DETAILED ACTION
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 .
Notice to Applicant
The following is a Final Office Action for Application Serial Number: 18/395,462, filed on December 22, 2023. In response to Examiner’s Non-Final Rejection dated July 16, 2025, Applicant on October 21, 2025, amended claims 1 and 16. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
The 35 U.S.C. § 102 rejections of claims 1, 3, 4, 7-9, 11, 14-16 and 20 are hereby withdrawn in light of Applicant’s amendments.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to Applicants amendments to claims 1 and 16. Updated 35 U.S.C. § 103 rejections have been applied to amended claims 2, 5, 6, 10, 12, 13 and 17-19.
Response to Arguments
Applicant's Arguments/Remarks filed October 21, 2025 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks regarding the pending rejections will be addressed herein below in the order in which they appear in the response filed October 21, 2025.
Regarding the 35 U.S.C. 101 rejection, Applicant states with regard to commercial or legal interactions, claim 1 does not recite a contract, a legal obligation, advertising, marketing or sales activities, or business relations. More specifically, and with reference to the set of commercial or legal interactions recognized in the MPEP, claim 1 does not recite managing a stable value protected life insurance policy, processing an insurance claim, hedging, mitigating settlement risk, arbitration, structuring a sales force, determining an optimal number of visits by a business representative to a client, offer-based price optimization, processing a credit application, or processing information through a clearing-house. See MPEP § 2106.04(a)(2)(II)(B).
With regard to the assertion that claim 1 is directed to managing personal behavior, claim
1 does not recite any of the types activities recognized in the MPEP as managing personal behavior. That is, claim 1 does not recite storing pre-set limits on spending, filtering content, considering historical usage information while inputting data, testing a patient for nervous system malfunctions, voting, providing information to a person while avoiding interruption of their current activity, playing a dice game, assigning hair designs to balance head shape, or hedging risk. See MPEP § 2106.04(a)(2)(II)(C). Accordingly, the Applicant respectfully submits that claim 1 is not directed to managing personal behavior.
In response, Examiner respectfully disagrees. Examiner notes the important issue is whether the concept (e.g., the idea of scheduling contact center agents to activity sessions by finding an optimal solution based on a mixed integer programming model and activity rules) is abstract (e.g., commercial and legal interactions and managing personal behavior, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper) - not whether the exact fact-pattern matches the particulars of previous court decisions. For example, in Planet Bingo, which dealt with the abstract idea of managing a game of bingo, the Federal circuit used Bilski and Alice to support the asserted abstract idea. Specifically the aspect of bingo game management which includes “solv[ing] a tampering problem and also minimiz[ing] other security risks” during bingo ticket purchases was determined to be similar to the abstract ideas of “risk hedging” during “consumer transactions,” (Bilski) and “mitigating settlement risk” in “financial transactions,” (Alice) that the Supreme Court found ineligible. Clearly, the fact patterns in Planet Bingo compared to Bilski and Alice were different, but the abstract concepts were similar. Additionally, to facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types; see MPEP 2106.04(a). Thus, Examiner finds Applicants aforementioned remarks are not persuasive and maintains the amended claims recite an abstract idea under Step 2A – Prong One for the reasons set forth in the office action.
Regarding the 35 U.S.C. 101 rejection, Applicant states the Office Action also asserts that claim 1 is directed to a mental process. The Applicant respectfully notes that "[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind."(See October Update, p. 7). The Applicant respectfully submits that is the case with the limitations of independent claim 1. The features of independent claim 1 simply cannot practically be performed in the human mind, which would be readily appreciated by any person of ordinary skill in the art. Indeed, the subject application discloses that the computational complexity associated with the recited operations, described in the subject application as "a computationally 'hard' problem," may even exceed the computational resources of a computer, in which case a fallback mechanism may be utilized. See subject application, [0106]. As such, claim 1 does not recite a mental process that can be practically performed in the human mind, regardless of whether a person attempting to perform the operations also has a pen and paper.
In response, Examiner respectfully disagrees. Examiner finds even in a computer environment, determining the mixed integer programming model based on a plurality of constraints and a plurality of optimization objectives; receiving an activity rule that defines properties of an activity session from a rule queue of activity rules to be scheduled; and scheduling a plurality of contact center agents to one or more activity sessions based on the activity rule by finding an optimal solution to a mixed integer programming problem generated based on the mixed integer programming model and the activity rule are still considered abstract by reciting limitations that mimic human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind. Claims can recite a mental process even if they are claimed as being performed on a computer; see MPEP 2106.04(a)(2)(III)(C). As stated in the 35 U.S.C. 101 rejection, the recitation of the additional elements do not take the claim out of the certain methods of organizing human activity and mental processes groupings. Examiner finds the pending claims recite similar limitations to claims the courts have indicated may not be sufficient in showing an improvement in computer-functionality, such as accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; see MPEP 2106.05(a)(I) and MPEP 2106.05(a)(II). Examiner maintains the claims recite an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant submits that the claims indicate integration of the alleged abstract idea into a practical application at least because the claims include specific recitations that place meaningful limits on the alleged judicial exception. With regard to the specificity of the claims, MPEP §2106.04(d) states:
A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception...
[T]he specificity of the claim limitation is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions to apply an exception.
(see p. 12, Applicant Remarks)
Additionally, Applicant argues Parker v. Flook (see p. 12-13, Applicant Remarks) and states unlike the claims in Parker v. Flook, claim 1 recites determining a specific type of model (i.e., a mixed integer programming model), and doing so specifically as a function of constraints and optimization objectives. Further, claim 1 specifically recites that the method includes scheduling the agents not only as a function of the specific model that was selected as a function of the constraints and objectives, but also based on an activity rule that is received from a rule queue and that defines properties of an activity session, as described in paragraph [0112] of the subject application. The specific features appear to be missing from the prior art as well, as discussed in more detail relative to the rejections under 35 U.S.C. §§ 102, 103. As such, the claims are recited with specificity and impose meaningful limits, such that the claims are more than a drafting effort designed to monopolize a judicial exception. Accordingly, for at least this reason, the Applicant respectfully submits that the claims do represent integration into a practical application.
In response, Examiner respectfully disagrees. First, Examiner respectfully reminds Applicant, although preemption is considered, the two-part analysis is used to determine patent eligibility. Preemption concerns are, thus fully addressed and rendered moot where a claim is determined to disclose patent ineligible subject matter under the two-part framework. While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). It is necessary to evaluate eligibility using the Alice/Mayo test, because while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible. Diamond v. Diehr, 450 U.S. 175, 191-92 n.14, 209 USPQ 1, 10-11 n.14 (1981) ("We rejected in Flook the argument that because all possible uses of the mathematical formula were not pyre-emptied, the claim should be eligible for patent protection"). Examiner notes Diamond v. Diehr discloses an example that recites meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Specifically, the claim is directed to the use of the Arrhenius equation in an automated process for operating a rubber‐molding press. The court found the claim recites meaningful limitations along with the judicial exception including installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, constantly recalculating the cure time and opening the press at the proper time. These limitations sufficiently limit the claim to the practical application of molding rubber products and are clearly not an attempt to patent the mathematical equation and thus recite improvements to the technology. Mackay Radio & Telegraph v. Radio Corp. of America is an example of applying a judicial exception with a particular machine. In this case, a mathematical formula was employed to use standing wave phenomena in an antenna system. The claim recited the particular type of antenna and included details of the shape of the antenna and the conductors, particularly the length and angle at which they were arranged. Tilghman v. Proctor provides an example of effecting a transformation of a particular article to a different state or thing. In that case, claimed process, which used the natural principle that the elements of neutral fat require that they be severally united with an atomic equivalent of water in order to separate and become free, resulted in the transformation of the fatty bodies into fat acids and glycerine. Examiner finds there are no similar technology, technological problem or solution here. It is noted that the mere manipulation or reorganization of data does not satisfy the transformation prong. See Cybersource Corp. v. Retail Decisions, Inc. Furthermore, Examiner finds the pending claims to be considerably similar to Flook by disclosing determining a specific type of model (i.e., a mixed integer programming model), and doing so specifically as a function of constraints and optimization objectives. Applicant merely makes a blanket statement that the methods provide a specific solution. Examiner respectfully reminds Applicant, regardless of complexity and/or granularity, data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Applicant has not identified any limitations in the claimed invention that show or submit that the technology used is being improved or there was a problem in the technology that the claimed invention solves.
Regarding the 35 U.S.C. 101 rejection, Applicant argues determining an inventive concept under Step 2B (see p. 13-15, Applicant Remarks) and submits that the prior art of record fails to teach or suggest each of the features recited in claims 1-20. That is, the claims include one or more "additional features" that are not well-understood, routine, or conventional. For example, independent claim 1 has been amended to recite "receiving, by the computing system, an activity rule that defines properties of an activity session from a rule queue of activity rules to be scheduled." Independent claim 16 has been similarly amended. The Applicant respectfully submits at least those features constitute "additional features" that are not well- understood, routine, or conventional.
Accordingly, the Applicant further submits that at least the claimed features not taught by the art of record, when interpreting the claim as a whole, constitute significantly more than the abstract idea. The Applicant also hereby preemptively traverses any taking of Official Notice that one or more of the claimed features is well-understood, routine, or conventional.
For at least the reasons set forth above, the Applicant submits that each of claims 1-20, is directed to statutory subject matter, and requests withdrawal of the rejections under § 101.
In response, Examiner respectfully disagrees. Applicant is respectfully reminded novelty and non-obviousness, have no bearing on whether a claim recites an abstract idea. The Federal Circuit has made this clear -rejecting an argument substantially similar to Applicants’ in Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) ("We do not agree . . . that the addition of merely novel or non-routine components to the claimed idea necessarily turns an abstraction into something concrete."). Furthermore, Examiner respectfully notes, the analysis in Step 2B addresses the question on whether an additional element (or combination of additional elements) represents well-understood, routine and/or conventional activities. Examiner finds Applicant is attempting to say the Step 2A-Prong One elements, the abstract idea, is what makes the claim eligible. Applicant has provided no detailed explanation to the configuration of the combination of additional elements nor has Applicant identified any disclosure in the claimed invention showing and/or submitting that the technology used is being improved, there was a technical problem in the technology that the claimed invention solves, or the ordered combinations of the known elements is significantly more than the abstract idea. Examiner maintains the additional elements recited in the claims do not perform any unconventional functions that can be considered “significantly more” than the judicial exception. Therefore, Examiner maintains the claims recite addition elements used as tools to perform the instructions of the abstract idea without disclosing limitations that integrates the abstract idea into a practical application, nor do these elements provide meaningful limitations that transforms the judicial exception into significantly more than the abstract idea itself. For at least these reasons, the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Applicant’s arguments, see pg. 8-13, filed October 21, 2025, with respect to the rejection(s) of claims 1-20 under 35 U.S.C. 102/103 have been fully considered. However, upon further consideration, a new ground(s) of rejection is made. Applicant’s arguments are considered moot because they are directed to newly amended subject matter and do not apply to the combination of references being used in the current rejection. Please refer to the 35 U.S.C. 103 rejection for further explanation and rationale.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-15 are directed towards a method and claims 16-20 are directed towards a system, both of which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite scheduling contact center agents to activity sessions by finding an optimal solution based on a mixed integer programming model and activity rules.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, determining the mixed integer programming model based on a plurality of constraints and a plurality of optimization objectives; receiving an activity rule that defines properties of an activity session from a rule queue of activity rules to be scheduled; and scheduling a plurality of contact center agents to one or more activity sessions based on the activity rule by finding an optimal solution to a mixed integer programming problem generated based on the mixed integer programming model and the activity rule constitutes methods based on commercial and legal interactions and managing personal behavior, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a computing system does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claim 16 recites certain method of organizing human activity and mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites a computing system at a high-level of generality such that it amounts to no more than generic computer component used as a tool to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional element does not integrate the abstract idea into practical application because it does not impose any meaningful limitations on practicing the abstract idea. Claim 1 is directed to an abstract idea. The computing system comprising a memory storing instructions executable by a processor recited in claim 16 also amounts to no more than mere instructions to apply the exception using generic computer components; see MPEP 2106.05(f). Thus, the additional elements recited in claim 16 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including computing system comprising a memory storing instructions executable by a processor amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 2-15 and 17-20 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in the independent claims. Therefore claims 2-15 and 17-20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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, 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.
Claims 1, 3, 4, 7-11, 13-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], and further in view of Rahimi et al., U.S. Publication No. 2025/0094896 [hereinafter Rahimi].
Referring to Claim 1, D’Attilio teaches:
A method for multi-objective schedule optimization in contact centers utilizing a mixed integer programming model, the method comprising (D’Attilio, [0044]; [0140]):
determining, by a computing system, the mixed integer programming model based on a plurality of constraints and a plurality of optimization objectives (D’Attilio, Table 1; [0081]), “… system solves the master problem and finds its dual values, which are subsequently used in the sub-problems further identify new columns that can benefit the coverage of requirements. Newly identified columns are added back to the master problem and solved… this cycle may be repeated until one of the predefined termination criteria is met… the system may perform one or more post-processing steps to improve the quality and usability of the schedules. Table 1 presented below lists various column generation models that may be used in conjunction with the column generation algorithm described herein”; (D’Attilio, [0096]), “If the system determines, in block 910, that one or more of the termination criteria has been satisfied (e.g., the optimal criteria and/or suboptimal criteria)…”; (D’Attilio, [0137]; [0140]; [0092]-[0093]; [0099]; [0044]; [0042]; [0052]);
receiving, by the computing system, an activity rule from a rule queue of activity rules to be scheduled (D’Attilio, [0081]), “the process of generating staffing requirement for each planning period starts with having the workload forecast as well as the KPI goals as inputs (e.g., service level, ASA, abandon rate, etc.)… After all the optimal staffing requirements for each planning period have been computed, the system may optimize the schedules of shifts to match the available staffing to anticipated needs or requirements as described below”; (D’Attilio, [0083]), “After staffing requirements are known from a previous phase, the system may derive shifts that can handle the requirements….”, Examiner considers the requirements to teach activity rules; (D’Attilio, [0046]; [0052]; [0054]; [0075]; [0077]-[0078]); and
scheduling, by the computing system, a plurality of contact center agents to one or more activity sessions based on the activity rule by finding an optimal solution to a mixed integer programming problem generated based on the mixed integer programming model and the activity rule (D’Attilio, [0085]), “system performs optimization based on the list of agents, staffing requirement forecasts, and/or other relevant data in order to generate shift schedules with the desired optimal (or approximately optimal output)”; (D’Attilio, [0045]-[0046]), “determining the expected number of workload interactions … as well as the service time associated with those interactions… in the planning horizon, converting the workload predictions from the first phase into a staffing or headcount requirement for the future planning horizon, and performing scheduling in which the headcount requirement is fulfilled through placement of staff throughout the planning horizon according to shift and schedule constraints, such that the final output is a schedule or roster that optimizes (or sub-optimizes) the coverage of workload with staffed agents…”; (D’Attilio, [0080]), “The generated models are then subsequently used to calculate staffing requirements for the purposes of scheduling”; (D’Attilio, [0049]; [0052]-[0053]; [0055]; [0081]; [0086]-[0088]; [0087]; [0097]; [0099]-[0101]).
D'Attilio teaches staffing requirement for planning periods having workload forecasts as well as the KPI goals as inputs (see par. 0081), but D’Attilio does not explicitly teach:
activity rule that defines properties of an activity session.
However Rahimi teaches:
activity rule that defines properties of an activity session (Rahimi, [0093]), “, the time-related constraints may also include additional data. The entity computing device 130 may determine the days of the week by defining which weekday shifts may be allocated… The entity computing device 130 may determine the break hours per shift based on specific hours during a shift when personnel cannot and/or may not work, often representing lunch breaks or meeting times, such as “12, 13, and 14” and/or meeting periods/times to reserve in hours and/or other suitable units”; (Rahimi, [0094]), “considering these time-related constraints, the entity computing device 130 may generate optimized schedules 214 that respect employee availability, legal requirements, and company policies. In this manner, the optimized schedules 214 output by the entity computing device 130 may ensure efficient and effective operations”; (Rahimi, [0090]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the staffing requirements for a planning period in D’Attilio to include the time-related constraints as taught by Rahimi. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to include the results of optimizing workforce scheduling process (see Rahimi par. 0091).
Referring to Claim 3, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
wherein the plurality of optimization objectives comprises an optimization objective to minimize unassigned contact center agents (D’Attilio, [0103]-[0105]), “the system may utilize various constraints in generating the optimized shift schedules… The first constraint is the demand constraint, the second set of constraints links agent decision variables… with planning group assignment decision variables μ.sub.t.sup.ar, and the third set of constraints ensured that all agents must be scheduled…”; (D’Attilio, [0107]), “the master problem formulation may minimize (or reduce) shift cost and understaffing, given parameters for labor cost and a penalty for missing requirement… it should be appreciated that this approach can support any number of custom objectives or multi-objective optimizations (e.g., minimize shift cost while maximizing service level and agent preferences). Multi-objective scheduling optimization allows the system to provide several, similarly “optimal,” shift schedule solutions and allow the end users choose depending on the objectives that are more important for their contact center”.
Referring to Claim 4, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
wherein the plurality of optimization objectives comprises an optimization objective to minimize understaffing caused by scheduling the one or more activity sessions (D’Attilio, [0107]), “the master problem formulation may minimize (or reduce) shift cost and understaffing, given parameters for labor cost and a penalty for missing requirement… it should be appreciated that this approach can support any number of custom objectives or multi-objective optimizations (e.g., minimize shift cost while maximizing service level and agent preferences). Multi-objective scheduling optimization allows the system to provide several, similarly “optimal,” shift schedule solutions and allow the end users choose depending on the objectives that are more important for their contact center”; (D’Attilio, [0140]), “The analytics module 1250 may further include an optimizer. As will be appreciated, an optimizer may be used to minimize a “cost function” subject to a set of constraints, where the cost function is a mathematical representation of desired objectives or system operation”
Referring to Claim 7, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
wherein the plurality of constraints comprises a constraint that agents must be either unassigned or assigned to one session (D’Attilio, [0104]), “the third set of constraints ensured that all agents must be scheduled”; (D’Attilio, [0087]), “the system must have both a restricted version of the problem and a way to find columns. In the illustrative embodiment, a column is defined as the set of shifts assigned to an agent (i.e., a “shift schedule”). The master problem ensures that the shift schedules selected meet the requirements for each planning group, and the sub-problem finds shift schedules that meet the scheduling constraints”; (D’Attilio, [0083]).
Referring to Claim 8, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
wherein the plurality of constraints comprises at least one constraint that a number of contact center agents assigned to a scheduled session must be at least a minimum group size and no greater than a maximum group size (D’Attilio, [0045]), “determining the expected number of workload interactions (e.g. calls, emails, chats, back-office work, etc.) as well as the service time associated with those interactions (e.g., average handle time) in the planning horizon, converting the workload predictions from the first phase into a staffing or headcount requirement for the future planning horizon, and performing scheduling in which the headcount requirement is fulfilled through placement of staff throughout the planning horizon according to shift and schedule constraints, such that the final output is a schedule or roster that optimizes (or sub-optimizes) the coverage of workload with staffed agents”.
Referring to Claim 9, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
wherein the plurality of constraints comprises a constraint that previously scheduled sessions cannot be unscheduled (D’Attilio, Fig. 10, Item: Constraint – doNOTResecheduleActivity; [0112]).
Referring to Claim 10, D’Attilio teaches the method of claim 1. D’Attilio teaches perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see par. 0006), but D’Attilio does not explicitly teach:
wherein the plurality of constraints comprises a constraint that a number of total sessions scheduled is no greater than a maximum total session count.
However Rahimi teaches:
wherein the plurality of constraints comprises a constraint that a number of total sessions scheduled is no greater than a maximum total session count (Rahimi, [0082]), “a first SLA may include requirements specifying: 0% of jobs must be addressed within an hour of their arrival, 50% of jobs must be addressed within 2 hours of arrival, 70% of jobs must be addressed within 5 hours of arrival, 80% of jobs must be addressed within 10 hours of arrival, and/or all jobs must be completed within 21 hours of arrival. Of course, it should be appreciated that the number of thresholds set for each job type may vary, such that a first job type may have only one threshold and no maximum limit required and a second job type may have ten thresholds with a maximum limit”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimization in D’Attilio to include the constraint limitation as taught by Rahimi. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of increasing required compliance (see Rahimi par. 0036).
Referring to Claim 11, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
wherein the plurality of constraints comprises a constraint that a number of concurrent sessions must be no greater than a maximum number of concurrent sessions (D’Attilio, [0045]), “determining the expected number of workload interactions (e.g. calls, emails, chats, back-office work, etc.) as well as the service time associated with those interactions (e.g., average handle time) in the planning horizon, converting the workload predictions from the first phase into a staffing or headcount requirement for the future planning horizon, and performing scheduling in which the headcount requirement is fulfilled through placement of staff throughout the planning horizon according to shift and schedule constraints”; (D’Attilio, [0084]; [0077]).
Referring to Claim 13, D’Attilio teaches the method of claim 1. D’Attilio teaches perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see par. 0006), but D’Attilio does not explicitly teach:
wherein the plurality of constraints comprises a constraint that defines whether understaffing below minimum staffing requirements for respective planning groups is permitted.
However Rahimi teaches:
wherein the plurality of constraints comprises a constraint that defines whether understaffing below minimum staffing requirements for respective planning groups is permitted (Rahimi, [0109]-[0110]), “the entity computing device 130 may establish personnel count restraints as part of the optimization block 206, and as indicated in the constraints 210. Personnel count (i.e., headcount) constraints may play a critical role in scheduling optimization… The entity computing device 130 may incorporate personnel count constraints by analyzing the total number of personnel available for each role and any restrictions on the number of personnel that can be assigned to a role within a given time period. Furthermore, the entity computing device 130 may analyze any maximum or minimum requirements for each role to guarantee that the workforce is efficiently utilized. Thus, by addressing personnel count constraints, the resulting optimization model 136d may ensure that the workforces capabilities are fully leveraged while preventing overstaffing or understaffing scenarios”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimization in D’Attilio to include the constraint limitation as taught by Rahimi. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of increasing required compliance (see Rahimi par. 0036).
Referring to Claim 14, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
further comprising:
updating, by the computing system, the activity rule in response to scheduling the activity rule based on one or more recurrence settings of the activity rule; and adding, by the computing system, the updated activity rule to the rule queue (D’Attilio, [0079]-[0080]), “… combination of queuing models with customer patience profiles, supporting different routing configurations of voice, chat, callback, email, casework, and/or back-office interactions allow for a robust and mathematically proven optimal solution… Like with workload forecasting, a big data infrastructure is leveraged to use the latest available historical ACD data (e.g., the last 90 days) to automatically perform a nightly batch process of building and validating steady-state contact center models… the nightly batch process provides a closed feedback loop for improving model accuracy accounting for new ACD interactions of the day”; (D’Attilio, [0154]).
Referring to Claim 15, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
further comprising adding, by the computing system, an initial set of activity rules to the rule queue; and wherein receiving the activity rule from the rule queue occurs subsequently to adding the initial set of activity rules to the rule queue (D’Attilio, [0124]-[0125]), “The IMR server 1216 may also be configured to ascertain why a customer is contacting the contact center so that the communication may be routed to the appropriate resource. The IMR configuration may be performed through the use of a self-service and/or assisted service tool which comprises a web-based tool for developing IVR applications and routing applications running in the contact center environment … The routing server 1218 may function to route incoming interactions. For example, once it is determined that an inbound communication should be handled by a human agent, functionality within the routing server 1218 may select the most appropriate agent and route the communication thereto… the selection of appropriate agent may be based on a routing strategy or algorithm that is implemented by the routing server 1218. In doing this, the routing server 1218 may query data that is relevant to the incoming interaction…”; (D’Attilio, [0154]), “when an event occurs (e.g., data is transferred to the system for handling), the virtual computing environment may be communicated with (e.g., via a request to an API of the virtual computing environment), whereby the API may route the request to the correct virtual function (e.g., a particular server-ambiguous computing resource) based on a set of rules”.
Referring to Claim 16, D’Attilio teaches:
A computing system for multi-objective schedule optimization in contact centers utilizing a mixed integer programming model, the system comprising:
at least one processor; and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor (D’Attilio, [0042]; [0044]; [0140]; [0149]-[0151]), causes the computing system to:
Claim 16 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Referring to Claim 20, D’Attilio teaches the computing system of claim 16.
wherein the plurality of instructions further causes the computing system to:
Claim 20 disclose substantially the same subject matter as claim 14, and is rejected using the same rationale as previously set forth.
Claims 2, 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], in view of Rahimi et al., U.S. Publication No. 2025/0094896 [hereinafter Rahimi], and further in view of Barto et al., U.S. Patent No. 7,069,097 [hereinafter Barto].
Referring to Claim 2, D’Attilio teaches the method of claim 1. D’Attilio further teaches:
further comprising:
receiving, by the computing system, schedule information for the contact center (D’Attilio, [0126]), “the contact center system 1200 may include one or more mass storage devices—represented generally by the storage device 1220—for storing data in one or more databases relevant to the functioning of the contact center…e. Agent data maintained by the contact center system 1200 may include, for example, agent availability and agent profiles, schedules… databases and/or data being accessible to the other modules or servers of the contact center system 1200 in ways that facilitate the functionality described herein. For example, the servers or modules of the contact center system 1200 may query such databases to retrieve data stored therein or transmit data thereto for storage”; (D’Attilio, [0044]; [0046]);
finding, by the computing system, possible candidate sessions based on the schedule information (D’Attilio, [0044]), “the technologies leverage a state-of-the art solver (e.g., IBM ILOG CPLEX) with a contact-center specific scheduling algorithm that takes workload and staffing requirement forecasts generated by the AI models as inputs, and uses column generation with linear programming (LP) for optimizing a set of specific objectives master problem (e.g., service performance, agent preference, paid cost, etc.) and constraint programming (CP) for solving sub-problems that find potential candidates of best agent shifts… determining the expected number of workload interactions (e.g. calls, emails, chats, back-office work, etc.) as well as the service time associated with those interactions (e.g., average handle time) in the planning horizon, converting the workload predictions from the first phase into a staffing or headcount requirement for the future planning horizon, and performing scheduling in which the headcount requirement is fulfilled through placement of staff throughout the planning horizon according to shift and schedule constraints, such that the final output is a schedule or roster that optimizes (or sub-optimizes) the coverage of workload with staffed agents”; (D’Attilio, [0101]; [0117]; [0126]);
estimating, by the computing system, contributions of each of the plurality of contact center agents and facilitators to each of a plurality of planning groups of the contact center (D’Attilio, [0089]), “the system performs initialization by solving the initial sub-problem. More specifically, at initialization, the system may find at least one valid shift schedule for each agent or agent grouping, for example, by solving the initial sub-problem, s.sub.init-SP, for each agent”; (D’Attilio, [0077]), “after the workload and agent handle time (AHT), for example, have been forecasted for the planning horizon, the system may determine how many agents are required to handle the forecasted workload, given certain KPI goals (e.g., such as service level, average speed of answer (ASA), abandon rate, customer satisfaction score, etc.). In some embodiments, this is done at the same granularity that the schedule operates on (e.g., often 15-/30-/60-minute periods). In some embodiments, such staffing requirements are generated for each planning group. In some embodiments, the inputs for determining the staffing requirements may include the workload forecast, the routing configuration of the contact center system, and/or other inputs”; (D’Attilio, [0083]), “the system receives/retrieves a list of agents and staffing request forecasts (e.g., in addition to the number of weeks/days to schedule). It should be appreciated that the list of agents may include, for example, working rules associated with the agents (e.g., work hours, regulatory requirements, etc.), capabilities of the respective agents (e.g., specializations in various areas, etc.), and/or other relevant criteria useful for deriving shifts from the staffing requirements and available agents”; (D’Attilio, [0085]), “The system performs optimization based on the list of agents, staffing requirement forecasts, and/or other relevant data in order to generate shift schedules with the desired optimal”; (D’Attilio, [0044]; [0074]-[0075]; [0087]-[0088]; [0109]-[0112]); and
determining, by the computing system, overstaffing with respect to minimum staffing requirements for each of the plurality of planning groups of the contact center (D’Attilio, [0101]), “the system may re-balance the agent allocation to planning groups with another small LP formulation so that each type of interaction receives similar service KPI. For example, when over-staffed, the system may spread the overstaffed agents equally to handle all interaction types… adding one agent to a requirement of ten agents may yield a 10% increase in service level, whereas the same agent allocated to a one hundred agents requirement may only yield a 1% increase in service level. In block 814, the system may output the optimized shift schedules. For example, in some embodiments, the system may return the resulting schedules for display. In some embodiments, the optimized shift schedules may be automatically incorporated into one or more aspects of the contact center system 1200”; (D’Attilio, [0081]).
D'Attilio teaches iteratively identifying good candidate shift schedules for covering requirements (see par. 0087), but D’Attilio does not explicitly teach:
identifying, by the computing system, concurrent sessions based on the possible candidate sessions; and
identifying, by the computing system, incompatible sessions based on the possible candidate sessions.
However Barto teaches:
identifying, by the computing system, concurrent sessions based on the possible candidate sessions; and identifying, by the computing system, incompatible sessions based on the possible candidate sessions (Barto, [col. 20, ln. 66]-[col. 21, ln. 6]), “If there is an overlap, a cancel award message 483 is sent to the machine scheduling agent 410 representing the process tool 115 for the next, overlapping engagement. The cancelled engagement is in turn replaced, and any overlap with the next scheduled engagement is identified. The rescheduling process continues until there are no overlaps or there are no more engagements scheduled for the lot 130”; (Barto, [col. 31, ln. 10-17]), “the machine scheduling agent 410 is to monitor the committed capacity of the process tool 115 to determine if it has overcommitted its resources. Each time a change occurs to the committed capacity of the process tool 115, the machine scheduling agent 410 runs a background task that looks for regions of violation (ROVs)”; (Barto, [col 32, ln. 4-33]; [col. 50, ln. 60]-[col. 51, ln. 10]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the iterative candidate shift scheduling for covering requirements in D’Attilio to include the session limitation as taught by Barto. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of reducing scheduling conflicts for a resource (see Barto col. 1, ln. 19-20).
Referring to Claim 12, D’Attilio teaches the method of claim 1. D’Attilio teaches perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see par. 0006), but D’Attilio does not explicitly teach:
wherein the plurality of constraints comprises a constraint that only one of two incompatible sessions can be scheduled.
However Barto teaches:
wherein the plurality of constraints comprises a constraint that only one of two incompatible sessions can be scheduled (Barto, [col. 9, ln. 29-33]), “A "working window" (WW) is a subset of the commitment window that the provider software agent 310 may use to constrain the engagement to accommodate other engagements and avoid overcommitting its resources”; (Barto, [col. 20, ln. 66]-[col. 21, ln. 6]), “If there is an overlap, a cancel award message 483 is sent to the machine scheduling agent 410 representing the process tool 115 for the next, overlapping engagement. The cancelled engagement is in turn replaced, and any overlap with the next scheduled engagement is identified. The rescheduling process continues until there are no overlaps or there are no more engagements scheduled for the lot 130”; (Barto, [col. 31, ln. 10-17]), “the machine scheduling agent 410 is to monitor the committed capacity of the process tool 115 to determine if it has overcommitted its resources. Each time a change occurs to the committed capacity of the process tool 115, the machine scheduling agent 410 runs a background task that looks for regions of violation (ROVs)”; (Barto, [col 32, ln. 4-33]; [col. 50, ln. 60]-[col. 51, ln. 10]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimization in D’Attilio to include the session limitation as taught by Barto. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of reducing scheduling conflicts for a resource (see Barto col. 1, ln. 19-20).
Referring to Claim 17, D’Attilio teaches the computing system of claim 16.
wherein the plurality of instructions further causes the computing system to:
Claim 17 disclose substantially the same subject matter as claim 2, and is rejected using the same rationale as previously set forth.
Claims 5, 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], in view of Rahimi et al., U.S. Publication No. 2025/0094896 [hereinafter Rahimi], and further in view of Bellini, III et al., U.S. Publication No. 2016/0036652 [hereinafter Bellini].
Referring to Claim 5, D’Attilio teaches the method of claim 1. D’Attilio teaches perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see par. 0006), but D’Attilio does not explicitly teach:
wherein the plurality of optimization objectives comprises an optimization objective to minimize interrupted activity sessions.
However Bellini teaches:
wherein the plurality of optimization objectives comprises an optimization objective to minimize interrupted activity sessions (Bellini, [0005]), “The ticketing system may support chat connections between the support technicians and end users. Chat sessions can facilitate improving the efficiency of technicians, and reducing frustration by end users, by reducing the delay introduced by waiting for messages, when using email for communication, or playing “phone tag” or waiting on hold, when using the phone for communication”; (Bellini, [0052]; [0054]; [0074]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimization in D’Attilio to include the objective limitation as taught by Bellini. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of provide customer support to customers to facilitate resolving problems (see Bellini par. 0003).
Referring to Claim 6, D’Attilio teaches the method of claim 1. D’Attilio teaches perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see par. 0006), but D’Attilio does not explicitly teach:
wherein the plurality of optimization objectives comprises an objective to minimize a percentage of opened sessions.
However Bellini teaches:
wherein the plurality of optimization objectives comprises an objective to minimize a percentage of opened sessions (Bellini, [0050]-[0051]), “The service schedule can indicate the times during which the SLT applies. For example, a service schedule can indicate service during normal business hours, and a different service schedule can indicate service at some or all times (24×7). Each response goal can include a time period and a percentage… The customers may then require measurement of the performance of the service provider against the SLA. For example, if the SLA requires an initial response of 2 hours at 80%, the service provider may show that for some or all the support tickets covered by that SLA, at least 80% of the support tickets had an initial response in 2 hours or less. If fewer than 80% of the support tickets covered by the SLA had an initial response in 2 hours or less, the service provider may not be in compliance with the SLA…”
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimization in D’Attilio to include the objective limitation as taught by Bellini. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of provide customer support to customers to facilitate resolving problems (see Bellini par. 0003).
Referring to Claim 18, D’Attilio teaches the computing system of claim 16. D’Attilio further teaches:
wherein the plurality of optimization objectives comprises:
a first optimization objective to minimize unassigned contact center agents (D’Attilio, [0101]; [0081]);
a second optimization objective to minimize understaffing caused by scheduling the one or more activity sessions (D’Attilio, [0107]; [0140]).
D’Attilio teaches perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see par. 0006), but D’Attilio does not explicitly teach:
a third optimization objective to minimize interrupted activity sessions; and
a fourth optimization objective to minimize a percentage of opened sessions.
However Bellini teaches:
a third optimization objective to minimize interrupted activity sessions (Bellini, [0005]; [0052]; [0054]; [0074]); and
a fourth optimization objective to minimize a percentage of opened sessions (Bellini, [0050]-[0051]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimization in D’Attilio to include the objective limitations as taught by Bellini. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of provide customer support to customers to facilitate resolving problems (see Bellini par. 0003).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], in view of Rahimi et al., U.S. Publication No. 2025/0094896 [hereinafter Rahimi], and further in view of Barto et al., U.S. Patent No. 7,069,097 [hereinafter Barto],
Referring to Claim 19, D’Attilio teaches the computing system of claim 16. D’Attilio further teaches:
wherein the plurality of constraints comprises:
a first constraint that contact center agents must be either unassigned or assigned to one session (D’Attilio, [0104]; [0087]; [0083]);
a second constraint that a number of contact center agents assigned to a scheduled session must be at least a minimum group size (D’Attilio, [0045]);
a third constraint that the number of contact center agents assigned to the scheduled session must be no greater than a maximum group size (“D’Attilio, [0045]);
a fourth constraint that previously scheduled sessions cannot be unscheduled (D’Attilio, Fig. 10, Item: Constraint – doNOTResecheduleActivity; [0112]); and
a sixth constraint that a number of concurrent sessions must be no greater than a maximum number of concurrent sessions (D’Attilio, [0045]; [0084]; [0077]).
D’Attilio teaches perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see par. 0006), but D’Attilio does not explicitly teach:
a fifth constraint that a number of total sessions scheduled is no greater than a maximum total session count;
a seventh constraint that only one of two incompatible sessions can be scheduled; and
an eighth constraint that defines whether understaffing below minimum staffing requirements for respective planning groups is permitted.
However Barto teaches:
a seventh constraint that only one of two incompatible sessions can be scheduled (Barto, [col. 9, ln. 29-33]; [col. 20, ln. 66]-[col. 21, ln. 6]; [col. 31, ln. 10-17]; [col 32, ln. 4-33]; [col. 50, ln. 60]-[col. 51, ln. 10]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimization in D’Attilio to include the session limitation as taught by Barto. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of reducing scheduling conflicts for a resource (see Barto col. 1, ln. 19-20).
D’Attilio teaches perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see par. 0006), but D’Attilio does not explicitly teach:
a fifth constraint that a number of total sessions scheduled is no greater than a maximum total session count; and
an eighth constraint that defines whether understaffing below minimum staffing requirements for respective planning groups is permitted.
However Rahimi teaches:
a fifth constraint that a number of total sessions scheduled is no greater than a maximum total session count (Rahimi, [0082]); and
an eighth constraint that defines whether understaffing below minimum staffing requirements for respective planning groups is permitted (Rahimi, [0109]-[0110]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimization in D’Attilio to include the constraint limitations as taught by Rahimi. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of increasing required compliance (see Rahimi par. 0036).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bourke et al. (US 7734783 B1) - Systems and methods for allocating resources, such as contact center agents, computer servers and recorders, among geographically distributed sites are provided. In this regard, a representative method comprises: creating a workload forecast, such as contact volume, and resource utilization, such as average interaction time, of events for a specified time frame as if the geographically distributed sites were co-located, performing discrete event-based simulation to assign or allocate the events to the resources as if the resources were co-located, and determining recommended allocations of the resources among the geographically distributed sites based on a relative distribution of events assigned to resources at each of the geographically distributed sites.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624