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
The following NON-FINAL Office action is in response to Applicant’s request for continued examination filed on 10/10/2025.
Status of Claims
Claims 1, 8, and 15 have been newly amended and Claim 22 newly added by Applicant.
Claims 1-5, 7-12,14-19, 21, 22 are currently pending and have been rejected as follows.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/10/2025 has been entered.
Response to Arguments
Applicant’s 08/07/2025 amendment necessitated new grounds of rejection in this office action.
Response to Applicant’s prior art rebuttal Arguments
- as per independent Claim 1 -
Prior art Argument I: Remarks 08/07/2026 p.9 ¶2-p.10 ¶2 argues Bondi does not teach
"predicting a predicted number of scheduling units for the set of one or more contact centers...wherein each scheduling unit of the scheduling units groups one or more of the agents into groups... with common scheduling requirements... each scheduling unit comprising a
computational module running on the computational system"
Applicant’s prior art argument I is moot in view of new grounds of rejection because now
Leggett; Ernest W. US 5185780 A hereinafter Leggett is relied upon by Examiner to teach
- predicting a predicted number of scheduling units for the set of one or more contact centers
(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 [or scheduling] units, each management unit (MU) having predetermined number of agent groups. Leggett 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),
- ...wherein each scheduling unit of the scheduling units groups one or more of the agents into groups... 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 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 MU allocations for the shift. Also, MPEP 2111.04 noting limited weight of a wherein limitation)
- each scheduling unit comprising a computational module running on the computational system
(Leggett column 17 lines 44-64: Referring to Fig.10, data flow within 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 the 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).
- as per independent Claim 15 -
Prior art Argument II: Remarks 08/07/2026 p.10 ¶3-p.11 ¶8 argues Johnston does not teach or suggest the limitation of "selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents"
Examiner fully considered the prior argument II but respectfully disagrees finding it unpersuasive.
Johnston et al US 11368588 B1 hereinafter Johnston teaches the Applicant’s contested
- "selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents"
(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. 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. column 3 lines 56-58: skills of the agent, as previously noted, may also be a factor in forecasting a need for agents and scheduling of the agents. column 5 lines 18-23: Thus, by forecasting an expected volume of communications to be received and scheduling [or selecting] an appropriate [or optimal] number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient [or optimal] and timely fashion, thereby providing the customers with good user experience. column 7 lines 25-27 corroborates that: Skills of the agents 144, as previously noted, may also be 25 a factor in forecasting a need for agents 114 and scheduling of the agents 114. Similarly, column 15 lines 22-29 states: The contact center management service 126 and ML model(s) 128 may improve forecast accuracy (better match [or select] of headcount of agents 114 required to achieve the target service level), better schedule efficiency [or optimization] (having the right [or optimal] 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. column 12 lines 62-66 provides a different example where: the increased occupancy may only be with respect to particular skill(s), e.g. a type of language or a communication transaction type. Thus, the need for additional agents may be a need for more agents 114 with particular skills(s)
Johnston column 15 lines 10-12: ML model(s) 128 predict that agents 114 and queues 116 will be extremely busy, etc. column 5 lines 18-23: Thus by forecasting an expected volume of communications to be received and scheduling an appropriate number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient and timely fashion, thereby providing customers with a good user experience. Johnston column 7 lines 25-42: Skills of agents 144, as previously noted, may also be a factor in forecasting a need for agents 114 and scheduling of the agents 114. For example, agent skills may include language skills, proficiency with one or more types of actions associated with a queue with which the agent is associated, and a type of communication. For example, some agents 114 may be proficient in French and thus, associated with queues 116 where French is a highly desirable skill. Likewise, some agents 114 may be proficient with certain types of transactions, e.g., refunds and/or returns. Thus, such agents 114 may be associated with queues 116 related to those types of transactions. Other skills include being familiar with handling certain types of communications 110, e.g., handling of text messages and/or web chats versus handling of telephone calls. Agents 114 may have multiple skills, and likewise, queues 116 may be associated with communications 110 where the multiple skills are desirable. column 7 lines 62-67: For example, the trained ML model(s) 128 may be utilized to determine that more agents 114 with certain skills are required. For example, more agents 114 may be needed who speak French, can handle the returns/refund process, handle credit card issues, etc. The needed agents 114 may possess one or more needed skills);
Thus, the prior art argument II is found unpersuasive.
- dependent Claim 2 -
Prior art argument III: Remarks 08/07/2026 p.11 ¶9 - p.12 ¶4 argues Johnston does not teach or suggest a machine learning algorithm to predict a number of scheduling units for a set of contact centers or select an actual number based on the number predicted by machine learning and the number of regions.
Examiner fully considered the Applicant’s argument but respectfully disagrees, applies the broadest reasonable interpretation by mapping Johnston to the explicit claim language, as follows
- “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” (Examiner interprets the predicted scheduling units as claimed with predicted agents group(s) of specific skill, location and/or sub-contact center within a contract center. Based on such broadest reasonable interpretation, Johnston provides many examples or a preponderance of evidence for teaching the above limitation starting with Johnston column 1 lines 25-31: some contact centers have thousands of agents available for scheduling. However, various limitations with respect to certain factors such as…skills of the agents, rules related to scheduling … can make it difficult to schedule agents for handling communications.
Johnston column 3 lines 56-58: skills of the agent, as previously noted, may also be a factor in forecasting a need for agents and scheduling of the agents.
Johnston column 4 lines 39-45: Additionally, the contact center management service may determine that more agents [scheduling units] with certain skills are needed. This need may be determined by the trained [or learning] models [or algorithms] used by the contact center management service with further details at column 7 lines 62-67, column 12 lines 8-42, column 15 lines 10-12, 22-29 explained below.
Johnston column 5 lines 18-23: thus by forecasting expected volume of communications to be received and scheduling [or selecting] an appropriate [or optimal] number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient and timely fashion, thereby providing the customers with good user experience.
Johnston column 7 lines 25-42: Skills of agents 144, as previously noted, may also be a factor in forecasting a need for agents 114 and scheduling of the agents 114. For example, agent skills may include language skills, proficiency with one or more types of actions associated with a queue with which the agent is associated, and a type of communication. For example, some agents 114 may be proficient in French and thus, associated with queues 116 where French is a highly desirable skill. Likewise, some agents 114 may be proficient with certain types of transactions, e.g., refunds and/or returns. Thus, such agents 114 may be associated with queues 116 related to those types of transactions. Other skills include being familiar with handling certain types of communications 110, e.g., handling of text messages and/or web chats versus handling of telephone calls. Agents 114 may have multiple skills, and likewise, queues 116 may be associated with communications 110 where the multiple skills are desirable.
Johnston column 7 lines 62-67: the trained ML [machine learning] model(s) 128 may be utilized to determine that more agents 114 [or scheduling units] with certain skills are required. For example, more agents 114 may be needed who speak French, can handle the returns/refund process, handle credit card issues etc. The needed agents114 possess one or more needed skills
Johnston column 12 lines 8-42: utilizing by contact center management service 126, ML [machine learning] model(s) 128 to manage agents 402 (similar to agents 114) and queues 404 (similar to queues 116). For example, t trained model(s) 128 determine agent 402a is not as occupied as expected, e.g. queue 404a is not receiving nearly as many communications 110 as forecast. For example, received communications 110 may only be 70% of forecast volume. Thus, agent 402 a may be reassigned to different queue 404b based upon skillset that agent 402 a possesses. For example, if agent 402 a speaks French and is able to handle refunds and returns, agent 402a may be reassigned to queue 404b experiencing higher volume of communications than forecast, to help agent 402b. Queue 404b may be directed to queuing communications directed to refunds and returns. Thus, French speaking skill of agent 402a may not be useful for queue 404b, but agent 402a's refunds and returns skills useful for communications received at queue 404 b. In some configurations, if agent 402a is not as occupied as expected, agent 402a may continue to be assigned to queue 404 a but communications 110 may be dynamically prioritized by ML [machine learning] model(s) 128 for routing to queue 404 a. Likewise, if forecast volume for queue 404a is much higher than the actually received volume of communications 110, then queue 404 a may be changed to handle a different type of communication 110. For example, queue 404 a may become a 2nd queue for handling of communications 110 directed to returns and refunds and agents 402 associated with queue 404 a that include that particular skill may continue to be assigned to queue 404 a. Those agents 402 that are not skilled in refunds and returns policy may be assigned to different queue 404 based on their skillset.
Johnston column 12 lines 62-66 provides a different example where: the increased occupancy may only be with respect to particular skill(s), e.g. a type of language or a communication transaction type. Thus, the need for additional agents [or scheduling units] may be a need for more agents 114 with particular skills(s)
Johnston column 15 lines 10-12: ML [machine learning] model(s) 128 predict that agents 114 and queues 116 will be extremely busy, etc. column 5 lines 18-23: Thus by forecasting an expected volume of communications to be received and scheduling an appropriate number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient and timely fashion, thereby providing customers with a good user experience.
Johnston column 15 lines 22-29 states: contact center management service 126 and ML [machine learning] model(s) 128 improve forecast accuracy (better match of headcount of agents 114 to achieve the target service level), better schedule efficiency [or optimization] (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 16 lines 54-60: each data centers 704 include computing devices that included software applications that receive and transmit data and handle communications 110. For instance, the computing devices included in data centers 704 include software components which transmit, retrieve, receive, or otherwise provide or obtain the data from the storage service 120. Specifically, per column 4 line 46-column 5 line 5: trained models can also be 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 [or additional scheduling units] that are currently off duty but have indicated a willingness to work extra hours. Thus, such agents [or additional scheduling units] 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 the forecast volume of communications to be received is detected by the trained models, then the contact center management service may 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 may be with respect to volume of specific communications, e.g., a larger than expected volume of communications related to returns and refunds, types of communications, and/or language specific communications).
Thus, the prior art argument III is found unpersuasive.
- new dependent Claim 22 -
Prior art argument IV: Remarks 08/07/2026 p12 last ¶ argues the prior art does not teach
- "wherein the predicting of a predicted number of scheduling units is performed using a machine learning algorithm... based on numbers of scheduling units for existing contact centers with known total numbers of agents"
Examiner fully considered prior art argument IV but respectfully disagrees finding it unpersuasive.
- "wherein the predicting of a predicted number of scheduling units is performed using a machine learning algorithm... based on numbers of scheduling units for existing contact centers with known total numbers of agents"
(Johnston column 8 lines 37-39: As is known, in configurations, the ML model(s) 128 may be trained based upon historical [or known] metrics and data related to the execution of the contact center 108. For example, at column 1 lines 25-31: some contact centers 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 15 lines 22-29 states: The contact center management service 126 and ML model(s) 128 may improve forecast accuracy (better match of headcount of agents 114 required to achieve the target service level), better schedule efficiency [or optimization] (having the right [or optimal] 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
Claim 4: The method of claim 1, further comprising: determining that an actual occupancy of the agent at the first queue is less than a forecasted occupancy of the agent; and based on the one or more factors and the determining that the actual occupancy of the agent at the first queue is less than the forecasted occupancy of the agent, dynamically determining, by the contact center management service using the trained machine learning model, to change assignment of the agent from the first queue to the second queue such that the agent no longer receives communications from the first queue).
Thus, the prior art argument IV is found unpersuasive.
Response to Applicant’s 101 rebuttal Arguments
Examiner reincorporates all findings and rationales at Final Act 06/10/2025 p.2-p.14 ¶3.
SME argument I: Remarks 08/07/2026 p.14 ¶ 1 argues recitation of “each scheduling unit comprising a computational module running on the computational system” of amended Claim 1 is a specific solution rooted in computer technology to a specific technological problem of initiating specific computer software and hardware - scheduling units- as part of configuring and controlling computer equipment in contact centers. Amended Claim 1 is argued to highlight that the limitation “initiating on at least one computational system, at least one scheduling unit” requires to initiating an element running on a computer system, and thus is directed to technology. Next,
Remarks 08/07/2026 p.14 ¶3-p.15 ¶2 cites Original Specification ¶¶ [0139], [0021], [0023] to argue that as amended independent Claim 1 improves technology (as opposed to improvement to an abstract idea) in part by controlling (initiating) computing units (scheduling units).
Examiner considered the SME argument 1 but respectfully disagrees finding it unpersuasive.
Examiner fully considered the Applicant’s argument but respectfully disagrees, by noting that while some features of Original Specification ¶ [0139], ¶ [0021], ¶ [0023] are reflected in the claims, other are not. This finding is important because 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
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v
PNG
media_image1.png
1
1
media_image1.png
Greyscale
. Guidewire Software, Inc. 728
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F.3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1336, 1345 108 USPQ2d 1173 Fed Cir. 2013: admonishing that the important inquiry for a 101 analysis is to look to the claim”, citing “Content Extraction & Transmission LLC
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v.
PNG
media_image1.png
1
1
media_image1.png
Greyscale
Wells Fargo Bank Nat’l Ass’n 776
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
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”. MPEP 2103 I C citing In re Hiniker Co 150 F3d 1362 1369 47 USPQ2d 1523, 1529 Fed Cir 1998.
As another issue of claim construction or claim interpretation, the Examiner reads the term “scheduling unit” in light of Original Specification ¶ [0032] 1st sentence as: a computational unit (e.g. hardware and/or software), computational module, computer program or similar, which may organize agents into groups with common scheduling requirements. For example, Fig.9 of the Original Disclosure depicts respective scheduling unit for respective skills.
PNG
media_image2.png
590
784
media_image2.png
Greyscale
Annotated excerpt of Fig. 9 from Original Fig. 9 depicting respective scheduling units
Accordingly, it is clear that, as read in light of the Applicant’s disclosure, the term “scheduling unit” is a mere logical grouping that partitions or organizes agents for scheduling purposes. The fact that such “scheduling unit” is now amended to be also represented by a “computational module running on the computer system” as opposed to be represented by pen and paper, does not necessarily render the independent Claim 1 less abstract and eligible because, expending upon the physical aids test, the MPEP 2106.04(a)(2) III C clarifies 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,
are all examples that do not preclude the claims from reciting the abstract idea.
Here, when tested per MPEP 2106.04(a)(2) III C the “scheduling unit”, as currently amended, represents such computer environment [MPEP 2106.04(a)(2) III C #2] upon which the abstract configuration, managing or scheduling for the “one or more contract centers” is being performed.
Additionally, or alternatively, when tested per MPEP 2106.04(a)(2) III C such “scheduling unit”, can also be viewed as a tool [MPEP 2106.04(A)(2) III C #3] to perform or produce the abstract “schedule for at least open agent”, as recited at the “wherein” limitation of independent Claim 1.
As per “initiating on at least one computational system, at least one scheduling unit” as argued by Remarks 08/07/2026 p.14 ¶1, the Examiner again notes that the Original Specification does not disclose what the term “initiating” means, as in “initiating on at least one computational system, at least one scheduling unit”. In the absence of an 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”, 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 scheduling units with common scheduling requirements would also represent an example falling within the Certain Methods of Organizing Human Activities.
Further, as articulated by MPEP 2106.04(a)(2) II ¶6, 4th sentence, it is also clear that activity that involves multiple people [akin here to groups of agents] and certain activity between a person and a computer [akin here to initiating the scheduling unit in light of Fig.9] may fall within the abstract Certain Methods of Organizing Human Activity grouping. Applicant admits at Original Specification ¶ [0002] that methods of configuring contact center technology already exist, albeit requiring guidance from experts with experience and expertise in the configuration of contact centers, to which, the Specification ¶ [0006] proposes partial, substantial or full automation. Yet such automation of human knowledge does correspond to the computer aided implementation of abstract processes as articulated by MPEP 2106.04(a)(2) III C #1,#2,#3 and/or the computer based activity of MPEP 2106.04(a)(2) II ¶6, 4th sentence, neither of which preclude the claims from reciting describing or setting forth the abstract exception. Thus, despite the allegation to the contrary, as made at by Remarks 08/07/2026 p.14 ¶1, in this case, both the problem of having to rely on human expert (Original Specification ¶ [0002]), and its solution achieved via automation as alternative to the cognitive capabilities of the human expert (Original Specification ¶ [0006]) remain entrepreneurial and abstract as opposed to technological. Simply put, the Applicant arrives at the conclusion of improving computer technology and computer efficiency by improvement to the abstract idea itself. 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 that even “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry”. It then follows that here any 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 previously 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, 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. Also, MPEP 2106.05(f)(2)(iii)1 articulated 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) I2 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 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, an 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, 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, once again, Examiner finds the SME argument I unpersuasive in demonstrating the claims are directed to technology, much less directed to an improvement in actual technology.
SME argument II: Remarks 08/07/2026 p.15 ¶ 3-p.16 ¶3 argues amended claim 1 recites a non-routine and unconventional algorithm of “predicting a number of scheduling units” used in “selecting an actual required number of scheduling units”, with said “actual required number of scheduling units” being considered when “initiating” “at least one scheduling unit” whose outputs the Applicant deems as improvement to the functioning of the computer running the computational module included in each scheduling unit - as well as the functioning of a contact center.
Examiner fully considered the SME argument II but disagrees finding it unpersuasive by reincorporating herein all findings and rationales above showing that improvement in the abstract idea is not improvement in technology. Further, Examiner also notes that the Specification does not disclose what the term initiating as in “initiating on at least one computational system, at least one scheduling unit” represents at independent Claim 1. The same holds true3 for configuring as in configuring the one or more contact centers at dependent Claim 5.
In the absence of their explicit definition, the Examiner applies the broadest reasonable interpretation test 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 or demand on “the actual required number of scheduling units”. Likewise, the Examiner interprets the term configuring in light of Original Specification ¶ [0026] 3rd sentence as establishing a number of scheduling units on computational systems. They all fall well within the equally abstract computer aided processes of MPEP 2106.04(a)(2) III C and/or fundamental economic practices or principle, interactions and/or managing of such interactions of MPEP 2106.04(a)(2) II A,B,C. Moreover, the fact that such broadly and generally recited “initiating” (Claim 1) and “configuring” (Claim 5) is based on “the actual required” [or demand] “number of scheduling units” (Claims 1,5) previously select[ed] “based on the predicted number of scheduling units” does not change the abstract character of the claims because said claims simply take into account a judgment of what is actual[y] needed based on an evaluation of what is predicted to be needed. Yet, such concepts of evaluation and judgment remain abstract as demonstrated by MPEP 2106.04(a)(2) III ¶2. In a similar vein MPEP 2106.05(a) I cites BSG Tech LLC v. Buyseasons, Inc. 899 F.3d 1281, 1287-88, 127 USPQ2d 1688,1693-94 (Fed. Cir. 2018); to show that providing historical usage information to improve the quality and organization of information added to a database, represent improvement to the information stored by a database, which is not equivalent to an improvement in actual technology such as database’s functionality. It would then follow, that here, a purported improvement to the ensuing information in configuring, managing or scheduling of the one or more contact centers would also not represent an improvement in actual technology.
Further still, the Examiner also submits, in the arguendo, that even when construing the predicting as an algorithm, as raised by Applicant at Remarks 08/07/2026 p.15 ¶3-p.16 ¶3, such algorithm, as expressed in words, per MPEP 2106.04(a)(2) I A, still falls within the auspices of abstract mathematical concepts of MPEP 2106.04(a)(2) I. Its alleged accuracy in predicting the “scheduling units” as raised by Applicant at Remarks 08/07/2026 p.15 ¶4, would at most represent an improvement in the abstract prediction of the required, demanded, or needed group or units of agents. Such improvement in the abstract process would not render the claims less abstract ad eligible. To justify such rationale, Examiner points to MPEP 2106.04(a)(2) I.C(i) which cites SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), to state that performing a resampled statistical analysis to generate a resampled distribution, does recite, describe or set forth the abstract idea. To justify such rationale, Examiner also points to MPEP 2106.04(a)(2) I.C (v) that finds that using an algorithm for determining the optimal configuration of number of business representative visits to clients does also recite, describe or set forth the abstract idea. For example here, similar to how Remarks 08/07/2026 p.15 ¶4-p.16 ¶1 argues in favor of a improved accuracy of values scheduling units used to configure (i.e. mange, schedule) contact centers, the ‘291 patent of SAP supra, described the analogous need for improving upon existing practices that performed rudimentary statistical functions not useful to investors in forecasting the behavior of financial markets because they relied upon assumptions that the probability distribution function (‘PDF’) for the financial data followed a normal or Gaussian distribution.” (’291 patent, col. 1, lines 24–36). Since “the PDF for financial market data is heavy tailed (i.e. the histograms of financial market data typically involve many outliers containing important information), rather than symmetric like a normal distribution, the SAP patent remedied those deficiencies by proposing utilization of resampled statistical methods for analysis of financial data, which did not assume a normal probability distribution. One such method is a bootstrap method, which estimated distribution of data in a pool (sample space) by repeated sampling of the data in the pool. A sample space in a boot-strap method defined by selecting specific investment or particular time period. Specifically, data samples were drawn from the sample space with replacement and then returned to the pool before next sample is drawn. Yet, the Federal Circuit noted: “Dependent method claims 2-7 and 10 add limitations… [that] require the resampling method to be a bootstrap method." SAP, 260 F. Supp. 3d at 715 . Likewise, "[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method." Id. at 716. Because bootstrap, jack-knife, and cross-validation methods are all "particular methods of resampling," those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE, 765 F.3d at 1353 (same). Dependent method claims 12-21 are no different”. “Here, the focus of the claims is not any improved computer or network, but the improved mathematical analysis”. Similarly, 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 ineligible. 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 and the current measurement of the process variable was substituted for the corresponding numbers in the original calculation.
Since the computerized resampled statistical model in SAP supra, the algorithm of optimal client-business representative in Maucorps, and the iterative or repetitive process of model substitution in configuring the petrochemical process in Flook supra, did not save their underlining claims from patent ineligibility, despite their specific data and the how-to details of performing the algorithm, the Examiner analogously reasons that here, a similar predictive algorithm, as argued by Remarks 08/07/2026 p.15 ¶4-p.16 ¶1, would also not preclude the current claims from reciting, describing or setting forth the abstract fundamental practices and its associated mathematical manipulations. To be also clear, according to MPEP 2106.04(a)(2) II A ¶2, the term fundamental is not used in the sense of necessarily being old or well-known4, but rather as a building block of modern economy. Here, selection of what is actually needed or required from what was predicted, as raised by Remarks 08/07/2026 p.15 ¶3, represents such fundamental, building block of modern economy regardless of whether or not its computerization via the argued algorithm is old or well-known. MPEP 2106.04(d)(1) is clear that “improvement in the judicial exception itself is not an improvement in technology”, with MPEP 2106.04 I ¶3, citing Flook, among others, to articulate that claims directed to narrow laws that have limited applications, remain patent ineligible. Further, MPEP 2106.04 I, cites Myriad, 569 U.S at 591, 106 USPQ2d at 1979 to stress that even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the 101 inquiry”. The “Myriad” rationale was corroborated by SAP Am Inc v InvestPic cited by MPEP 2106.04(a)(2) I.C(i). Digging deeper into the Court’s rationale in SAP supra, Examiner finds the Court ruled 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”. That is, “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” with no plausibly alleged innovation in non-abstract application realm.
Here, as in Myriad, 569 U.S at 591, 106 USPQ2d at 1979 and SAP Am Inc v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ.2d 1638 (Fed. Cir. 2018), no matter how much of an advance or accuracy in scheduling resources or units the claims would recite, said advance would still lie entirely within the realm of Certain Methods of Organizing Human Activities with no plausibly of the alleged innovation entering the non-abstract realm. The “SAP” findings were corroborated by Versata Dev Grp Inc v SAP Am Inc 115 USPQ2d 1681 Fed Cir 2015 again undelaying the difference between improvement to entrepreneurial goal objective versus improvement to actual technology. see MPEP 2106.04. Here, the accuracy in scheduling, managing or configuring resources or units as argued by Remarks 08/07/2026 p.15 ¶4, would correspond to such abstract, mathematical analysis to achieve the equally abstract fundamental or economic concept of configuration, management or scheduling for the one or more contact centers, as a fundamental building block, which, no matter of its alleged robustness, would be ineligible following the legal tests of SAP, Flook, Maucorps, Myriad, Versata cited by MPEP supra.
Accordingly, the Examiner has provided a preponderance of legal evidence showing the SME argument II is unpersuasive.
SME argument III: Remarks 08/07/2026 p.16 ¶4 argues that newly added dependent claim 22 specifies what data is used in a specific prediction using the machine learning algorithm, where the predicted number of scheduling unit is then used in selecting the actual required number of scheduling units, based on which at least one scheduling unit is initiated, which is argued to integrate the abstract idea into a practical application and amount to significantly more.
Examiner fully considered the SME argument III but respectfully disagrees finding it unpersuasive. First, it is noted that MPEP 2106.04 I ¶3 cites Flook 437 U.S. at 589-90, 198 USPQ at 197, to argue that narrow laws that may have limited applications were still held ineligible. Here,
considering or specificizing known information, such as “numbers of scheduling units for existing contact centers with known total numbers of agents”, to be used in the “algorithm” for “predicting” “a predicted number of scheduling units” represents such narrowing of the abstract prediction to limited applications in the configuring, managing or scheduling of the one or more contact centers
which does not render the claims less abstract and eligible. For example, MPEP 2106.04(a)(2) I.C(i) cites SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), to state that performing a resampled statistical analysis to generate a resampled distribution, does recite, describe or set forth the abstract exception. Specifically, the ‘291 patent of SAP supra, described an analogous need for improving upon existing practices that performed rudimentary statistical functions not useful to investors in forecasting the behavior of financial markets because they relied upon assumptions that the probability distribution function (‘PDF’) for the financial data followed a normal or Gaussian distribution.” (’291 patent, col. 1, lines 24–36). Yet, it was found that “the PDF for financial market data is heavy tailed (i.e., the histograms of financial market data typically involve many outliers containing important information),” rather than symmetric like a normal distribution. Id., col. 1, lines 36– 37, 41–44. To remedy those deficiencies, the patent in “SAP” proposed utilization of resampled statistical methods for analysis of financial data, which did not assume a normal probability distribution. One such method is a bootstrap method, which estimates distribution of data in a pool (a sample space) by repeated sampling of the data in the pool. A sample space in a boot-strap method can be defined by selecting a specific investment or a particular period of time. Data samples are drawn from the sample space with replacement: samples are drawn from the sample space and then returned to the pool before next sample is drawn. Yet, the Federal Circuit noted: “Dependent method claims 2-7 and 10 add limitations… [that] require the resampling method to be a bootstrap method." SAP, 260 F. Supp. 3d at 715 . Likewise, "[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method." Id. at 716. Because bootstrap, jack-knife, and cross-validation methods are all "particular methods of resampling," those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE, 765 F.3d at 1353 (same). Dependent method claims 12-21 are no different”. “Here, the focus of the claims is not any improved computer or network, but the improved mathematical analysis”. Similarly, 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 the selected time intervals, and in each updating computation, the most recently calculated alarm base and the current measurement of the process variable was substituted for the corresponding numbers in the original calculation.
Since the specification of a data pool or sample space to be used in the computerized resampled statistical model in SAP supra did not render the claims less abstract and eligible, and since the specification of present or known values to be used in the determining algorithm of Flook
supra also did not render the claims less abstract and eligible, the Examiner also reasons that here, the analogous use of the “numbers of scheduling units for existing contact centers with known total numbers of agents” in the algorithm of predicting a number of scheduling units, as argued by Applicant at Remarks 08/07/2026 p.16 ¶4 with respect to newly added dependent Claim 22, would also not render said claim less abstract and eligible.
Also, the fact that such “algorithm” is “a machine learning algorithm”, does not integrate the underlining abstract concepts into a practical appclaition or provide significantly more, because it represents a mere invocation of machinery to apply the aforementioned abstract processes, such as a mathematical algorithm being applied on a general-purpose computer, as exemplified in the non-limiting example of MPEP 2106.05(f)(2)(i). Thus, the SME argument III is unpersuasive.
SME argument IV: Remarks 08/07/2026 p.17 ¶1-p.18 ¶1 argues that similar to the hypothetical claim 2 of the USPTO’s Example 48, the currently and newly added dependent claim 22 recites a specific, technological how to, including a specific use of a machine learning algorithm, which is argued to provides a specific technology solution and improvement.
Examiner fully considered SME argument IV but respectfully disagrees finding it unpersuasive.
Examiner reincorporates all findings and rationales above that demonstrated that the claims are not directed to a solution in actual technology and improvement in actual technology.
As per Applicant’s reliance on claim 2 of USPTO’s Examples 48 at Remarks 08/07/2026 p.17 ¶1-p.18 ¶1, the Examiner reminds that all 101 examples provided by USPTO, including Example 48 are hypothetical and non-precedential. USPTO “2019 PEG, 101 Examples 37-42 document entitled “Subject Matter Eligibility Examples: Abstract Ideas” p.1, ¶1 2nd sentence. “The examples below are hypothetical and only intended to be illustrative of the claim analysis under the 2019 PEG” corroborating “May 2016 Update: Memorandum - Formulating a Subject Matter Eligibility Rejection and Evaluating the Applicant’s Response to a Subject Matter Eligibility Rejection”, p.5 ¶2 Section C: “USPTO issued examples in conjunction with the Interim Eligibility Guidance, including […] July 2015 Update Appendix I: Examples […]; These examples, many of which are hypothetical, were drafted to show exemplary analyses under the Interim Eligibility Guidance and are intended to be illustrative of the analysis only. While some of the fact patterns draw from U.S. Supreme Court and U.S. Court of Appeals for the Federal Circuit decisions, the examples do not carry the weight of court decisions. Therefore, the examples should not be used as a basis for a subject matter eligibility rejection. Similarly see July 2024 Subject Matter Eligibility Examples, pertaining to Examples 47-49, p.1, ¶1, 2nd sentence: “The examples below are hypothetical and only intended to be illustrative of the claim analysis performed using MPEP 2106, and of the particular issues noted below in the Issue Spotting Chart”.
In any event here, the current claims are irreconcilably different than steps (f) and (g) in hypothetical claim 2 of Example 48. This is because, at no point do any of the current claims recite anything remotely analogous to: (f) “synthesizing speech waveforms from the masked clusters, wherein each speech waveform corresponds to a different source sn”; and “(g) “combining the speech waveforms to generate a mixed speech signal x' by stitching together the speech waveforms corresponding to the different sources sn, excluding the speech waveform from a target source ss such that the mixed speech signal x' includes speech waveforms from the different sources sn and excludes the speech waveform from the target source ss“;
Rather, as explained by Applicant at Remarks 08/07/2026 p.16 ¶4, and subsequently at Remarks 08/07/2026 p.18 ¶1, newly added dependent Claim 22, merely uses the “numbers of scheduling units for existing contact centers with known total numbers of agents” on the algorithm of predicting a predicted number of scheduling units, which still recites, describes or sets forth the abstract exception, in a manner not meaningfully different than SAP, Flook, and Maucorps supra.
Further, the fact that such “algorithm” is “a machine learning algorithm”, does not integrate the underlining abstract concepts into a practical appclaition or provide significantly more, because it represents a mere invocation of machinery to apply the aforementioned abstract processes, such as a mathematical algorithm being applied on a general-purpose computer, as exemplified in the non-limiting example of MPEP 2106.05(f)(2)(i).
Thus, the SME argument IV is unpersuasive.
SME argument V: Remarks 08/07/2026 p.18 ¶2-p.18 ¶3 argues the current claims are directed to patent eligible technology and provide improvements to technology. For example, it is argued that amended independent Claim 1 recites a detailed, non-generic technological algorithm which provides specific outputs and a technological improvement, which integrates any alleged abstract idea or concept into practical application; that amount to significantly more than any alleged abstract idea. Moreover, Remarks 08/07/2026 p.18 ¶4-p.19 ¶ 1 argues that unlike Versata DevGrp, Inc v SAP Am, Inc 115 USPQ2d 1681 (Fed. Cir 2015), the amended independent claim 1 and new dependent claim 22 recite detailed and unconventional technological algorithms rooted in computer technology and producing technological outputs and improvements.
Examiner fully considered the SME argument V but respectfully disagrees finding it unpersuasive, by reincorporating all findings and rationales above. For example, the Examiner reiterates that MPEP 2106.04 I ¶3 cites Flook 437 U.S. at 589-90, 198 USPQ at 197, among others, to argue that narrow laws that may have limited applications were still held ineligible. Here,
considering specific, known information, of demand or what is needed, such as “numbers of scheduling units for existing contact centers with known total numbers of agents”, to be used in the “algorithm” for an output of “predicting” “a predicted number of scheduling units” represents such narrowing of the abstract prediction to limited applications in the configuring, managing or scheduling of the one or more contact centers. This does not render the claims less abstract and eligible. For example, MPEP 2106.04(a)(2) I.C(i) cites SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), to state that performing a resampled statistical analysis to generate a resampled distribution, does recite, describe or set forth the abstract exception. Specifically, the ‘291 patent of SAP supra, described an analogous need for improving upon existing practices that performed rudimentary statistical functions not useful to investors in forecasting the behavior of financial markets because they relied upon assumptions that the probability distribution function (‘PDF’) for the financial data followed a normal or Gaussian distribution.” (’291 patent, col. 1, lines 24–36). Yet, it was found that “the PDF for financial market data is heavy tailed (i.e., the histograms of financial market data typically involve many outliers containing important information),” rather than symmetric like a normal distribution. Id., col. 1, lines 36– 37, 41–44. To remedy those deficiencies, the patent in “SAP” proposed utilization of resampled statistical methods for analysis of financial data, which did not assume a normal probability distribution. One such method is a bootstrap method, which estimates distribution of data in a pool (a sample space) by repeated sampling of the data in the pool. A sample space in a boot-strap method can be defined by selecting a specific investment or a particular period of time. Data samples are drawn from the sample space with replacement: samples are drawn from the sample space and then returned to the pool before next sample is drawn. Yet, the Federal Circuit noted: “Dependent method claims 2-7 and 10 add limitations… [that] require the resampling method to be a bootstrap method." SAP, 260 F. Supp. 3d at 715 . Likewise, "[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method." Id. at 716. Because bootstrap, jack-knife, and cross-validation methods are all "particular methods of resampling," those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE, 765 F.3d at 1353 (same). Dependent method claims 12-21 are no different”. “Here, the focus of the claims is not any improved computer or network, but the improved mathematical analysis”. Similarly, 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 the selected time intervals, and in each updating computation, the most recently calculated alarm base and the current measurement of the process variable was substituted for the corresponding numbers in the original calculation.
Since the specification of a data pool or sample space to be used in the computerized resampled statistical model in SAP supra did not render the claims less abstract and eligible, and since the specification of present or known values to be used in the determining algorithm of Flook
supra also did not render the claims less abstract and eligible, despite their specific data and the how-to details of performing the algorithm, the Examiner also reasons that here, the analogous use of the “numbers of scheduling units for existing contact centers with known total numbers of agents” to be used on the algorithm of predicting a predicted number of scheduling units as outputs argued by Remarks 08/07/2026 p.18 ¶2-p.18 ¶3 with respect to independent Claim 1 and newly added dependent Claim 22, would also not render said claim less abstract and eligible. Also, the fact that such “algorithm” is “a machine learning algorithm”, does not integrate the underlining abstract concepts into a practical appclaition or provide significantly more, because it represents a mere invocation of machinery to apply the aforementioned abstract processes, such as a mathematical algorithm being applied on a general-purpose computer, as exemplified in the non-limiting example of MPEP 2106.05(f)(2)(i) to achieve automation of a desired, yet still abstract, entrepreneurial, commercial managerial or scheduling goal or objective, as read in light of the at least Original Specification ¶ [0002] and ¶ [0006]. Yet, the use of such known data represented by “known total numbers of agents” in “exiting contact centers” for “predicting a predicted number of scheduling units for the set of one or more contact centers” (dependent Claim 22) for subsequent configuration, management or scheduling of the contact centers as demonstrated 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; outputting the actual required number of scheduling units; and initiating on at least one computational system, at least one scheduling unit, based on the actual required number of scheduling units” (independent Claim 1) does not render the claims patent eligible since MPEP 2106.04(a)(2) II C is clear that considering historical usage information while inputting data5 and automatization by providing information to a person without interfering with the person’s primary activity6 both set forth the abstract grouping of Certain Methods of Organizing Human activities. In fact, MPEP 2106.04(a)(2) II ¶6, 4th sentence is clear that certain activity between a person and a computer may still fall within certain methods of organizing human activity. Also according to BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018), as cited by MPEP 2106.05(a) I providing historical usage information to users while inputting data, in order to improve the quality and organization of information added to a database, represents an improvement to the information stored by a database which is not equivalent to an improvement in the database’s functionality. Thus, the SME argument V is unpersuasive.
SME argument VI: Remarks 08/07/2026 p.19 ¶2-p.19 ¶5 argues that the currently amend independent Claim 1 and the newly added dependent Claim 22 are not directed to fundamental economic principles or practices because they rely on, or require, computer systems for their operation, which is not the case for the categories of inventions or examples enumerated by USPTO to help define claims directed to “Certain Method of Organizing Human Activity”.
Examiner fully considered the Applicant’s SME argument VI but respectfully disagrees finding it unpersuasive by reincorporating all findings and rationales above. Examiner also reminds the Applicant that MPEP 2106.04(A)(2) II ¶6, 4th sentence, states that the sub-groupings (i.e. fundamental economic principles or practices) of the main abstract grouping of Certain Methods of Organizing Human Activities encompass activity that involves multiple people. The same MPEP 2106.04(A)(2) II ¶6, 4th sentence, also stresses that certain activity between a person and a may still fall within the “certain methods of organizing human activity” grouping. Here, the multiple people are represented by the “total number of agents” (independent Claim 1), further explained as the “known total numbers of agents” (dependent Claim 22). Also here, the computer interaction is evidenced by “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, each scheduling unit comprising a computational module running on the computational system” (independent Claim 1). Thus, despite Applicant’s allegation to contrary, the MPEP 2106.04(A)(2) II ¶6, 4th sentence, discloses abstract principles or practices that rely on, or require computer systems for their operation, without necessarily rendering their underlining claims less abstract and eligible. This is to be expected because, according to MPEP 2106.04(a)(2) II A, the term fundamental is not used in the sense of necessarily being old or well-known but rather from the perspective of a building block of modern economy. Here, the configuring, managing or scheduling of the “one or more contact centers” represents such building block of modern economy no matter if its implementation or initiation on a computer system is old or well-known.
In fact, MPEP 2106.04(a)(2) III C comes to corroborate such findings by stating that:
#1. Performing an abstract process on a generic computer,
#2 Performing an abstract process in a computer environment, and
# 3. Using a computer as a tool to perform an abstract process,
all do not preclude their underlining claims from reciting the abstract exception.
Further, even when more granularly investigating such level of automation or computerization from the prism of additional computer-based elements, such automation or computerization still represent mere invocation of machinery or computer components such as:
- mathematical algorithm being applied on a general-purpose computer [her “machine learning”], as tested per MPEP 2106.05(f)(2)(i),
- monitoring audit log data [here known, predict[ed] and required number], as tested per MPEP 2106.05(f)(2) (iii), and
- requiring use of software to tailor information and provide it to the user on a computer [here “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, each scheduling unit comprising a computational module running on the computational system”] as tested per MPEP 2106.05(f)(2) (v).
MPEP 2106.05(f)(2) finds such invocation of machinery or computer components to apply the abstract idea does not integrate it into a practical appclaition or provide significantly more.
Based on the preponderance of legal evidence above, the Examiner finds the SME argument VI is unpersuasive
In conclusion, Examiner submits that the argued limitations still recite, describe or set forth the abstract exception (Step 2A prong 1), with no additional, computer-based elements, capable to integrate the abstract idea into a practical application (Step 2A prong 2), and for similar reasons incapable to provide significantly more (Step 2B). Thus, the argued claims are patent ineligible.
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 1-5,7-12,14,21,22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), ¶2, 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.
Claims 1,8 are independent and have been each amended to recite, among others:
- “wherein each scheduling unit of the scheduling units groups one or more of the agents into groups, each group with common scheduling requirements”;
Claims 1,8 are thus rendered vague and indefinite because it is unclear, how “one” [single] “agent” as broadly covered by the breadth of expression “one or more of the 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 a plurality of the total number of agents into groups, each group with common scheduling requirements;
Claims 2-5,7,21,22 are dependent and rejected based on rejected parent Claim 1.
Claims 9-12,14 are dependent and rejected based on rejected parent Claim 8.
Clarification and/or correction is/are required.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 1-5, 7-12, 14-19, 21,22 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 the agents into groups, each group with common scheduling requirements” at independent Claims 1,8,15; and “configuring a set of one or more contact centers” 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 Claims 1,8,17, 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, including “initiating”, “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” at Claims 1,8, and similarly “initiating”, “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” at Claim 15. Examiner reads these limitations in light of Original Spec. ¶ [0002]-¶ [0006] to recite, describe or set forth the abstract managing, initiating or configuring interactions pertaining to scheduling of “agents” in “one or more contact centers”. The abstract, fundamental character of the claims is further corroborated by the business relationship management in the newly added dependent Claim 21 reciting “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”. 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 the agents into groups, each group with common scheduling requirements; group[ing] of scheduling units with common scheduling requirements, comprising one or more of: operating days and hours, shifts, location, and department, would similarly represent a grouping of building blocks (i.e. units) 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” comprising “initiating on at least one computational system, at least one scheduling unit” “to produce at least one schedule for at least one agent” at independent Claims 1,8, and “initiating on at least one computational system, at least one skill management platform” “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, the Examiner relies on MPEP 2106.04(a)(2) II B which states that structuring a work force7, [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 the 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 in “initiating”, “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 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 ii8 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 implementable9 through 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 Clams 1,8,17, “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
- 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 optimal number of skills” 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, 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 Claims 2, 9, 18, could equally be argued as an 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 “scheduling units for the set of one or more contact centers” at Claims 1,2,5,6,8,9,12,13,17,18 and recitation of “automatically”, as in “automatically configuring the set of one or more contact centers” at Claims 5,6,12,13,19,20, could perhaps be argued as an example of #2, namely a computer environment upon which the mental 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.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 Claims 2,9,18,22 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)10. 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 the 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 exception into a practical application. Also, the benchmarking by such computer components of a “predicted” versus “actual required number of scheduling units” (Claims 1,8,18), “numbers of scheduling units for existing contact centers with known total numbers of agents” (dependent Claim 22) 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 of “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, would correspond to requiring mere use of software to tailor information and provide it to the user on a generic computer, as tested per MPEP 2106.05(f)(2) v11. None of these examples, as tested MPEP 2106.05(f)(2), integrate the abstract idea into a practical application. In fact, MPEP 2106.05(f)(2) ¶112 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 with MPEP 2106.05(f)(2)13 further clarifying 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)14 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 particular technological environment characterized by “scheduling units for the set of one or more contact centers” at Claims 1,2,5,6,8,9, 12,13,17,18 and by automation at Claims 5,12,19, would similarly 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 independent Claim 15, would also constitute narrowing of abstract idea to a computerized technological environment which, per MPEP 2106.05(h) x15, would not integrate the abstract idea into a practical application.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 recordkeeping16 / gathering statistics and presenting offers17, receiving/transmitting data over network, including utilizing an intermediary computer to forward information18, arranging hierarchy of groups and sorting information19, performing repetitive calculations20. 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” 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 1-5, 7-12, 14-19, 21 and 22 although directed to statutory categories (methods or processes at Claims 1-5,7,21,22 and Claims 15-19, and system or machine at Claims 8-12, 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 1-5, 7-12, 14-19, 21 and 22 are believed to be patent ineligible.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claim Rejections - 35 USC § 102
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5, 8, 12, and 21 are rejected under 35 U.S.C. 102(a)(1) based upon a public use or sale or other public availability of the invention as disclosed by:
Leggett; Ernest W. US 5185780 A hereinafter Leggett. As per,
Claims 1,8 Leggett 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, 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), “wherein each scheduling unit of the scheduling units groups one or more of the argents 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 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 computational system” (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 the 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 halfhour 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)
Claims 5, 12 Leggett 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)
Claim 21 Leggett teaches all the limitations in claim 1 above. Furthermore,
Leggett teaches “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 the values for each interval of each day), “location” (Leggett column 6 lines 20-23: accommodate both geographical dispersal of management units and call types among multiple management units and multiple geographical locations) “and department” (Leggett column 6 lines 50-52: Therefore, a number of small call center offices [or departments] can be interconnected and function as one large, efficient call-handling team).
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 15 and 19 are rejected under 35 U.S.C. 102(a)(1) based upon a public use or sale or other public availability of the invention as disclosed by:
Johnston et al US 11368588 B1 hereinafter Johnston. As per
Claim 15 Johnston 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” (Johnston column 12 lines 8-42: utilizing by contact center management service 126, ML model(s) 128 to manage agents 402 (similar to agents 114) and queues 404 (similar to queues 116). For example, t trained model(s) 128 determine agent 402a is not as occupied as expected, e.g. queue 404a is not receiving nearly as many communications 110 as forecast. For example, received communications 110 may only be 70% of forecast volume. Thus, agent 402 a may be reassigned to different queue 404b based upon skillset that agent 402 a possesses. For example, if agent 402 a speaks French and is able to handle refunds and returns, agent 402a may be reassigned to queue 404b experiencing higher volume of communications than forecast, to help agent 402b. Queue 404b may be directed to queuing communications directed to refunds and returns. Thus, French speaking skill of agent 402a may not be useful for queue 404b, but agent 402a's refunds and returns skills useful for communications received at queue 404 b. In some configurations, if agent 402a is not as occupied as expected, agent 402a may continue to be assigned to queue 404 a but communications 110 may be dynamically prioritized by ML model(s) 128 for routing to queue 404 a. Likewise, if forecast volume for queue 404a is much higher than the actually received volume of communications 110, then queue 404 a may be changed to handle a different type of communication 110. For example, queue 404 a may become a 2nd queue for handling of communications 110 directed to returns and refunds and agents 402 associated with queue 404 a that include that particular skill may continue to be assigned to queue 404 a. Those agents 402 that are not skilled in refunds and returns policy may be assigned to different queue 404 based on their skillset. column 16 lines 29-37: data centers 704 may be configured in different arrangements depending on service provider network 102. For example data centers 704 may be included or otherwise make-up an availability zone. Further, availability zones may make-up or be included in a region. Thus, service provider network 102 may comprise one or more availability zones, regions, etc. The regions may be based on geographic areas, such as being located within a predetermined geographic perimeter):
- “selecting a predicted optimal number of skills for the set of one or more contact centers based on the total number of agents” (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. 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. column 3 lines 56-58: skills of the agent, previously noted, may also be a factor in forecasting a need for agents and scheduling of the agents. column 5 lines 18-23: Thus, by forecasting an expected volume of communications to be received and scheduling [or selecting] an appropriate [or optimal] number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient [optimal] and timely fashion, thereby providing customers with good user experience. column 7 lines 25-27 corroborates that: Skills of agents 144, as previously noted, may also be 25 a factor in forecasting a need for agents 114 and scheduling of agents 114. Similarly, column 15 lines 22-29 states: contact center management service 126 and ML model(s) 128 may improve forecast accuracy (better match [or select] of headcount of agents 114 required to achieve the target service level), better schedule efficiency [or optimized] (having the right [optimal] 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. column 12 lines 62-66 provides a different example where: the increased occupancy may only be with respect to particular skill(s), e.g. a type of language or a communication transaction type. Thus, the need for additional agents may be a need for more agents 114 with particular skills(s)
Johnston column 15 lines 10-12: ML model(s) 128 predict that agents 114 and queues 116 will be extremely busy, etc. column 5 lines 18-23: Thus by forecasting an expected volume of communications to be received and scheduling an appropriate number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient and timely fashion, thereby providing customers with a good user experience. Johnston column 7 lines 25-42: Skills of agents 144, as previously noted, may also be a factor in forecasting a need for agents 114 and scheduling of the agents 114. For example, agent skills may include language skills, proficiency with one or more types of actions associated with a queue with which the agent is associated, and a type of communication. For example, some agents 114 may be proficient in French and thus, associated with queues 116 where French is a highly desirable skill. Likewise, some agents 114 may be proficient with certain types of transactions, e.g., refunds and/or returns. Thus, such agents 114 may be associated with queues 116 related to those types of transactions. Other skills include being familiar with handling certain types of communications 110, e.g., handling of text messages and/or web chats versus handling of telephone calls. Agents 114 may have multiple skills, and likewise, queues 116 may be associated with communications 110 where the multiple skills are desirable. column 7 lines 62-67: For example, the trained ML model(s) 128 may be utilized to determine that more agents 114 with certain skills are required. For example, more agents 114 may be needed who speak French, can handle the returns/refund process, handle credit card issues, etc. The needed agents 114 may possess one or more needed skills);
- “selecting an actual optimal number of skills for the set of one or more contact centers based on the optimal number of skills” (Johnston column 12 lines 8-42: Fig.4 illustrates contact center management service 126 utilizing the ML model(s) 128 to manage agents 402 (similar to agents 114) and queues 404 (similar to queues 116). For example, t trained model(s) 128 determine that agent 402a is not as occupied as expected, e.g. queue 404a is not receiving nearly as many communications 110 as forecast. For example, the received communications 110 may only be 70% of forecast volume. Thus, agent 402 a may be reassigned to a different queue 404 b based upon the skillset that the agent 402 a possesses. For example, if agent 402 a speaks French and is able to handle refunds and returns, agent 402a may be reassigned to queue 404b experiencing higher volume of communications than forecast, to help agent 402b. Queue 404 b may be directed to queuing communications directed to refunds and returns. Thus, French speaking skill of agent 402a may not be useful for queue 404b, but agent 402 a's refunds and returns skills may be useful for communications received at queue 404 b. In some configurations, if agent 402a is not as occupied as expected, the agent 402 a may continue to be assigned to queue 404 a but communications 110 may be dynamically prioritized by ML model(s) 128 for routing to queue 404 a. Likewise, in configurations, if the forecast volume for queue 404 a is much higher than the actually received volume of communications 110, then queue 404 a may be changed to handle a different type of communication 110. For example, queue 404 a may become a 2nd queue for handling of communications 110 directed to returns and refunds and agents 402 associated with queue 404 a that include that particular skill may continue to be assigned to queue 404 a. Those agents 402 that are not skilled in refunds and returns policy may be assigned to a different queue 404 based on their skillset);
- “outputting the actual optimal number of skills”
(Johnston column 12 line 64-column 13 line 5: Thus, the need for additional agents may be need for more agents 114 with particular skills. Accordingly, contact center management service 126 send [or output] a notification to operator of contact center 108 to notify the operator that more agents 114 need to be trained, at 512, with respect to the particular skills. Additionally, a notification may be sent, at 514, indicating that the operator needs to hire more agents that have the particular skills. Similar column 13 lines 12-20: Thus, at 520 contact center management service 126 provide notification to the operator of contact center 108 that more agents 114 should be brought on board at to work at contact center 108)
- “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” (Johnston column 17 lines 46-52: load balancing devices or other types of network infrastructure components are utilized for balancing a load between each of data centers 704A-N, between each of server computers 802A-F in each data center 704, and, between computing resources in each of server computers 802. column 15 lines 44-52: Thus, automated process using trained models for work force management improves functioning of computing devices, reduces processing time to schedule and manage staff, reduces needed manpower in scheduling and managing a workforce, and in case of contact centers, more quickly manages agents and associated queues for capacity issues, and dynamically routing of communications to appropriate agents and associated queues. To this end
Johnston column 2 lines 59-63: contact center management service defines [initiate] routing profiles [118 within contact center 108 Fig.1] to dynamically link queues to agents. Specifically at column 3 lines 2-6: when a routing profile is created [initiated], it is specified how many agents may handle the chat conversation. For example, column 12 lines 62-66: In specific configurations [initiations] increased occupancy is with respect to particular skill(s), e.g type of language or communication transaction type. Thus, the need for additional agents may be a need for more agents 114 with particular skill(s). This initiation of skill routing model or platform is reflected at column 9 lines 45-51: if the agent 204a is scheduled to end their shift at 5:00 and the average amount of time to handle a communication 110 is 6 minutes, around 4:50 to 4:54, communication 110 may no longer be routed to particular agent 204a but rather to other agent 202 having the corresponding skills, either at sub-contact center 202a or 202b. Similarly, column 9 line 66 to column 10 line 4: if, agents 204 having particular skill are finished, or close to finished, at sub-contact center 202a, then contact center management service 126 begin routing particular communications 110 to different centers 202b-202x based on needed agent skills.
Johnston provides the most comprehensive example at column 12 lines 8-42: Fig.4 illustrates contact center management service 126 utilizing ML model(s) 128 to manage agents 402 (similar to agents 114) and queues 404 (similar to queues 116). For example, t trained model(s) 128 determine that agent 402a is not as occupied as expected, e.g. queue 404a is not receiving nearly as many communications 110 as forecast. For example, received communications 110 may only be 70% of forecast volume. Thus, agent 402 a may be reassigned to different queue 404 b based upon the skillset that the agent 402 a possesses. For example, if agent 402 a speaks French and is able to handle refunds and returns, agent 402a may be reassigned to queue 404b experiencing higher volume of communications than forecast, to help agent 402b. Queue 404 b may be directed to queuing communications directed to refunds and returns. Thus, French speaking skill of agent 402a may not be useful for queue 404b, but agent 402 a's refunds and returns skills may be useful for communications received at queue 404 b. In some configurations, if agent 402a is not as occupied as expected, agent 402 a may continue to be assigned to queue 404 a but communications 110 may be dynamically prioritized by ML model(s) 128 for routing to queue 404a. Likewise, in configurations, if forecast volume for queue 404 a is much higher than the actually received volume of communications 110, then queue 404 a may be changed to handle a different type of communication 110. For example, queue 404 a may become a 2nd queue for handling of communications 110 directed to returns and refunds and agents 402 associated with queue 404 a that include that particular skill may continue to be assigned to queue 404 a. Those agents 402 that are not skilled in refunds and returns policy may be assigned to a different queue 404 based on their skillset).
“each skill management platform comprising a computational module running on the computational system” (Johnston column 14 lines 9-12: Fig.6 illustrates aspects of the functions performed at least partly by the service provider network 102 as described in Figs.1-5. The logical operations described with respect to Fig.6 may be implemented (1) as a sequence of computer -implemented acts or program modules running on computing system and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. column 17 lines 46-52: load balancing devices or other types of network infrastructure components are utilized for balancing a load between each of data centers 704A-N, between each of server computers 802A-F in each data center 704, and, between computing resources in each of server computers 802. column 15 lines 44-52: Thus, automated process using trained models for work force management improves functioning of computing devices, reduces processing time to schedule and manage staff, reduces needed manpower in scheduling and managing a workforce, and in case of contact centers, more quickly manages agents and associated queues for capacity issues, and dynamically routing of communications to appropriate agents and associated queues).
Claim 19 Johnston teaches all the limitations in claim 15 above. Furthermore,
Johnston 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” (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. column 15 lines 44-52: Thus, the automated process for using trained models for work force management improves the functioning of computing devices, e.g., reduces processing time to schedule and manage staff, reduces needed manpower in scheduling and managing a workforce, and in case of contact centers, more quickly manages agents and associated queues for capacity issues, as well as dynamically routing of communications to appropriate agents and associated queues. Specifically column 12 lines 8-42 Fig.4 illustrates contact center management service 126 utilizing ML model(s) 128 to manage agents 402 (similar to agents 114) and queues 404 (similar to queues 116). For example t trained model(s) 128 determine that agent 402a is not as occupied as expected, e.g. queue 404a is not receiving nearly as many communications 110 as forecast. For example, received communications 110 may only be 70% of forecast volume. Thus, agent 402 a may be reassigned to a different queue 404 b based upon the skillset that the agent 402 a possesses. For example, if agent 402 a speaks French and able to handle refunds and returns, agent 402a may be reassigned to queue 404b experiencing higher volume of communications than forecast to help agent 402b. Queue 404 b may be directed to queuing communications directed to refunds and returns. Thus, French speaking skill of agent 402a may not be useful for queue 404b, but agent 402 a's refunds and returns skills may be useful for communications received at queue 404 b. In some configurations, if agent 402a is not as occupied as expected, agent 402 a may continue to be assigned to queue 404 a but communications 110 may be dynamically prioritized by ML model(s) 128 for routing to queue 404a. Likewise, in configurations, if forecast volume for queue 404a is much higher than the actually received volume of communications 110 then queue 404a may be changed to handle different type of communication 110. For example, queue 404 a may become a 2nd queue for handling of communications 110 directed to returns and refunds and agents 402 associated with queue 404 a that include that particular skill may continue to be assigned to queue 404 a. Those agents 402 that are not skilled in refunds and returns policy may be assigned to a different queue 404 based on their skillset);
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 2,3,9,10,22 are rejected under 35 U.S.C. 103 as being unpatentable over:
Leggett as applied to claims 1,8 above, and in view of
Johnston et al US 11368588 B1 hereinafter Johnston. As per,
Claims 2,9 Leggett teaches all the limitations at claims 1,8 above.
Leggett 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 managing contact centers teaches or suggests:
- “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” (Examiner interprets the predicted scheduling units as claimed with predicted agents group(s) of specific skill, location and/or sub-contact center within a contract center. Based on such broadest reasonable interpretation, Johnston provides many examples or a preponderance of evidence for teaching the above limitation starting with: column 1 lines 25-31: some contact centers have thousands of agents available for scheduling. However, various limitations with respect to certain factors such as…skills of the agents, rules related to scheduling … can make it difficult to schedule agents for handling communications.
Johnston column 3 lines 56-58: skills of the agent, as previously noted, may also be a factor in forecasting a need for agents and scheduling of the agents.
Johnston column 4 lines 39-45: Additionally, the contact center management service may determine that more agents [scheduling units] with certain skills are needed. This need may be determined by the trained models used by the contact center management service.
Johnston column 5 lines 18-23: thus by forecasting expected volume of communications to be received and scheduling [or selecting] an appropriate [or optimal] number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient and timely fashion, thereby providing the customers with good user experience.
Johnston column 7 lines 25-42: Skills of agents 144, as previously noted, may also be a factor in forecasting a need for agents 114 and scheduling of the agents 114. For example, agent skills may include language skills, proficiency with one or more types of actions associated with a queue with which the agent is associated, and a type of communication. For example, some agents 114 may be proficient in French and thus, associated with queues 116 where French is a highly desirable skill. Likewise, some agents 114 may be proficient with certain types of transactions, e.g., refunds and/or returns. Thus, such agents 114 may be associated with queues 116 related to those types of transactions. Other skills include being familiar with handling certain types of communications 110, e.g., handling of text messages and/or web chats versus handling of telephone calls. Agents 114 may have multiple skills, and likewise, queues 116 may be associated with communications 110 where the multiple skills are desirable.
Johnston column 7 lines 62-67: the trained ML model(s) 128 may be utilized to determine that more agents 114 [or scheduling units] with certain skills are required. For example, more agents 114 may be needed who speak French, can handle the returns/refund process, handle credit card issues, etc. The needed agents 114 may possess one or more needed skills
Johnston column 12 lines 8-42: utilizing by contact center management service 126, ML model(s) 128 to manage agents 402 (similar to agents 114) and queues 404 (similar to queues 116). For example, t trained model(s) 128 determine agent 402a is not as occupied as expected, e.g. queue 404a is not receiving nearly as many communications 110 as forecast. For example, received communications 110 may be 70% forecast volume. Thus agent 402a may be reassigned to different queue 404b based upon skillset that agent 402 a possesses. For example, if agent 402a speaks French and able to handle refunds and returns, agent 402a may be reassigned to queue 404b experiencing higher volume of communications than forecast, to help agent 402b. Queue 404b may be directed to queuing communications directed to refunds and returns. Thus, French skill of agent 402a may not be useful for queue 404b, but agent 402a's refunds and returns skills useful for communications received at queue 404b. if agent 402a is not as occupied as expected, agent 402a may continue to be assigned to queue 404a but communications 110 may be dynamically prioritized by ML model(s) 128 for routing to queue 404 a. Likewise, if forecast volume for queue 404a is much higher than the actually received volume of communications 110, then queue 404 a may be changed to handle a different type of communication 110. For example, queue 404 a may become a 2nd queue for handling of communications 110 directed to returns and refunds and agents 402 associated with queue 404 a that include that particular skill may continue to be assigned to queue 404 a. Those agents 402 that are not skilled in refunds and returns policy may be assigned to different queue 404 based on their skillset.
Johnston column 12 lines 62-66 provides a different example where: the increased occupancy may only be with respect to particular skill(s), e.g. a type of language or a communication transaction type. Thus, the need for additional agents [or scheduling units] may be a need for more agents 114 with particular skills(s)
Johnston column 15 lines 10-12: ML model(s) 128 predict that agents 114 and queues 116 will be extremely busy, etc. column 5 lines 18-23: Thus by forecasting an expected volume of communications to be received and scheduling an appropriate number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient and timely fashion, thereby providing customers with a good user experience.
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 to achieve the target service level), better schedule efficiency [or optimization] (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 16 lines 54-60: each data centers 704 include computing devices that included software applications that receive and transmit data and handle communications 110. For instance, the computing devices included in data centers 704 include software components which transmit, retrieve, receive, or otherwise provide or obtain the data from the storage service 120. Specifically, per column 4 line 46-column 5 line 5: trained models can also be 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 [or additional scheduling units] that are currently off duty but have indicated a willingness to work extra hours. Thus, such agents [or additional scheduling units] 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 the forecast volume of communications to be received is detected by the trained models, then the contact center management service may 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 may be with respect to volume of specific communications, e.g., a larger than expected volume of communications related to returns and refunds, types of communications, and/or language specific communications).
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 teachings to have included Johnston’s teachings 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 it did 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 in view of Johnston, 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).
Claims 3,10 Leggett teaches all the limitations in claims 1,8.
Leggett 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 optimal number of skills”;
- “outputting the actual optimal number of skills” as claimed. However,
Johnston in analogous managing contact enters teaches or suggests: “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”; (Johnston column 15 lines 10-12: ML model(s) 128 predict agents 114 and queues 116 will be extremely busy) etc. column 5 lines 18-23: Thus by forecasting an expected volume of communications to be received and scheduling an appropriate number of agents with appropriate skills, the contact center may be able to handle communications from customers in an efficient and timely fashion, thereby providing customers with a good user experience. column 7 lines 25-42: Skills of agents 144, as previously noted, may also be a factor in forecasting a need for agents 114 and scheduling of the agents 114. For example, agent skills may include language skills, proficiency with one or more types of actions associated with a queue with which the agent is associated, and a type of communication. For example, some agents 114 may be proficient in French and thus, associated with queues 116 where French is a highly desirable skill. Likewise, some agents 114 may be proficient with certain types of transactions, e.g., refunds and/or returns. Thus, such agents 114 may be associated with queues 116 related to those types of transactions. Other skills include being familiar with handling certain types of communications 110, e.g., handling of text messages and/or web chats versus handling of telephone calls. Agents 114 may have multiple skills, and likewise, queues 116 may be associated with communications 110 where the multiple skills are desirable. column 7 lines 62-67: For example, the trained ML model(s) 128 may be utilized to determine that more agents 114 with certain skills are required. For example, more agents 114 may be needed who speak French, can handle the returns/refund process, handle credit card issues, etc. The needed agents 114 may possess one or more needed skills)
- “selecting an actual optimal number of skills for the set of one or more contact centers based on the optimal number of skills”; (Johnston column 12 lines 8-42: Fig.4 illustrates contact center management service 126 utilizing the ML model(s) 128 to manage agents 402 (similar to agents 114) and queues 404 (similar to queues 116). For example, t trained model(s) 128 determine that agent 402a is not as occupied as expected, e.g. queue 404a is not receiving nearly as many communications 110 as forecast. For example, the received communications 110 may only be 70% of forecast volume. Thus, agent 402 a may be reassigned to a different queue 404 b based upon the skillset that the agent 402 a possesses. For example, if agent 402 a speaks French and is able to handle refunds and returns, agent 402a may be reassigned to queue 404b experiencing higher volume of communications than forecast, to help agent 402b. Queue 404 b may be directed to queuing communications directed to refunds and returns. Thus, French speaking skill of agent 402a may not be useful for queue 404b, but agent 402 a's refunds and returns skills may be useful for communications received at queue 404 b. In some configurations, if agent 402a is not as occupied as expected, the agent 402 a may continue to be assigned to queue 404 a but communications 110 may be dynamically prioritized by ML model(s) 128 for routing to queue 404 a. Likewise, in configurations, if the forecast volume for queue 404 a is much higher than the actually received volume of communications 110, then queue 404 a may be changed to handle a different type of communication 110. For example, queue 404 a may become a 2nd queue for handling of communications 110 directed to returns and refunds and agents 402 associated with queue 404 a that include that particular skill may continue to be assigned to queue 404 a. Those agents 402 that are not skilled in refunds and returns policy may be assigned to a different queue 404 based on their skillset) “and”
- “outputting the actual optimal number of skills” (Johnston column 12 line 64-column 13 line 5: Thus, the need for additional agents may be need for more agents 114 with particular skill(s). Accordingly, contact center management service 126 may send [or output] a notification to operator of contact center 108 to notify the operator more agents 114 need to be trained, at 512, with respect to the particular skill(s). Additionally, or alternatively, a notification may be sent, at 514, indicating that the operator needs to hire more agents that have the particular skill(s). Similar column 13 lines 12-20: Thus, at 520 contact center management service 126 provide notification to the operator of the contact center 108 that more agents 114 should be brought on board at to work at the contact center 108).
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 teachings to have included Johnston’s teachings 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 it did 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 in view of Johnston, 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).
Claim 22 Leggett teaches all the limitations in claim 1 above.
Leggett does not explicitly recite: “wherein the predicting of a predicted number of scheduling units is performed using a machine learning algorithm, and wherein the machine learning algorithm is based on numbers of scheduling units for existing contact centers with known total numbers of agents” as claimed.
Johnston however in analogous call center management teaches or suggests
wherein the predicting of a predicted number of scheduling units is performed using a machine learning algorithm, and wherein the machine learning algorithm is based on numbers of scheduling units for existing contact centers with known total numbers of agents”
(Johnston column 8 lines 37-39: 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, at column 1 lines 25-31: some contact centers 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 15 lines 22-29 states: The contact center management service 126 and ML model(s) 128 may improve forecast accuracy (better match of headcount of agents 114 required to achieve the target service level), better schedule efficiency [or optimization] (having the right [or optimal] 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
Claim 4: The method of claim 1, further comprising: determining that an actual occupancy of the agent at the first queue is less than a forecasted occupancy of the agent; and based on the one or more factors and the determining that the actual occupancy of the agent at the first queue is less than the forecasted occupancy of the agent, dynamically determining, by the contact center management service using the trained machine learning model, to change assignment of the agent from the first queue to the second queue such that the agent no longer receives communications from the first queue).
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 teachings to have included Johnston’s teachings 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 it did 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 in view of Johnston, 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).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 4,11 are rejected under 35 U.S.C. 103 as being unpatentable over:
Leggett / Johnston as applied to claims 3,10 above, and in further view of
Crockett et al, US 6044355 A hereinafter Crockett. As per,
Claims 4,11 Leggett / Johnston / teaches all the limitations in claims 3,10 above.
Leggett / Johnston does not explicitly recite as claimed: “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”.
Crockett however in analogous managing contact enters teaches/suggests: “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”
(Crockett Abstract: scheduling personnel (agents) in a work environment based on personnel skill levels. The method facilitates true skills-based scheduling of agents in a telephone call center using a simulation tool to predict what fraction of scheduled agents from each skill group will be available to each call type during each time interval being scheduled. A feedback mechanism is used to adjust net staffing and skills usage data between iterations of a call handling simulation until a given schedule being tested through the simulator meets some acceptance criteria. Specifically, per column 6 lines 13-18: the initial [skill] estimates are derived from historical call center data. The method then continues at Step 14 to apply the current net staff array(s) and skill group availability array(s) (one for each call type, respectively) to a Scheduler. column 2 lines 20-22: If, for example, a call center has 10 skills defined, then agents could in principle have any of 1024 possible combinations (210) of those skills. column 9 lines 44-62: Fig.4A illustrates the graph of net staff versus schedule interval after making a first pass through the scheduler. This is the first pass at creating an agent schedule, using the initial net staff and skills usage estimates. In this example, to get call type 3 adequately staffed, the scheduler had to greatly over-staff call Types 1 and 2. This is because the initial skills usage estimates were unrealistically high. Fig.4B illustrates the output of the 1st pass through the simulator. As noted above, the simulator simulates the handling of the predicted call volume by the agents in the first schedule attempt. The results are (as expected) not acceptable. Long call delays build up at the beginning and end of the day (seen in Average Speed of Answer graph), and in the middle of the day all Call Types are significantly over-staffed (as seen in the net staff graph). This output, however, provides a 1st refinement of the net staff data and a more realistic view of skills usage data. Both the net staff and skills usage data the serve as input for a 2nd scheduling pass, as previously described).
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 / Johnston’s teachings to have included Crockett’s teachings in order to have created a work schedule for that agent (and other agents scheduled to work at the same time during a given scheduling interval) that maximizes the quality of service offered by the call center while making efficient use of call center resources by utilizing a series of call handling simulations are run to generate incremental or "interim" schedules that, through a feedback mechanism, progress toward some "optimum" scheduling solution for the call center (Crockett column5 lines 23-29 in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one or ordinary skills in the art as articulated by Crockett at column 10 lines 61-65.
Further, the claimed invention could have also been considered as a mere combination of old elements in a similar managing contact enters field of endeavor. In such combination each element merely would have performed same analytical and managerial 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 Leggett / Johnston in further view of Crockett, 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).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 7,14 are rejected under 35 U.S.C. 103 as being unpatentable over:
Leggett as applied to claims 1,8 above, in view of
Bondi et al, US 6333979 B1 hereinafter Bondi. As per,
Claims 7,14 Leggett teaches all the limitations in claims 1,8 above. Furthermore,
Leggett column 6 lines 20-23: accommodates multiple call types dispersed among multiple management units and multiple geographical locations.
Leggett does not go so far to explicitly recite: “wherein the number of regions comprises a number of time zones the set of one or more contact centers operates in” as claimed.
Bondi however in analogous art of managing contact centers teaches and/or suggests “wherein the number of regions comprises a number of time zones the set of one or more contact centers operates in” (Bondi column 6 lines 36-45: Fig.3 is an exemplary map of a geography-based destination plan for United States to illustrate a noncoincidence of call volume peaks shown in Fig.4. In this example, the geography-based destination plan divides United States into 2 predetermined category regions, a West Region and an East Region Served by communication processing centers CPC 301 and 302, respectively. Calls that originate in the West Region are first received by CPC 301 while calls originating in the East Region are first received by CPC 302. column 6 lines 47-65: Because the regions of FIG. 3 span different time zones, the daily call volume pattern of the West Region is shifted later in time from that of the East Region. Thus, the daily call volume peaks for the two regions occur at different times. The non-coincidence of call volume peaks provides potential efficiencies which can be realized by sharing communication processing center resources across different time zones. By exploiting noncoincidence of call volume peaks in different regions, CPCs 301, 302 operated more efficiently by requiring fewer agents for given maximum time-to-answer for the peak time of day. For example, at 12:00 pm EST, East Region is experiencing a peak of noontime incoming calls while West Region is operating at lower level since it is mid-morning in West Region. Therefore, some of West Region calls could be rerouted to CPC 302 in East Region to more evenly distribute incoming calls between CPCs 301,302).
PNG
media_image3.png
419
720
media_image3.png
Greyscale
Bondi Fig.4 in support of rejection arguments
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 teachings to have included Bondi’s teachings in order to have more efficiently managed the contact center by reallocating incoming communications so that the amount of smallest free capacity is maximized for better spreading out the distribution of communications more evenly over the communication processing centers, thus reducing the average time to answer calls, while taking advantage of the noncoincidence of daily incoming communication volume peaks due to seasonal and regional variations and time zone differences (Bondi column 1 lines 50-57 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 managing contact centers field of endeavor. In such combination each element merely would have performed the same analytical and managerial 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 Bondi, 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).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable over:
Johnston as applied to claim 15 above, in view of
Crockett et al, US 6044355 A hereinafter Crockett. As per,
Claim 16 Johnston teaches al the limitations in claim 15 above.
Johnston column 8 lines 37-65 still recites ML model(s) trained based upon historical metrics and data related to execution of contact center 108. For example, ML model(s) 128 analyze historical metrics and data related to execution of the contact center 108 in the service provider network 102. The historical metrics and data based on gathered metrics and data collected over past 6 months, one year, or longer by data analytics service 124. In configurations, historical metrics and data may have been collected during a period shorter than 6 months. Based upon the analysis of the historical metrics and data by the ML model(s) 128, the ML model(s) 128 learn to recognize patterns of the metrics and data by the ML model(s) 128, ML model(s) 128 learn to recognize patterns of the metrics and data. The patterns of the metrics and data may indicate a baseline or normal operation with respect to metrics and data of the contact center 108 within the service provider network 102. Furthermore,
Johnston column 7 lines 1-18 exemplifies such metrics or factors as skills of an agent 114.
* Therefore *
Johnston might suggest but does not explicitly recite to clearly anticipate:
- “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”
* However *
Crockett in analogous managing contact enters teaches or suggests: “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”
(Crockett Abstract: scheduling personnel (agents) in a work environment based on personnel skill levels. The method facilitates true skills-based scheduling of agents in a telephone call center using a simulation tool to predict what fraction of scheduled agents from each skill group will be available to each call type during each time interval being scheduled. A feedback mechanism is used to adjust net staffing and skills usage data between iterations of a call handling simulation until a given schedule being tested through the simulator meets some acceptance criteria. Specifically, per column 6 lines 13-18: initial [skill] estimates are derived from historical call center data. The method then continues at Step 14 to apply the current net staff array(s) and skill group availability array(s) (one for each call type, respectively) to a Scheduler. column 2 lines 20-22: If, for example, a call center has 10 skills defined, then agents could in principle have any of 1024 possible combinations (210) of those skills. column 9 lines 44-62: Fig.4A illustrates the graph of net staff versus schedule interval after making a first pass through the scheduler. This is the first pass at creating an agent schedule, using the initial net staff and skills usage estimates. In this example, to get call type 3 adequately staffed, the scheduler had to greatly over-staff call Types 1 and 2. This is because the initial skills usage estimates were unrealistically high. Fig.4B illustrates the output of the 1st pass through the simulator. As noted above, the simulator simulates the handling of the predicted call volume by the agents in the first schedule attempt. The results are (as expected) not acceptable. Long call delays build up at the beginning and end of the day (seen in Average Speed of Answer graph), and in the middle of the day all the Call Types are significantly over-staffed (as seen in the net staff graph). This output, however, provides a 1st refinement of the net staff data and a more realistic view of skills usage data. Both the net staff and skills usage data serve as input for a 2nd scheduling pass, previously described)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention to have modified Johnston teachings to have included Crockett’s teachings to have created a work schedule for that agent and other agents scheduled to work at same time during a given scheduling interval that maximizes quality of service by call center while making efficient use of call center resources by utilizing a series of call handling simulations to generate incremental or interim schedules that, through a feedback mechanism, to progress toward some optimum scheduling solution for the center (Crockett column5 lines 23-29 in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one or ordinary skills in the art as articulated by Crockett at column 10 lines 61-65.
Further, the claimed invention could have also been considered as a mere combination of old elements in a similar managing contact enters field of endeavor. In such combination each element merely would have performed same analytical and managerial 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 Johnston in view of Crockett, 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).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 17,18 is rejected under 35 U.S.C. 103 as being unpatentable over:
Johnston as applied to claim 15 above, and in view of
Bondi et al, US 6333979 B1 hereinafter Bondi. As per,
Claim 17 Johnston teaches al the limitations in claim 15 above. Further,
Johnston teaches 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”; (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. Specifically, see column 4 line 46- column 5 line 5: trained models can also be 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 the forecast volume of communications to be received is detected by the trained models, then the contact center management service may 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 may be with respect to volume of specific communications, e.g., a larger than expected volume of communications related to returns and refunds, types of communications, and/or language specific communications)
- “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 ”
(Johnston column 4 line 46-column 5 line 5: … 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. 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 provide a notification to a manager associated with the contact center to contact agents 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 increased volume of communications being received. Likewise, if protracted decrease in volume of communications received compared to the 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 may be 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);
- “outputting the actual required number of scheduling units”
(Johnston column 4 line 51-column5 line 5: 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 provide a notification [or output] to a manager associated with the contact center to contact agents 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 the forecast volume of communications to be received is detected by the trained models, then the contact center management service provide a notification [or output] 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);
* While *
Johnston still recites at column 16 lines 29-37: data centers 704 may be configured in different arrangements depending on service provider network 102. For example, data centers 704 may be included in or otherwise make-up an availability zone. Further, availability zones may make-up or be included in a region. Thus, service provider network 102 may comprise one or more availability zones, regions, and so forth. The regions may be based on geographic areas, such as being located within a predetermined geographic perimeter
* Nevertheless *
Johnston does not explicitly recite, to clearly anticipate:
- “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”
* However *
Bondi in analogous art of managing contact centers teaches or suggests:
- “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”
(Bondi column 8 lines 19-30: call volume data collected include the average call volume and peak call volume. Such data is collected for each time interval of interest. One exemplary set of time intervals is set of all 15 minute intervals between 7:00am and 7:00pm. The average call volume and peak call volume can be measured in calls per unit of time (e.g. hour) in Erlangs representing 3600 call-seconds per hour. This loosely translates to 1 operator-hour per hour. column 12 lines 43-48: In actual practice, call time intervals, communication processing centers, service agent teams or predetermined categories (i.e. 15 minute time intervals), may be used to divide up relevant busy period of network (7:00 am to 9:00 pm), or even to divide up all 24 hours of day. column 6 lines 55-65: By exploiting noncoincidence of call volume peaks in different regions, CPCs 301, 302 can be operated more efficiently by requiring fewer agents for a given maximum time-to-answer for the peak time of day. For example, at 12:00 pm EST, East Region is experiencing a peak of noontime incoming calls while West Region is operating at lower level since it is mid-morning in West Region. Therefore, some of West Region calls could be rerouted to CPC 302 in East Region to more evenly distribute incoming calls between CPCs 301,302. column 8 lines 31-39: The free capacity of one communication processing centers 116-119 in a given hour is defined to be difference between its capacity to handle calls at a given quality of service (parameter B) and the call volume directed towards it (parameter A). Both of parameters A and B may be measured in units of Erlangs, or in calls per hour. In general, when relationship: B−A>0 is true, the resulting quality of service will exceed target quality of service).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Johnston’s teachings to have included Bondi’s teachings in order to have more efficiently managed the contact center by reallocating incoming communications so that the amount of smallest free capacity is maximized for better spreading out the distribution of communications more evenly over the communication processing centers, thus reducing the average time to answer calls, while taking advantage of the noncoincidence of daily incoming communication volume peaks due to seasonal and regional variations and time zone differences (Bondi column 1 lines 50-57 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 managing contact centers field of endeavor. In such combination each element merely would have performed the same analytical and managerial 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 Johnston in view of Bondi, 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).
Claim 18 Johnston / Bondi teaches all the limitations in claim 17 above. Furthermore,
Johnston 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 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).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Conclusion
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.
PNG
media_image4.png
587
648
media_image4.png
Greyscale
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.
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
March 23rd, 2026
1 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
2 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
3 Original Specification ¶ [0026] 3rd sentence
4 OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept);
5 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
6 Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018).
7 In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009)
8 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
9 Per MPEP 2106.04(a): “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible…”.
10 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);
11 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
12 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)
13 TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1747, 1748 (Fed. Cir. 2016)
14 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)
15 buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014).
16 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);
17 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
18 Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362
19 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
20 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)