but does not determine the number of units to create
(Remarks 07/01/2026 p.24 ¶2)
- #3 “initiating on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units” because Applicant interprets the claim language “initiating … based on the actual required number” to suggests a dynamic process of creating scheduling units which allegedly Leggett does not disclose (Remarks 07/01/2026 p.24 ¶2).
Examiner fully considered the Applicant’s arguments on independent Claims 1,8, above.
As per, the amended “wherein predicting the predicted number of scheduling units comprises computing a weighted average of (i) a standard number of scheduling units associated with a standard configuration corresponding to the total number of agents and (ii) an average actual number of scheduling units for one or more existing sets of contact centers having a number of agents corresponding to the standard configuration, wherein a weight assigned to the average actual number of scheduling units in the weighted average is based on a quantity of the one or more existing sets of contact centers”, such argument is moot in view of new grounds of rejection.
Examiner relies on Dong et al US 20240362550 A1 hereinafter Dong to teach or suggest:
- “wherein predicting the predicted number of scheduling units comprises computing a weighted average of (i) a standard number of scheduling units associated with a standard configuration corresponding to the total number of agents and (ii) an average actual number of scheduling units for one or more existing sets of contact centers having a number of agents corresponding to the standard configuration, wherein a weight assigned to the average actual number of scheduling units in the weighted average is based on a quantity of the one or more existing sets of contact centers”,
(Dong ¶ [0014] 1st sentence: use of contact centers is becoming increasingly common with ¶ [0066] 2nd sentence stating that: historical contact center data is associated with such contact centers, and with ¶ [0067] 1st sentence stating that: historical contact center data 502 obtained by tracking contact center conditions of the contact centers. For example, ¶ [0064] 4th sentence: discloses agent device 404 or server which implements software usable by contact center agents to address contact center engagements requested by contact center users. ¶ [0057] 2nd sentence: examples include, resource provisioning and deployment software. ¶ [0074] 4th-5th sentence: some implementations use a traffic modeling technique (e.g., a traffic modeling such as Erlang-c) in contact center scheduling by calculating the proposed number of agents by taking the estimated engagement volume and the average handling time for the modeling engine, from the administrator device. One such example is disclosed at ¶ [0092] 4th sentence: users tend to contact [existing] medical contact centers when they are sick with viral or bacterial diseases, historically tracked at ¶ [0091] when levels of community spread of viral or bacterial disease were within 10% of current levels. Then at ¶ [0068] 1st sentence: modeling engines 512A-512C access such historical contact center data 502.Then at ¶ [0076] the modeling engines 512A-512C include at least one of a weighted weekly moving average engine... A weighted weekly moving average engine calculates target value representing demand for contact center agents of a set of data over a predetermined period of time. In particular, it uses a weighted average approach to give greater importance to more recent data points. The weighted weekly moving average engine receives input data on a weekly basis and maintains a rolling window of the most recent n weeks of data, where n is a predetermined number. The weighted weekly moving average engine then calculates a weighted average of the data within the window, with the weights assigned to each data point based on its position within the window. More specifically, the weight assigned to each data point is inversely proportional to its age, such that more recent data points are given greater weight. The weighted weekly moving average engine use any weighting function to determine the weights…. The weighted average is provided as the output (e.g., corresponding to the demand for the contact center agents).
Dong ¶ [0094] 1st-2nd sentences: Using the above technique, the medical contact center determine 50 contact center agents are required to reach a specified service level (e.g., 75% of contact center users being connected with a contact center agent within 5 minutes of requesting connection to a contact center agent) on Apr. 22, 2023. The medical contact center may determine that only 40 contact center agents are scheduled to be working on Apr. 22, 2023.
Dong ¶ [0095] The above use case could be modified for other industries. For example, a contact center of a stock brokerage could leverage modeling engines that include a first modeling engine that takes into account contact center data of the previous week, a second modeling engine that takes into account contact center data of the previous month, and a third modeling engine that takes into account the contact center data from times with similar events to a current time. A combination engine could be used to combine the outputs of these three modeling engines. Initially, when little training data is available, the outputs of the first modeling engine and the second modeling engine are more heavily weighted by the combination engine. However, as the amount of training data increases, the third modeling engine would begin making better predictions of contact center agent demand than the first modeling engine and the second modeling engine. Thus, when more training data becomes available, the combination engine would increase the weight applied to the output of the third modeling engine, while reducing the weight applied to the first modeling engine and the second modeling engine).
Examiner also maintains that Leggett; Ernest W. US 5185780 A still teaches/suggests the features argued above at #1, #2, #3 as follows:
* As per *
- #1 predict a number of scheduling units for the set of one or more contact centers based on the total number of agents, Examiner notes that at no point does said limitation recite how many management units should be created as alleged by Remarks 07/01/2026 p.24 ¶1.
Examiner continues to interpret said limitation on broadest reasonable interpretation of MPEP 2111. Examiner also reminds the Applicant that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Based on such tests Examiner submits that Leggett; teaches or at a minimum suggests:
- #1 predict a number of scheduling units for the set of one or more contact centers based on the total number of agents,
(Leggett column 3 lines 62-65: invention facilitates efficient management of the individual agents of the management unit based on real-time performance statistics and meaningful display of the generated agent schedules. For example, at column 6 lines 6-10, 24-26: With reference to Figs.1-2, a team of call center agents is organized into management units, each management unit (MU) having predetermined number of agent groups. column 7 lines 14-21: if there are 4 MU’s expected [or predicted] to have equivalent amounts of staffing but MU1, closes 1 hour before the other MU's, the call center supervisor can setup the allocations for each MU at 25% until the end of the day, when the other 3 MU’s are then each allocated 33.3% of the calls. When MU1 closes, no falloff in service level then occurs. tour templates are correlated with the forecast to generate a set of tours for each management unit. column 15 lines 41-51 noting an example disclosing the staffing to comprise total agents number. Also, column 18 lines 38-42: The number of MU agents required for reforecast MU call volumes is calculated using Erlang C method),
* As per *
- # 2 “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions”,
Examiner notes that at no point does said limitation recite the number of units to create as alleged by Remarks 07/01/2026 p.24 ¶1. Examiner continues to interpret said limitation on broadest reasonable interpretation of MPEP 2111. Examiner also reminds the Applicant that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Leggett; teaches or at a minimum suggests:
- # 2 “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions”,
(Leggett column 6 lines 20-23: the system accommodate not only geographical dispersal of management units but also multiple call types dispersed among multiple management units and multiple geographical locations. For example, at column 7 lines 14-21: 4 MU’s expected to have equivalent amounts of staffing but MU1 closes 1 hour before the other MU's, the call center supervisor setup the allocations for each MU at 25% until end of day, when the other 3 MU’s are then each allocated 33.3% of calls. Such scheduling function is disclosed at column 8 lines 12-22: to allocate work hours according to staffing requirements that have been forecast. Scheduling has 3…components: tour generation, agent assignment and schedule generation…. Tour generation is the process of matching the staffing requirements with staffing possibilities, defined by staffing restrictions such as hours of operation. column 8 lines 31-34, 37-41: referring to Fig.4, generate tours routine 52 is used to create tours for theoretical agents of each management unit based on the tour templates and forecast FTE requirements for a particular period. Thereafter, supervisor(s) assign agents to generated tours using a list of named agents. Alternatively, agents can be assigned using automatic process as described below.
Leggett column 12 lines 45-50: The method begins at step 73 calculating the offered load a. At step 75, Erlang C calculation C(n,a) is tun for n=a+1, which is minimum agents for which meaningful Erlang C calculation can be made. column 13 lines 57-column 14 line 4: Thereafter, according to Fig.7, 2 initial guesses (namely, minimum number of agents n and n+1) are used as predictor values and a 1st loop is run up in step 85 to determine a value (100-ePWt) which is approximately desired service level. At this point the method checks the estimate by calculating Erlang C for p-1, p and p+1. Calculating the actual C(p-1,a), C(p,a) and C(p+1,a) and service level exact [or actual] values, 1 of 3 is hopefully a winning value. Generally, the winning value will be the central predicted value p for most common input data. Stated differently, the routine uses the numbers for n and n+1 to predict the value p which brings a result close to the desired objective. The Erlang C loop is then continually run up to calculate C(P-1,a), C(p,a) and C(p+1,a). column 18 lines 17-30: The Staff column 84 of Fig.9 is the staffing for the MU. The required MU values (Req) are calculated from the required team values and the MU allocations for the shift. The number of Open agents for the shift is calculated by subprocess 114 from the workstation's Individual Schedule dataset. After the end of every half hour period, actual performance data is received from central computer 12. The team values for that half-hour, namely, AHT, Occupancy and Service Level, are written directly from the data. The MU Call Volume is calculated from the MU allocation of the actual team call volume. MU Required staffing remains unchanged and the actual MU staffing is obtained [or selected] from the central computer by way of the MIS).
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Leggett Fig.7 in support of rejection argument
* As per *
- #3 initiating on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units”.
Examiner notes that at no point does said limitation recite a dynamic process of creating scheduling as alleged by Applicant at Remarks 07/01/2026 p.24 ¶2).
Examiner continues to interpret said limitation on broadest reasonable interpretation of MPEP 2111. Examiner also reminds the Applicant that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Based on such tests Examiner submits
Leggett; teaches or at a minimum suggests:
- #3 initiating on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units”.
(Leggett column 2 lines 23-25: The invention thus provides the flexibility and control to properly take advantage of the capacities of modern digital switches. Specifically, per column 7 lines 5-23: Following the generation of a forecast, the method continues at step 31 to allocate the expected call load among the management units. This function enables centralized computer 12 of overall team to distribute responsibility for answering calls according to expected available staffing as administratively determined. Moreover, the allocation is variable by each ½ hour of each day of the week enabling the call center to realize significant facilities and management cost savings. For example, if there are 4 MU's expected to have equivalent amounts of staffing but MU1, closes 1 hour before the other MU's, the call center supervisor can setup the allocations for each MU at 25% until the end of the day, when the other three MU's are then each allocated 33.3% of calls. When MU1 closes, no falloff in service level then occurs. Of course, in operation, incoming calls always go to first available agent, regardless of the location of that agent. column 6 lines 15-18: a single telephone switch or module may likewise be associated with more than one team, and more than one MU can be associated with more than one team. Thereafter at
Leggett column 17 lines 6-12: local staffing changes at the MU level are transmitted back to the central computer databases and are rebroadcast back in real-time as data is received to all affected management units in the system column 18 lines 17-20,31-51,66-67: Staff column 84 of Fig.9 is staffing for the MU. required MU values (Req) are calculated from required team values and the MU allocations for the shift. As described above with respect to the intra-day reforecasting capability, every ½ hour throughout the shift, call volumes for the rest of the shift are recalculated based on the actual call volume data received earlier in the shift. The recalculation is performed by subprocess 116. Again, the calls are forecasted for the MU on an allocated basis. The number of MU agents required for the reforecast MU call volumes is calculated using an Erlang C method. An example of intra-day reforecasting provided by subprocess 116 can now be described. According to the technique, reforecast ratio (Rf) is first generated equal to the summation of Actual data divided by summation of Forecast data for periods having actual data. A so-called reality ratio is then generated and is defined as equal to N/(N-1)2, where N is the number of periods of actual data. When actual MIS data is received, the reforecast process is automatically redone)
Examiner thus submits that by the above automatic, flexible, iterative, repetitive allocation or initiation of the management units MU, as disclosed above, Leggett teaches or at least suggests even the over-narrowly dynamic interpretation argued by Remarks 07/01/2026 p.24 ¶2.
Based on the preponderance of both legal and factual evidence above, Examiner submits that the prior art teaches or suggests the prior art arguments with respect to Claims 1,8.
- independent Claim 15, dependent Claims 3,10 -
Remarks 07/01/2026 p.10 ¶3-p.11 ¶2 argues Johnston et al US 11368588 B1 does not disclose a determination of cardinality, of distinct skill categories that should be defined for a proposed or onboarding set of contact centers, nor the specific bidirectional relationship whereby the actual optimal number of skills is increased relative to a baseline when proposed skill types are agent-light and decreased when proposed skill types are agent-intensive.
Examiner fully considered the argument which is moot in view of new grounds of rejection.
Examiner first points to an issue of claim construction or claims interpretation by noting that the features upon which applicant relies namely the actual optimal number of skills is increased relative to a baseline when proposed skill types are agent-light and decreased when proposed skill types are agent-intensive, as asserted above at Remarks 07/01/2026 p.10 ¶3-p.11 ¶1 are not recited in the rejected claim(s). Simply said, the Applicant is mischaracterizing what independent Claim 15 recites because, even as amended, said Claim 15, and similarly Claims 3,10, do not recite cardinality, to define for the purpose of onboarding set of contact centers1, nor a specific bidirectional relationship in active voice. Rather, consistent with the argument above, the amended independent Claim 15 simply recites a wherein limitation, whose patentable weight is tested per MPEP 2111.04 I. Here, said wherein limitation recites:
- “wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide”.
It is thus clear that the amended independent Claim 15 merely recognizes two instances: i. one, where “the actual optimal number of skills is greater”… “when” “proposed skill types require a low number of agents” … “and”
ii. another, where “the actual optimal number of skills is less”… “when the one or more proposed skill types require a high number of agents to provide”.
This is different than deliberate increasing, in active diathesis or active voice, the actual optimal number of skills relative to a baseline when proposed skill types are agent-light, and decreasing, in active diathesis or active voice, the proposed skill types require a high number of agents to provide, as asserted by Applicant above at Remarks 07/01/2026 p.10 ¶3-p.11 ¶1.
Examiner once again reminds the Applicant that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In any event, no matter if the claims are interpreted based on the correct claim construction as identified by the Examiner above, or interpreted based on the much narrower, and incorrect claim construction, as asserted by Applicant above, the prior art of Babine US 20070129996 A1
hereinafter Babine, as now relied upon by Examiner teaches or at least suggests:
- “wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide”;
(Babine ¶ [0003] A problem that arises in call centers with small skill groups is that the small group agents are often either underutilized or overutilized. Underutilization occurs when there are unduly restrictive barriers to using the small group agents to handle large group calls. Underutilization results in low occupancy and low productivity for the small group agents, and thus a higher cost per transaction for these small group agents. Overutilization occurs when the barriers to using the small group agents to handle large group calls are too easy to overcome. Overutilization results in high occupancy for the small group agents and poor service levels for the kinds of contacts that only the small group agents handle.
Babine ¶ [0054], A given agent thus have multiple small skills and may be designated as borrowable for multiple large skills. Since there may be many more than two skills in the call center, and more than two possible skills per agent, there are numerous permutations of the manner in which skills are assigned an agent. Examples of such permutations are given in the following list: ¶ [0057] C) Small skill assigned as primary, large skill as borrowable-agent will help on the large skill as long as the small skill has acceptable service level and work would not deplete the small group available agents below a designated level ¶ [0058], D) Small skill assigned as primary, large skill assigned as reserve-agent helps out when large skill needs help, ¶ [0060] F) Large skill assigned as primary and small skill assigned as reserve-agent helps out the small skill group when needed
Babine Annotated Figs. 3-4 extracted below and associated text.
Babine ¶ [0042] In step 304, a check is made as to whether or not current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0043] If current service level is not equal to or greater than the service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 306, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 304 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0044] If step 304 determines that current service level is equal to or greater than the service level target, the process moves to step 308, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than specified minimum threshold X or at least 3 surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 310. ¶ [0047] If the total number of surplus agents in the small skill group for Skill 22 is at least 1 more than the specified minimum threshold X, that is, if there are three or more surplus agents for Skill 22, the surplus agent with the lowest occupancy is selected to be the “borrowable” agent for the large skill, as indicated in step 312.
Babine ¶ [0066] noting a similar example where In step 404, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0067] If the current service level for Skill 22 is not equal to or greater than the 80/20 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 406, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 404 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0068] If step 404 determines that the current service level is equal to or greater than the service level target, the process moves to step 408, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 410. ¶ [0069] In step 412, a check is made as to whether or not the current service level for Skill 24 is equal to or greater than the 90/10 service level target for Skill 24.
Babine ¶ [0070] If the current service level for Skill 24 is not equal to or greater than the 90/10 service level target, the agent is not eligible to help with the large volume skill (Skill 1) in step 414, and hence cannot be borrowed to handle a call requiring the large volume skill.
Babine ¶ [0071] If step 412 determines that the current service level for Skill 24 is equal to or greater than the 90/10 service level target, the process moves to step 416, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, or at least four surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 414. ¶ [0074] If the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, that is, if there are four or more surplus agents for Skill 24, the surplus agent with the lowest occupancy is selected to be a borrowable agent for the large skill, as indicated in step 418. Although a LOA selection technique is used in this example, other techniques, such as MIA, or combinations of such techniques, can be used instead. Again, the selection can also or alternatively take into account surplus agent proficiency at the small skill, the large skill, or both. Any combination of such criteria may be used, and the invention is not limited in this regard. ¶ [0075] In step 420, a determination is made as to whether there is a call surplus in the large skill, that is, if there are more calls waiting in queue than there are agents available to service the calls. If so, the borrowable small group agent as determined in step 418 is used to handle a call, as indicated in step 422. Otherwise, the borrowable agent determined in step 418 enters the agent queue(s) for both the large skill (Skill 1) and the small skills (Skill 22 and Skill 24). Step 424 indicates that, as a result, the next call when it arrives may be handled by the borrowed agent determined in step 418, or by an agent that has Skill 1 as a primary skill).
It thus appears that Babine’s management of underutilization and overutilization teaches not only the above wherein limitation based on broadest reasonable interpretation, as tested per MPEP 2111, but also the overly narrow interpretation of Remarks 07/01/2026 p.10 ¶3-p.11 ¶1.
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Babine Annotated Fig. 3(shown left) and Annotated Fig.4 (shown right) in support of rejection arguments
Thus, the prior art teaches or at least suggests the contested limitation.
- independent Claim 15 and dependent Claim 19 -
Remarks 07/01/2026 p.24 last ¶ - p.25 ¶2 argues again that Johnston does not teach:
- #1 “selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents” and
- #2 “initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills”.
Examiner fully considered the argument which is moot in view of new grounds of rejection.
Examiner now relies on Babine US 20070129996 A1 hereinafter Babine to teach:
- #1 “selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents” (Babine ¶ [0002] 6th sentence: as expected or as predicted, a large skill group typically comprises larger number of agents than a small skill group, but actual number vary depending upon total number of agents in the call center. ¶ [0035] 1st sentence: in a typical implementation, a given skill must be designated as warranting protections associated with small group status. ¶ [0036]1st-2nd sentences: Another trigger is based on % rule specifying that a small group surplus agent may be used to handle a large group call if that small group surplus agent does not represent more Y % of total number of surplus small group agents. ¶ [0037] 1st sentence: another example of a trigger is one based on a different type of % rule specifying that a small group surplus agent may be used to handle a large group call if the number of small group surplus agents represents more than Z % of the total number of small group agents)
- #2 “initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills” (Babine ¶ [0018] 3rd sentence the disclosed techniques can be used with automatic call distribution (ACD) systems, telemarketing systems, private-branch exchange (PBX) systems, computer-telephony integration (CTI)-based systems, and combinations of these and other types of call centers. ¶ [0020] Fig.2 shows a simplified block diagram of implementation of ACD system 101. The system in Fig.2 is stored-program-controlled system that includes interfaces 112 to external communication links, a communications switching fabric 113, service circuits 114, memory 115 for storing control programs and data, and processor 116 for executing the stored control programs to control the interfaces and the fabric, to provide automatic call distribution functionality. Specifically, per ¶ [0021] data elements stored in memory 115 of ACD system 101 include set of call queues 120... Each call queue 121-129 in the set of call queues 120 corresponds to a different agent skill as detailed above... calls are prioritized … in different ones of a plurality of call queues that correspond to a skill and each one of which corresponds to a different priority. Similarly, each agent's skills are prioritized according to his or her level of expertise in that skill, and agents may be, enqueued in individual ones of agent queues 130 in their order of expertise level, or enqueued in different ones of agent queues that correspond to a skill and each one of which corresponds to a different expertise level in that skill),
Thus, the prior art teaches or at least suggests the contested limitations.
- dependent Claims 3 and 10 -
Remarks 07/01/2026 p.26-p.28 ¶2 then argues that in addition to the amended limitation of parent independent Claims 1,8, the prior art of Leggett and Johnston also does not teach, either alone or together with rationale:
- “selecting an actual optimal number of skills for the set of one or more contact centers based on the optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers, wherein the actual optimal number of skills is greater than the optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide”
as amended at each of dependent Claims 3,10.
Examiner fully considered the argument which is moot in view of new grounds of rejection.
Examiner reincorporates similar fact patterns and rationales as articulated above. Specifically,
Examiner now relies on Babine US 20070129996 A1 hereinafter Babine to teach or suggest:
- selecting an actual optimal number of skills for the set of one or more contact centers based on the optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers, (Babine ¶ [0035] 3rd sentence: designation of small skills is updated periodically, so the system provide, on request or in accordance with a schedule, an analysis indicating or specifying which skills should be designated. ¶ [0053] a given agent template in the call center specify small skills, large skills, traditional and other skills types.
¶ [0002] 6th sentence: a large skill group is typically larger than small skill group, but actual number in a group vary depending upon factors. ¶ [0054]-¶ [0062] such permutations are:
A) Small skill assigned as reserve-agent helps only when small skill is in trouble
B) Small skill assigned as primary-agent works only on small skill
C) Small skill assigned as primary, large skill as borrowable-agent will help on the large skill as long as the small skill has acceptable service level and work would not deplete the small group available agents below a designated level
D) Small skill assigned as primary, large skill assigned as reserve-agent helps out when large skill needs help
E) Small skill and large skill assigned as primary-agent handles each type of call routinely
F) Large skill assigned as primary and small skill assigned as reserve-agent helps out the small skill group when needed
G) Large skill assigned as primary-agent handles only large skill calls
H) Large skill assigned as reserve-agent handles large skill calls only when large skill group needs help)
Babine ¶ [0040] 2nd-4th sentence: noting an example of designating Skill 22 as a small skill having associated small skill group. In this example, the headcount rule is utilized, and threshold X is set to 2. The hunt group form similarly indicates service level target is 80/20, that is, 80% of calls requiring Skill 22 must be serviced within 20 seconds. Also within settings 300, specify Skill 22 as the primary small skill for that agent, and further specify Skill 1 as large volume skill. Similarly
Babine ¶ [0064] 3rd-5th sentences: hunt group form for Skill 22 designates X value of 2, and a service level target of 80/20, just as in the Fig.3. The hunt group form for Skill 24 designates an X value of 3, and a service level target of 90/10, that is, 90% of the calls requiring Skill 24 must be serviced within 10 seconds. The settings 400 further include an agent login form for a given agent. The agent login form specifies Skill 22 and Skill 24 as primary small skills for that agent, and further specifies Skill 1 as a large skill, also referred to as a large volume skill in this example
Babine ¶ [0079] an occupancy calculation may be performed for small skill agents to show the percentages of occupancy contributed by each large skill they support. In other words, if an agent had an overall 74% occupancy for the day, the system could perform calculations to indicate, for example, that the agent had 58% occupancy based on core work, with 7% added due to Large Skill 1 and 9% added due to Large Skill 2);
- “wherein the actual optimal number of skills is greater than the optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide”
(Babine ¶ [0003] A problem that arises in call centers with small skill groups is that the small group agents are often either underutilized or overutilized. Underutilization occurs when there are unduly restrictive barriers to using the small group agents to handle large group calls. Underutilization results in low occupancy and low productivity for the small group agents, and thus a higher cost per transaction for these small group agents. Overutilization occurs when the barriers to using the small group agents to handle large group calls are too easy to overcome. Overutilization results in high occupancy for the small group agents and poor service levels for the kinds of contacts that only the small group agents handle.
Babine ¶ [0054], A given agent thus have multiple small skills and may be designated as borrowable for multiple large skills. Since there may be many more than two skills in the call center, and more than two possible skills per agent, there are numerous permutations of the manner in which skills are assigned an agent. Examples of such permutations are given in the following list: ¶ [0057] C) Small skill assigned as primary, large skill as borrowable-agent will help on the large skill as long as the small skill has acceptable service level and work would not deplete the small group available agents below a designated level ¶ [0058], D) Small skill assigned as primary, large skill assigned as reserve-agent helps out when large skill needs help, ¶ [0060] F) Large skill assigned as primary and small skill assigned as reserve-agent helps out the small skill group when needed
Babine Annotated Figs. 3-4 extracted below and associated text.
Babine ¶ [0042] In step 304, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0043] If the current service level is not equal to or greater than the service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 306, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 304 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0044] If step 304 determines that the current service level is equal to or greater than the service level target, the process moves to step 308, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 310. ¶ 0047]
If the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, that is, if there are three or more surplus agents for Skill 22, the surplus agent with the lowest occupancy is selected to be the “borrowable” agent for the large skill, as indicated in step 312.
Babine ¶ [0066] noting a similar example where In step 404, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0067] If the current service level for Skill 22 is not equal to or greater than the 80/20 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 406, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 404 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0068] If step 404 determines that the current service level is equal to or greater than the service level target, the process moves to step 408, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 410. ¶ [0069] In step 412, a check is made as to whether or not the current service level for Skill 24 is equal to or greater than the 90/10 service level target for Skill 24.
¶ [0070] If the current service level for Skill 24 is not equal to or greater than the 90/10 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 414, and hence cannot be borrowed to handle a call requiring the large volume skill. ¶ [0071] If step 412 determines that the current service level for Skill 24 is equal to or greater than the 90/10 service level target, the process moves to step 416, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, or at least four surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 414. ¶ [0074] If the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, that is, if there are four or more surplus agents for Skill 24, the surplus agent with the lowest occupancy is selected to be a borrowable agent for the large skill, as indicated in step 418. Although a LOA selection technique is used in this example, other techniques, such as MIA, or combinations of such techniques, can be used instead. Again, the selection can also or alternatively take into account surplus agent proficiency at the small skill, the large skill, or both. Any combination of such criteria may be used, and the invention is not limited in this regard. ¶ [0075] In step 420, a determination is made as to whether there is a call surplus in the large skill, that is, if there are more calls waiting in queue than there are agents available to service the calls. If so, the borrowable small group agent as determined in step 418 is used to handle a call, as indicated in step 422. Otherwise, the borrowable agent determined in step 418 enters the agent queue(s) for both the large skill (Skill 1) and the small skills (Skill 22 and Skill 24). Step 424 indicates that, as a result, the next call when it arrives may be handled by the borrowed agent determined in step 418, or by an agent that has Skill 1 as a primary skill).
Thus, the prior art teaches or at least suggests the contested limitation.
- dependent Claims 4 and 11 -
Remarks 07/01/2026 p.28 ¶3-p.29 then argues that, in addition to the amended and contested limitations in the parent claims above, Leggett and Johnston and Crockett, also does not teach alone or in combination with rationale, wherein selecting a predicted optimal number of skills for the set of one or more contact centers is further based on actual numbers of skills for at least one existing set of contact centers at dependent Claims 4,11 above.
Examiner fully considered the argument which is moot in view of new grounds of rejection.
Examiner now relies on Moran et al, US 20180191906 A1 to teach or suggest:
- “wherein selecting the predicted optimal number of skills for the set of one or more contact centers is further based on actual numbers of skills for at least one existing set of contact centers”
(Moran ¶ [0003] 3rd sentence: Existing contact centers typically track each resource's skillset(s) and utilize data from one or more sources, including historical data captured from automatic call distribution (ACD) systems in the contact center, to predict future staffing needs and the associated resource skillset mix that will be required to service incoming contacts.
Moran ¶ [0005] 2nd sentence: For example, an attribute-based contact center may be able accurately predict that contacts with a certain set of attributes are more likely to occur at one point during the workday, e.g., early morning, and that the contact type will change to a different mix of attributes or skills during a different part of the workday, e.g., late morning. Specifically per
Moran ¶ [0070] 3rd-5th sentences: The selection of the resource is based on a comparison between the plurality of additional resource attributes and the plurality of contact attributes. For example, the contact request a resource with the first resource attribute and also wish to communicate with a resource located in a specified region and in a particular language [interpreted as an example of attribute or skill]. The attributes of available resources are compared to the contact attributes and the most suitable resource is selected)
Thus, the prior art teaches or suggests the contested limitation.
- dependent Claims 7 and 14 -
Remarks 07/01/2026 p.30 then argues that, in addition to the amended and contested limitations in the parent claims above, Leggett in view of Bondi does not teach, either alone or with combined rationale, the fundamental limitations of amended independent Claims 1 and 8
computing a weighted average of a standard configuration value and averaged historical data, where the weight depends on the quantity of historical examples at parent Claims 1,8
As identified above, Examiner relied on Dong et al US 20240362550 A1 hereinafter Dong
to teach or suggest: computing a weighted average of a standard configuration value and averaged historical data, where the weight depends on the quantity of historical examples
(Dong ¶ [0014] 1st sentence: use of contact centers is becoming increasingly common with ¶ [0066] 2nd sentence stating that: historical contact center data is associated with such contact centers, and with ¶ [0067] 1st sentence stating that: historical contact center data 502 obtained by tracking contact center conditions of the contact centers. ¶ [0066] 4th sentence: the historical contact center data 502 includes table 504 including columns for time range 506, engagement volume 508, and wait time distribution 510. Each row is associated with a time range (e.g. 9:00 am-10:00 am Eastern Daylight Time on Apr.1, 2022) and stores engagement volume (e.g total number of engagements or number of engagements per unit time where an engagement include a user accessing the contact center via a medium, such as voice call, video call, or instant message; the engagement volume may be a total engagement volume) during the time range and a data structure indicating a wait time distribution of users who accessed the contact center during the time range. Per ¶ [0064] 4th sentence: agent device 404 or server which implements software used by contact center agents to address contact center engagements requested by contact center users. ¶ [0057] 2nd sentence: examples include, resource provisioning and deployment software. ¶ [0074] 4th-5th sentence: some implementations use a traffic modeling technique (e.g., a traffic modeling such as Erlang-c) in contact center scheduling by calculating the proposed number of agents by taking the estimated engagement volume and the average handling time for the modeling engine, from the administrator device. One such example is at ¶ [0092] 4th sentence: users tend to contact [existing] medical contact centers when they are sick with viral or bacterial diseases, historically tracked at ¶ [0091] when levels of community spread of disease were within 10% of current levels. Then ¶ [0068] 1st sentence: modeling engines 512A-512C access such historical contact center data 502.Then at ¶ [0076] the modeling engines 512A-512C include at least one of a weighted weekly moving average engine... A weighted weekly moving average engine calculates target value representing demand for contact center agents of a set of data over a predetermined period of time. In particular, it uses a weighted average approach to give greater importance to more recent data points. The weighted weekly moving average engine receives input data on a weekly basis and maintains a rolling window of the most recent n weeks of data, where n is a predetermined number. The weighted weekly moving average engine then calculates a weighted average of the data within the window, with the weights assigned to each data point based on its position within the window. More specifically, the weight assigned to each data point is inversely proportional to its age, such that more recent data points are given greater weight. The weighted weekly moving average engine use any weighting function to determine the weights…. The weighted average is provided as the output (e.g., corresponding to the demand for the contact center agents).
Dong ¶ [0094] 1st-2nd sentences: Using the above technique, the medical contact center determine 50 contact center agents are required to reach a specified service level (e.g., 75% of contact center users being connected with a contact center agent within 5 minutes of requesting connection to a contact center agent) on Apr. 22, 2023. The medical contact center may determine that only 40 contact center agents are scheduled to be working on Apr. 22, 2023.
Dong ¶ [0095] The above use case could be modified for other industries. For example, a contact center of a stock brokerage could leverage modeling engines that include a first modeling engine that takes into account contact center data of the previous week, a second modeling engine that takes into account contact center data of the previous month, and a third modeling engine that takes into account the contact center data from times with similar events to a current time. A combination engine could be used to combine the outputs of these three modeling engines. Initially, when little training data is available, the outputs of the first modeling engine and the second modeling engine are more heavily weighted by the combination engine. However, as the amount of training data increases, the third modeling engine would begin making better predictions of contact center agent demand than the first modeling engine and the second modeling engine. Thus, when more training data becomes available, the combination engine would increase the weight applied to the output of the third modeling engine, while reducing the weight applied to the first modeling engine and the second modeling engine).
Thus, the prior art teaches or at least suggests the contested limitation.
- dependent Claim 16 -
Remarks 07/01/2026 p.31 ¶1-¶3 argues that neither Johnston not Crockett, teaches alone or in combination with rationales, the fundamental limitations at parent independent Claim 15 of: # 1 selecting predicted optimal number of skills based on the total number of agents; # 2 initiating skill management platforms based on the actual optimal number of skills, and the amended resource-aware skill count adjustment, now amended at parent independent Claim 15. Then, Remarks 07/01/2026 p.31 ¶4 argues, that Crockett, does not teach “wherein selecting a predicted optimal number of skills for the set of one or more contact centers is further based on actual numbers of skills for at least one existing set of contact centers” at dependent Claim 16.
Examiner fully considered the argument which is moot in view of new grounds of rejection.
Examiner now relies on Babine US 20070129996 A1 hereinafter Babine to teach at claim 15:
- #1 “selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents” (Babine ¶ [0002] 6th sentence: as expected or predicted, a large skill group typically comprises larger number of agents than a small skill group, but actual number vary depending upon total number of agents in the call center. ¶ [0035] 1st sentence: in a typical implementation, a given skill must be designated as warranting protections associated with small group status. ¶ [0036]1st-2nd sentences: Another trigger is based on % rule specifying that a small group surplus agent may be used to handle a large group call if that small group surplus agent does not represent more Y % of total number of surplus small group agents. ¶ [0037] 1st sentence: another example of a trigger is one based on a different type of % rule specifying that a small group surplus agent may be used to handle a large group call if the number of small group surplus agents represents more than Z % of the total number of small group agents)
- #2 “initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills” (Babine ¶ [0018] 3rd sentence the disclosed techniques can be used with automatic call distribution (ACD) systems, telemarketing systems, private-branch exchange (PBX) systems, computer-telephony integration (CTI)-based systems, and combinations of these and other types of call centers. ¶ [0020] Fig.2 shows a simplified block diagram of implementation of ACD system 101. The system in Fig.2 is stored-program-controlled system that includes interfaces 112 to external communication links, a communications switching fabric 113, service circuits 114, memory 115 for storing control programs and data, and processor 116 for executing the stored control programs to control the interfaces and the fabric, to provide automatic call distribution functionality. Specifically, per ¶ [0021] data elements stored in memory 115 of ACD system 101 include set of call queues 120... Each call queue 121-129 in the set of call queues 120 corresponds to a different agent skill... calls are prioritized … in different ones of a plurality of call queues that correspond to a skill and each one of which corresponds to a different priority. Similarly, each agent's skills are prioritized according to his or her level of expertise in that skill, and agents may be, enqueued in individual ones of agent queues 130 in their order of expertise level, or enqueued in different ones of agent queues that correspond to a skill and each one of which corresponds to a different expertise level in that skill),
Examiner also now relies on Moran et al, US 20180191906 A1 to teach or suggest at Claim 16:
- wherein selecting the predicted optimal number of skills for the set of one or more contact centers is further based on actual numbers of skills for at least one existing set of contact centers.
(Moran ¶ [0003] 3rd sentence: Existing contact centers typically track each resource's skillset(s) and utilize data from one or more sources, including historical data captured from automatic call distribution (ACD) systems in the contact center, to predict future staffing needs and the associated resource skillset mix that will be required to service incoming contacts. [0005] 2nd sentence: For example, an attribute-based contact center may be able accurately predict that contacts with a certain set of attributes are more likely to occur at one point during the workday, e.g., early morning, and that the contact type will change to a different mix of attributes during a different part of the workday, e.g., late morning. [0070] 3rd – 5th sentences: The selection of the resource is based on a comparison between the plurality of additional resource attributes and the plurality of contact attributes. For example, the contact may request a resource with the first resource attribute and may also wish to communicate with a resource located in a specified region and in a particular language. The attributes of available resources are compared to the contact attributes and the most suitable resource is selected).
Thus, the prior art teaches the contested limitation.
- dependent Claims 17,18 -
Remarks 07/01/2026 p.32 ¶2-¶4 argues Johnston and Bondi does not teach/suggest base limitations of parent independent Claim 15, and the specific resource-aware skill count adjustment recited in amended independent Claim 15. Then, Remarks 07/01/2026 p.32 ¶5-p.33 ¶2 argues Johnston and Bondi are not “predicting a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents; selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions; outputting the actual required number of scheduling units”. Then Remarks 07/01/2026 p.33 ¶3 argues Johnston's machine learning models perform pattern recognition and anomaly detection on operational metrics, not computing predictions of number of scheduling units using historical data about scheduling unit counts in existing contact centers. Finally, Remarks 07/01/2026 p.33 ¶4 argues no support combining Johnston and Bondi.
Examiner fully considered the argument which is moot in view of new grounds of rejection.
Examiner now relied on Babine US 20070129996 A1 to teaches/suggest at parent claim 15 the argued resource-aware skill count adjustment of Remarks 07/01/2026 p.32 ¶1-¶4 as follows:
Babine ¶ [0003] A problem that arises in call centers with small skill groups is that the small group agents are often either underutilized or overutilized. Underutilization occurs when there are unduly restrictive barriers to using the small group agents to handle large group calls. Underutilization results in low occupancy and low productivity for the small group agents, and thus a higher cost per transaction for these small group agents. Overutilization occurs when the barriers to using the small group agents to handle large group calls are too easy to overcome. Overutilization results in high occupancy for the small group agents and poor service levels for the kinds of contacts that only the small group agents handle.
Babine ¶ [0054], A given agent thus have multiple small skills and may be designated as borrowable for multiple large skills. Since there may be many more than two skills in the call center, and more than two possible skills per agent, there are numerous permutations of the manner in which skills are assigned an agent. Examples of such permutations are given in the following list: ¶ [0057] C) Small skill assigned as primary, large skill as borrowable-agent will help on the large skill as long as the small skill has acceptable service level and work would not deplete the small group available agents below a designated level ¶ [0058], D) Small skill assigned as primary, large skill assigned as reserve-agent helps out when large skill needs help, ¶ [0060] F) Large skill assigned as primary and small skill assigned as reserve-agent helps out the small skill group when needed
Babine Annotated Figs. 3-4 extracted below and associated text.
Babine ¶ [0042] In step 304, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0043] If the current service level is not equal to or greater than the service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 306, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 304 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0044] If step 304 determines that the current service level is equal to or greater than the service level target, the process moves to step 308, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 310. ¶ 0047]
If the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, that is, if there are three or more surplus agents for Skill 22, the surplus agent with the lowest occupancy is selected to be the “borrowable” agent for the large skill, as indicated in step 312.
Babine ¶ [0066] noting a similar example where In step 404, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0067] If the current service level for Skill 22 is not equal to or greater than the 80/20 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 406, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 404 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0068] If step 404 determines that the current service level is equal to or greater than the service level target, the process moves to step 408, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 410. ¶ [0069] In step 412, a check is made as to whether or not the current service level for Skill 24 is equal to or greater than the 90/10 service level target for Skill 24.
¶ [0070] If the current service level for Skill 24 is not equal to or greater than the 90/10 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 414, and hence cannot be borrowed to handle a call requiring the large volume skill. ¶ [0071] If step 412 determines that the current service level for Skill 24 is equal to or greater than the 90/10 service level target, the process moves to step 416, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, or at least four surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 414. ¶ [0074] If the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, that is, if there are four or more surplus agents for Skill 24, the surplus agent with the lowest occupancy is selected to be a borrowable agent for the large skill, as indicated in step 418. Although a LOA selection technique is used in this example, other techniques, such as MIA, or combinations of such techniques, can be used instead. Again, the selection can also or alternatively take into account surplus agent proficiency at the small skill, the large skill, or both. Any combination of such criteria may be used, and the invention is not limited in this regard. ¶ [0075] In step 420, a determination is made as to whether there is a call surplus in the large skill, that is, if there are more calls waiting in queue than there are agents available to service the calls. If so, the borrowable small group agent as determined in step 418 is used to handle a call, as indicated in step 422. Otherwise, the borrowable agent determined in step 418 enters the agent queue(s) for both the large skill (Skill 1) and the small skills (Skill 22 and Skill 24). Step 424 indicates that, as a result, the next call when it arrives may be handled by the borrowed agent determined in step 418, or by an agent that has Skill 1 as a primary skill).
As per dependent Claim 17, as argued at Remarks 07/01/2026 p.32 ¶4-p.33 ¶2, Examiner relies on Leggett Ernest W. US 5185780 A to teach or suggest:
- “predicting a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents”; (Leggett column 3 lines 62-65: The invention also facilitates the efficient management of the individual agents of the management unit based on real-time performance statistics and meaningful display of the generated agent schedules. For example, at column 6 lines 6-10, 24-26: With reference to Figs.1-2, a team of call center agents is organized into management units, each management unit (MU) having predetermined number of agent groups. column 7 lines 14-21: if there are 4 MU’s expected [or predicted] to have equivalent amounts of staffing but MU1, closes 1 hour before the other MU's, the call center supervisor can setup the allocations for each MU at 25% until the end of the day, when the other 3 MU’s are then each allocated 33.3% of the calls. When MU1 closes, no falloff in service level then occurs. tour templates are correlated with the forecast to generate a set of tours for each management unit. column 18 lines 38-42: The number of MU agents required for the reforecast MU call volumes is calculated using an Erlang C method),
- “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions”
(Leggett column 6 lines 20-23: the system accommodate not only geographical dispersal of management units but also multiple call types dispersed among multiple management units and multiple geographical locations. For example, at column 7 lines 14-21: 4 MU’s expected to have equivalent amounts of staffing but MU1 closes 1 hour before the other MU's, the call center supervisor setup the allocations for each MU at 25% until end of day, when the other 3 MU’s are then each allocated 33.3% of calls. Such scheduling function is disclosed at column 8 lines 12-22: to allocate work hours according to staffing requirements that have been forecast. Scheduling has 3…components: tour generation, agent assignment and schedule generation…. Tour generation is the process of matching the staffing requirements with staffing possibilities, defined by staffing restrictions such as hours of operation. column 8 lines 31-34, 37-41: referring to Fig.4, generate tours routine 52 is used to create tours for theoretical agents of each management unit based on the tour templates and forecast FTE requirements for a particular period. Thereafter, the supervisor(s) assign agents to generated tours using a list of named agents. Alternatively, agents can be assigned using an automatic process instead of manually as described below.
Leggett column 12 lines 45-50: The method begins at step 73 by calculating the offered load a. At step 75, Erlang C calculation C(n,a) is tun for n=a+1, which is minimum agents for which meaningful Erlang C calculation can be made. column 13 lines 57-column 14 line 4: Thereafter, according to the method of Fig.7, 2 initial guesses (namely, the minimum number of agents n and n+1) are used as predictor values and a first loop is run up in step 85 to determine a value (100-ePWt) which is approximately desired service level. At this point the method checks the estimate by calculating Erlang C for p-1, p, p+1. Calculating the actual C(p-1,a), C(p,a) and C(p+1,a) and service level exact values, 1 of 3 is hopefully a winning value. Generally, the winning value will be the central predicted value p for most common input data. Stated differently, the routine uses the numbers for n and n+1 to predict the value p which brings a result close to the desired objective. The Erlang C loop is then continually run up to calculate C(P-1,a), C(p,a) and C(p+1,a). column 18 lines 17-20:The Staff column 84 of Fig.9 is the staffing for the MU. The required MU values (Req) are calculated from the required team values and the MU allocations for the shift) “and”
- “outputting the actual required number of scheduling units”
(Leggett column 4 lines 34-40: Staffing changes at the management unit are transmitted to the centralized computer of the force management system then regularly broadcast back to the other management units of the system. The performance analysis screen at the management unit is thus continuously updated with modified team call handling performance data. column 16 lines 8-11: The screen includes a Management Unit identifier field 70 to identify the MU performance data being displayed. The performance analysis screen shows the MU's allocation of team data
Leggett column 17 lines 6-15: local staffing changes at MU level are transmitted back to the central computer databases and rebroadcast back to all affected management units in the system. Therefore, all of MU supervisors can continuously view the team statistics even as local staffing changes are dynamically implemented at other management units
Leggett column 18 lines 17-20: Staff column 84 of Fig.9 is the staffing for the MU),
As per dependent Claim 18, as argued at Remarks 07/01/2026 p.33 ¶3, Examiner starts from Applicant’s own admission that the machine learning models of Johnston et al US 11368588 B1 hereinafter Johnston perform pattern recognition and anomaly detection on operational metrics but still contests that Johnston's operational metrics refer to predictions of the number of scheduling units using historical data about scheduling unit counts in existing contact centers. Examiner disagrees by submitting, that in addition to the base teachings above, Johnston further recites at column 8 lines 37-49: As is known, in configurations, the ML model(s) 128 may be trained based upon historical metrics and data related to the execution of the contact center 108. For example, the ML model(s) 128 may analyze historical metrics and data related to execution of the contact center 108 in the service provider network 102. The historical metrics and data may be based on gathered metrics and data that has been collected over the past six months, one year, or longer by the data analytics service 124. In configurations, the historical metrics and data may have been collected during a period shorter than six months. An operator of the service provider network 102 may determine how long historical metrics and data may be retained, e.g., stored in the storage service 120. column 7 lines 1-6: forecasting and scheduling service 122 may utilize the metrics and data from the data analytics service 124 and other factors in forecasting a need for agents 114 based on forecasting anticipated volumes of communications 110 and scheduling of agents 114 in accordance with the anticipated volumes of communications 110.
Johnston column 15 lines 22-29 states: contact center management service 126 and ML model(s) 128 improve forecast accuracy (better match of headcount of agents 114 required to achieve the target service level), better schedule efficiency (having the right number of agents 114 in all intervals/time periods [or units] to achieve the target service level without having idle agents 114 or overworked agents 114), and better adherence, thereby increasing productivity Similarly column 17 lines 46-52: Appropriate load balancing devices or other types of network infrastructure components are utilized for balancing a load between each of data centers 704A-704N, between each of server computers 802A-802F in each data center 704, and, between computing resources in each of the server computers 802).
Thus, the prior art teaches or at least suggests the contested limitations.
Response to Applicant’s § 101 rebuttal Arguments
Examiner reincorporates all findings and rationales at Non-Final Act 03/25/2026 p.10-p.25.
Remarks 07/01/2026 p.12 ¶ 1 argues independent Claim 1 limits the “scheduling unit” to a computational module running on the computational system and is the very structure initiated on at least one computational system based on the actual required number of scheduling units.
- First, as an issue of claim construction and claim interpretation, an argument can be made that independent Claim 1, and similarly, sister independent Claim 8, does not necessarily require the “computational module” to be the very structure initiated, as alleged by Remarks 07/01/2026 p.12 ¶ 1. This is because at independent Claim 1, and similarly at Claim 8, the “scheduling unit” is the one being initiat[ed]. While the “scheduling unit” compris[es] the “computational module”, when testing, the open-ended expression “comprising” per MPEP 2111.03 I, under claim construction, it becomes clear that the “scheduling unit” can comprise, in the open-end, many elements, with no indication of which one of said elements would be solely representative of the composition or of the whole, namely, the “scheduling unit”, so as to be initiat[ed]. Simply put, Claim 1 does not equate “scheduling unit” to the “computational module” as apparently asserted by Applicant above. Thus, by stating that by being compris[ed] in the “scheduling unit”, the “computational module” is the very structure being initiated, the Applicant’s argument is suffering from fallacy of division logical error, namely that the erroneous interpretation into the claim, that the “initiating” property of the parent, the whole, or the group, [in this case the “scheduling unit”], applies to a specific one of its individual parts [in this case, the “computational module”].
- Second, and separate, from the above fallacy of division rebuttal, the Examiner also submits, in the arguendo, that even if the “computational module” would be the one initiat[ed], as argued by Applicant at Remarks 07/01/2026 p.12 ¶ 1, it would still not necessarily render the claims less abstract and eligible. This is because both the claims themselves and the Original Specification ¶ [0032] are silent on how said “computational module”, as part of the “scheduling unit”, is “running on the at least one computational system”. The only disclosure of the “scheduling unit”, [abbreviated as SU] “running on the at least one computational system” was found by the Non-Final Act 03/25/2026 p.11 ¶1 at Annotated excerpt of Fig.9 extracted below from Original Fig. 9 depicting respective scheduling units SU1, SU2, SU3, SU4, SU5.
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Annotated excerpt of Fig. 9 from Original Fig. 9 depicting respective scheduling units
Original Specification ¶ [0138] 3rd, 5th sentences corroborates: “A user may be shown a scheduling unit setup, which they may be given the opportunity to amend”…“Example screen or display 910 also shows a button displaying “Submit”, which may be configured to configure contact centers according to setups once pressed by a user”. Examiner finds that this is the only citation and depiction in the Original Disclosure that would purportedly provide any support for an SU or “scheduling unit”, as possibly represented by a “computational module”, “running on the at least one computational system”. In such case, the recited “running” of the “computational module” as an example of a resource or representation of the “scheduling unit” would correspond to a mere execution or result of a view or depiction of a resource or a group of resources (i.e. SU1, SU2, SU3, SU4, SU5), to configure, initiate, produce or define, via the yet to be claimed, submit button, a schedule for at least one agent of the contact center shown at Annotated Fig.9.
Accordingly, as read in light of Original Disclosure, the “computational module”, as an example of a “scheduling unit”, appears to be a mere logical representation of resource selected and initiated for scheduling purposes for at least one agent. Thus, the mere fact that such “scheduling unit” was amended to comprise, or be represented by, a “computational module”, possibly depicted at Annotated Fig.9, as “running on the computer system”, to configurate, initiate, define or produce the schedule, can be argued as a mere example of a computer environment or tool upon which to perform the abstract process of schedule configuration, which, as revealed by MPEP 2106.04 (a)(2) III C #2 and/or #3, do not preclude the recitation of the abstract exception. Thus here, consistent with the above test at MPEP 2106.04(a)(2) III C # 2, #3, the initiation or execution, of the “scheduling unit”, even when represented or associated as by a “computational module”, would represent such use of computer tool and/or computer environment in which to produce the abstract process scheduling as evidenced, at same limitation of independent Claims 1,8 by explicit recitation of “wherein each scheduling unit is configured to produce at least one schedule for at least one agent”.As such, similar to any other purported, open-ended elements, included in expression “scheduling unit comprising”, as tested per MPEP 2111.03 I, the “computational module”, compris[ed] in the “scheduling unit”, would equally represents a mere component upon which the abstract processes to produce “at least one schedule for at least one agent are being performed”. Later on, it can also be argued that such “computational module” as a tool would merely apply the abstract idea [when more granularly tested per MPEP 2106.05(f) (2)(v)] or would represent a technological environment or field of use [when more granularly tested per MPEP 2106.05(h)] narrowing the abstract scheduling or configuration. For now, given the preponderance of evidence above, the “computational module”, does not preclude the claims to recite, describe or set forth the abstract exception. Thus, the argument is unpersuasive.
Remarks 07/01/2026 p.12 ¶2-p.13 ¶1 further argues the claims do not describe or set forth the abstract structuring a workforce, because the recited total number of agents is not merely a count of human workers organized for an economic or business purpose in the abstract, but rather a parameter that governs the provisioning of cloud-based computing infrastructure, namely the number of computational modules, in the form of scheduling units, that must be instantiated on at least one computational system to operate the tenant’s contact center technology. Thus, it is argued that, the claimed invention is more accurately characterized as automated cloud resource provisioning for tenant onboarding, than structuring a workforce.
Examiner fully considered the Applicant’s argument but respectfully disagrees that the claims do not describe or set forth the abstract structuring of a workforce, because the claims “produce at least one schedule for at least one agent” (independent Claims 1,8) and similarly “assign a contact center interaction to at least one agent based on a skill associated with the at least one agent” (independent Claim 15) including “selecting an actual optimal number of skills for the set of one or more contact centers based on the predicted optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers, wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide” (independent Claim 15, and similarly at dependent Claims 3,10). All these are abstract example of workforce structuring. Also, although not entirely claimed, Applicant repeatedly asserts at Remarks 07/01/2026 p.8 ¶4, p.11 ¶1, p.13 ¶1, p.17 ¶2 that the claims do provision scheduling infrastructure during onboarding of a new client which would also correspond to a commercial and/or fundamental economic practice, falling within the abstract certain methods of organizing human activities.
Equally important, despite the Applicant’s allegation to the contrary, at no point do the claims recite automated cloud. Thus, the Applicant’s interpretation of the claims as automated cloud resource provisioning for tenant onboarding, is uncorroborated and flawed.
Examiner further submits, in the arguendo, without conceding, just for sake of argument, that even if the claims would explicitly recite, automated cloud resource provisioning for tenant onboarding, they would still recite, describe or set forth the abstract fundamental economic practices and principles of resource provisioning for tenant onboarding. The fact that such resources would allegedly be, in the arguendo, automated cloud resources, would not change the abstract character of the claims. This is because, according to MPEP 2106.04(a)(2) II A, ¶2, the term fundamental, as in fundamental economic practices or principles, is not used in the sense of being old or well-known but rather as a building block of modern economy. Here, the configuration of a schedule for one or more contact centers by providing or provisioning resources, represents such a fundamental example of building block of modern economy no matter if such resources are human resources or computing resources be it hardware, software computational module, or even cloud resources, as alleged by Applicant above. In a similar vein, MPEP 2106.04(a)(2) III C # 2, has separately demonstrated that a computer environment upon which to perform the abstract processes does not preclude the claims to recite, describe or set forth the abstract exception. Later, it will similarly be shown that, as tested per MPEP 2106.05(h), the narrowing the abstract processes, to technological environment, or field of use, as argued here by the yet to be claimed cloud resources, would also not integrate the abstract exception into a practical application or provide significantly more.
Based on the preponderance of legal evidence above, the Examiner finds the Applicant’s argument unpersuasive.
Remarks 07/01/2026 p.13 ¶2 argues the weighted-average computation of independent Claim 1 is distinguishable from the resampled statistical analysis in SAP and the iterative alarm-limit recalculation in Flook, because it recites that the output of a weighted-average computation, namely the predicted number of scheduling units, used to select an actual required number of scheduling units, which in turn is used to initiate, on at least one computational system, at least one scheduling unit, wherein each scheduling unit is a computational module running on the computational system and is configured to produce at least one schedule for at least one agent. The claimed weighted average therefore is argued not to terminate in the production of an abstract numerical output, but rather directly determine how many computational modules are instantiated and executed on the underlying computational system, a structural and operational consequence to the computing system itself.
Examiner fully considered the Applicant’s argument but respectfully disagrees.
- First, as an issue of claim construction or claim interpretation, the Examiner notes that sister independent Claim 15 is yet to recite the weighted-average computation.
- Second with respect to the argued Claim 1, the fact that the “weighted average” “predicting” of the “number of” [resources or] “scheduling units” based on comparing “standard” [business practices] “corresponding to the total number of agents” to “one or more existing sets of contact centers”, is further used to “select an actual required number of” [resources or] “scheduling units for the set of one or more contact centers based on the predicted number of” [resources or] “scheduling units and the number of regions” to further “initiate on at least one computational system, at least one scheduling unit” to finally “produce at least one schedule for at least one agent”, does not render said independent Claim 1 and similarly independent Claim 8, less abstract and eligible because, said “weighted average” “predicting”, represent mathematical relationships expressed in words of MPEP 2106.04(a)(2) I A, used to implement, through equally abstract evaluation, as encompassed by MPEP 2106.04(a) III ¶2, the equally abstract commercial and/or fundamental, economic practices and principles of MPEP 2106.04(a)(2) II A, B, “to produce at least one schedule for at least one agent” at the last limitation of independent Claim 1 in the “configuring a set of one or more contact centers” as summarized at the preamble of independent Claim 1, with the “initiating on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units” as argued by Applicant above at independent Claim 1, representing the computer environment or computer tools upon which said abstract processes are performed. Such computer environment or computer tools, as identified above, do not preclude the recitation of the abstract exception as tested per MPEP 2106.04(a)(2) III C #1,#2,#3, or at most represent use or invocation of tools to apply the aforementioned abstract processes, which according to MPEP 2106.05(f)(2)(i),(iii),(v) etc., as later tested below, do not integrate the abstract exception into a practical application or provide significantly more.
Based on the preponderance of legal evidence above, the Examiner finds the Applicant’s argument unpersuasive.
Remarks 07/01/2026 p.14 ¶1 argues that different than BSG Tech v. Buyseasons and Interval Licensing v. AOL as identified by Non-Final Act 03/25/2026 p.14 ¶1, p.23 ¶2 etc., the
independent claim 1 does not merely provide historical or recommended information to a human
administrator for review, but recites, in the alternative and independent of any human review step, that the actual required number of scheduling units used to automatically initiate computational modules on a computational system, with the initiating step directly modifying the operative resource allocation of the computing system rather than merely displaying information to a person.
Examiner fully considered the argument but is unpersuasive to render the claims eligible.
Specifically, in Interval Licensing, cited by MPEP 2106.04(a)(2) II C, the patentee claimed acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content.
Similarly, BSG, as cited by MPEP 2106.05(a) I provided historical usage information to users while inputting data, to improve the quality and organization of information added to a database, which was found to represent an improvement to the information stored by a database which is not equivalent to an improvement in the database’s functionality
Here, similar to acquiring content from an information source, as in Interval Licensing, and similar to the historical usage information found ineligible in BSG, the current independent Claims 1,8, consider “(i) a standard number of scheduling units associated with a standard configuration corresponding to the total number of agents and (ii) an average actual number of scheduling units for one or more existing sets of contact centers having a number of agents corresponding to the standard configuration”, for “selecting an actual required number of scheduling units”.
Also here, similar to the subsequent controlling the timing of the display of acquired content and displaying the content found ineligible in Interval Licensing, the current independent Claims 1,8, are “outputting the actual required number of scheduling units” subsequent to “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions”;
Finally, here, similar to acquiring an updated version of the previously-acquired content when the information source updates its content, found ineligible in Interval Licensing, the current independent Claims 1,8, “produce at least one schedule for at least one agent”.
Thus, while not necessarily identical, the current claims still describe or set forth concepts that are not meaningfully different than the abstract concepts of Interval Licensing, and BSG.
Examiner finds nothing at Remarks 07/01/2026 p.14 ¶1 that would preclude the current claims to recite, describe or set forth the abstract concepts above as demonstrated above.
Applicant’s response to the prior Claim Rejections - 35 USC 112
Remarks 07/01/2026 p.14 last ¶-p.15 ¶2 argues independent claims 1,8 were amended as suggested by Examiner and, as such, the 112(b) rejection in the prior art should be withdrawn.
Examiner fully considered the 112(b) argument which is found unpersuasive.
Examiner first notes that Non-Final Act 03/25/2026 p.25-p.26 ¶1 recommended:
Claims 1,8 to be amended to each recite, as an example only:
- wherein each scheduling unit of the scheduling units groups a plurality of the total number of agents into groups, each group with common scheduling requirements;
Now, the Examiner notes that the Applicant’s 07/01/2026 amendment, amended
Claims 1,8 is a manner different than what was recommended, to recite, among others:
- wherein each scheduling unit of the scheduling units groups one or more of a plurality of the ; [bolded emphasis added].
Claims 1,8 thus remain vague and indefinite because it is unclear, how “one” [single] “agent” as broadly covered by the breadth of expression “one or more of a plurality of the total number of agents”, can himself or herself be grouped into a “group”. It is also unclear to whom would said “one” [single] “agent” have “common scheduling requirements” to.
Claims 1,8 are recommended to be amended to each recite, as an example only:
- wherein each scheduling unit of the scheduling units groups ;
OR
Claims 1,8 are recommended to be amended to each recite, as another option as:
- wherein each scheduling unit of the scheduling units groups one or more pluralities of the total number of agents into groups, each group with common scheduling requirements;
Clarification and/or correction is/are required.
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Applicant’s response to the prior Claim Rejections - 35 USC 101
Step 2A Prong One: Remarks 07/01/2026 p.16 ¶2-¶3 cites Original Specification ¶ [0063] to argue independent Claim 1, involve instantiating computational modules on computational systems, namely: “initiating on at least one computational system, at least one scheduling unit” ... “each scheduling unit comprising a computational module running on the at least one computational system” which cannot be performed mentally or with pen and paper, and are not organizing human activity. Remarks 07/01/2026 p.16 ¶4-p.17 ¶1 argues sister independent Claim 15 similarly “initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills, wherein each skill management platform is configured to assign a contact center interaction to at least one agent based on a skill associated with the at least one agent, each skill management platform comprising a computational module running on the at least one computational system” requiring instantiating computational modules, argued as technological operation beyond abstract concepts.
Further, Remarks 07/01/2026 p.17 ¶2 further argues the claims are directed to a technological solution for automated provisioning of cloud-based computing resources used to operate contact center technology for an onboarding tenant, rather than to a fundamental economic practice or a mental process performed on a generic computer, and do not describe or set forth the abstract structuring a workforce, because the recited total number of agents is not merely a count of human workers organized for an economic or business purpose in the abstract, but rather a parameter that governs the provisioning of cloud-based computing infrastructure, namely the number of computational modules, in the form of scheduling units, that must be instantiated on at least one computational system to operate the tenant’s contact center technology. Thus, it is argued that the claimed invention is more accurately characterized as automated cloud resource provisioning for tenant onboarding, than structuring a workforce.
Examiner fully considered the Step 2A prong one arguments but respectfully disagrees.
* First, Examiner addresses Remarks 07/01/2026 p.17 ¶2 to resubmit and maintain that the claims still recite, or at minimum describe or set forth the abstract structuring of a workforce because the claims still “produce at least one schedule for at least one agent” (independent Claims 1,8) and similarly “assign a contact center interaction to at least one agent based on a skill associated with the at least one agent” (independent Claim 15) including “selecting an actual optimal number of skills for the set of one or more contact centers based on the predicted optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers, wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide” (independent Claim 15). All these are abstract example of workforce structuring. Applicant repeatedly admits at Remarks 07/01/2026 p.8 ¶4, p.11 ¶1, p.13 ¶1, p.17 ¶2 that the claims provision scheduling infrastructure during onboarding of a new client which sets for the commercial or fundamental practices of certain methods of organizing human activities. Equally important, despite the Applicant’s allegation to the contrary, at no point do the claims recite such automated cloud. Thus, Applicant’s interpretation of the claims as automated cloud resource provisioning for tenant onboarding, is both uncorroborated and flawed. Examiner further submits, in the arguendo, without conceding, just for the sake of argument, that even if the claims would actually recite, automated cloud resource provisioning for tenant onboarding, they would still recite, describe or set forth the abstract fundamental economic practices and principles of resource provisioning for tenant onboarding, even if such resources would be automated cloud resources. This is because according to MPEP 2106.04(a)(2) II A, ¶2, the term fundamental, as in fundamental economic practices or principles, is not used in the sense of being old or well-known but rather as a building block of modern economy. Here, the configuration of a schedule for one or more contact centers by providing or provisioning resources, represents such a fundamental example of building block of modern economy no matter if such resources are human resources or computing resources be it hardware, software or a computational module, skill management platform, or even cloud resources as alleged by Applicant above. As such the argued solution, of Remarks 07/01/2026 p.17 ¶2 is not a technological solution and the argued problem is not a technological problem.
In a similar vein, as demonstrated above, MPEP 2106.04(a)(2) III C # 2, has separately demonstrated that a computer environment, as identified above, upon which to perform the abstract processes does not preclude the claims to recite, describe or set forth the abstract exception. Later, it will similarly be shown, as tested per MPEP 2106.05(h), that narrowing the abstract processes, to technological environment, or field of use, as alleged here as cloud resources by Applicant above, does not integrate the abstract exception into a practical application or provide significantly more.
** Second, Examiner further addresses the “initiating” argument at Remarks 07/01/2026 p.16 ¶2-¶3 for Claim 1, and Remarks 07/01/2026 p.16 ¶4-p.17 ¶1 for Claim 15, by resubmitting that here, independent Claims 1,8 merely recite: initiating/initiate on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units”. Also, here, independent Claim 15 merely recites initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills.
However, the Examiner again notes that Original Specification does not disclose what the term “initiating” means, as in “initiating” / “initiate on at least one computational system, at least one scheduling unit” at independent Claims 1,8 and “initiating on at least one computational system, at least one skill management platform” at independent Claim 15. To address this the Applicant cites at Remarks 07/01/2026 p.16 ¶3, to Original Specification ¶ [0063]. Yet, the Original Specification ¶ [0063] fails to define what such “initiating” means. In the absence of a clear, deliberate and explicit definition for the term “initiating”, Examiner applies the broadest reasonable interpretation, as instructed by MPEP 2111, and interprets the term initiating” “at least one scheduling unit” in light of Original Specification ¶ [0104] as setting up, configuring, starting, generating for scheduling resources for the “one or more contact centers” based on their need, require or demand represented here by “the actual required number of scheduling units” (independent Claims 1,8) or “based on the actual optimal number of skills” (independent Claim 15) which still fall well within the equally abstract fundamental economic practices or principle, interactions and/or managing of such interactions as listed by MPEP 2106.04(a)(2) II A,B,C. As a non-limiting example, MPEP 2106.04(a)(2) II B cites In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009), to show that structuring a workforce still falls within the abstract grouping of Certain Methods of Organizing Human Activities. It then follows that here grouping or structuring the work agents into computerized representation of skills (independent Claim 15) or scheduling units with common scheduling requirements (independent Claims 1,8) would also represent an example falling within Certain Methods of Organizing Human Activities, with MPEP 2106.04(a)(2) II A ¶6, 4th sentence stating that certain activity between a person and a computer still fall within the "certain methods of organizing human activity" grouping. Here, the closest evidence for “initiating” of “at least one” “scheduling unit” (independent Claims 1,8) or “skill management platform” (independent Claim 15), as an activity, via a submit button, between a person and computer, appears disclosed at Annotated Fig. 9 depicting as a list of scheduling units and a list skills. This is corroborated by Original Specification ¶ [0138] 3rd, 5th sentences: “A user may be shown a scheduling unit setup, which they may be given the opportunity to amend”…“Example screen or display 910 also shows a button displaying “Submit”, which may be configured to configure contact centers according to setups once pressed by a user”.
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Annotated excerpt of Fig. 9 from Original Fig. 9 depicting respective scheduling units
Based on the preponderance of both the legal and factual evidence above, the Examiner finds that the “initiating” of “at least one” “scheduling unit” (independent Claims 1,8) or “skill management platform” (independent Claim 15), represents an activity between a person and a computer, that according to MPEP 2106.04(a)(2) II A ¶6, 4th sentence, does not preclude the claims to describe or set forth the abstract certain methods of organizing human activity grouping.
Alternatively, when tested per MPEP 2106.04(a)(2) III C #,1,#2, #3, the Applicant’s asserted level of computerization in “initiating” of “at least one” “scheduling unit” (independent Claims 1,8) or “skill management platform” (independent Claim 15), could also be argued as a computer environment or use or a computer tool (here in light of Fig.9), upon which the abstract exception is being performed. No matter of which of the two tests is employed (i.e. MPEP 2106.04(a)(2) II A ¶6, 4th sentence, or MPEP 2106.04(a)(2) III C #,1,#2, #3), it remains clear that the argued claims still recite describe or set forth the abstract exception. Step 2A prong one. Even when the level of computerization, as asserted by Remarks 07/01/2026 p.16 ¶2-p.17 ¶1 is more granularly tested at the subsequent steps below, it merely applies the identified abstract processes, as tested per MPEP 2106.05(f), and/or narrow it to a field of use or technological environment, as tested per MPEP 2106.05(h), none of which render the claims eligible.
Step 2A Prong two: Remarks 07/01/2026 p.17 ¶3-p.20 ¶3 argues the claims integrate any alleged abstract idea into a practical application.
Step 2A Prong two - i. Remarks 07/01/2026 p.17 ¶3-p.18 ¶1, cites Original Specification ¶ [0006], ¶ [0139] to argue the claims provide improvement to contact center configuration technology. For example, it is argued that the time and skillset required by administrator or supervisor to configure the technology of contact centers may be reduced, the speed of configuring a contact center may be increased, and the accuracy and/or precision of values used to configure the contact centers may be increase[d].
Examiner fully considered the Step 2A Prong two i argument but respectfully disagrees finding it unpersuasive because here, the Applicant arrives at a conclusion of improving computer technology and computer efficiency by improvement to the abstract idea. Yet, MPEP 2106.05(a) II is clear that improvement in the abstract idea itself (e.g. fundamental economic concept) is not improvement in technology. Similarly, MPEP 2106.04 I cites Myriad, 569 U.S. at 591, 106 USPQ2d at 1979: to state even “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the 101 inquiry”. It follows that here the purported groundbreaking, innovative, or even brilliant improvement in the abstract managing, scheduling, or configuring of the “one or more contact centers” would also not satisfy the 101 inquiry. The “Myriad” rationale was further corroborated in “SAP Am, Inc v InvestPic” cited by MPEP 2106.04(a)(2) I.C(i). Digging deeper, into the rationale, it was found in SAP that “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. Here, as in SAP supra, even if one were to submit in the arguendo, that by automating the configuration (i.e. management, scheduling) of contact centers, as read in light of Original Specification ¶ [0006] 2nd sentence, ¶ [0139] 2nd sentence and cited by Remarks 07/01/2026 p.17 ¶3-p.18 ¶1, the time and skillset required by an administrator or supervisor may be reduced, to purportedly result in the increased speed of configuring a contact center, and accuracy and/or precision of values used to configure the contact centers, this ensuing benefit would still represent latent results of the alleged improvement in the efficiency of the abstract idea itself not an improvement in either actual technology or the computer itself.
Further, MPEP 2106.05(f)(2)(iii)2 demonstrates that an increased in speed in a process that comes from the capabilities of the computer, represents mere invocation of machinery that merely applies the abstract exception, which does not render the claims eligible. In a similar vein MPEP 2106.05(a) I3 states that accelerating a process such as analyzing audit log data when the increased speed comes from the capabilities of the computer, does not represent an improvement in computer-functionality. It then follows that here, the purported increase in speed in configuring, managing or scheduling the contact center as argued by Applicant above would also not render the current claims patent eligible. Also, Original Specification ¶ [0037] clarifies, right from the onset, that even the metrics used for benchmarking, namely productivity, efficient use of computational resources, efficient use of human resources, high customer service metrics, are themselves entrepreneurial and abstract, and not technological. Therefore, when tested per “Myriad” and “SAP” as cited by MPEP 2106.04, the Examiner finds that the Applicant arrives at the conclusion of increased speed of configuring (i.e. managing, scheduling) a contact center, and accuracy and/or precision of values used to configure (i.e. mange, schedule) the contact centers, by means of improvement to the abstract idea in reducing the time and skillset required by an administrator, as read in light of Original Specification ¶ [0002], ¶ [0006] 2nd sentence, ¶ [0139] 2nd sentence, with no plausibly of innovation in non-abstract application realm. This does not render the claims eligible. These findings are further corroborated by MPEP 2106.04(a)(2) II C citing Interval Licensing LLC, v. AOL Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018) to state that providing information to a person without interfering with the person’s primary activity still recited, described or set forth the abstract exception. 896 F.3d at 1344, 127 USPQ2d 1553 citing Interval Licensing LLC v. AOL, Inc., 193 F. Supp.3d 1184, 1188 (W.D. 2014)). Specifically, in Interval Licensing supra the patentee claimed an attention manager for acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content. 896 F.3d at 1339-40, 127 USPQ2d at 1555. The Federal Circuit ruled that "[s]tanding alone, the act of providing someone an additional set of information without disrupting the ongoing provision of an initial set of information is an abstract idea… 896 F.3d at 1344-45, 127 USPQ2d at 1559. It follows that here, analogous automation of managing, scheduling, or “configuring” “a set of one or more contact centers” at Claims 1,5,8,10,12,15,19, that would somehow reduce the time and skillset required by an administrator or supervisor to “configure” (i.e. schedule, manage ) “the one or more contact centers” would be equally abstract as providing information without disrupting the user in “Interval Licensing”. By such test, the improvement is not technological but rather abstract. Similarly, Versata Dev Grp, Inc v SAP Am, Inc 115 USPQ2d 1681 Fed Cir 2015 underlined the difference between improvement to entrepreneurial goal objective versus improvement germane to actual technology, and found that using fewer software tables and searches than prior-art software, to group, sort and eliminate less restrictive information did not render the claims eligible despite its dramatic improvement computer performance and ease of maintenance.
By such standards, the Examiner reiterates that here, the analogous alleged automation that would somehow reduce the time and skillset required by an administrator or supervisor to “configure” (i.e. scheduled) “the one or more contact centers” would also not render the claims eligible. Thus, Examiner finds the Step 2A Prong two - i. unpersuasive in demonstrating the claims are directed to technology, much less directed to an improvement in actual technology.
Step 2A Prong two - ii: Remarks 07/01/2026 p.18 ¶2-¶3 argues at Claims 1,8, that the quantity dependent weighting mechanism of: (1) identifying standard configuration corresponding to the total number of agents; (2) retrieving and averaging actual scheduling unit counts from multiple existing contact centers; (3) computing a weight based on the quantity of existing data points; and (4) calculating a weighted average of the standard value and the averaged historical value, cannot be performed in the human mind or reduced to a fundamental economic practice. Remarks 07/01/2026 p.18 ¶4-p.19 ¶1 further argues at Claims 3,10,15, that “selecting an actual optimal number of skills for the set of one or more contact centers based on the optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers, wherein the actual optimal number of skills is greater than the optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide” recites a rule that evaluates the resource intensity of proposed skill types and adjusts the skill taxonomy accordingly, thus argued to represent a computational optimization strategy that balances competing technical constraints (skill granularity versus agent allocation efficiency) in a manner argued to be fundamentally technological rather than abstract.
Examiner fully considered the Applicant’s Step 2A Prong two-ii argument above but respectfully disagreed finding it unpersuasive, because here, use of mathematical relationships expressed in words, such as: “predicting the predicted number of scheduling units comprises computing a weighted average” (independent Claims 1,8) using, among others “a weight assigned to the average actual number of scheduling units in the weighted average is based on a quantity of the one or more existing sets of contact centers”, (independent Claims 1,8), tested per of MPEP 2106.04(a)(2) I A, to “produce” through predict[ive] evaluation, as tested per MPEP 2106.04(a) III ¶2, and use of standard business practices, such as “standard number of scheduling units associated with a standard configuration” as compared to “a quantity of the one or more existing sets of contact centers” (independent Claims 1,8) and similarly to “select”, “optimal number of skills” (dependent Claims 3,10 and independent Claim 15), through an evaluation and judgment
(MPEP 2106.04(a) III ¶2) such that “wherein the actual optimal number of skills is greater than the optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide”
(dependent Claims 3,10 and independent Claim 15), still represent, akin to an as-is versus a to-be analysis, cognitive (MPEP 2106.04(a)(2) III) and/or commercial and fundamental, mitigative economic practices or principles (MPEP 2106.04(a)(2) II), to “produce” the equally abstract “at least one schedule for at least one agent” (last limitation of independent Claims 1,8), in “configuring a set of one or more contact centers” (summarized at the preamble of independent Claims 1,8), and to “select an actual optimal number of skills for the set of one or more contact centers” (dependent Claims 3,10, and independent Claim 15).
Equally important MPEP 2106.04(a)(2) II A, ¶2, clarifies that the term fundamental, as in fundamental economic practices or principles, is not used in the sense of necessarily being old or well-known but rather as a building block of modern economy. Here, when tested per MPEP 2106.04(a)(2) II A, ¶2, the “scheduling units”, represent such building blocks of modern economy, in the abstract scheduling or assigning at least one agent for “configuring a set of one or more contact centers”, no matter whether or not said “scheduling units” were old or well-known, to compris[e] a computational module running on the at least one computational system. This rationale is analogous to MPEP 2106.04(a)(2) III C finding that an abstract process performed in a computer environment (MPEP 2106.04(a)(2) III C #2) or by use of a computer as a tool MPEP 2106.04(a)(2) III C #3), does not preclude the recitation of the abstract exception. Further still,
MPEP 2106.05(f)(2) (i),(iii),(v) articulates that use of computer or other machinery or tools to apply the aforementioned abstract processes, also do not integrate the aforementioned abstract exception into a practical application.
Based on the preponderance of the legal evidence above, the Examiner finds the Step 2A Prong two - ii argument unpersuasive.
Step 2A Prong two - iii: Remarks 07/01/2026 p.19 ¶2 argues the characterization of the claims as Certain Methods of Organizing Human Activities fails to account for these specific technical features. Said claims are argued as not directed to the abstract goal of organizing agents or managing contact centers, they are directed to specific computational methodologies (weighted average prediction and resource-aware optimization) that are integrated into the technological process of configuring computational systems. It is then argued that the fact that the configured systems may ultimately be used in contact centers does not render the configuration process itself abstract, just as claims to configuring database systems or network routers would not be abstract merely because the configured systems store or route information related to human activities.
Examiner fully considered the Step 2A Prong two-iii argument above but respectfully disagrees finding it unpersuasive because here, far from any configuring of database systems or network routers, as raised above by Remarks 07/01/2026 p.19 ¶2, the current claims simply use weighted average prediction (independent Claims 1,8), as example of mathematical relationships expressed in words (MPEP 2106.04(a)(2) I A) along with skill selection, as an example of fundamental mitigative practice (MPEP 2106.04(a)(2) II A) such that “the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types required to provide a low number of agents, and wherein the actual optimal number of skills is less than the predicted optimal number of skills when the one or more proposed skill types required to provide a high number of agents” (dependent Claims 3,10 and independent Claim 15).
Thus, here, unlike the configuring of database systems or network routers, as raised by Remarks 07/01/2026 p.19 ¶2, the claims’ character as a whole, remains undeniably abstract.
Accordingly, the Step 2A Prong two – iii argument is found unpersuasive.
Step 2A Prong two-iv: Remarks 07/01/2026 p.19 ¶3-p.20 ¶1 argues that here, as in DDR Holdings, LLCv. Hotels.com, L.P., 773 F.3d 1245, 1257 (Fed. Cir. 2014), the claims recite specific technological solutions (weighted average prediction and resource-aware optimization) to the problem of configuring contact center computational infrastructure, rather than simply automating conventional manual configuration processes. Similarly, Remarks 07/01/2026 p. 20 ¶2 -¶3 argues that similar to Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336-38 (Fed. Cir. 2016) and the USPTO's SMED memorandum, the claims recite specific computational architecture: scheduling units comprising computational modules running on computational systems, initiated based on predicted and selected numbers.
Examiner fully considered the Step 2A Prong two-iv argument above but respectfully disagrees finding it unpersuasive because here, far from a patent eligible real-world technological application, the current claims simply use weighted average prediction (independent Claims 1,8), as example of mathematical relationships expressed in words (MPEP 2106.04(a)(2) I A) along with skill selection, as an example of fundamental mitigative practice (MPEP 2106.04(a)(2) II A) such that “the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types required to provide a low number of agents, and wherein the actual optimal number of skills is less than the predicted optimal number of skills when the one or more proposed skill types required to provide a high number of agents” (dependent Claims 3,10 and independent Claim 15).
At no point do the amended claims provide anything remotely analogous to the plurality of classification structures for repeated extraction and importing as required precursors for the mapping, for the self-referential data structures, as was the case in Enfish, 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016) as cited by MPEP 2106.04(a). Also, at no point do the amended claims provide anything remotely analogous to the systems and methods of generating a composite webpage that combines certain visual elements of a host website with the content of a third-party merchant, as in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 113 USPQ2d 1097 (Fed. Cir. 2014), as cited by MPEP 2106.05(d). Digging deeper into DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1248, 113 USPQ2d at 1099, the Examiner finds the Court ruled that the eligible claim had additional elements that amounted to significantly more than the abstract idea, because they modified conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, which differed from the conventional operation of Internet hyperlink protocol that transported the user away from the host’s webpage to the third party’s webpage when the hyperlink was activated.
Here, there is nothing similar to such patent eligible technological arrangement.
According, the Step 2A Prong two-iv is found unpersuasive.
Step 2B: Remarks 07/01/2026 p.20 ¶4-p.23 ¶4 argues the claims recite significantly more.
Step 2B i: Remarks 07/01/2026 p.20 ¶5-p.21 ¶1 argues independent Claims 1,8 represent non-conventional and non-generic technical solution combining (i) standard number of scheduling units from a theoretical configuration with (ii) an average actual number from existing contact centers, where the weight assigned to the empirical data depends on the quantity of existing examples. It is argued that this quantity-dependent weighting mechanism provides a specific technical solution to the problem of making predictions when limited historical data is available: when few existing contact centers are available for comparison, the prediction relies more heavily on standard configuration (theoretical baseline), as more historical examples become available, the prediction increasingly incorporates empirical evidence. It is argued that this is significantly more than the abstract idea because it provides a concrete technical approach to balancing theoretical models against empirical data in a manner that adapts based on data availability.
Likewise, Remarks 07/01/2026 p.21 ¶2 argues that the resource-aware skill count adjustment recited in dependent Claims 3,10 and independent Claim 15 similarly represents a non-conventional and non-generic technical solution that adjusts the number of skill categories based on the resource intensity of proposed skill types: increasing the skill count when proposed skills require few agents (allowing greater granularity without excessive specialization) and decreasing the skill count when proposed skills require many agents (consolidating the taxonomy to avoid over-fragmentation), as a specific optimization strategy that balances competing technical constraints in contact center configuration. This is similarly argued to represent significantly more than any alleged abstract idea because it allegedly provides a concrete technical rule for optimizing skill taxonomy based on resource requirements, a specific solution to the technical problem of configuring contact center systems efficiently.
Examiner fully considered the Step 2B i but respectfully disagrees finding it unpersuasive.
Examiner first notes that the Applicant’s solution of the prediction relying more heavily on the standard configuration (theoretical baseline) when few existing contact centers are available for comparison, and then the prediction increasingly incorporates empirical evidence as more historical examples become available, as alleged by Applicant at Remarks 07/01/2026 p.21 ¶ 1,
is not reflected in the actual language of independent Claims 1,8. Similarly, there is no recitation in active voice or active diathesis of increasing the skill count when proposed skills require few agents (allowing greater granularity without excessive specialization) and decreasing the skill count when proposed skills require many agents (consolidating the taxonomy to avoid over-fragmentation), as equally alleged by Applicant at Remarks 07/01/2026 p.21 ¶2
Examiner reminds the Applicant that the “101 inquiry must focus on language of Asserted Claims themselves” as in “Synopsys, Inc. v Mentor Graphics Corp, U.S. Court of Appeals Federal Circuit, No 2015-1599, October 17 2016 2016 BL 344522 839 F3d 1138” citing “Accenture Global Servs., GmbH
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1343, 1346 113 USPQ2d 1354 (Fed. Cir. 2014): We focus here on whether the claims of the asserted patents fall within the excluded category of abstract ideas”, cert. denied, 136 S Ct 119, 193 L. Ed. 2d 208 2015). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”.
Indeed, here, as attested by Remarks 07/01/2026 p.20 ¶5-p.21 ¶1 the argued independent Claims 1,8 merely predict the predicted number of scheduling units using (i) a standard number of scheduling units from a theoretical configuration with (ii) an average actual number from existing contact centers, where the weight assigned to the empirical data depends on the quantity of existing examples. Similarly, dependent Claims 3,10 and independent Claim 15, far from an active increase and decrease of a skill count, merely recite at an “wherein” limitation, tested per
MPEP 2111.04 I, “wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide”.
Thus, the Examiner finds that such solution still addresses the abstract prediction model for the equally abstract scheduling “for at least one agent” (independent Claims 1,18) and “assign a contact center interaction to at least one agent based on a skill associated with the at least one agent” (independent Claim 15), not an improvement in actual technology or the computer itself. Specifically here, use of mathematical relationships expressed in words, such as: “predicting the predicted number of scheduling units comprises computing a weighted average” (independent Claims 1,8) using, among others “a weight assigned to the average actual number of scheduling units in the weighted average is based on a quantity of the one or more existing sets of contact centers”, (independent Claims 1,8), tested per of MPEP 2106.04(a)(2) I A, to “produce” through predict[ive] evaluation, as tested per MPEP 2106.04(a) III ¶2, and use of standard business practices, such as “standard number of scheduling units associated with a standard configuration” as compared to “a quantity of the one or more existing sets of contact centers” (independent Claims 1,8) and similarly to “select”, “optimal number of skills” (dependent Claims 3,10 and independent Claim 15), through an evaluation and judgment (MPEP 2106.04(a) III ¶2) such that “wherein the actual optimal number of skills is greater than the optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide” (dependent Claims 3,10 and independent Claim 15), still represent, akin to an as-is versus a to-be analysis, cognitive (MPEP 2106.04(a)(2) III) and/or commercial and fundamental economic or mitigative practices or principles (MPEP 2106.04(a)(2) II), to “produce” the equally abstract “at least one schedule for at least one agent” (last limitation of independent Claims 1,8), in “configuring a set of one or more contact centers” (summarized at the preamble of independent Claims 1,8), and to “select an actual optimal number of skills for the set of one or more contact centers” (dependent Claims 3,10, and independent Claim 15).
Examiner also submits, in the arguendo, without conceding, just for the sake of argument, that even if the current independent claims 1,8 above would reflect a prediction relying more heavily on the standard configuration (theoretical baseline) when few existing contact centers are available for comparison, and then the prediction increasingly incorporates empirical evidence as more historical examples become available, such scenario would still represent improvement in the abstract exception, not an improvement in technology. The same rationale would apply, in the arguendo, at dependent Claims 3,10 and independent Claim 15 for actively increasing the skill count when proposed skills require few agents (allowing greater granularity without excessive specialization) and decreasing the skill count when proposed skills require many agents (consolidating the taxonomy to avoid over-fragmentation).
Thus, even before reaching Step 2B of the analysis, the Examiner discovers that, such improvement in the abstract exception, does not render the claims less abstract and eligible when tested per MPEP 2106.04(d)(1). Such rationale is justified by SAP Am, Inc v InvestPic, as cited by MPEP 2106.04(a)(2) I. C (i) that used of a resampled statistical model to analyze or forecast data, which had an analogous problem of not having a normal probability distribution, yet was similarly found to be directed to the abstract exception. In a similar vein, the Supreme Court also found that an iterative formula for computing an alarm limit, by repeatedly substituting the model with a most recent model, remained patent ineligible. see Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978), as cited by MPEP 2106.04(a)(2) I. Specifically, in Flook, the process was repeated at selected time intervals, and in each updating computation, the most recently calculated alarm base [akin here to what Applicant alludes as standard configuration or theoretical baseline] and the current measurement of process variable was substituted for the corresponding numbers in the original calculation [akin here to what Applicant alludes as the increasing incorporation of empirical evidence as more historical examples become available, the prediction].
Further still, separate from the evidence above, an argument can be also made, that the lack of data, as alleged here, when few existing contact centers are available for comparison, sets forth an equally abstract entrepreneurial problem of the fundamental economic practices, of “configuring contact centers” “to produce at least one schedule for at least one agent”, when tested per MPEP 2106.04(a)(2) II A. Its solution, to consider both (i) a standard number of scheduling units from a theoretical configuration with (ii) an average actual number from existing contact centers, as raised by Remarks 07/01/2026 p.20 ¶5-p.21 ¶1, is also abstract and entrepreneurial rather than technological. To corroborate such rationale, Examiner again points to MPEP 2106.04(a)(2) II A ¶2 to clarify that the term fundamental, as in a fundamental economic practice, is not used in the sense of necessarily being old or well-known, but rather as a building block of modern economy. Here, the alleged improvement in the prediction of scheduling units to address the insufficiency of data as raised by Remarks 07/01/2026 p.20 ¶5-p.21 ¶1, would present such abstract, fundamental, building block of modern economy regardless of whether or not such abstract improvement would have been old or well-known. Thus, the claims character, as a whole, remains undeniably abstract, no matter whether or not the abstract manipulation argued by Applicant Remarks 07/01/2026 p.21 ¶2, last sentence is conventional or routine. This finding also runs complementary to MPEP 2106.04(d)(1) establishing that improvement in the judicial exception itself is not an improvement in technology.
Based on the preponderance of the legal evidence above, Examiner finds Step 2B i unpersuasive.
Step 2B ii: Remarks 07/01/2026 p.21 ¶3-p.22 ¶2, cites BASCOM Global Internet Services v. AT & T Mobility LLC, 827 F .3d 1341, 13 50 (Fed. Cir. 2016), to argue the independent claims recite a specific technical process for automatically configuring contact center computational infrastructure based on a multistep prediction and selection process, culminating in the initiation of computational modules with the ordered combination of claim elements providing an inventive concept of: (1) predicting a number of scheduling units using the weighted average methodology based on total agents; (2) selecting an actual required number based on the prediction and the number of regions; (3) outputting the result; and (4) initiating computational modules based on the selected number, represents a non-routine and non-conventional arrangement of elements that transforms the abstract concept (if any) into a patent-eligible application. Remarks 07/01/2026 p.22 ¶2 argues that the weighted average methodology, the resource-aware skill adjustment, and the overall configuration pipeline represent concrete technical solutions that improve contact center configuration technology
Examiner considered the Step 2B ii argument but respectfully disagrees finding it unpersuasive.
Examiner points to MPEP 2106.04, and 2019 PEG Advanced Module Slide 20, USPTO Memorandum-Recent Subject Matter Eligibility Decisions McRO, Inc. dba Planet Blue v. Bandai Namco Games America Inc. and BASCOM Global Internet Services v. AT&T Mobility LLC, November 2, 2016 p.2 ¶5-¶6 to assert that a particular or specific solution still needs to be a technological solution, rather than an entrepreneurial or abstract solution. Here, despite Remarks 07/01/2026 p.21 ¶3-p.22 ¶2 alleging to the contrary, the claims recite the latter not the former.
Examiner considered the argument but respectfully disagrees finding it unpersuasive by submitting that the argued features are irreconcilably different than the technological improvements of MPEP 2106.05(a), 2106.05(d) I.3, and the legal findings of BASCOM. For once, the Examiner notes that in BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341,1350-51,119 USPQ2d 1236,1243-44 (2016), as relied upon by Appellant above, and cited by MPEP 2106.05(d) I.3, the additional computer-based elements amounted to significantly more than the abstract idea due to a non-conventional and non-generic arrangement that provided a technical improvement in the art. Indeed, digging deeper into Bascom supra, the Examiner finds that its claims were found eligible because they utilized a hybrid filtering scheme implemented on ISP server, which, for its time (1990s), was found by the Federal Circuit to be a technology-based solution that filtered content on Internet to overcome existing problems with other Internet filtering systems. Specifically, in Bascom, the claims took a prior art filter solution (one-size-fits-all filter at the ISP server) and made it more dynamic and efficient (providing individualized filtering at ISP server), raising the claims to a level of software-based invention that improved performance of the computer system itself Bascom Glob. Internet Servs. v. AT&T Mobility, LLC, U.S. Court of Appeals Federal Circuit, No. 2015-1763, June 27,2016,2016 BL 204401,827 F.3d 1341.
Here however, there is nothing remotely technologically similar to the dual filtering scheme of Bascom. Rather the current claims are argued to merely recite an alleged improvement in the abstract configuration of contact centers, “to produce at least one schedule for at least one agent” (independent Claims 1,8) and “assign a contact center interaction to at least one agent based on a skill associated with the at least one agent” (independent Claim 15). Here, the (1) predicting a number of scheduling units using the weighted average methodology based on total agents; (2) selecting an actual required number based on the prediction and the number of regions; and (3) outputting the result, as alleged by Remarks 07/01/2026 p.21 ¶3 are not meaningfully different than the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, as in Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) cited by MPEP 2106.04(a)(2) III. Also, here (4) initiating computational modules based on the selected number, when tested per MPEP 2106.05(f)(2), can be argued as a computer tool or machinery to apply such abstract processes, or, when tested per MPEP 2106.05(h)(i) it can be argued as a technological field to limit the above combination of collecting information, analyzing it, and displaying certain results of the collection and analysis.
Based on preponderance of the legal evidence above, the Step 2B ii argument is unpersuasive.
Step 2B iii: Remarks 07/01/2026 p.22 ¶3 argues the amended independent claims do not preempt or monopolize the abstract idea.
Examiner fully considered the Step 2B ii argument by pointing to MPEP 2106.04 “Eligibility Step 2: Whether a Claim is Directed to a Judicial Exception I” & “MPEP 2106.07(b) Evaluating Applicant’s Response” states that the Courts do not use the narrowing argument as stand-alone test for eligibility. For example, even though the claims in Flook did not wholly preempt mathematical formula, and the claims in Mayo were directed to narrow laws that may have limited applications, the Supreme Court nonetheless held them ineligible because they failed to amount to significantly more than the recited exceptions (Flook at 589-590; Mayo at 1302 cited at July 2015 Update: Subject Matter Eligibility p.8 Section VI, p.11 footnotes 26 to 29). Examiner also submits that the Federal Circuit followed the Supreme Court’s lead in rejecting arguments that a lack of total preemption equates with eligibility (buySafe 765 F.3d at 1355; Ultramercial, 772 F.3d at 716. Also “Fairwarning Page IP, LLC v. Iatric Sys., Inc. U.S. Court of Appeals Federal Circuit, No. 2015-1985 October 11, 2016, 2016 BL 337879, 120 USPQ2d 1293” citing “Ariosa”. Also “McRO Inc. v. Bandai Namco Games Am. Inc. U.S. Court of Appeals Federal Circuit, Nos. 2015-1080-1081,-1082,-1083,-1084,-1086,-1087,-1088,-1089,-1090,-1092,-1093,-1094,-1095,-1096,-1097,-1098,-1099,-1100,-1101, September 13, 2016, 2016 BL 297537, 837 F.3d 1299, 120 USPQ2d 1091” at p.1102 last ¶ 1st sentence, and Synopsys, Inc. v Mentor Graphics Corp, U.S. Court of Appeals Federal Circuit, No 2015-1599, October 17,2016,2016 BL 344522,839 F3d 1138” citing Ariosa. Instead, the questions of preemption are inherent in the two-part framework from “Alice Corp” and “Mayo” and are resolved by using this framework to distinguish between preemptive claims, and “those that integrate the building blocks into something more. Yet, it is important to note that while a preemptive claim may be ineligible, absence of complete preemption does not guarantee that the claim is eligible. Even arguing that there are other ways to practice not to preempt an abstract idea, does not make the claim “less abstract” and eligible (“OIP Technologies, Inc. v. Amazon.com, 115 USPQ2d 1090 at page 1092 2nd to last ¶ citing buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014)” and “Accenture Global Servs., GmbH v. Guidewire Software, 728 F.3dast 1345”), or said differently “the availability of other possible computer-implemented methods […] does not assuage fears of blocking further innovation” (The Money Suite Co. v 21st Century Ins. & Fin. Co. v 21st Century Ins. & Fin again citing OIP Techs, Inc. v. Amazon.com, Inc., No. C-12-1233 EMC, 2012 WL 3985118, at *12 N.D. Cal. Sept 11, 2012. Also “[w]here a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot.” “Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015)”. “OIP Tech., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed Cir. 2015)” further corroborated by “MPEP 2106.05(h)”. Said differently, mere restatement of what was already determined as abstract idea by narrowing or reformulating the abstract idea is not enough to save the claims from ineligibility (“BSG Tech LLC v. BuySeasons, Inc., U.S. Court of Appeals, Federal Circuit, No. 2017-1980, August 15, 2018, 2018 BL 291291, 899 F.3d 1281”, p.1695 citing “SAP Am Inc. v. InvestPic LLC, No. 2017-2081, at*14, Fed. Cir May 15, 2018”). Indeed, limiting or narrowing of the abstract idea does not render the claims any less abstract. For example, MPEP 2106.04 I cites Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971 to state claims directed to "narrow laws that may have limited applications" [were still] held ineligible. Similarly, MPEP 2106.04 I cites “Flook, 437 U.S. at 589-90, 198 USPQ at 197” to state that claims that did not "wholly preempt the mathematical formula" held ineligible.
Remarks 07/01/2026 p.22 ¶4 further argues that the remarks presented for claim 1 also apply to amended independent claim 15 as well. Examiner thus reincorporates all findings and rationales with respect to independent Claim 1 to independent Claim 15.
- dependent Claim 18 -
Step 2B - iv: Remarks 07/01/2026 p.22 ¶5 states dependent claim 18 adds the prediction is performed using a machine learning algorithm based on historical data from existing contact centers. It is then argued that when combined with the weighted average methodology of the independent claims, this represents a specific technical implementation where machine learning computes the weighted average using historical scheduling unit data, a concrete technological application that provides significantly more than any alleged abstract idea.
Examiner fully considered the Step 2B iv argument but respectfully disagrees finding it unpersuasive. First, as an issue of claim construction it is noted that dependent Claim 18 does not explicitly recite historical data. Second, no matter of presence or absence of the historical data, the “using a machine learning algorithm” in “predicting” “a predicted number of scheduling units”, represents mere invocation of a computer component as tool or machinery to perform the abstract “predicting”. As demonstrated MPEP 2106.05(f)(2)(i), such invocation of a mathematical algorithm applies on computer, does not provide significantly more.
Thus, the Step 2B - iv argument is unpersuasive.
- dependent Claims 3-5, 10-12, 7,14,19 -
Step 2B-v: Remarks 07/01/2026 p.22 ¶6 states dependent Claims 3,10, recite resource-aware skill count adjustment methodology which is agued to provides significantly more than any alleged abstract idea for the reasons discussed. Similarly Remarks 07/01/2026 p.22 ¶ 7 states dependent Claims 4 and 11 add that the skill prediction is further based on actual numbers of skills for existing contact centers, thus argued to provide additional empirical grounding that represents significantly more than any alleged abstract idea. Remarks 07/01/2026 p.23 ¶ 1 further dependent Claims 5, 12, and 19, recite automatically configuring the contact centers based on the predicted and selected values, representing the culmination of the technological process in automated system configuration, which is argued as significantly more than any alleged abstract idea. Finally, Remarks 07/01/2026 p.23 ¶2 states dependent Claims 7 and 14 specify that the regions comprise time zones, providing a specific technical context for the region-based selection that represents significantly more than any alleged abstract idea.
Examiner fully considered the Step 2B-v argument but respectfully disagrees finding it unpersuasive by resubmitting that here, at dependent Claims 3,10, the use of mathematical relationships expressed in words (MPEP 2106.04(a)(2) I A), such as: “the predicted optimal number of skills” “and” “on one or more proposed skill types for the set of one or more contact centers, wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types required to provide a low number of agents, and wherein the actual optimal number of skills is less than the predicted optimal number of skills when the one or more proposed skill types required to provide a high number of agents” are used as part of an evaluation (MPEP 2106.04(a)(2) III) and/or commercial and fundamental economic or mitigative practices or principles (MPEP 2106.04(a)(2) II), to “select an actual optimal number of skills for the set of one or more contact centers” . These are integral to the abstract exception, and not representative of additional elements, much less additional elements capable to provide significantly more than what was already identified as abstract.
Similarly, the features argued above with respect to dependent claims 4,11, dependent claims 5,12,19 and dependent claims 7,14 merely narrow the abstract idea to equally abstract concepts or to an automatic technological environment (here “automatically configuring” at claims 5,12,19) as tested per MPEP 2106.05(h), or an application of the abstract processes by a computer as tool, as tested per MPEP 2106.05(f), none of which provide significantly more than what was already identified as abstract.
Examiner also incorporates all findings and rationales at Non-Final Act 03/25/2026 p.29-p.32 ¶4
Thus, the Step 2B - v argument is unpersuasive.
Objection
Claim 3 is objected for informally reciting:
- “wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types required to provide a low number of agents, and wherein the actual optimal number of skills is less than the predicted optimal number of skills when the one or more proposed skill types required to provide a high number of agents”;
* instead of *
- wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents, and wherein the actual optimal number of skills is less than the predicted optimal number of skills when the one or more proposed skill types require a high number of agents; in a manner similar to siter dependent Claim 10 and independent Claim 15.
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Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-5, 7, 8, 10-12, 14-19, and 21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Examiner notes Non-Final Act 03/25/2026 p.25-p.26 ¶1 recommended:
Claims 1,8 to be amended to each recite, as an example only:
- wherein each scheduling unit of the scheduling units groups a plurality of the total number of agents into groups, each group with common scheduling requirements;
Examiner now notes that the Applicant’s 07/01/2026 amendment, amended independent
Claims 1,8, to recite, among others:
- wherein each scheduling unit of the scheduling units groups one or more of a plurality of the ; [bolded emphasis added].
Claims 1,8 thus remain vague and indefinite because it is still unclear, how “one” [single] “agent” as broadly covered by the breadth of expression “one or more of a plurality of the total number of agents”, can himself or herself be grouped into a “group”. It is also unclear to whom would said “one” [single] “agent” have “common scheduling requirements” to.
Claims 1,8 are recommended to be amended to each recite, as an example only:
- wherein each scheduling unit of the scheduling units groups ; OR
Claims 1,8 are recommended to be amended to each recite, as another option:
- wherein each scheduling unit of the scheduling units groups one or more pluralities of the total number of agents into groups, each group with common scheduling requirements;
Claims 3-5,7,21 are dependent and rejected based on rejected parent independent Claim 1.
Claims 10-12,14 are dependent and rejected based on rejected parent independent Claim 8.
Claims 16-19 are dependent and rejected based on rejected parent independent Claim 15.
Claim 15 is independent and has been amended to recite, among others:
- selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents;
- selecting an actual optimal number of skills for the set of one or more contact centers based on the predicted optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers, wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide;
- outputting the actual optimal number of skills; and
- initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills, wherein each skill management platform is configured to assign a contact center interaction to at least one agent based on a skill associated with the at least one agent, each skill management platform comprising a computational module running on the at least one computational system.
In summary, claim 15 starts by repeatedly reciting “actual optimal number of skills” and “predicted optimal of skills” at the first and second “selecting” limitations, then recites only “the optimal number of skills” at the end of the second “selecting” limitation with no indication if it refers back to the antecedental “predicted optimal number of skills” at the first and second “selecting” limitations, and then reverts back to “the actual optimal number of skills” at “outputting” and “initiating” limitations. Therefore,
Claim 15 is rendered vague and indefinite because it is unclear if: “the optimal number of skills” as subsequently recited at the second “selecting” limitation of said claim 15, relates back to “a predicted optimal number of skills” and “the predicted optimal number of skills” as antecedently recited at the first and second “selecting” limitations of said independent Claim 15.
Claim 15 is recommended be further amended to recite, among others, and as example only
- selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents;
- selecting an actual optimal number of skills for the set of one or more contact centers based on the predicted optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers, wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the predicted optimal number of skills when the one or more proposed skill types require a high number of agents to provide; etc.
Claims 16-19 are dependent and rejected based on rejected parent independent Claim 15.
Clarifications and/or corrections are required.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1,3-5,7,8,10-12,14-19 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) set forth or describe the abstract grouping of “Certain Methods of Organizing Human Activities”, as tested per MPEP 2106.04(a)(2) II, namely: “groups one or more of a plurality of the total number of agents into groups, each group with common scheduling requirements”
“produce at least one schedule for at least one agent” at independent Claims 1,8 and similarly
“selecting an actual optimal number of skills for the set of one or more contact centers” and “assign a contact center interaction to at least one agent based on a skill associated with the at least one agent” at independent Claim 15 and dependent Claims 3,10, with further consideration for “a number of regions” at independent Claims 1,8,15, such as “time zones” per Claims 7,14, and similarly “selecting an actual optimal number of skills for the set of one or more contact centers based on the optimal number of skills” at Claims 3,10,15. The abstract, fundamental character of the claims is further corroborated by the business relationship management in the newly added dependent Claim 21 reciting “common scheduling requirements, the requirements comprising one or more of: operating days and hours, shifts, location, and department”. It is worth noting that per MPEP 2106.04(a)(2) II A ¶2 the term fundamental is not used in the sense of necessarily being old or well-known, but rather as building block of modern economy. It then follows that here, the group[ing] one or more of a plurality of the total number of agents into groups, each group with common scheduling requirements; at independent Claims 1,8 and dependent Claim 21, including group[ing] of scheduling units with common scheduling requirements, comprising one or more of: operating days and hours, shifts, location, and department, at dependent Claim 21 would similarly represent a grouping of building blocks in the business practices of “configuring one or more contact centers”. For example, with respect to “configuring the set of one or more contact centers” “to produce at least one schedule for at least one agent” at independent Claims 1,8, and “to assign a contact center interaction to at least one agent based on a skill associated with the at least one agent” at independent Claim 15, “wherein selecting the predicted optimal number of skills for the set of one or more contact centers is further based on actual numbers of skills for at least one existing set of contact centers” at dependent Claims 4,16 and similarly at Claim 11.
Examiner relies on MPEP 2106.04(a)(2) II B which states that structuring a work force4, [akin here by schedule[ing] or assign[ing] agents above] falls within the abstract commercial interactions. Examiner also points to MPEP 2106.04 (A) (2) II ¶6, 4th sentence to submit that Certain Methods of Organizing Human Activities, encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer may [still] fall within the "certain methods of organizing human activity" grouping. Thus here, even certain computerization “to produce at least one schedule for at least one agent” at independent Claims 1,8 and “to assign a contact center interaction to at least one agent based on a skill associated with the at least one agent” at independent Claim 15 would not preclude the claims from reciting, describing or setting forth Certain Methods of Organizing Human Activities. More to the point, MPEP 2106.04(a)(2) II C ii5 states that considering historical usage information while inputting data, still falls within the abstract organizing of human activities. It then follows that here, the preponderantly recited “selecting an actual required number of scheduling units” / “skills” at claims 1,3,4,8,10,11,15-17 similarly sets forth the abstract organizing of human activities.
Further, the Examiner submits that here, the “Certain Methods of Organizing Human Activities”, could be argued as implementable6 through abstract mathematical relationships expressed in words, as broadly defined by MPEP 2106.04(a)(I) A used by computer-aided mental processes, as tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, such as by computer-aided evaluation, judgement and observation. For example, MPEP 2106.04(a)(2) III cites Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); to state that examples of collecting information, analyzing it, and displaying certain results of the collection and analysis, recite the abstract mental processes.
- Here, such evaluation, judgement or analysis are set forth by: “predicting a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents” at dependent Claim 17, “predicting a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents, wherein predicting the predicted number of scheduling units comprises computing a weighted average of (i) a standard number of scheduling units associated with a standard configuration corresponding to the total number of agents and (ii) an average actual number of scheduling units for one or more existing sets of contact centers having a number of agents corresponding to the standard configuration, wherein a weight assigned to the average actual number of scheduling units in the weighted average is based on a quantity of the one or more existing sets of contact centers” at independent Claims 1,8, “scheduling units are organized into groups of scheduling units with common scheduling requirements, the requirements comprising one or more of: operating days and hours, shifts, location, and department” at dependent Claim 21; “wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide” at independent Claim 15;
- Here, such collection and judgement are set forth by: “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions” at independent Claims 1,8,17, and by “selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents”; “selecting an actual optimal number of skills for the set of one or more contact centers based on the predicted optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers” at Claims 3,10,15;
- Here such displaying or observation of certain results of the collection and analysis is set forth by: “outputting the actual required number of scheduling units” at Claims 1,8,17, and similarly “outputting the actual optimal number of skills” at dependent Claims 3, 10,15.
Examiner also points to MPEP 2106.04(a)(2) III C to submit that: # 1. Performing a mental process on a generic computer; #2. Performing a mental process in a computer environment, and #3. Using a computer as a tool to perform a mental process, do not preclude the claims from reciting, a mental process. Here, the capabilities of the “memory” at Claim 8 and the “processor” at Claims 1,3,5,8,10,12,15,17,19, to perform the aforementioned mental processes, such as
“initiating on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units” at independent Claims 1,8 and similarly “initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills” could perhaps be argued as example of # 1. Performing a mental process on a generic computer; and/or #3. Using a computer as a tool to perform a mental process, as tested per MPEP 2106.04(a)(2) III C above. Similarly, here, given its high level of generality, the recitation of “using a machine learning algorithm” in “predicting” “a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents” at dependent Claim 18, could equally be argued as example of # 1. Performing a mental process on a generic computer; and/or #3. Using a computer as a tool to perform a mental process, when tested per MPEP 2106.04(a)(2) III C above. Finally, here, the preponderant recitations of resources such as “scheduling units for the set of one or more contact centers” at Claims 1,5,8,12,17,18 and recitation of “automatically”, as in “automatically configuring the set of one or more contact centers” at Claims 5,12,19 could perhaps be argued as an example of #2, namely a computer environment upon which the abstract process is being performed.
In an abundance of caution, the Examiner will more granularly test the effect of such computer elements below. For now, it is clear that, given the preponderance of legal evidence as demonstrated above, the character as a whole of the claims remains undeniably abstract.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual or combination of the computer elements identified above appear to represent mere physical aids to implement the aforementioned abstract exception, as tested at the prior step. Even when construed, in the arguendo, as additional elements such computer elements of “memory” at Claim 8 and “processor” at Claims 1,3,5,8,10,12,15,17,19, and use of “machine learning algorithm” at Claim 18 would merely apply the abstract idea, such as applying the aforementioned business method and/or its underlining machine learning or mathematical algorithm on a computer, tested per MPEP 2106.05(f)(2)(i)7. For example “at least one scheduling unit” used for the abstract produc[ing] [of] at least one schedule for at least one agent” by applying or “initiating, on at least one computational system”, (independent Claims 1,8), and the “skill management platform” used for the abstract assign[ing] [of] a contact center interaction to at least one agent based on a skill associated with the at least one agent by applying or “initiating, on at least one computational system” (independent Claim 15) would represent invocation of computer components or machinery to execute abstract processes, representative here of business method of “configuring” “one or more contact centers” for scheduling operations. Such automation would not integrate the abstract idea into a practical application as tested per MPEP 2106.05(f)(2)(iii).
Also, the benchmarking by such computer components of a “predicted” versus “actual required number of scheduling units” (Claims 1,8) and the analogous benchmarking by such computer components of a “predicted” versus “actual” “skills” (Claims 3,10,15), could also be argued as a computerized attempt at monitoring audit log data executed on a general-purpose computer, as tested per MPEP 2106.05(f)(2) iii. This too, would not integrate the abstract exception into a practical application. Finally, any computerization, recited at a high level such as
“initiating on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units” at independent Claims 1,8, and “initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills”, at independent Claim 15, as read in light of Fig. 9 and ¶ [0138] 3rd, 5th sentences of the Original Disclosure, would constitute along with “outputting the actual required number of scheduling units” at Claims 1,8,17, and similarly “outputting the actual optimal number of skills” at dependent Claims 3,10,15, a mere requirement to use [or initiate] of software to tailor information and provide it to the user on a generic computer, as tested per MPEP 2106.05(f)(2) v8.
None of these examples, as tested MPEP 2106.05(f)(2), integrate the abstract idea into a practical application because they represent mere invocation of machinery or computer tools to apply existing processes or the abstract idea itself. In fact, MPEP 2106.05(f)(2) ¶19 is clear that use of a computer or other machinery for economic or other tasks (receive, store, or transmit data) does not integrate the abstract idea into a practical application, and then MPEP 2106.05(f)(2)10 further clarifies that the combination of computer server and telephone unit performing recording, administration and archiving of data still performing to the abstract idea in an organized manner. In a similar vein, MPEP 2106.05(h)11 states that narrowing the combination of collecting information, analyzing, and displaying certain results of the collection and analysis to a particular field of use or technological environment does not integrate the abstract idea into a practical application. It then follows that here, narrowing collecting information, analyzing, and displaying certain results of the collection and analysis to a field of use or technological environment characterized by “scheduling units for the set of one or more contact centers” at Claims 1,5,8,12,17,18 further narrowed to compris[e] “a computational module running on the at least one computational system” at independent Claims 1,8. A similar narrowing of the abstract exception to a field of use or technological environment is exemplified here by the by the automation of the “configuring the set of one or more contact centers” at dependent Claims 5,12,19, with such configuring elucidated “to produce at least one schedule for at least one agent” at independent Claims 1,8 and “to assign a contact center interaction to at least one agent based on a skill associated with the at least one agent” at independent Claim 15, with such scheduling, assigning or configuring being “based on at least the actual required number of scheduling units” at dependent Claims 5,12, and “based on at least the actual optimal number of skills” at dependent Claim 19. Thus, in addition to the equally valid apply it test of MPEP 2106.05(f), when also tested per MPEP 2106.05(h), such narrowing of the abstract exception to the aforementioned automation technological environment or field of use would equally not integrate the abstract exception into a practical application. Similarly narrowing the abstract produc[ing] [of] at least one schedule for at least one agent to “initiating”, “on at least one computational system, at least one scheduling unit” at independent Claims 1,8, and narrowing the abstract assign[ing] [of] a contact center interaction to at least one agent based on a skill associated with the at least one agent to “initiating, on at least one computational system, at least one skill management platform” at Claim 15, would also constitute narrowing abstract idea to a computerized technological environment which, per MPEP 2106.05(h) x12, would not integrate the abstract idea into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the computer elements are merely aids performing the aforementioned abstract exception, or at most represent additional elements that would merely apply the already identified abstract idea and/or link use of abstract idea to a field of use or technological environment, as tested per MPEP 2106.05(f),(h). Specifically, Examiner points to MPEP 2106.05 (d) II and carries over the finings tested per MPEP 2106.05 (f) and (h) and submits that the additional computer-based elements also do not provide significantly more. Examiner submits that the above tests show the applying of the abstract idea [MPEP 2106.05 (f)] and narrowing the abstract idea to a field of use or technological environment [MPEP 2106.05 (h)], suffice in showing that the additional computer-based elements also do not provide significantly more without having to rely on the conventionality test [MPEP 2106.05(d)]. Yet, even assuming arguendo, that further evidence would now be require to demonstrate conventionality of additional, computer elements, Examiner would point to MPEP 2106.05(d) to demonstrate conventionality of computer components performing: electronic recordkeeping13 / gathering statistics and presenting offers14, receiving/transmitting data over network, including utilizing an intermediary computer to forward information15, arranging hierarchy of groups and sorting information16, performing repetitive calculations17. Here, the electronic recordkeeping, gathering statistics, arranging hierarchy of groups, and performing repetitive calculations, are reflected in the capabilities of the “memory” and “processors” to benchmark predicted versus actual number of “scheduling units” or “skills”. Also here, the sorting of information is reflected in the alleged computerization of the preponderantly recited “selecting” of actual required number of scheduling units, predicted optimal number of skills, actual optimal number of skills etc. and possibly “wherein scheduling units are organized into groups of scheduling units with common scheduling requirements, the requirements comprising one or more of: operating days and hours, shifts, location, and department” at Claim 21 assuming arguendo computer implementation.
If necessary, the Examiner would also follow MPEP 2106.05(d) I.2.(a), and point as evidence for the conventionality of the additional elements, their interpretation as read in light of:
Original Specification ¶ [0040] reciting at high level of generality: “ML models used herein may, for example, include (artificial) neural networks (NNs), decision trees, regression analysis, Bayesian networks, Gaussian networks, genetic processes, etc. Additionally or alternatively, ensemble learning methods may be used which may use multiple/modified learning algorithms, for example, to enhance performance. Ensemble methods, may, for example, include “Random Forest” methods or “XGBoost” methods”.
As it can be seen, Applicant has not invented machine learning, nor is Applicant alleging as much.
Original Specification ¶ [0121] “Server(s) 810 and computers 840 and 250, may include one or more controller(s) or processor(s) 816, 846, and 856, respectively, for executing operations according to embodiments of the invention and one or more memory unit(s) 818, 848, and 858, respectively, for storing data (e.g., interactions) and/or instructions executable by the processor(s). Processor(s) 816, 846, and/or 856 may include, for example, a central processing unit (CPU), a digital signal processor (DSP), a microprocessor, a controller, a chip, a microchip, an integrated circuit (IC), or any other suitable multi-purpose or specific processor or controller. Memory unit(s) 818, 848, and/or 858 may include, for example, a random-access memory (RAM), a dynamic RAM (DRAM), a flash memory, a volatile memory, a non-volatile memory, a cache memory, a buffer, a short-term memory unit, a long-term memory unit, or other suitable memory units or storage units”.
Original Specification ¶ [0140] reciting at high level of generality: “Different embodiments are disclosed herein. Features of certain embodiments may be combined with features of other embodiments; thus, certain embodiments may be combinations of features of multiple embodiments. The foregoing description of the embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. It should be appreciated by persons skilled in the art that many modifications, variations, substitutions, changes, and equivalents are possible in light of the above teaching. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention”.
Original Specification ¶ [0141] reciting at high level of generality: “While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention”.
In conclusion, Claims 1,3-5,7,8,10-12,14-19,21 although directed to statutory categories (methods or processes at Claims 1,3-5,7,21 and Claims 15-19, and system or machine at Claims 8,10-12,14) they still do recite, describe or set forth the abstract exception (Step 2A prong one), with no additional, computer-based elements, capable to integrate, either alone or in combination the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B).
Therefore, the Claims 1,3-5,7,8,10-12,14-19 and 21 are believed to be patent ineligible.
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Rejections under 35 § U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,5,7,8,12,14 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over:
Leggett; Ernest W. US 5185780 A hereinafter Leggett, in view of
Dong et al, US 20240362550 A1 hereinafter Dong. As per,
Claims 1,8 Leggett teaches or suggests “A method for configuring a set of one or more contact centers, the set of one or more contact centers associated with a total number of agents and a number of regions, the method comprising, using a computer processor”: / “A system for configuring a set of one or more contact centers, the set of one or more contact centers associated with a total number of agents and a number of regions, the system comprising: a memory; at least one processor configured to” (Leggett column 6 lines 33-38):
- “predicting a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents” (Leggett column 3 lines 62-65: The invention also facilitates the efficient management of the individual agents of the management unit based on real-time performance statistics and meaningful display of the generated agent schedules. For example, at column 6 lines 6-10, 24-26: With reference to Figs.1-2, a team of call center agents is organized into management units, each management unit (MU) having predetermined number of agent groups. column 7 lines 14-21: if there are 4 MU’s expected [or predicted] to have equivalent amounts of staffing but MU1, closes 1 hour before the other MU's, the call center supervisor can setup the allocations for each MU at 25% until the end of the day, when the other 3 MU’s are then each allocated 33.3% of the calls. When MU1 closes, no falloff in service level then occurs. tour templates are correlated with the forecast to generate a set of tours for each management unit. column 18 lines 38-42: The number of MU agents required for the reforecast MU call volumes is calculated using an Erlang C method),
- “and”
- “wherein each scheduling unit of the scheduling units groups one or more of a plurality of the total number of agents into groups, each group with common scheduling requirements” (Leggett column 6 lines 6-15, 18-20: with reference to Figs.1-2, a team of call center agents is organized into management units, each MU having predetermined number of agent groups. An MU is thus a set of agent groups managed locally as single unit. In this manner, each management unit can have its individual work rules and hours of operation. One team can therefore handle one call type while another team handles 2nd call type. Similarly, column 6 lines 42-45: organizing the team of agents into a plurality of management units, each management unit having one or more groups of individual agents. column 18 lines 17-21: Staff column 84 of Fig.9 is the staffing for the MU. The required MU values (Req) are calculated from the required team values and the MU allocations for the shift)
- “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions”; (Leggett column 6 lines 20-23: the system accommodate not only geographical dispersal of management units but also multiple call types dispersed among multiple management units and multiple geographical locations. For example, at column 7 lines 14-21: 4 MU’s expected to have equivalent amounts of staffing but MU1 closes 1 hour before the other MU's, the call center supervisor setup the allocations for each MU at 25% until end of day, when the other 3 MU’s are then each allocated 33.3% of calls. Such scheduling function is disclosed at column 8 lines 12-22: to allocate work hours according to staffing requirements that have been forecast. Scheduling has 3…components: tour generation, agent assignment and schedule generation…. Tour generation is the process of matching the staffing requirements with staffing possibilities, defined by staffing restrictions such as hours of operation. column 8 lines 31-34, 37-41: referring to Fig.4, generate tours routine 52 is used to create tours for theoretical agents of each management unit based on the tour templates and forecast FTE requirements for a particular period. Thereafter, the supervisor(s) assign agents to generated tours using a list of named agents. Alternatively, agents can be assigned using an automatic process instead of manually as described below.
Leggett column 12 lines 45-50: The method begins at step 73 by calculating the offered load a. At step 75, Erlang C calculation C(n,a) is tun for n=a+1, which is minimum agents for which meaningful Erlang C calculation can be made. column 13 lines 57-column 14 line 4: Thereafter, according to the method of Fig.7, 2 initial guesses (namely, the minimum number of agents n and n+1) are used as predictor values and a first loop is run up in step 85 to determine a value (100-ePWt) which is approximately desired service level. At this point the method checks the estimate by calculating Erlang C for p-1, p and p+1. Calculating the actual C(p-1,a), C(p,a) and C(p+1,a) and service level exact values, 1 of 3 is hopefully a winning value. Generally, the winning value will be the central predicted value p for most common input data. Stated differently, the routine uses the numbers for n and n+1 to predict the value p which brings a result close to the desired objective. The Erlang C loop is then continually run up to calculate C(P-1,a), C(p,a) and C(p+1,a). column 18 lines 17-20:The Staff column 84 of Fig.9 is the staffing for the MU. The required MU values (Req) are calculated from the required team values and the MU allocations for the shift.
- “outputting the actual required number of scheduling units”
(Leggett column 4 lines 34-40: Staffing changes at the management unit are transmitted to the centralized computer of the force management system then regularly broadcast back to the other management units of the system. The performance analysis screen at the management unit is thus continuously updated with modified team call handling performance data. column 16 lines 8-11: The screen includes a Management Unit identifier field 70 to identify the MU performance data being displayed. The performance analysis screen shows the MU's allocation of team data
Leggett column 17 lines 6-15: local staffing changes at MU level are transmitted back to the central computer databases and rebroadcast back to all affected management units in the system. Therefore, all of MU supervisors can continuously view the team statistics even as local staffing changes are dynamically implemented at other management units
Leggett column 18 lines 17-20: Staff column 84 of Fig.9 is the staffing for the MU), “and”
- “initiating on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units, wherein each scheduling unit is configured to produce at least one schedule for at least one agent,
(Leggett column 2 lines 23-25: The invention thus provides the flexibility and control to properly take advantage of the capacities of modern digital switches. Specifically, per column 7 lines 5-23: Following the generation of a forecast, the method continues at step 31 to allocate the expected call load among the management units. This function enables centralized computer 12 of overall team to distribute responsibility for answering calls according to expected available staffing as administratively determined. Moreover, the allocation is variable by each ½ hour of each day of the week enabling the call center to realize significant facilities and management cost savings. For example, if there are 4 MU's expected to have equivalent amounts of staffing but MU1, closes 1 hour before the other MU's, the call center supervisor can setup the allocations for each MU at 25% until the end of the day, when the other three MU's are then each allocated 33.3% of calls. When MU1 closes, no falloff in service level then occurs. Of course, in operation, incoming calls always go to first available agent, regardless of the location of that agent. column 6 lines 15-18: a single telephone switch or module may likewise be associated with more than one team, and more than one MU can be associated with more than one team.
Leggett column 7 lines 5-23: Following the generation of a forecast, the method continues at step 31 to allocate the expected call load among the management units. This function enables centralized computer 12 of overall team to distribute responsibility for answering calls according to expected available staffing as administratively determined. Moreover, the allocation is variable by each ½ hour of each day of the week enabling the call center to realize significant facilities and management cost savings. For example, if there are 4 MU's expected to have equivalent amounts of staffing but MU1, closes 1 hour before the other MU's, the call center supervisor can setup the allocations for each MU at 25% until the end of the day, when the other three MU's are then each allocated 33.3% of calls. When MU1 closes, no falloff in service level then occurs. Of course, in operation, incoming calls always go to first available agent, regardless of the location of that agent.
Leggett column 17 lines 6-12: local staffing changes at the MU level are transmitted back to the central computer databases and are rebroadcast back in real-time as data is received to all affected management units in the system column 18 lines 17-20,31-51,66-67: Staff column 84 of Fig.9 is staffing for the MU. required MU values (Req) are calculated from required team values and the MU allocations for the shift. As described above with respect to the intra-day reforecasting capability, every ½ hour throughout the shift, call volumes for the rest of the shift are recalculated based on the actual call volume data received earlier in the shift. The recalculation is performed by subprocess 116. Again, the calls are forecasted for the MU on an allocated basis. The number of MU agents required for the reforecast MU call volumes is calculated using an Erlang C method. An example of intra-day reforecasting provided by subprocess 116 can now be described. According to the technique, reforecast ratio (Rf) is first generated equal to the summation of Actual data divided by summation of Forecast data for periods having actual data. A so-called reality ratio is then generated and is defined as equal to N/(N-1)2, where N is the number of periods of actual data. When actual MIS data is received, the reforecast process is automatically redone)
- “each scheduling unit comprising a computational module running on the at least one computational system” (Leggett column 2 lines 23-25: The invention thus provides the flexibility and control to properly take advantage of the capacities of modern digital switches. Specifically, per column 7 lines 5-23: Following the generation of a forecast, the method continues at step 31 to allocate the expected call load among the management units. This function enables centralized computer 12 of overall team to distribute responsibility for answering calls according to expected available staffing as administratively determined. Moreover, the allocation is variable by each ½ hour of each day of the week enabling the call center to realize significant facilities and management cost savings. For example, if there are 4 MU's expected to have equivalent amounts of staffing but MU1, closes 1 hour before the other MU's, the call center supervisor can setup the allocations for each MU at 25% until the end of the day, when the other three MU's are then each allocated 33.3% of calls. When MU1 closes, no falloff in service level then occurs. Of course, in operation, incoming calls always go to first available agent, regardless of the location of that agent. column 6 lines 15-18: a single telephone switch or module may likewise be associated with more than one team, and more than one MU can be associated with more than one team.
Leggett column 17 lines 6-12: local staffing changes at the MU level are transmitted back to the central computer databases and are rebroadcast back in real-time as data is received to all affected management units in the system column 18 lines 17-20,31-51,66-67: Staff column 84 of Fig.9 is staffing for the MU. required MU values (Req) are calculated from required team values and the MU allocations for the shift. As described above with respect to the intra-day reforecasting capability, every ½ hour throughout the shift, call volumes for the rest of the shift are recalculated based on the actual call volume data received earlier in the shift. The recalculation is performed by subprocess 116. Again, the calls are forecasted for the MU on an allocated basis. The number of MU agents required for the reforecast MU call volumes is calculated using an Erlang C method. An example of intra-day reforecasting provided by subprocess 116 can now be described. According to the technique, reforecast ratio (Rf) is first generated equal to the summation of Actual data divided by summation of Forecast data for periods having actual data. A so-called reality ratio is then generated and is defined as equal to N/(N-1)2, where N is the number of periods of actual data. When actual MIS data is received, the reforecast process is automatically redone.
Leggett column 17 lines 44-64: Referring to Fig.10, data flow within an MU workstation is shown in detail. The workstation datastores are those required by the system to support local (MU) needs of in-charge supervisor for a particular MU. Thus, the data brought down and stored at workstation is only that necessary and sufficient for MU management. in Fig.10, the workstation includes Individual Schedules dataset 102 which contains the schedules of a particular day for the agents of a particular MU. A Results dataset 104 contains the actual call volume and agent performance statistics recorded for the entire team at half-hourly intervals. It is a replica of the set stored on central computer, but generally only the actual data for current shift is stored locally. A Detailed Forecast dataset 106 contains the predicted half-hourly call volume and AHT. Carried with this dataset are the MU allocation numbers for the corresponding half hour periods. An MU Allocations dataset 108 provides the definitions of the MU allocation numbers. The definitions give the percentage of the incoming calls directed to particular MU's.
Leggett column 5 lines 52-62, column 6 lines 3-5: centralized computer 12 is linked to workstations 24 organized into distinct groups or so-called management units [MU] 22a, 22b, 22n.
Leggett column 6 lines 16-17: a single telephone switch may likewise be associated with more than one team, and more than one MU can be associated with more than one team)
* However *
Leggett does not explicitly recite: “wherein predicting the predicted number of scheduling units comprises computing a weighted average of (i) a standard number of scheduling units associated with a standard configuration corresponding to the total number of agents and (ii) an average actual number of scheduling units for one or more existing sets of contact centers having a number of agents corresponding to the standard configuration, wherein a weight assigned to the average actual number of scheduling units in the weighted average is based on a quantity of the one or more existing sets of contact centers” as claimed.
* Nevertheless*
Dong in analogous art of configuration or management of a call center teaches/suggests:
- “wherein predicting the predicted number of scheduling units comprises computing a weighted average of (i) a standard number of scheduling units associated with a standard configuration corresponding to the total number of agents and (ii) an average actual number of scheduling units for one or more existing sets of contact centers having a number of agents corresponding to the standard configuration, wherein a weight assigned to the average actual number of scheduling units in the weighted average is based on a quantity of the one or more existing sets of contact centers”
(Dong ¶ [0014] 1st sentence: use of contact centers is becoming increasingly common with ¶ [0066] 2nd sentence stating that: historical contact center data is associated with such contact centers, and with ¶ [0067] 1st sentence stating that: historical contact center data 502 obtained by tracking contact center conditions of the contact centers. For example, ¶ [0064] 4th sentence: discloses agent device 404 or server which implements software usable by contact center agents to address contact center engagements requested by contact center users. ¶ [0057] 2nd sentence: examples include, resource provisioning and deployment software. ¶ [0074] 4th-5th sentence: some implementations use a traffic modeling technique (e.g., a traffic modeling such as Erlang-c) in contact center scheduling by calculating the proposed number of agents by taking the estimated engagement volume and the average handling time for the modeling engine, from the administrator device. One such example is disclosed at ¶ [0092] 4th sentence: users tend to contact [existing] medical contact centers when they are sick with viral or bacterial diseases, historically tracked at ¶ [0091] when levels of community spread of viral or bacterial disease were within 10% of current levels. Then at ¶ [0068] 1st sentence: modeling engines 512A-512C access such historical contact center data 502.Then at ¶ [0076] the modeling engines 512A-512C include at least one of a weighted weekly moving average engine... A weighted weekly moving average engine calculates target value representing demand for contact center agents of a set of data over a predetermined period of time. In particular, it uses a weighted average approach to give greater importance to more recent data points. The weighted weekly moving average engine receives input data on a weekly basis and maintains a rolling window of the most recent n weeks of data, where n is a predetermined number. The weighted weekly moving average engine then calculates a weighted average of the data within the window, with the weights assigned to each data point based on its position within the window. More specifically, the weight assigned to each data point is inversely proportional to its age, such that more recent data points are given greater weight. The weighted weekly moving average engine use any weighting function to determine the weights…. The weighted average is provided as the output (e.g., corresponding to the demand for the contact center agents.
Dong ¶ [0094] 1st-2nd sentences: Using the above technique, the medical contact center determine 50 contact center agents are required to reach a specified service level (e.g., 75% of contact center users being connected with a contact center agent within 5 minutes of requesting connection to a contact center agent) on Apr. 22, 2023. The medical contact center may determine that only 40 contact center agents are scheduled to be working on Apr. 22, 2023.
Dong ¶ [0095] The above use case could be modified for other industries. For example, a contact center of a stock brokerage could leverage modeling engines that include a first modeling engine that takes into account contact center data of the previous week, a second modeling engine that takes into account contact center data of the previous month, and a third modeling engine that takes into account the contact center data from times with similar events to a current time. A combination engine could be used to combine the outputs of these three modeling engines. Initially, when little training data is available, the outputs of the first modeling engine and the second modeling engine are more heavily weighted by the combination engine. However, as the amount of training data increases, the third modeling engine would begin making better predictions of contact center agent demand than the first modeling engine and the second modeling engine. Thus, when more training data becomes available, the combination engine would increase the weight applied to the output of the third modeling engine, while reducing the weight applied to the first modeling engine and the second modeling engine).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Leggett’s method/system to have included Dong’s teachings or suggestions in order to have better collaborated and exchanged information in a coordinated manner to improve the overall performance of a predicting contact center agent demand based on a service level target (Dong ¶ [0073] 1st sentence in view of MPEP 2143 G)
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with configuration or management of a call centers. In such combination each element would have merely performed the same analytical or predictive function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Leggett in view of Dong, the to be combined elements would have fitted together, like puzzle pieces in a complementary, logical, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claims 5,12 Leggett / Dong teaches all the limitations in claims 1,8 above.
Leggett further teaches “further comprising, using a computer processor”;
- “automatically configuring the set of one or more contact centers based on at least the actual required number of scheduling units” (Leggett column 2 lines 34-37: this architecture provides flexibility to accommodate significant changes in the organization and configuration of call handling units and the relationship between them. Specifically, per column 18 lines 28-30, 66-67: actual MU staffing is obtained from central computer by way of MIS. When actual MIS data is received, the reforecast process is automatically redone. column 7 lines 28-32: method continues at step 35 by assigning the individual agents of each management unit).
Claims 7,14 Leggett / Dong teaches all the limitations in claims 1,8 above. Furthermore,
Leggett teaches geographical dispersal of management units but falls short to associate the respective time zone as required by: “wherein the number of regions comprises a number of time zones the set of one or more contact centers operates in” as claimed. However,
Dong in analogous configuration or management of a call center teaches/suggests
“wherein the number of regions comprises a number of time zones the set of one or more contact centers operates in” (Dong ¶ [0066] noting time ranges or zones such as Eastern Daylight Time).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have explained or at most complementarily modified Leggett’s teaching of geographical dispersal of management units to have been associated with respective time zones such as Eastern Daylight Time of Dong in order to have allowed for better training of the modeling engines to predict contact center agent demand (Dong ¶ [0066] sentence in view of MPEP 2143 G). Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with configuration or management of a call centers. In such combination each element would have merely performed the same analytical or predictive function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Leggett in view of Dong, the to be combined elements would have fitted together, like puzzle pieces in a complementary, logical, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 21 Leggett / Dong teaches all the limitations in claim 1 above. Furthermore,
Leggett teaches or suggests “wherein scheduling units are organized into groups of scheduling units with common scheduling requirements” (Leggett column 6 lines 6-13: with reference to Figs.1-2, a team of call center agents is organized into management units, each MU having predetermined number of agent groups. An MU is thus a set of agent groups managed locally as single unit. In this manner, each management unit can have its individual work rules and hours of operation), “the requirements comprising one or more of: operating days and hours” (Leggett column 6 lines 11-13: In this manner, each management unit can have its individual work rules and hours of operation), “shifts” (Leggett column 18 lines 17-20: required MU values (Req) are calculated from required team values and MU allocations for the shift. column 20 lines 11-17: using the staffing requirements and the tour evaluation heuristics, the method continues at step 134 to create a 2D evaluation array (one dimension is the day of the week and the other is each 15 minute interval of the day) containing a running sum of values for each interval of each day), “location” (Leggett column 6 lines 20-23: accommodate both geographical dispersal of MUs and call types among multiple management units and multiple geographical locations) “and department” (Leggett column 6 lines 50-52: Thus, a number of small call center offices [or departments] can be interconnected and function as one large, efficient call-handling team).
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Claims 3,10 are rejected under 35 U.S.C. 103 as being unpatentable over
Leggett / Dong as applied to claims 1,8 above, and in view of
Babine et al, US 20070129996 A1 hereinafter Babine, and in further view of
Jaiswal et al, US 20230394392 A1 hereinafter Jaiswal. As per,
Claims 3,10 Leggett / Dong teaches all the limitations in claims 1,8.
Leggett / Dong does not explicitly recite: “using the computer processor”:
- “selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents”;
- “selecting an actual optimal number of skills for the set of one or more contact centers based on the predicted optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers, wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types required to provide a low number of agents, and wherein the actual optimal number of skills is less than the predicted optimal number of skills when the one or more proposed skill types required to provide a high number of agents”;
- “outputting the actual optimal number of skills” as claimed. However,
Babine in analogous art of configuring g or managing call centers teaches/suggests:
- “selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents”; (Babine ¶ [0002] 3rd sentence: upon needs associated with a particular skill, there is a large skill group and a small number skill group. ¶ [0002] 6th sentence: as expected or as predicted, the large skill group typically comprises a larger number of agents than a small skill group, but actual number vary depending upon total number of agents in the call center. see Annotated Fig.3 below and ¶ [0040] 2nd-4th sentences and Annotated Fig.4 below and
¶ [0064] 2nd sentence noting two examples of designating or selecting Skill 22 as a small skill having an associated small skill group for a specific headcount and a service level target of 80/20.
¶ [0035] 1st sentence: in a typical implementation, a given skill is designated as associated with small group status. ¶ [0036]1st-2nd sentences: a trigger is based on a % rule specifying that a small group surplus agent may be used to handle a large group call if that small group surplus agent does not represent more Y % of total number of surplus small group agents. ¶ [0037] 1st sentence: another example of a trigger is one based on a different type of % rule specifying that a small group surplus agent may be used to handle a large group call if the number of small group surplus agents represents more than Z % of the total number of small group agents)
- “selecting an actual optimal number of skills for the set of one or more contact centers based on the predicted optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers”
(Babine ¶ [0035] 3rd sentence: designation of small skills is updated periodically, so the system provide, on request or in accordance with a schedule, an analysis indicating or specifying which skills should be designated. ¶ [0053] a given agent template in the call center specify small skills, large skills, traditional and other skills types. ¶ [0002] 6th sentence: a large skill group is typically larger than small skill group, but actual number in a group vary depending upon factors. ¶ [0054]-¶ [0062] such permutations are:
A) Small skill assigned as reserve-agent helps only when small skill is in trouble
B) Small skill assigned as primary-agent works only on small skill
C) Small skill assigned as primary, large skill as borrowable-agent will help on the large skill as long as the small skill has acceptable service level and work would not deplete the small group available agents below a designated level
D) Small skill assigned as primary, large skill assigned as reserve-agent helps out when large skill needs help
E) Small skill and large skill assigned as primary-agent handles each type of call routinely
F) Large skill assigned as primary and small skill assigned as reserve-agent helps out the small skill group when needed
G) Large skill assigned as primary-agent handles only large skill calls
H) Large skill assigned as reserve-agent handles large skill calls only when large skill group needs help)
Babine ¶ [0040] 2nd-4th sentence: noting an example of designating Skill 22 as a small skill having associated small skill group. In this example, the headcount rule is utilized, and threshold X is set to 2. The hunt group form similarly indicates service level target is 80/20, that is, 80% of calls requiring Skill 22 must be serviced within 20 seconds. Also within settings 300, specify Skill 22 as the primary small skill for that agent, and further specify Skill 1 as large volume skill. Similarly
Babine ¶ [0064] 3rd-5th sentences: hunt group form for Skill 22 designates X value of 2, and a service level target of 80/20, just as in the Fig.3. The hunt group form for Skill 24 designates an X value of 3, and a service level target of 90/10, that is, 90% of the calls requiring Skill 24 must be serviced within 10 seconds. The settings 400 further include an agent login form for a given agent. The agent login form specifies Skill 22 and Skill 24 as primary small skills for that agent, and further specifies Skill 1 as a large skill, also referred to as a large volume skill in this example
Babine ¶ [0079] an occupancy calculation may be performed for small skill agents to show the percentages of occupancy contributed by each large skill they support. In other words, if an agent had an overall 74% occupancy for the day, the system could perform calculations to indicate, for example, that the agent had 58% occupancy based on core work, with 7% added due to Large Skill 1 and 9% added due to Large Skill 2);
- “wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types required to provide a low number of agents, and wherein the actual optimal number of skills is less than the predicted optimal number of skills when the one or more proposed skill types required to provide a high number of agents”;
(Babine ¶ [0003] A problem that arises in call centers with small skill groups is that the small group agents are often either underutilized or overutilized. Underutilization occurs when there are unduly restrictive barriers to using the small group agents to handle large group calls. Underutilization results in low occupancy and low productivity for the small group agents, and thus a higher cost per transaction for these small group agents. Overutilization occurs when the barriers to using the small group agents to handle large group calls are too easy to overcome. Overutilization results in high occupancy for the small group agents and poor service levels for the kinds of contacts that only the small group agents handle.
Babine ¶ [0054], A given agent thus have multiple small skills and may be designated as borrowable for multiple large skills. Since there may be many more than two skills in the call center, and more than two possible skills per agent, there are numerous permutations of the manner in which skills are assigned an agent. Examples of such permutations are given in the following list: ¶ [0057] C) Small skill assigned as primary, large skill as borrowable-agent will help on the large skill as long as the small skill has acceptable service level and work would not deplete the small group available agents below a designated level ¶ [0058], D) Small skill assigned as primary, large skill assigned as reserve-agent helps out when large skill needs help, ¶ [0060] F) Large skill assigned as primary and small skill assigned as reserve-agent helps out the small skill group when needed
Babine Annotated Figs. 3-4 extracted below and associated text.
Babine ¶ [0042] In step 304, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0043] If the current service level is not equal to or greater than the service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 306, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 304 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0044] If step 304 determines that the current service level is equal to or greater than the service level target, the process moves to step 308, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 310. ¶ 0047]
If the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, that is, if there are three or more surplus agents for Skill 22, the surplus agent with the lowest occupancy is selected to be the “borrowable” agent for the large skill, as indicated in step 312.
Babine ¶ [0066] noting a similar example where In step 404, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0067] If the current service level for Skill 22 is not equal to or greater than the 80/20 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 406, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 404 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0068] If step 404 determines that the current service level is equal to or greater than the service level target, the process moves to step 408, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 410. ¶ [0069] In step 412, a check is made as to whether or not the current service level for Skill 24 is equal to or greater than the 90/10 service level target for Skill 24.
¶ [0070] If the current service level for Skill 24 is not equal to or greater than the 90/10 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 414, and hence cannot be borrowed to handle a call requiring the large volume skill. ¶ [0071] If step 412 determines that the current service level for Skill 24 is equal to or greater than the 90/10 service level target, the process moves to step 416, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, or at least four surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 414. ¶ [0074] If the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, that is, if there are four or more surplus agents for Skill 24, the surplus agent with the lowest occupancy is selected to be a borrowable agent for the large skill, as indicated in step 418. Although a LOA selection technique is used in this example, other techniques, such as MIA, or combinations of such techniques, can be used instead. Again, the selection can also or alternatively take into account surplus agent proficiency at the small skill, the large skill, or both. Any combination of such criteria may be used, and the invention is not limited in this regard. ¶ [0075] In step 420, a determination is made as to whether there is a call surplus in the large skill, that is, if there are more calls waiting in queue than there are agents available to service the calls. If so, the borrowable small group agent as determined in step 418 is used to handle a call, as indicated in step 422. Otherwise, the borrowable agent determined in step 418 enters the agent queue(s) for both the large skill (Skill 1) and the small skills (Skill 22 and Skill 24). Step 424 indicates that, as a result, the next call when it arrives may be handled by the borrowed agent determined in step 418, or by an agent that has Skill 1 as a primary skill).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Leggett/Dong teachings to have included Babine’s teachings or suggestions to have improved techniques for handling small group agents in a call center to effectively mitigate overutilizing and underutilization of agents (Babine ¶ [0002]-¶ [0004] in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skills of one of ordinary skills in the art articulated by Babine ¶ [0086].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar managing contact centers field of endeavor. In such combination each element would have merely performed same analytical and managerial function as separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Leggett / Dong in further view of Babine, the to be combined elements would have fitted together, like puzzle pieces, in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that, the results of the combination would have been predictable (MPEP 2143 A).
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Babine Annotated Fig. 3(shown left) and Annotated Fig.4 (shown right) in support of rejection arguments
* Further *
Leggett / Dong / Babine does not recite:
- “outputting the actual optimal number of skills” as explicitly claimed.
* However *
Jaiswal in analogous configuration or management of contact centers teaches/suggests
- “outputting the actual optimal number of skills”
(Jaiswal ¶ [0048] 1st-4th sentences: The skill set is information indicating skills required for the job. The number of skills required for the job may be one or more. The skill is represented by identification information of the skill. The skill required for the job may be indicated by the type and level of the skill. ¶ [0151] in step S206, processor 202 creates skill list L2. ¶ [0152] 1st sentence: in step S207, processor 202 displays the list L2 using the skill selection GUI U101. ¶ [0153] 1st sentence: In the example of Fig.10, four skills are displayed [or ourputed])
It would have been obvious to one skilled in the art, before effective filling date of the claimed invention to have modified Leggett/Dong/Babine’s method to have included Jaiswal’s teaching/ suggestion to have more effectively secured personnel having skills necessary for jobs when an amount of skills is insufficient (Jaiswal ¶ [0034] in view of MPEP 2143 G), and, at the same time having recognized a more efficient training scheme to have improved the skills of educators while setting personnel with high necessary skills as educators and personnel who should improve the necessary skills as trainees (Jaiswal ¶ [0002] 3rd sentence in view of MPEP 2143 G). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as further articulated by Jaiswal ¶ [0204].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor configuration or management of contact centers. In such combination each element would have merely performed same analytical and display function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Leggett/Dong/Babine in further view of Jaiswal, the to be combined elements would have fitted together, like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination’s results would have been predictable (MPEP 2143 A).
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Claims 4,11 are rejected under 35 U.S.C. 103 as being unpatentable over
Leggett/Dong/Babine/Jaiswal as applied to claims 3,10 above, and in further view of
Moran et al, US 20180191906 A1 hereinafter Moran. As per,
Claims 4,11 Leggett/Dong/Babine/Jaiswal teaches all the limitations in claims 3,10, above.
Leggett/Dong/Babine/Jaiswal does not explicitly recite as explicitly claimed:
“wherein selecting the predicted optimal number of skills for the set of one or more contact centers is selected further based on actual numbers of skills for at least one existing set of contact centers”
Moran however in analogous configuring or managing call centers teaches/suggests:
“wherein selecting the predicted optimal number of skills for the set of one or more contact centers is further selected based on actual numbers of skills for at least one existing set of contact centers”
(Moran ¶ [0003] 3rd sentence: Existing contact centers typically track each resource's skillset(s) and utilize data from one or more sources, including historical data captured from automatic call distribution (ACD) systems in the contact center, to predict future staffing needs and the associated resource skillset mix that will be required to service incoming contacts.
Moran ¶ [0005] 2nd sentence: For example, an attribute-based contact center may be able accurately predict that contacts with a certain set of attributes are more likely to occur at one point during the workday, e.g., early morning, and that the contact type will change to a different mix of attributes or skills during a different part of the workday, e.g., late morning. Specifically, per
Moran ¶ [0070] 3rd-5th sentences: The selection of the resource is based on a comparison between the plurality of additional resource attributes and the plurality of contact attributes. For example, the contact request a resource with the first resource attribute and also wish to communicate with a resource located in a specified region and in a particular language [interpreted as an example of attribute or skill]. The attributes of available resources are compared to the contact attributes and the most suitable resource is selected)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Leggett/Dong/Babine/Jaiswal to have further included Moran’s teachings or suggestions to have mitigated the existing and impractical creation of a resource roster that would have been needed to be changed dynamically to have exactly matched the prediction (Moran ¶ [0005] in view of MPEP 2143 G, F) as incentivized by the need for efficient and optimal scheduling of resources having become an increasingly important component of effective contact center management (Moran ¶ [0003] in view of MPEP 2143 G, F).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with the configuration or management of contact centers. In such combination each element merely would have performed the same analytical or predictive function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Leggett / Dong / Babine/Jaiswal in further view of Moran, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination results would have been predictable (MPEP 2143 A).
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Claims 15,19 are rejected under 35 U.S.C. 103 as being unpatentable over:
Babine et al, US 20070129996 A1 hereinafter Babine, in view of
Jaiswal et al, US 20230394392 A1 hereinafter Jaiswal. As per,
Claim 15 Babine teaches “A method for configuring a set of one or more contact centers, the set of one or more contact centers associated with a total number of agents and a number of regions, and the method comprising, using a computer processor” (Babine ¶ [0020], ¶ [0025], ¶ [0085]):
- “selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents” (Babine ¶ [0002] 3rd sentence: upon needs associated with a particular skill, there is a large skill group and a small number skill group. ¶ [0002] 6th sentence: as expected or as predicted, the large skill group typically comprises a larger number of agents than a small skill group, but actual number vary depending upon total number of agents in the call center. see Annotated Fig.3 below and ¶ [0040] 2nd-4th sentences and Annotated Fig.4 below and
¶ [0064] 2nd sentence noting two examples of designating or selecting Skill 22 as a small skill having an associated small skill group for a specific headcount and a service level target of 80/20.
¶ [0035] 1st sentence: in a typical implementation, a given skill is designated as associated with small group status. ¶ [0036]1st-2nd sentences: a trigger is based on a % rule specifying that a small group surplus agent may be used to handle a large group call if that small group surplus agent does not represent more Y % of total number of surplus small group agents. ¶ [0037] 1st sentence: another example of a trigger is one based on a different type of % rule specifying that a small group surplus agent may be used to handle a large group call if the number of small group surplus agents represents more than Z % of the total number of small group agents)
- “selecting an actual optimal number of skills for the set of one or more contact centers based on the predicted optimal number of skills and based on one or more proposed skill types for the set of one or more contact centers”
(Babine ¶ [0035] 3rd sentence: designation of small skills is updated periodically, so the system provide, on request or in accordance with a schedule, analysis indicating or specifying which skills should be designated. i.e. ¶ [0053] specify small skills, large skills, traditional and other skills types. ¶ [0002] 6th sentence: a large skill group is larger than small skill group, but actual number in a group vary depending upon factors. ¶ [0054]-¶ [0062] such permutations are:
A) Small skill assigned as reserve-agent helps only when small skill is in trouble
B) Small skill assigned as primary-agent works only on small skill
C) Small skill assigned as primary, large skill as borrowable-agent will help on the large skill as long as the small skill has acceptable service level and work would not deplete the small group available agents below a designated level
D) Small skill assigned as primary, large skill assigned as reserve-agent helps out when large skill needs help
E) Small skill and large skill assigned as primary-agent handles each type of call routinely
F) Large skill assigned as primary and small skill assigned as reserve-agent helps out the small skill group when needed
G) Large skill assigned as primary-agent handles only large skill calls
H) Large skill assigned as reserve-agent handles large skill calls only when large skill group needs help)
Babine ¶ [0040] 2nd-4th sentence: noting an example of designating Skill 22 as a small skill having associated small skill group. In this example, the headcount rule is utilized, and threshold X is set to 2. The hunt group form similarly indicates service level target is 80/20, that is, 80% of calls requiring Skill 22 must be serviced within 20 seconds. Also within settings 300, specify Skill 22 as the primary small skill for that agent, and further specify Skill 1 as large volume skill. Similarly
Babine ¶ [0064] 3rd-5th sentences: hunt group form for Skill 22 designates X value of 2, and a service level target of 80/20, just as in the Fig.3. The hunt group form for Skill 24 designates an X value of 3, and a service level target of 90/10, that is, 90% of the calls requiring Skill 24 must be serviced within 10 seconds. The settings 400 further include an agent login form for a given agent. The agent login form specifies Skill 22 and Skill 24 as primary small skills for that agent, and further specifies Skill 1 as a large skill, also referred to as a large volume skill in this example
Babine ¶ [0079] an occupancy calculation may be performed for small skill agents to show the percentages of occupancy contributed by each large skill they support. In other words, if an agent had an overall 74% occupancy for the day, the system could perform calculations to indicate, for example, that the agent had 58% occupancy based on core work, with 7% added due to Large Skill 1 and 9% added due to Large Skill 2);
- “wherein the actual optimal number of skills is greater than the predicted optimal number of skills when the one or more proposed skill types require a low number of agents to provide, and wherein the actual optimal number of skills is less than the optimal number of skills when the one or more proposed skill types require a high number of agents to provide”;
(Babine ¶ [0003] A problem that arises in call centers with small skill groups is that the small group agents are often either underutilized or overutilized. Underutilization occurs when there are unduly restrictive barriers to using the small group agents to handle large group calls. Underutilization results in low occupancy and low productivity for the small group agents, and thus a higher cost per transaction for these small group agents. Overutilization occurs when the barriers to using the small group agents to handle large group calls are too easy to overcome. Overutilization results in high occupancy for the small group agents and poor service levels for the kinds of contacts that only the small group agents handle.
Babine ¶ [0054], A given agent thus have multiple small skills and may be designated as borrowable for multiple large skills. Since there may be many more than two skills in the call center, and more than two possible skills per agent, there are numerous permutations of the manner in which skills are assigned an agent. Examples of such permutations are given in the following list: ¶ [0057] C) Small skill assigned as primary, large skill as borrowable-agent will help on the large skill as long as the small skill has acceptable service level and work would not deplete the small group available agents below a designated level ¶ [0058], D) Small skill assigned as primary, large skill assigned as reserve-agent helps out when large skill needs help, ¶ [0060] F) Large skill assigned as primary and small skill assigned as reserve-agent helps out the small skill group when needed
Babine Annotated Figs. 3-4 extracted below and associated text.
Babine ¶ [0042] In step 304, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0043] If the current service level is not equal to or greater than the service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 306, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 304 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0044] If step 304 determines that the current service level is equal to or greater than the service level target, the process moves to step 308, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 310.
Babine ¶ [0047] If the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, that is, if there are three or more surplus agents for Skill 22, the surplus agent with the lowest occupancy is selected to be the “borrowable” agent for the large skill, as indicated in step 312.
Babine ¶ [0066] noting a similar example where In step 404, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. ¶ [0067] If the current service level for Skill 22 is not equal to or greater than the 80/20 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 406, and hence cannot be borrowed to handle a call requiring the large volume skill. The process then returns to step 404 so that additional checks can be made periodically to determine if conditions have changed. ¶ [0068] If step 404 determines that the current service level is equal to or greater than the service level target, the process moves to step 408, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 410. ¶ [0069] In step 412, a check is made as to whether or not the current service level for Skill 24 is equal to or greater than the 90/10 service level target for Skill 24.
Babine ¶ [0070] If current service level for Skill 24 is not equal to or greater than the 90/10 service level target, the agent is not eligible to help with the large volume skill (Skill 1) as indicated in step 414, and hence cannot be borrowed to handle a call requiring the large volume skill.
Babine ¶ [0071] If step 412 determines that the current service level for Skill 24 is equal to or greater than the 90/10 service level target, the process moves to step 416, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, or at least four surplus agents in this example. If not, there are insufficient surplus agents, and the agent is not eligible to help with the large volume skill, as indicated in step 414.
Babine ¶ [0074] If the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, that is, if there are four or more surplus agents for Skill 24, the surplus agent with the lowest occupancy is selected to be a borrowable agent for the large skill, as indicated in step 418. Although a LOA selection technique is used in this example, other techniques, such as MIA, or combinations of such techniques, can be used instead. Again, the selection can also or alternatively take into account surplus agent proficiency at the small skill, the large skill, or both. Any combination of such criteria may be used, and the invention is not limited in this regard.
Babine ¶ [0075] In step 420, a determination is made as to whether there is a call surplus in the large skill, that is, if there are more calls waiting in queue than there are agents available to service the calls. If so, the borrowable small group agent as determined in step 418 is used to handle a call, as indicated in step 422. Otherwise, the borrowable agent determined in step 418 enters the agent queue(s) for both the large skill (Skill 1) and the small skills (Skill 22 and Skill 24). Step 424 indicates that, as a result, the next call when it arrives may be handled by the borrowed agent determined in step 418, or by an agent that has Skill 1 as a primary skill).
- “”
- “initiating on at least one computational system, at least one skill management platform, based on the actual optimal number of skills” (Babine ¶ [0018] 3rd sentence the disclosed techniques can be used with automatic call distribution (ACD) systems, telemarketing systems, private-branch exchange (PBX) systems, computer-telephony integration (CTI)-based systems, and combinations of these and other types of call centers. ¶ [0020] Fig.2 shows a simplified block diagram of implementation of ACD system 101. The system in Fig.2 is stored-program-controlled system that includes interfaces 112 to external communication links, a communications switching fabric 113, service circuits 114, memory 115 for storing control programs and data, and processor 116 for executing the stored control programs to control the interfaces and the fabric, to provide automatic call distribution functionality. Specifically, per ¶ [0021] data elements stored in memory 115 of ACD system 101 include set of call queues 120... Each call queue 121-129 in the set of call queues 120 corresponds to a different agent skill... calls are prioritized … in different ones of a plurality of call queues that correspond to a skill and each one of which corresponds to a different priority. Similarly, each agent's skills are prioritized according to his or her level of expertise in that skill, and agents may be, enqueued in individual ones of agent queues 130 in their order of expertise level, or enqueued in different ones of agent queues that correspond to a skill and each one of which corresponds to a different expertise level in that skill),
“wherein each skill management platform is configured to assign a contact center interaction to at least one agent based on a skill associated with the at least one agent each skill management platform comprising a computational module running on the at least one computational system” (Babine ¶ [0021] Referring again to Fig.1, exemplary data elements stored in memory 115 of ACD system 101 include set of call queues 120... Each call queue 121-129 in the set of call queues 120 corresponds to a different agent skill... calls are prioritized … in different ones of a plurality of call queues that correspond to a skill and each one of which corresponds to a different priority. Similarly, each agent's skills are prioritized according to his or her level of expertise in that skill, and agents may be, enqueued in individual ones of the agent queues 130 in their order of expertise level, or enqueued in different ones of a plurality of agent queues that correspond to a skill and each one of which corresponds to a different expertise level in that skill)
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Babine Annotated Fig. 3(shown left) and Annotated Fig.4 (shown right) in support of rejection arguments
* While *
Babine disclosure at Annotated Fig.3, ¶ [0042], [0044], Annotated Fig.4, ¶ [0066], ¶ [0068], [0069], [0071] 1st sentence, [0079] may or may not suggest, it does not exactly recite to anticipate:
- “outputting the actual optimal number of skills” as explicitly claimed.
* However *
Jaiswal in analogous configuration or management of contact centers teaches/suggests
- “outputting the actual optimal number of skills”
(Jaiswal ¶ [0048] 1st-4th sentences: The skill set is information indicating skills required for the job. The number of skills required for the job may be one or more. The skill is represented by identification information of the skill. The skill required for the job may be indicated by the type and level of the skill. ¶ [0151] in step S206, processor 202 creates skill list L2. ¶ [0152] 1st sentence: in step S207, processor 202 displays the list L2 using the skill selection GUI U101. ¶ [0153] 1st sentence: In the example of Fig.10, four skills are displayed [or ourputed])
It would have been obvious to one skilled in the art, before effective filling date of the claimed invention to have modified Babine’s method to have included Jaiswal’s teaching/suggestion to have more effectively secured personnel having skills necessary for jobs when an amount of skills is insufficient (Jaiswal ¶ [0034] in view of MPEP 2143 G), and, at same time having recognized a more efficient training scheme to have improved the skills of educators while setting personnel with high necessary skills as educators and personnel who should improve the necessary skills as trainees (Jaiswal ¶ [0002] 3rd sentence in view of MPEP 2143 G). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Babine ¶ [0038], ¶ [0052], ¶ [0086] in view of Jaiswal ¶ [0204].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor configuration or management of contact centers. In such combination each element would have merely performed same analytical and display function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Babine in view of Jaiswal, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination’s results would have been predictable (MPEP 2143 A).
Claim 19 Babine / Jaiswal teaches all limitations in claim 15 above. Furthermore,
Babine teaches further comprising, “using the computer processor”:
- “automatically configuring the set of one or more contact centers based on at least the actual optimal number of skills”; (Babine ¶ [0016] Fig.2 is a block diagram of an automatic call distribution (ACD) system implemented in the call center of Fig. 1.
Babine Annotated Fig.3 below and ¶ [0042] In step 304, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. Then at ¶ [0044] If step 304 determines that the current service level is equal to or greater than the service level target, the process moves to step 308, where a determination is made as whether the total number of surplus agents in small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. ¶ [0052] In a given embodiment, an otherwise conventional agent selection process may be altered such that it is biased toward using the borrowable agents. This will avoid a situation in which the borrowable agents remain idle while large skill agents are assigned calls. One type of bias toward use of borrowable agents to handle large skill calls is reflected in steps 314 and 316 of the Fig.3 example, in the immediate use of the borrowable agent to handle a call in a large skill call surplus situation. However, the agent selection [or configuration] process in step 318 may also be biased toward use of the borrowed agents. For example, the borrowable agent may be used automatically for the next available call, or the use of borrowable and large skill agents may be alternated as long as borrowable agents are available. Numerous other techniques for biasing a given call selection process toward using borrowable agents to handle large skill calls will be apparent to those skilled in the art.
Babine Annotated below and Fig.4 ¶ [0066] noting a similar example at In step 404, a check is made as to whether or not the current service level for Skill 22 ≥ 80/20 service level target for Skill 22. Then at ¶ [0068] 1st sentence: If step 404 determines that the current service level is equal to or greater than the service level target, the process moves to step 408, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 22 is at least one more than the specified minimum threshold X, or at least three surplus agents in this example. Then at ¶ [0069] In step 412, a check is made as to whether or not the current service level for Skill 24 ≥ 90/10 service level target for Skill 24. Then at ¶ [0071] 1st sentence: If step 412 determines that the current service level for Skill 24 is equal to or greater than the 90/10 service level target, the process moves to step 416, where a determination is made as whether the total number of surplus agents in the small skill group for Skill 24 is at least one more than the specified minimum threshold X, or at least four surplus agents in this example.
Babine ¶ [0079] As a more particular illustration, an occupancy calculation may be performed for small skill agents to show the percentages of occupancy contributed by each large skill they support. In other words, if an agent had an overall 74% occupancy for the day, the system could perform calculations to indicate, for example, that the agent had 58% occupancy based on core work, with 7% added due to Large Skill 1 and 9% added due to Large Skill 2.
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Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over:
Babine / Jaiswal as applied to claim 15 above, and in further view of
Moran et al, US 20180191906 A1 hereinafter Moran. As per,
Claim 16 Babine / Jaiswal does not exactly recite as claimed:
- “wherein selecting the predicted optimal number of skills for the set of one or more contact centers is further based on actual numbers of skills for at least one existing set of contact centers”.
Moran in analogous optimal scheduling of resources in a contact center teaches/suggest
-“wherein selecting the predicted optimal number of skills for the set of one or more contact centers is further based on actual numbers of skills for at least one existing set of contact centers”.
(Moran ¶ [0003] 3rd sentence: Existing contact centers typically track each resource's skillset(s) and utilize data from one or more sources, including historical data captured from automatic call distribution (ACD) systems in the contact center, to predict future staffing needs and the associated resource skillset mix that will be required to service incoming contacts. [0005] 2nd sentence: For example, an attribute-based contact center may be able accurately predict that contacts with a certain set of attributes are more likely to occur at one point during the workday, e.g., early morning, and that the contact type will change to a different mix of attributes during a different part of the workday, e.g., late morning. [0070] 3rd – 5th sentences: The selection of the resource is based on a comparison between the plurality of additional resource attributes and the plurality of contact attributes. For example, the contact may request a resource with the first resource attribute and may also wish to communicate with a resource located in a specified region and in a particular language. The attributes of available resources are compared to the contact attributes and the most suitable resource is selected).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Babine / Jaiswal’s “method” to have further included
Moran’s teachings in order to have provided a more optimal scheduling of resources in the contact center as necessitated by the ever increasingly important component of effective contact center management of efficient workforce management. (Moran ¶ [0002] -¶ [0003], ¶ [0033], ¶ [0034], ¶ [0042], ¶ [0064], ¶ [0067] in view of MPEP 2143 G and/or F). The predictability of such modification would have been further corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Babine ¶ [0038], ¶ [0052], ¶ [0086] in view of Jaiswal ¶ [0204], and in further view of Moran ¶ [0015], ¶ [0083].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor of optimal scheduling of resources in a contact center. In such combination each element would have merely performed same analytical and selective function as separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Babine / Jaiswal in further view of Moran, the to be combine elements would have fitted together, like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination’s results would have been predictable (MPEP 2143 A).
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Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over
Babine / Jaiswal as applied to claim 15 above, in further view of
Leggett; Ernest W. US 5185780 A hereinafter Leggett. As per,
Claim 17. Babine / Jaiswal teaches all the limitations in claim 15 above. Further,
Babine / Jaiswal does not recite “further comprising, using the computer processor:”
- “predicting a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents”;
- “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions”; “and”
- “outputting the actual required number of scheduling units” as claimed.
* However *
Leggett.in analogous art of managing contact centers teaches or suggests:
- “predicting a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents”;
(Leggett column 3 lines 62-65: invention facilitates efficient management of the individual agents of the management or scheduling unit based on real-time performance statistics and meaningful display of the generated agent schedules. For example, at column 6 lines 6-10, 24-26: With reference to Figs.1-2, a team of call center agents is organized into management units, each management unit (MU) having predetermined number of agent groups. column 7 lines 14-21: if there are 4 MU’s expected [or predicted] to have equivalent amounts of staffing but MU1, closes 1 hour before the other MU's, the call center supervisor can setup the allocations for each MU at 25% until the end of the day, when the other 3 MU’s are then each allocated 33.3% of the calls. When MU1 closes, no falloff in service level then occurs. tour templates are correlated with the forecast to generate a set of tours for each management unit. column 18 lines 38-42: The number of MU agents required for the reforecast MU call volumes is calculated using Erlang C method),
- “selecting an actual required number of scheduling units for the set of one or more contact centers based on the predicted number of scheduling units and the number of regions”
(Leggett column 6 lines 20-23: system accommodate not only geographical [or regional] dispersal of management units but also multiple call types dispersed among multiple management units and multiple geographical locations [or regions]. For example, at column 7 lines 14-21: 4 MU’s expected to have equivalent amounts of staffing but MU1 closes 1 hour before the other MU's, the call center supervisor setup allocations for each MU at 25% until end of day, when the other 3 MU’s are then each allocated 33.3% of calls. Such scheduling function is disclosed at column 8 lines 12-22: to allocate work hours according to staffing requirements that have been forecast. Scheduling has 3…components: tour generation, agent assignment and schedule generation…. Tour generation is the process of matching the staffing requirements with staffing possibilities, defined by staffing restrictions such as hours of operation. column 8 lines 31-34, 37-41: referring to Fig.4, generate tours routine 52 is used to create tours for theoretical agents of each management unit based on the tour templates and forecast FTE requirements for a particular period. Thereafter, the supervisor(s) assign agents to generated tours using a list of named agents. Alternatively, agents can be assigned using an automatic process instead of manually as described below.
Leggett column 12 lines 45-50: The method begins at step 73 by calculating the offered load a. At step 75, Erlang C calculation C(n,a) is tun for n=a+1, which is minimum agents for which meaningful Erlang C calculation can be made. column 13 lines 57-column 14 line 4: Thereafter, according to the method of Fig.7, 2 initial guesses (namely, the minimum number of agents n and n+1) are used as predictor values and a first loop is run up in step 85 to determine a value (100-ePWt) which is approximately desired service level. At this point the method checks the estimate by calculating Erlang C for p-1, p, p+1. Calculating the actual C(p-1,a), C(p,a) and C(p+1,a) and service level exact values, 1 of 3 is hopefully a winning value. Generally, the winning value will be the central predicted value p for most common input data. Stated differently, the routine uses the numbers for n and n+1 to predict the value p which brings a result close to the desired objective. The Erlang C loop is then continually run up to calculate C(P-1,a), C(p,a) and C(p+1,a). column 18 lines 17-20:The Staff column 84 of Fig.9 is the staffing for the MU. The required MU values (Req) are calculated from the required team values and the MU allocations for the shift) “and”
- “outputting the actual required number of scheduling units”
(Leggett column 4 lines 34-40: Staffing changes at the management unit are transmitted to the centralized computer of the force management system then regularly broadcast back to the other management units of the system. The performance analysis screen at the management unit is thus continuously updated with modified team call handling performance data. column 16 lines 8-11: The screen includes a Management Unit identifier field 70 to identify the MU performance data being displayed. The performance analysis screen shows the MU's allocation of team data
Leggett column 17 lines 6-15: local staffing changes at MU level are transmitted back to the central computer databases and rebroadcast back to all affected management units in the system. Therefore, all of MU supervisors can continuously view the team statistics even as local staffing changes are dynamically implemented at other management units
Leggett column 18 lines 17-20: Staff column 84 of Fig.9 is the staffing for the MU).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Babine/Jaiswal’s “method” to have included Leggett’s teachings of suggestions to have continuously track team statistics even as local staffing would dynamically change at other management units, while at the same time having allowed for improved what-if learning in the management unit such as when there is some unexpected agent absences, or when the terminal is depressed in order to be mitigated by further modifying agent schedules as necessary (Leggett column 17 lines 6-43 in view of MPEP 2143 G and/or F)
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with managing contact centers. In such combination each element merely would have performed same analytical, predictive, selective and outputting or display function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced above by Babine/Jaiswal in further view of Leggett, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over
Babine / Jaiswal / Leggett as applied to claim 17 above, in further view of
Johnston et al US 11368588 B1 hereinafter Johnston. As per,
Claim 18 Babine / Jaiswal / Leggett teaches all the limitations in claim 17 above. Furthermore,
Babine / Jaiswal / Leggett combination does not explicitly recite: “wherein predicting a predicted number of scheduling units for the set of one or more contact centers comprises”:
- “predicting, using a machine learning algorithm, a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents, wherein the machine learning algorithm is based at least on actual numbers of scheduling units for at least one existing set of contact centers” as claimed.
Johnston however in analogous art of call center management teaches or suggests: “wherein predicting a predicted number of scheduling units for the set of one or more contact centers comprises”:
- “predicting, using a machine learning algorithm, a predicted number of scheduling units for the set of one or more contact centers based on the total number of agents, wherein the machine learning algorithm is based at least on actual numbers of scheduling units for at least one existing set of contact centers”
(Johnston column 1 lines 25-31: some contact centers may have thousands of agents available for scheduling. However, various limitations with respect to certain factors such as…skills of the agents… can make it difficult to schedule agents for handling communications. Johnston column 4 lines 39-45: Additionally, the contact center management service may determine that more agents with certain skills are needed. This need may be determined by the trained models used by the contact center management service
Johnston column 4 line 46 - column 5 line 5: trained models used to quickly detect anomalies with respect to actual volume of communications received at the contact center and the forecast volume of communications received, as well as differences between expected handle time and actual handle time (or expected after call work and actual after call work). For example, if the trained models determine that a much larger amount of communications are currently being received with respect to the forecast, the contact center management service may provide a notification to a manager associated with the contact center to contact agents that are currently off duty but have indicated a willingness to work extra hours. Thus, such agents may be contacted to see if they are interested in working to help deal with the increased volume of communications being received. Likewise, if protracted decrease in volume of communications received compared to forecast volume of communications to be received is detected by the trained models, then the contact center management service provide a notification to the manager indicating that a decrease in the number of agents may be desirable, e.g., allowing some agents to quit work early. The anomalies may be with respect to overall volume or with respect to volume of specific communications, e.g. larger than expected volume of communications related to returns and refunds, types of communications, and/or language specific communications.
Johnston column 8 lines 37-49: As is known, in configurations, the ML model(s) 128 may be trained based upon historical metrics and data related to the execution of the contact center 108. For example, the ML model(s) 128 may analyze historical metrics and data related to execution of the contact center 108 in the service provider network 102. The historical metrics and data may be based on gathered metrics and data that has been collected over the past six months, one year, or longer by the data analytics service 124. In configurations, the historical metrics and data may have been collected during a period shorter than six months. An operator of the service provider network 102 may determine how long historical metrics and data may be retained, e.g., stored in the storage service 120. column 7 lines 1-6: forecasting and scheduling service 122 may utilize the metrics and data from the data analytics service 124 and other factors in forecasting a need for agents 114 based on forecasting anticipated volumes of communications 110 and scheduling of agents 114 in accordance with the anticipated volumes of communications 110.
Johnston column 15 lines 22-29 states: contact center management service 126 and ML model(s) 128 improve forecast accuracy (better match of headcount of agents 114 required to achieve the target service level), better schedule efficiency (having the right number of agents 114 in all intervals/time periods to achieve the target service level without having idle agents 114 or overworked agents 114), and better adherence, thereby increasing productivity
Johnston column 17 lines 46-52: Appropriate load balancing devices or other types of network infrastructure components are utilized for balancing a load between each of data centers 704A-704N, between each of server computers 802A-802F in each data center 704, and, between computing resources in each of the server computers 802).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Babine / Jaiswal / Leggett’s “method” to have included Johnston’s teachings or suggestions in order to have employed machine learning models capable to improve forecast accuracy for better match of headcount of agents 114 required to achieve the target service level, better schedule efficiency (having the right number of agents 114 in all intervals/time periods to achieve the target service level without having idle agents 114 or overworked agents 114), and better adherence, thereby increasing productivity (Johnston column 15 lines in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skills of one of ordinary skills in the art as further articulated by Johnston at column 22 lines 17-26.
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar managing contact centers field of endeavor. In such combination each element would have merely performed same analytical and managerial function as separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Babine/Jaiswal/Leggett in further view of Johnston, the to be combined elements would have fitted together, like puzzle pieces, in logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that, the results of the combination would have been predictable (MPEP 2143 A).
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
-Marengo Nancy, Skill-based routing in multi-skill call centers, BMI paper, Vrije Universiteit, Amsterdam, 2004
- WO 2021113798 A1 predicting performance for a contact center via machine learning
- US 20240364815 A1 ¶ [0101] At 1006, the server trains a combination engine (e.g., the combination engine 514) to generate a combination of modeling engines (e.g., the combination of modeling engines 516). The training of the combination engine may be based on the historical contact center data and performance data of the multiple modeling engines. The performance data may be generated by back testing the multiple modeling engines on past time ranges for which data is stored in the historical contact center data. The back testing may include comparing, for the service level that was provided, the predicted number of agents with the actual number of agents that were working. ¶ [0108] last two sentences: when the future time occurs, the server may determine an actual number of agents working at the future time and a service level provided by the contact center at the future time. The server may further train the modeling engines and/or the combination engine based on the actual number of agents and the provided service level.
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US 20240364815 A1 Fig.8 as relevant to the currently claimed outputting limitations
- US 20030043832 A1 ¶ [0101] 2nd sentence: In observance of Erlang's formula, it is noted that pk is the probability that k servers (agents) are busy.
- US 20020123983 A1 ¶ [0225] The Erlang metrics can be used to develop comparisons of these figures. The following example highlights the differences between under and over staffing. If a call center is receiving 500 calls an hour at 240 seconds per call, and aims to answer 90% of calls within 30 seconds, 40 agents will be required. If 35 agents are employed, the average speed of answer (ASA) will be 100 seconds. If 45 agents are employed, the ASA will be one second. This shows that significant variation in customer satisfaction can result from relatively small changes of agent numbers.
- US 20160088153 A1 teaching Prediction of Contact Allocation, Staff Time Distribution, and Service Performance Metrics in a Multi-Skilled Contact Center Operation Environment
- US 6075848 A column 8 lines 48-53: The graph in FIG. 6 shows a comparison of the rate at which calls were handled (#suc) with the rate at which first call attempts were made (#first), calculated using the "first call" analysis described above. It is clear from this graph that the actual difference between stimulated and handled traffic is much less than is suggested by the graph in Fig. 5. Also, column 8 lines 54-65: The graph in Fig.7 shows the results of the Erlang traffic calculations based on the call pattern numbers of the graphs in FIGS. 5 and 6. The "est" line shows the calculated number of call stations required to handle #suc calls (where the number of call stations was in fact 75). The "all" line shows that if the total number of calls (#tot) was used to calculate the number of call stations required, the number would be roughly double the number actually being used. Finally, using the calculated number of first calls (#first) the "first" line shows that an increase in the number of lines and answering stations of around only 10% would be sufficient to make sure that no calls were blocked.
- US 20210174288 A1 ¶ [0001] 1st-3rd sentences: One problem faced by many customer contact centers is how to efficiently use resources of the contact center, including hardware and software resources, to process customer interactions. When a contact center agent is not proficient at his job, the resources are not used as efficiently as they could. For example, the resources may unnecessarily be used to transfer a current interaction to another agent who might be more proficient, to process repeated call-backs due to the customer's issue not being resolved the first time, and/or for a prolonged interaction with the customer due to the agent's lack of proficiency. ¶ [0012] 3rd-4th sentences: Agents who perform well allow more efficient use of contact center resources by, for example, allowing shorter interactions with customers, avoiding call transfers, avoiding repeat calls, and the like. Avoiding such tasks help avoid unnecessary tying up of resources such as processors, communication ports, queues, and the like. ¶ [0026] 1st sentence: the machine learning model is trained with data of agents of the contact center for which actual performance scores have been computed. ¶ [0026] 1st sentence: the machine learning model is trained with data of agents of the contact center for which actual performance scores have been computed. ¶ [0056] In one embodiment, the threshold is selected based on a correlation of performance scores of actual agents of the contact center, and particular events associated with those agents. ¶ [0059] According to one example embodiment, the contact center system 1160 manages resources (e.g. personnel, computers, and telecommunication equipment) to enable delivery of services via telephone or other communication mechanisms. Such services may vary depending on the type of contact center, and may range from customer service to help desk, emergency response, telemarketing, order taking, and the like.
- US 11706345 B1 column 9 lines 15-44: A priority and a delay of a queue may be specified in a routing profile that names the queue. If the routing profile names multiple queues, then the priority of the queues determines which queue is serviced by an agent before other queues. For example, consider a group of agents assigned to a “Sales” routing profile. The Sales routing profile may name a “Sales” queue with priority 1 and a “Support” queue with priority 2. In this case, contacts in the lower priority Support queue are routed to an agent when there are no contacts in the higher priority Sales queue. A queue in a routing profile may also be associated with a delay (e.g., in seconds) with priority taking precedence over delay. In this case, if there is a contact in a queue associated with a delay (e.g., a delay greater than zero) and all higher priority queues are empty, then the contact is routed to an agent only after the contact has been waiting in the queue for at least the delay amount of time. For example, consider a group of agents assigned to a “Support” routing profile. The Support routing profile may name a “Tier 1 Support” queue with priority 1 and a delay of zero seconds, a “Tier 2 Support” queue with priority 2 and a delay of twenty seconds, and a “Tier 3 Support” queue with priority 3 and a delay of eighty seconds. In this case, a contact in the Tier 2 Support queue may be routed to an agent when the contact has been waiting in the queue for at least twenty seconds and the Tier 1 Support queue is empty. Likewise, a contact in the Tier 3 Support queue may be routed to an agent when the contact has been waiting in the queue for at least eighty seconds and both the Tier 1 and the Tier 2 Support queues are empty.
- US 20240330828 A1 ¶ [0015] 2nd sentence: Contact centers with an existing store of historic data that includes the type of input data for a predictive routing server, such as agent data and customer-agent interaction data, may be able to leverage the historic data into estimating and modeling how the contact center might have performed with the predictive routing server over a baseline of actual performance information over the same time period. Mid-¶ [0039] A variance analysis may also be generated for agent variance, which may show the range of agent performance for each category as specified group of agents. Examples of a grouping of agents can include, but are not limited to, an agent's role and an agent's skills.
- US 20200293922 A1 ¶ [0067] In one arrangement, during the training process, the predictor device 116 is configured to utilize a mean absolute error (MAE) metric 252 as the training quality metric 250. Mean absolute error relates to a measured difference between two variables. As such, during operation, the predictor device 116 can utilize each model 150 to identify the predicted output for a particular variable, such as EWT, and can utilize the contact center operational data 136 to identify the actual output value for a particular variable, such as EWT. The predictor device 116 can then apply the following MAE metric 252 to both the model 150 and the contact center operational data 136… ¶ [0068] For each actual output value, y, of the contact center operational data 136, the predictor device 116 utilizes the MAE metric 252 to identify a magnitude of a residual, …is the predicted output value from the model 150. The MAE metric 252 utilizes the absolute value of the residual to mitigate the cancellation of negative and positive residual values. The predictor device 116 further utilizes the MAE metric 252 to calculate the average of the residual values, where n is the total number of data points within the contact center operational data 136. The predictor device 116 provides the average of the residual values as a mean error score 253 the given model 150. The predictor device 116 can output the mean error score 253 as the model quality value 152 for the model 150. ¶ [0071] With application of the EV metric 254 to the contact center operational data 136 and to each model 150, the predictor device 116 is configured to identify any discrepancy between the model 150 and the actual contact center operational data 136. For example, during application of the EV metric 254, the predictor device 116 can identify a coefficient of determination for the contact center operational data 136 relative to a given model 150. The predictor device 116 provides coefficient of determination as an explained variance score 255 for the given model 150. The predictor device 116 can output the explained variance score 255 as the model quality value 152 for the model 150.
- US 10771628 B1 Fig.7B column 13 lines 8-21: Fig.7B depicts call center dashboard report 710 displayed via a graphical user interface, according to some embodiments. As disclosed in relation to Fig.7A, call center dashboard 702 may further include an interface configured to generate reports (e.g. graphs, meat maps, histograms, and icons) to visual depict collected and forecasted data regarding call data and user data. Specifically, call center dashboard report 710 is graph representing actual inbound calls and predicted inbound calls. Such reports are beneficial in that they visually represent the output accuracy of forecasting models implemented by employees 114 and other entities with access to call center dashboard 702.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624 A
July 25th, 2026
1 USPTO’s training entitled Focus on Computer/Software-related Claims dated May 2015 slides 16-17,20-21 citing MPEP 2111.04
2 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
3 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
4 In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009)
5 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
6 Per MPEP 2106.04(a): “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible…”.
7 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
8 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
9 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit)
10 TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1747, 1748 (Fed. Cir. 2016)
11 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)
12 buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014).
13 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts");
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
14 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
15 Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362
16 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
17 Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values);
Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
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