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 . Claims 1-2, 4-9, 11-15, and 17-20 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Non-Final Office action. Applicant, on 01/05/2026, amended claims, and cancelled claims 3, 10, and 16. Claims 1-2, 4-9, 11-15, and 17-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The arguments presented by the Applicant are persuasive regarding the 102 Rejection facilitating a new line of 103 Rejections.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that the claims recite specific technological improvements in Step 2-P2 of the analysis and further points to the Specification.
Examiner respectfully disagrees. The additional elements are each identified below and are determined to be performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) and as such do not integrate the abstract idea into a practical application.
Applicant further contends that the claims provide a technical improvement and not merely automating scheduling. Applicant further asserts that identifying… subset of jobs, and determining.. a tentative solution represents a technical solution and improves computer system performance. Applicant contends the claims improve a computer system capabilities.
Examiner respectfully disagrees. The claims merely improve upon the abstract idea itself and do not constitute an improvement to the technology or technological field as a whole. Further the cited limitations are merely concepts capable of being performed in the human mind (i.e. via pen and paper) and further certain methods of organizing human activity.
The 101 Rejection is updated and maintained below.
Response to Arguments - 35 USC § 102/103
Applicant’s arguments with respect to the 35 USC 103 rejections have been fully considered, but they are moot in view of the new line of 103 Rejections seen below.
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 1-2, 4-9, 11-15, and 17-20 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) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the Claims is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims recite a series of steps for the abstract idea recited below.
Regarding independent Claims, (additional elements bolded)
Regarding Claims 1, 9, and 15, A method, comprising: identifying, by a processing device, a plurality of variables associated with scheduling a plurality of workers to perform a plurality of jobs during respective shifts defined by corresponding time periods, wherein each variable of the plurality of variables is associated with a corresponding shift;
identifying a plurality of constraints associated with the plurality of variables;
identifying a scheduling problem for optimizing an objective function defined on the plurality of variables subject to the plurality of constraints;
identifying, based on a historic schedule assigning at least a subset of the plurality of workers to perform at least a subset of the plurality of jobs during a plurality of past time periods, a subset of the plurality of variables of the scheduling problem;
determining, based on the subset of the plurality of variables, a tentative solution of the scheduling problem;
responsive to determining that the tentative solution fails a predefined quality criterion, modifying the tentative solution; and
generating, based on the tentative/tentative modified solution, a schedule to assign at least the plurality of workers to perform at least the plurality of jobs during at least the plurality of time periods.
Further regarding Claim 9, A system, comprising: a memory; and a processing device coupled to the memory, the processing device configured to:
Further regarding Claim 15, A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a processing device, cause the processing device to:
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards identifying a plurality of variables, identifying a plurality of constraints, identifying a scheduling problem, identifying a subset of the plurality of variables, and determining a tentative solution of the scheduling problem all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards addressing the various scheduling problem and allowing generation of workforce schedules (See Specification, [11]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least an processing device; system, comprising: a memory; and a processing device coupled to the memory, the processing device configured to:; and computer-readable non-transitory storage medium comprising executable instructions that, when executed by a processing device, cause the processing device to. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claims 2, 4-8, 11-14, and 17-20, the claim further narrows the abstract idea or recite additional elements previously rejected in the independent claims.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the Claims amounts 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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 7- 9, 14-15, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aykin (US 7725339 B1), Wang et al. (US 20200151649 A1), and Izadi et al. (US 20200380451 A1).
Regarding Claims 1, 9, and 15, Aykin teaches: A method, comprising: identifying, by a processing device, a plurality of variables associated with scheduling a plurality of workers to perform a plurality of jobs during respective shifts defined by corresponding time periods, wherein each variable of the plurality of variables is associated with a corresponding shift; (Aykin, [co. 12, li. 33-41]; The RA algorithm then rounds the values of the integer restricted decision variables with fractional values either up or down in an attempt to find an integer feasible solution. After rounding the values of the decision variables, the RA algorithm also checks the constraints of the MILP model. If the rounded solution is violating one or more constraints of the MILP model, values of the variables are adjusted to restore feasibility including addition of new agents to meet the agent requirements in every period and Aykin, [co. 15, li. 60-63]; Break constraints (36-38) ensure that all required breaks are scheduled for each agent who is assigned to tour k. These constraints as well as the break variables are defined only for valid breaks for a shift length and Aykin, [co. 17, li. 4-46]; Each pseudo tour k has only one daily start time in the daily start time set QI.sub.k. Consequently, formulation (34-41) will have work pattern variables Q.sub.k1, and one daily shift variable QX.sub.khi, and the associated daily break variables for that shifts for tour k, and day h=1, . . . ,7, for each such pseudo tour and Aykin, [co. 24, li. 46-49]; Compare the days-off variables Y and Z with shift variables X for tours with consistent daily start time and shift length, and daily shift variables QX with work pattern variables Q).
identifying a plurality of constraints associated with the plurality of variables; (Aykin, [co. 4, li. 54-59]; The formulation step also includes the expression of contact center constraints, and agent and skill requirements in all periods to be scheduled as mathematical equalities or inequalities. Finally, constraints that restrict the values of all decision variables to nonnegative values, and some variables to nonnegative integer values only are added to the formulation.
identifying a scheduling problem for optimizing an objective function defined on the plurality of variables subject to the plurality of constraints; (Aykin, [abstract]; develops a Mixed Integer Linear Programming (MILP) model for the scheduling environment; applied an optimization algorithm that uses the Branch and Bound algorithm with a Rounding Algorithm to improve performance; and locates a globally optimal or near optimal workforce schedule in total cost or paid time or agent satisfaction and Aykin, [co. 4, li. 14-31]; The present invention also discloses a solution algorithm that solves a number of sub-problems (or nodes) with the use of the standard Branch and Cut (B&C) algorithm for MILP problems. The solution method supplements the B&C algorithm by a Rounding Algorithm (RA algorithm) to locate the optimal solution of the MILP model of the invention. The optimality condition (both necessary and sufficient) for an MILP model is well documented in the field of Optimization; An all-integer solution that satisfies all of the constraints of an MILP with a minimization (maximization) type objective function, and has an objective value that is better than the best lower bound (upper bound) for any of the sub-problems in the B&C algorithm is a global (i.e. absolute) optimal solution to that MILP model (Wolsey, 1998). Once the optimality condition is satisfied by an all-integer solution found during the execution of this solution algorithm (either in the B&C algorithm or in the RA algorithm), an optimal agent schedule is reported using the information in the optimal solution of the MILP model found and Aykin, [col. 5, li, 54-58]; the B&C algorithm updates the best integer solution known for its own use, and forms new sub-problems (nodes) by adding new constraints (Wolsey, 1998). The entire process is then repeated; the B&C algorithm chooses a new sub-problem, and solves its LP relaxation).
identifying a subset of the plurality of variables of the scheduling problem, wherein the subset comprises one or more variables associated with the plurality of shifts, (Aykin, [co. 23, l. 21-34]; The MILP formulation generator ((3) in FIG. 1) of the invention is implemented in a computer program. Given the agent and skills requirements for each planning period t.epsilon.QT.sub.h, h=1, . . . ,7, and center information regarding agent groups, tours and shift parameters, the MILP formulation generation program develops a formulation (step (11)) of the scheduling problem using the MILP formulations and their extensions (1-89) of the invention disclosed before. To generate a formulation, the MILP formulation generation program calculates the coefficients of various shift, break, days-off, work pattern, overage and underage, agent allocation variables in the respective equations and inequalities of the MILP model of the invention for the scheduling environment considered). Examiner interprets the skill/agent requirements, various shifts, breaks, etc. as the subset of variables.
determining, based on the subset of the plurality of variables, a tentative solution of the scheduling problem; (Aykin, [co. 5-6; li. 53-3]; If the best known (integer feasible) solution was changed in the RA algorithm, the B&C algorithm updates the best integer solution known for its own use, and forms new sub-problems (nodes) by adding new constraints (Wolsey, 1998). The entire process is then repeated; the B&C algorithm chooses a new sub-problem, and solves its LP relaxation. If the LP relaxation has a feasible solution satisfying all of the constraints but some of the integrality constraints for some decision variables, the B&C algorithm transfers control to the RA algorithm which searches for an integer feasible solution through rounding and adding more agents to the schedules. The solution algorithm of the present invention terminates when all sub-problems in the B&C algorithm are terminated (sub-problem termination conditions in the B&C algorithm are given in Wolsey, 1998). That is when the optimality condition is satisfied by an integer feasible solution to the MILP model. When this condition is satisfied, control is transferred to the schedule-reporting step).
responsive to determining that the tentative solution fails a predefined quality criterion, modifying the tentative solution; and (Aykin, [co.33, li. 3-14]; repeating steps (viii), (ix), and (x) until all agent requirements are met, or agent requirements for some contact types are not met in all periods and all available agents in constraint (c8) who can serve these contact types are scheduled; xii. Examining the tours scheduled in the integer feasible solution found and eliminating redundant tours that do not create new agent shortages in any period when removed by lowering the values of related shift, break, and work pattern variables). Examiner notes that if the tentative solution does not satisfy the criterion, the process repeats the steps until all criteria are satisfied and Aykin, [co. 4, li. 26-31]; Once the optimality condition is satisfied by an all-integer solution found during the execution of this solution algorithm (either in the B&C algorithm or in the RA algorithm), an optimal agent schedule is reported using the information in the optimal solution of the MILP model found).
generating, based on the tentative/tentative modified solution, a schedule to assign at least the plurality of workers to perform at least the plurality of jobs during at least the plurality of time periods. (Aykin, [co.33, li. 3-14]; repeating steps (viii), (ix), and (x) until all agent requirements are met, or agent requirements for some contact types are not met in all periods and all available agents in constraint (c8) who can serve these contact types are scheduled; xii. Examining the tours scheduled in the integer feasible solution found and eliminating redundant tours that do not create new agent shortages in any period when removed by lowering the values of related shift, break, and work pattern variables). Examiner notes that if the tentative solution does not satisfy the criterion, the process repeats the steps until all criteria are satisfied and Aykin, [co. 4, li. 26-31]; Once the optimality condition is satisfied by an all-integer solution found during the execution of this solution algorithm (either in the B&C algorithm or in the RA algorithm), an optimal agent schedule is reported using the information in the optimal solution of the MILP model found).
While Aykin teaches identifying historic work patterns, tasks requiring only specific skills (i.e. Chinese speaking only), and subsets of tasks, Aykin does not appear to teach a frequency. However, Aykin in view of the analogous art of Wang (i.e. scheduling) does teach: identifying, in a historic schedule assigning at least a subset of the plurality of workers to perform at least a subset of the plurality of jobs during a plurality of past time periods, a subset of jobs that were most frequently assigned to a particular worker (Wang, [206]; Historical-based assignment rules assign work items to agents that are frequently assigned to the users that generated the work items. The selection criteria for historical-based assignment rules may evaluate a given work item's content to ascertain which agents have been frequently assigned to work items associated with the same user that generated the given work item. For example, work item 1020 indicates that assignment engine 722 assigned agent A 724 to work items associated with user 1004 ten times and assigned agent B 726 to work items associated with user 1004 two times and Wang, [248]; In some embodiments, the assignment rules include at least one historically-based assignment rule with historically-based selection criteria that evaluate a given work item to determine frequencies in which at least some of the plurality of agents were previously assigned to address past work items submitted by a user that submitted the work item. In such embodiments, the one or more candidate agents were previously assigned to address the past work items). Examiner interprets the jobs created by the same user as a subset of jobs.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Aykin including identifying historic work patterns, tasks requiring only specific skills (i.e. Chinese speaking only), and subsets of tasks with the teachings of Wang including frequently assigned jobs in order to assist in scheduling workers with greater experience with a task (Wang, [207]; historical-based assignment rules may assign the given work item to the agent most frequently assigned to work items associated with the particular user. For example, since assignment engine 722 previously assigned ten work items associated with user 1002 to agent A 724 and previously assigned two work items associated with user 1002 to agent B 726, assignment engine 722 should assign work items associated with user 1002 to agent A 724 rather than to agent B 726).
While Aykin/Wang teach identifying historic scheduling and subsets of jobs, neither appear to explicitly teach identifying historic shifts assigned to particular workers. However, Aykin/Wang in view of the analogous art of Izadi (i.e. scheduling) does teach: identifying, in the historic schedule, a plurality of shifts during which the particular worker was assigned to perform a job of the subset of jobs; and (Izadi, [05]; Data such as the previous schedule shift data, which may be influential in determining the current cycle schedule, may be difficult to accommodate on the screen as well as the current cycle schedule. As a result, access to, comparing and interpretation of the previous and current shift data of the employees is difficult. If employee details are displayed within the table, then favoritism for certain employees may occur when scheduling advantageous shifts and Izadi, [66-67]; queries extract the data needed for each task from the server database 34, such as the previous cycle shift pattern, employees' availability for the current cycle, employees' details, etc. The algorithms used by the scheduling interface 44 utilize this data to recommend the current cycle shift pattern using the functions and the algorithms…. the previous schedule 48 and the recommended current cycle schedule 59 shift data are represented in the same dynamic employee information box 56 with color-coded geometric shapes (circles, squares, hexagons, etc.). This dual schedule or “dual rosterogram” is a visual representation where previous shift data and current shift data are viewable in one interface. This dual rostergram within the employee information box 56 allows for a user to view and compare previous shift data and current shift data to make informed decisions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Aykin/Wang including identifying historic scheduling and subsets of jobs with the teachings of identifying historic shifts in order to determine if biases/favoritism is being shown to certain employees (Izadi, [05]; schedule, may be difficult to accommodate on the screen as well as the current cycle schedule. As a result, access to, comparing and interpretation of the previous and current shift data of the employees is difficult. If employee details are displayed within the table, then favoritism for certain employees may occur when scheduling advantageous shifts).
Further regarding Claims 9, Aykin teaches: A system, comprising: a memory; and a processing device coupled to the memory, the processing device configured to: (Aykin, [co. 1, li. 38-42]; The invention pertains to the field of computer systems and computer implemented methods. More specifically, the invention pertains to methods and computer systems for scheduling agents with multiple skills optimally at contact centers to satisfy time varying agent staffing requirements for multiple contact types).
Further regarding Claims 15, Aykin teaches: A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a processing device, cause the processing device to: (Aykin, [co. 23, li. 21-28]; The MILP formulation generator ((3) in FIG. 1) of the invention is implemented in a computer program. Given the agent and skills requirements for each planning period t.epsilon.QT.sub.h, h=1, . . . ,7, and center information regarding agent groups, tours and shift parameters, the MILP formulation generation program develops a formulation (step (11)) of the scheduling problem using the MILP formulations and their extensions).
Regarding Claims 2, Aykin teaches: The method of claim 1, further comprising:
responsive to determining that the tentative solution fails a predefined quality criterion, modifying the subset of the plurality of variables of the scheduling problem. (Aykin, [co. 5. Li. 35-41]; After rounding the values of the decision variables, the RA algorithm also checks the constraints of the MILP model. If the rounded solution is violating one or more constraints of the MILP model, values of the variables are adjusted to restore feasibility including addition of new agents to meet the agent requirements in every period, and for all skill types).
Regarding Claims 7, 14, and 19, Aykin teaches: The method of claim 1, wherein modifying the tentative solution further comprises performing at least one of: modifying a start time of a chosen shift of the tentative solution, modifying an end time of a chosen shift of the tentative solution, modifying duration of a chosen shift of the tentative solution, or swapping a first shift and a second shift of the tentative solution. (Aykin, [co. 13-14; 62-5]; If operating hours are shorter for certain days (e.g. regular hours of 5:00am to 10:00pm with Saturday operating hours of 5:00am to 4:00pm) for a tour or a contact center, the present invention extends the MILP model by formulating each such tour as consisting of two tours with identical work rules except for their start times as follows: (i) tour 1 includes the start times for which the latest daily shift completion time not exceeding the operating hours for the early closure day, and (ii) tour 2 has the start times not covered by tour group 1 (ending after closure on the early closure day and Aykin, [co. 14, li. 21-25; li. 36-44]; The present invention also extends the MILP model to tours not requiring consistent daily shift start times and same daily shift length requirements. For these tours, shift start time as well as shift duration for an agent may vary from one day to another… Thus, the latest start time for a tour plus the length of the longest daily shift type are not allowed to exceed 24 hours minus the desired minimum rest period between consecutive daily shifts. To prevent this for a tour type, the present invention creates separate tour types each with start times allowing a minimum required time between the latest possible end of a shift on one day and the earliest possible start of a shift on the following day)
Regarding Claims 8 and 20, Aykin teaches: The method of claim 1, wherein modifying the tentative solution further comprises: identifying a sub-schedule of a tentative solution; and (Aykin, [co. 5-6, li. 53-4]; the best known (integer feasible) solution was changed in the RA algorithm, the B&C algorithm updates the best integer solution known for its own use, and forms new sub-problems (nodes) by adding new constraints (Wolsey, 1998). The entire process is then repeated; the B&C algorithm chooses a new sub-problem, and solves its LP relaxation. If the LP relaxation has a feasible solution satisfying all of the constraints but some of the integrality constraints for some decision variables, the B&C algorithm transfers control to the RA algorithm which searches for an integer feasible solution through rounding and adding more agents to the schedules. The solution algorithm of the present invention terminates when all sub-problems in the B&C algorithm are terminated (sub-problem termination conditions in the B&C algorithm are given in Wolsey, 1998). That is when the optimality condition is satisfied by an integer feasible solution to the MILP model. When this condition is satisfied, control is transferred to the schedule-reporting step).
replacing one or more shifts of the sub-schedule. (Aykin, [co. 14, li. 26-40]; Assume that the scheduling environment involves only tours not requiring consistent daily start time and shift length. Let QK be the set of all such tour types. Assume that agents assigned to tour type k can start at any one of a set of predetermined start times given in QI.sub.k. Let the daily shift lengths allowed for tour type k be F.sub.k. F.sub.k may also change from one day to another. Note that tour k has a minimum weekly work limit specified by the shortest shift in F.sub.k, and a maximum weekly work limit specified by the longest shift in F.sub.k. The daily shift start times should not allow for back-to-back shift schedules for agents without allowing enough time for rest. Thus, the latest start time for a tour plus the length of the longest daily shift type are not allowed to exceed 24 hours minus the desired minimum rest period between consecutive daily shifts.
Claims 4, 11, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aykin (US 7725339 B1) in view of Wang et al. (US 20200151649 A1), Izadi et al. (US 20200380451 A1), and Yamasaki et al (US 20230359182 A1).
Regarding Claims 4, 11, and 17, Aykin teaches: The method of claim 1, wherein identifying the subset of the plurality of variables further comprises: identifying a subset of shifts of the historic schedule; and (Aykin, [co. 3-4, li. 60-8]; A further limitation of the prior art MILP models is that they only consider agent scheduling environments involving a single agent skill group. Recent technological developments in ACD's used in call centers to queue and assign calls to agents, and incorporation of other types of contact media such as email and fax, made contact center managers to realize that agents with different types of skills are needed to be scheduled to handle these contact types (e.g. a Spanish speaking …). Thus, the agent scheduling task became more complex since the agent scheduling method used should take the time-varying demand for different agent skill types, and available agent groups with different skill sets (e.g. Spanish & English speaking ….) into consideration in scheduling agents…. formulating MILP models for scheduling environments involving a plurality of agent skill groups, and a plurality of contact types with time-varying workload for specific agent skills and (Aykin, [co. 6, li. 39-48]; This sub-process uses statistical techniques to analyze patterns in the historical data, and develop forecasts. Agent and skill requirements planning module (2) combines contact volume and average handling time forecasts with service level targets (e.g. 80% of incoming calls answered within 20 seconds of their receipt) to determine agent and skill requirements in each planning period to be scheduled. In a multi-skilled environment, this module determines the agent and skills mix from the available agent skill groups that are needed in each planning period to deliver the targeted service levels to all contact types and Aykin, [co. 15, li. 3-15]; A work pattern in this case is a string of zeros and ones indicating work days (=1) and non-work days (=0) scheduled for agents assigned to this work pattern. All possible work patterns for a week are shown in FIG. 3 Let the set of all allowed work and non-work day patterns for the agents assigned to tour k be QL.sub.k. Only the work patterns in FIG. 3 satisfying the work and non-work day rules specified for tour k are included in QL.sub.k (e.g. a minimum of two consecutive days off no work on Sunday, etc.). A work pattern in this case specifies work and non-work days explicitly. Define the days-off pattern variable Q.sub.k1 as the number of agents assigned to tour k with a work pattern of 1. Let A.sub.kIh be equal to one if day h is a work day for agents assigned to work pattern 1 of tour k, and zero otherwise). Examiner interprets the pattern of shifts such as needing a specific skill as the subset of shifts.
append, to the subset of variables, one or more variables associated with at least one of; (Aykin, co. 10, l. 57-62]; Another extension of MILP model (1-8) involves agent scheduling when there aren't enough agents to meet the agent requirements in every period to be scheduled. In this case, objective function (1), and constraint (2) are modified by adding overstaffing and understaffing variables).
While Aykin does teach start and end times of shifts. Aykin does not appear to teach determining start/end times for each of historic shift. However, Aykin in view of the analogous art of Yamasaki (i.e. scheduling) does teach: for each shift of the subset of shifts, identify a start time and an end time of the shift; (Yamasaki, [37]; The past performance 336 includes information such as an actual time period as opposed to a scheduled time period, a cumulative time period, the number of defects, start/completion information acquisition accuracy, and the like regarding all processes and work performed until now by the worker or one or more pieces of work included in the work information 303).
While Aykin does teach start and stop times of new shifts. Aykin does not appear to teach determining if a shift starts within a predetermined time period. However, Aykin in view of the analogous art of Yamasaki (i.e. scheduling) does teach: a first shift that starts within a predefined timeframe from the start time or a second shift that ends within a predefined timeframe from the end time. (Yamasaki, [37]; The past performance 336 includes information such as an actual time period as opposed to a scheduled time period, a cumulative time period, the number of defects, start/completion information acquisition accuracy, and the like regarding all processes and work performed until now by the worker or one or more pieces of work included in the work information 303 and Yamasaki, [03]; detecting entry of a target to be processed into a predetermined bound in the vicinity of a facility that performs the processes and a start/completion judgment section (11) that sets, as a start time, a time when the entry of the target to be processed into the predetermined bound is detected or a time after the elapse of a predetermined time period from the time in question).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Aykin including start and stop times of shift with the teachings of Yamasaki including determining a start time within a predefined in order to determine historic accuracy of prior job schedules (Yamasaki, [37]; past performance 336, and the like. The past performance 336 includes information such as an actual time period as opposed to a scheduled time period, a cumulative time period, the number of defects, start/completion information acquisition accuracy, and the like regarding all processes and work performed until now by the worker or one or more pieces of work included in the work information 303).
Claims 5-6, 12-13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aykin (US 7725339 B1) in view of Wang et al. (US 20200151649 A1), Izadi et al. (US 20200380451 A1), and Noble et al (US 8935172 B1).
Regarding Claims 5, 12, and 18, Aykin teaches: The method of claim 1, wherein identifying the subset of the plurality of variables further comprises: identifying a subset of shifts of the historic schedule; and (Aykin, [co. 3-4, li. 60-8] and Aykin, [co. 6, li. 39-48] and Aykin, [co. 15, li. 3-15]; citations provided above).
append, to the subset of variables, one or more variables associated with at least one of; (Aykin, co. 10, l. 57-62]; Another extension of MILP model (1-8) involves agent scheduling when there aren't enough agents to meet the agent requirements in every period to be scheduled. In this case, objective function (1), and constraint (2) are modified by adding overstaffing and understaffing variables).
While Aykin does teach start and end times of shifts. Aykin does not appear to teach determining start/end times for each of historic shift. However, Aykin in view of the analogous art of Noble (i.e. scheduling) does teach: for each shift of the subset of shifts, identify a start time of the shift; (Noble, [co. 21, li. 40-46]; the managing schedule module may be configured to look at the start and end times (e.g., dates) for the campaigns and determine the campaigns have been conducted for a period of time and still have a period of time before they are finished. Thus, the managing schedule module may import performance data for the campaign(s) automatically and may revise the campaign(s), forecast, roster template, and/or schedule accordingly).
While Aykin does teach a responsive action to demand. Aykin does not appear to explicitly teach: responsive to determining that the start time is within a predefined timeframe of a predicted demand rise time, However, Aykin in view of the analogous art of Noble (i.e. scheduling) does teach the entirety of the limitation: (Noble, [co. 26]; this forecasted information may include a predicted number of communications that are to occur for each time interval, a forecasted number of staff that is to work each time interval, and/or a forecasted average handle time for each communication that occurs in each time interval… the planned performance information may include a threshold value for the number of communications that are to occur for each time interval of a shift for the outbound campaign. One of ordinary skill in the art can envision other information that may be included in the planned performance information in light of this disclosure… as a result of the monitoring module conducting a comparison between the actual performance information and the planned performance information and applying the defined rules, the monitoring module determines whether a change in staffing requirements with respect to the outbound campaign has occurred in Step 504)... FIG. 6 lists the planned performance information for the outbound campaign as the number of forecasted outbound calls to be placed 601, the forecasted average handle time (AHT) 602 in minutes, the number of forecasted staff members 603, and the number of staff members actually scheduled 604 for every one-hour time interval of the eight-hour shift. Further, the table lists the actual performance information for the outbound campaign as the actual number of outbound calls 605, the actual AHT 606, and the actual number of staff members 607 for the first six one-hour time intervals of the eight-hour shift. In this particular instance, the remaining two one-hour intervals have yet to occur for the shift).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Aykin including a responsive action to demand with the teachings of Noble including determining an end time is within a predefined demand change in order to have adequate staffing to ensure jobs can be performed and save costs (Noble, [co. 27 li. 51-59]; monitoring the actual performance of the outbound campaign may not only identify changes in staffing requirements that may need to be addressed by adding staff so that goals for the campaign may be reached, but may also identify changes in staffing requirements that signal reducing staff as a result of better-than-forecasted performance, which can lead to cost savings for the call center).
Regarding Claims 6 and 13, Aykin teaches: The method of claim 1, wherein identifying the subset of the plurality of variables further comprises: identifying a subset of shifts of the historic schedule; and (Aykin, [co. 3-4, li. 60-8] and Aykin, [co. 6, li. 39-48] and Aykin, [co. 15, li. 3-15]; citations provided above).
append, to the subset of variables, one or more variables associated with at least one of; (Aykin, co. 10, l. 57-62]; Another extension of MILP model (1-8) involves agent scheduling when there aren't enough agents to meet the agent requirements in every period to be scheduled. In this case, objective function (1), and constraint (2) are modified by adding overstaffing and understaffing variables).
While Aykin does teach start and end times of shifts. Aykin does not appear to teach determining start/end times for each of historic shift. However, Aykin in view of the analogous art of Noble (i.e. scheduling) does teach: for each shift of the subset of shifts, identify an end time of the shift; (Noble, [co. 21, li. 40-46]; the managing schedule module may be configured to look at the start and end times (e.g., dates) for the campaigns and determine the campaigns have been conducted for a period of time and still have a period of time before they are finished. Thus, the managing schedule module may import performance data for the campaign(s) automatically and may revise the campaign(s), forecast, roster template, and/or schedule accordingly).
While Aykin does teach a responsive action to demand. Aykin does not appear to explicitly teach: responsive to determining that the end time is within a predefined timeframe of a predicted demand drop time, However, Aykin in view of the analogous art of Noble (i.e. scheduling) does teach the entirety of the limitation: (Noble, [co. 26]; this forecasted information may include a predicted number of communications that are to occur for each time interval, a forecasted number of staff that is to work each time interval, and/or a forecasted average handle time for each communication that occurs in each time interval… the planned performance information may include a threshold value for the number of communications that are to occur for each time interval of a shift for the outbound campaign. One of ordinary skill in the art can envision other information that may be included in the planned performance information in light of this disclosure… as a result of the monitoring module conducting a comparison between the actual performance information and the planned performance information and applying the defined rules, the monitoring module determines whether a change in staffing requirements with respect to the outbound campaign has occurred in Step 504)... FIG. 6 lists the planned performance information for the outbound campaign as the number of forecasted outbound calls to be placed 601, the forecasted average handle time (AHT) 602 in minutes, the number of forecasted staff members 603, and the number of staff members actually scheduled 604 for every one-hour time interval of the eight-hour shift. Further, the table lists the actual performance information for the outbound campaign as the actual number of outbound calls 605, the actual AHT 606, and the actual number of staff members 607 for the first six one-hour time intervals of the eight-hour shift. In this particular instance, the remaining two one-hour intervals have yet to occur for the shift).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Aykin including a responsive action to demand with the teachings of Noble including determining an end time is within a predefined demand change in order to have adequate staffing to ensure jobs can be performed and save costs (Noble, [co. 27 li. 51-59]; monitoring the actual performance of the outbound campaign may not only identify changes in staffing requirements that may need to be addressed by adding staff so that goals for the campaign may be reached, but may also identify changes in staffing requirements that signal reducing staff as a result of better-than-forecasted performance, which can lead to cost savings for the call center).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JEREMY L GUNN/ Examiner, Art Unit 3624
Prosecution Insights