DETAILED ACTION
Status of the Application
The following is a Final Office Action. In response to Examiner's communication of February 5, 2026, Applicant, on May 1, 2026, amended claims 1 & 11. Claims 1-20 are now pending in this application and have been rejected below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Amendment
Applicant's amendments are not sufficient to overcome the 35 USC 101 rejection set forth in the previous action. Therefore, these rejections are maintained below.
Applicant's amendments render moot the 35 USC 103 rejection set forth in the previous action. Therefore, new grounds for rejection necessitated by Applicant’s amendment are set forth below.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 have been fully considered, but they are not persuasive.
Applicant argues that the claims do not recite an abstract idea under Prong 1 of Step 2A because the claims are necessarily rooted in computer technology to overcome a problem specifically arising in the graphical user interfaces and improve graphical user interfaces by specifically reciting a system that provides an input interface for receiving optimization input data from a user and using the input along with optimization models to generate shift schedules presented by a schedule output interface, the claim features do not fall into the methods of organizing human activity grouping of abstract ideas since the claimed features related to receiving optimization input data and presenting generated shift schedules via different user interfaces do not fall into the interactions associated with methods of organizing human activity, and the claim features do not fall in the mental process groupings of abstract ideas since the claimed features related to receiving optimization input data and presenting generated shift schedules via different user interfaces cannot be performed by the human mind.. Examiner respectfully disagrees.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
Here, under Prong 1 of Step 2A, claim 1, and similarly claims 2-20), in view of the first prong of Step 2A, recite “optimizing labor resources at a facility, … comprising: … a first optimization model and a second optimization model, wherein the first optimization model when executed determines optimal shift patterns for full-time employee schedules and fixed part-time employee schedules over a period of time, and wherein the second optimization model when executed determines headcounts and variable part-time shifts; … store resource data used in determining forecasted demand; … calculate an average forecasted demand for each day of a week based on the resource data; generate a one-week demand estimate based on the calculated average forecasted demand; receive optimization inputs relating to scheduling constraints for full-time shifts, fixed part-time shifts, and variable part-time shifts, wherein the optimization inputs are received …; generate, by executing the first optimization model using the one-week demand estimate and the optimization inputs, the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules; and generate, by executing the second optimization model using the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules from the first optimization model and the optimization inputs, headcounts and the variable part-time shifts; and generate a shift schedule comprising the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules, the headcounts, and the variable part-time shifts; and causing the shift schedule to be displayed ….” Claims 1-20, in view of the claim limitations, recite the abstract idea of optimizing labor resources at a facility by collecting resource data used to determine forecast demand, calculating and generating a forecasted demand, receiving full, part, and variable time shift constraints, generating optimal full and part time shift patterns, headcounts, and variable time shift by executing a first and second model, generating a shift schedule based on the full and part time shift patterns, headcounts, and variable time shift, and outputting the shift schedule.
As a whole, each of these aforementioned limitations manage the personal human behavior of based on personal human behavior and sales activity by optimizing labor including generating employee shift patterns, schedules and headcounts based on forecasted demand and employee shift constraints, and thus, the claims recite to a certain method of organizing human activity. Further, as a whole, in view of the claim limitations, but for the generic computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited collecting resource data used to determine forecast demand, calculating and generating a forecasted demand, receiving full, part, and variable time shift constraints, generating optimal full and part time shift patterns, headcounts, and variable time shift by executing a first and second model, generating a shift schedule based on the full and part time shift patterns, headcounts, and variable time shift, and outputting the shift schedule could all be reasonably interpreted as a human observing information regarding a demand for resources and shift constraints, a human performing an evaluation of the observed information to calculate and generate forecasted demand, a human performing an evaluation to execute models and determine the patterns, schedules, and headcounts, and a human outputting the result manually and/or with pen and paper; therefore, the claims recite mental processes.
As noted above, under Prong 1 of Step 2A, claims recite an abstract idea if they recite any limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas. That is, the generically recite computer components of the generic first user interface for input and generic second user interface for output referred to by Applicant do not preclude the claims from reciting an abstract idea, and under Prong 1 of Step 2A, the claims recite an abstract if they recite limitations that fall within one of the enumerated groupings of abstract ideas.
Specifically with respect to the alleged improvements asserted by Applicant, contrary to Applicant’s assertions, merely implementing an abstract idea with the recited generic first user interface for input and generic second user interface for output referred to by Applicant does not make the claims directed to an improvement in computer technology or other technology. “[A]n improvement in the abstract idea itself (e.g., a recited fundamental economic concept) is not an improvement in technology.” MPEP 2106.05(a). Mere automation of manual processes is not an improvement to computer technology. Id.
As detailed below when addressing Applicant’s assertion that the claims do not recite a certain method of organizing human activity nor a mental process, the claims do indeed recite a certain method of organizing human activity and a mental process and merely implementing that abstract idea with a generic first and second user interface is nothing more than applying the abstract idea with a generic computer component, which is not an improvement in computer technology. The alleged improvement of “a system that provides an input interface for receiving optimization input data from a user and using the input along with optimization models to generate shift schedules presented by a schedule output interface” recites an abstract idea because, in this alleged improvement, the received optimization inputs are constraints for employee shift schedules, the optimization models are used to generate shift schedules for employees, and the presented schedule is a schedule of employees and all these elements manage human behavior of employees, and thus, the alleged improvement recites a certain method of organizing human activity, which is an abstract idea. Furthermore, but for the generic user interfaces for input and output in this alleged improvement, a human can observe information regarding input optimization parameters to receive optimization input, a human can use judgment and perform evaluations based on the observed information and optimization models to generate a shift schedule, and a human can output the results of the evaluation manually and/or with a pen and paper, and thus, the alleged improvement also recites a mental process, which is also an abstract idea. With respect to the “input interface” and “output interface” referred to by Applicant in the alleged improvement, these elements amount to nothing more than implementing the abstract idea using generic computer components, which is not an improvement to technology nor otherwise sufficient to transform an abstract idea into a patent-eligible invention.
Moreover, as in the claims at issue in Electric Power Group, the present claims are not focused on a specific improvement in computers or any other technology, but instead on certain independently abstract ideas that simply invokes computers as tools to implement the abstract idea. Electric Power Group, LLC v. Alstom S.A., et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a).
Regarding Applicant’s assertions the claims do not fall into the methods of organizing human activity grouping of abstract ideas, a claim recites certain methods of organizing human activity when the claim recites fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). 84 Fed. Reg. at 52. Here, ss a whole, each of the aforementioned limitations manage the personal human behavior of based on personal human behavior and sales activity by optimizing labor including generating employee shift patterns, schedules and headcounts based on forecasted demand and employee shift constraints. Thus, contrary to Applicant’s assertions, the claims recite to a certain method of organizing human activity.
Further, regarding Applicant’s assertions that the claims do not fall in the mental process groupings of abstract ideas, a claim recites mental processes when the claim recites concepts performed in the human mind (including an observation, evaluation, judgment, opinion), wherein if the claim, under its broadest reasonable interpretation, covers the claim being practically performed in the mind but for the recitation of generic computer components, then the claim is in the mental process category. 84 Fed. Reg. 52 n.14. Here, as a whole, in view of the claim limitations, but for the generic computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited collecting resource data used to determine forecast demand, calculating and generating a forecasted demand, receiving full, part, and variable time shift constraints, generating optimal full and part time shift patterns, headcounts, and variable time shift by executing a first and second model, generating a shift schedule based on the full and part time shift patterns, headcounts, and variable time shift, and outputting the shift schedule could all be reasonably interpreted as a human observing information regarding a demand for resources and shift constraints, a human performing an evaluation of the observed information to calculate and generate forecasted demand, a human performing an evaluation to execute models and determine the patterns, schedules, and headcounts, and a human outputting the result manually and/or with pen and paper. Therefore, despite Applicant’s assertions, the claims recite mental processes.
Accordingly, since the claims recite mental processes and certain methods of organizing human activity, the claims recite an abstract idea under the first prong of Step 2A.
Applicant argues that “even if it is still determined that claim 1 recites an abstract idea in light of the amendments and arguments above (which is disputed herein by the Applicant), the alleged abstract idea is integrated into a practical application pursuant to step 2A prong 2” because the claims integrate “the alleged abstract idea into the particular technological environment of scheduling user interface systems,” the claims recite “receiving optimization patterns [sic] from a user from an input user interface,” upon “finalization of the user-provided parameters, the system processes data through a series of optimization models to optimize full-time, fixed part-time, and variable part-time schedules, and finally causes the shift schedule to be displayed on an output interface,” and the claims, as amended, recite “features that firmly link the claimed invention to the particular technological environment of scheduling user interface systems.” Examiner respectfully disagrees.
As noted above, under Prong 2 of Step 2A, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. However, aside for the generic system and input and output user interfaces, the features referred to by Applicant are not additional elements beyond the recited abstract idea, but rather, they part of and directed to the recited abstract idea because, as detailed above, the scheduling, receiving optimization parameters, processing data through a series of optimization models to optimize full-time, fixed part-time, and variable part-time schedules, and causing the shift schedule to be displayed referred to by Applicant all manage the personal human behavior of employees by managing and scheduling their shifts, and thus, is a certain method of organizing human activity. Further, a human can mentally receive optimization parameters by observing the parameters, a human can mentally process the data through the optimization models to optimize the shift schedules by performing evaluations using the observed data and the models, and a human can output the resulting schedule with a pen and paper, and thus, the elements referred to by Applicant include a mental process. The system, the input user interface, and the output user interface amount to nothing more than applying an abstract idea using generic computer components, which is not sufficient to integrate an abstract idea into a practical application.
In the interest of providing a comprehensive response regarding Prong 2 of Step 2A, the claims recite the additional elements beyond the recited abstract idea of “[a] system …, the system comprising: a memory configured to store …; one or more databases configure to store …,” “via a first user interface of an application executed on an electronic device,” and “on a second user interface of the application executed on the electronic device” in claim 1, and similarly claim 11; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components, which is not sufficient to integrate an abstract idea into a practical application. Further, these elements merely generally link the abstract idea to a field of use/technological environment, namely a generic computing environment, which is also not sufficient to integrate an abstract idea into a practical application.
Applicant argues “that, even if it is still determined that claim 1 recites an abstract idea in light of the amendments and arguments above (which is disputed herein by the Applicant), the additional elements of claim 1 amount to significantly more than the alleged abstract idea,” the claims recite “receiving optimization patterns from a user from an input user interface,” upon “finalization of the user-provided parameters, the system processes data through a series of optimization models to optimize full- time, fixed part-time, and variable part-time schedules, and finally causes the shift schedule to be displayed on an output interface,” “Applicant contends that these features furnish an inventive concept that amounts to significantly more than the alleged judicial exceptions of methods of organizing human activities or mental processes,” and for “at least these reasons, Applicant respectfully submits that claim[s] recite[] significantly more than the alleged abstract idea and is patent-eligible under step 2B.” Examiner respectfully disagrees.
As noted above, under Step 2B, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, are significantly more than the abstract idea itself. However, like in Applicant’s argument regarding Prong 2 of Step 2A, aside for the generic system and input and output user interfaces, the features referred to by Applicant are not additional elements beyond the recited abstract idea, but rather, they part of and directed to the recited abstract idea because, as detailed above, scheduling, receiving optimization parameters, processing data through a series of optimization models to optimize full-time, fixed part-time, and variable part-time schedules, and causing the shift schedule to be displayed referred to by Applicant all manage the personal human behavior of employees by managing and scheduling their shifts, and thus, is a certain method of organizing human activity. Further, a human can mentally receive optimization parameters by observing the parameters, a human can mentally process the data through the optimization models to optimize the shift schedules by performing evaluations using the observed data and the models, and a human can output the resulting schedule with a pen and paper, and thus, the elements referred to by Applicant include a mental process. The system, the input user interface, and the output user interface amount to nothing more than applying an abstract idea using generic computer components, which is not sufficient to be significantly more than the abstract idea itself.
In the interest of providing a comprehensive response regarding Prong 2 of Step 2A, the claims recite the additional elements beyond the recited abstract idea of “[a] system …, the system comprising: a memory configured to store …; one or more databases configure to store …,” “via a first user interface of an application executed on an electronic device,” and “on a second user interface of the application executed on the electronic device” in claim 1, and similarly claim 11; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components, which is not sufficient to be significantly more than the abstract idea itself. Further, these elements merely generally link the abstract idea to a field of use/technological environment, namely a generic computing environment, which is also not sufficient to be significantly more than the abstract idea itself.
Response to Arguments - 35 USC § 103
Applicant’s arguments with respect to the prior art rejections have been fully considered, but they are now moot in view of new grounds for rejection necessitated by Applicant’s amendments.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims (claim 1, and similarly claims 2-20), in view of the first prong of Step 2A, recite “optimizing labor resources at a facility, … comprising: … a first optimization model and a second optimization model, wherein the first optimization model when executed determines optimal shift patterns for full-time employee schedules and fixed part-time employee schedules over a period of time, and wherein the second optimization model when executed determines headcounts and variable part-time shifts; … store resource data used in determining forecasted demand; … calculate an average forecasted demand for each day of a week based on the resource data; generate a one-week demand estimate based on the calculated average forecasted demand; receive optimization inputs relating to scheduling constraints for full-time shifts, fixed part-time shifts, and variable part-time shifts, wherein the optimization inputs are received …; generate, by executing the first optimization model using the one-week demand estimate and the optimization inputs, the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules; and generate, by executing the second optimization model using the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules from the first optimization model and the optimization inputs, headcounts and the variable part-time shifts; and generate a shift schedule comprising the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules, the headcounts, and the variable part-time shifts; and causing the shift schedule to be displayed ….” Claims 1-20, in view of the claim limitations, recite the abstract idea of optimizing labor resources at a facility by collecting resource data used to determine forecast demand, calculating and generating a forecasted demand, receiving full, part, and variable time shift constraints, generating optimal full and part time shift patterns, headcounts, and variable time shift by executing a first and second model, generating a shift schedule based on the full and part time shift patterns, headcounts, and variable time shift, and outputting the shift schedule.
As a whole, each of these limitations are directed to managing the personal human behavior based on personal human behavior and sales activity by optimizing labor including generating employee shift patterns, schedules and headcounts based on forecasted demand and employee shift constraints, and thus, the claims recite to a certain method of organizing human activity. Further, as a whole, in view of the claim limitations, but for the generic computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited collecting resource data used to determine forecast demand, calculating and generating a forecasted demand, receiving full, part, and variable time shift constraints, generating optimal full and part time shift patterns, headcounts, and variable time shift by executing a first and second model, generating a shift schedule based on the full and part time shift patterns, headcounts, and variable time shift, and outputting the shift schedule could all be reasonably interpreted as a human observing information regarding a demand for resources and shift constraints, a human performing an evaluation of the observed information to calculate and generate forecasted demand, a human performing an evaluation to execute models and determine the patterns, schedules, and headcounts, and a human outputting the result manually and/or with pen and paper; therefore, the claims recite mental processes. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2-10 & 12-20 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the generic computer components and systems performing the claimed functions, the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper and certain methods of organizing human activity that manages human behavior. Accordingly, since the claims recite mental processes and certain methods of organizing human activity, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] system …, the system comprising: a memory configured to store …; one or more databases configure to store …,” “via a first user interface of an application executed on an electronic device,” and “on a second user interface of the application executed on the electronic device” in claim 1 and “by a control circuit communicatively coupled to a memory and one or more databases,” “stored in the one or more databases,” “via a first user interface of an application executed on an electronic device,” and “on a second user interface of the application executed on the electronic device” in claim 11; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Further, these elements merely generally link the abstract idea to a field of use/technological environment, namely a generic computing environment. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-10 & 12-20 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e., apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0136]-[0137] (describing the circuits, circuitry, systems, devices, processes, methods, techniques, functionality, services, servers, sources and the like described herein may be utilized, implemented and/or run on many different types of devices and/or systems., and by way of example, the system may comprise a processor module, memory, and one or more communication links). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, electronic record keeping, storing and retrieving information in memory, and presenting offers, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-10 & 12-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-6, 9, 11-16, & 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kiran, et al. (US 20050240465 A1), hereinafter Kiran, in view of Putcha, et al. (US 20190295204 A1), hereinafter Putcha, in further view of Ogawa, et al. (US 20250078102 A1), hereinafter Ogawa.
Regarding Claim 1, Kiran discloses a system for optimizing labor resources at a facility, the system comprising ([0095]-[0100], fig. 10):
a memory configured to store ([0005], [0095]-[0100], fig. 10) a first optimization model and a second optimization model, wherein the first optimization model when executed determines optimal shift patterns for full-time employee schedules and fixed part-time employee schedules over a period of time, and wherein the second optimization model when executed determines headcounts ([0091], in the optimization process, the workforce requirements management system employs an integer linear programming ("ILP") model divided into two models, wherein in the first model, the requirement model, the optimum number and type of the resource schedule templates are determined (i.e., optimal shift patterns for full-time employee schedules and fixed part-time employee schedules) to cover the FTE requirements of the organization for transaction volume, and in the second model, the planning model, the number of resources (i.e., headcounts) of each type (e.g., full time, part time, etc.) (i.e., full-time and part-time employee) and their weekly working schedules are determined based on human resource management rules, [0086]-[0087], in step 510 the system then executes the FTE requirement optimization model (per location) in order to in step 515 identify the number of required resources with different shift templates (i.e., optimal shift patterns for full-time employee schedules and fixed part-time employee schedules), step 520 executes the planning optimization model (per location) to determine in step 525 the number of full time equivalents and part time equivalents needed to meet the forecasted transaction (i.e., headcounts), and step 530 determines an optimized FTE and PTE work schedule (per location) that presents the most efficient combination of FTE and PTE employees to meet the forecast transaction) …;
one or more databases configure to store resource data ([0020]-[0021], workforce requirements management according to an embodiment of the present invention comprises a resource planning server 20 configured with a data storage area 30 implemented as a conventional) used in determining forecasted demand ([0024]-[0025], forecast module 100 analyzes historical transaction data stored in the data storage area 30 and create a forecast of transaction volume for a particular location);
a control circuit communicatively coupled to the memory and the one or more databases, the control circuit configured to:
calculate an average forecasted demand for each day of a week based on the resource data ([0039], [0041], step 250, where the workforce requirements management system calculates the total daily transaction volume per transaction type, to calculate the total daily transaction volume, historical transaction data, in step 270 the system defines an appropriate function that represents the particular daily plot for the transaction level, in step 275 the forecasted daily transaction volumes for the next month is calculated);
generate a … demand estimate based on the calculated average forecasted demand ([0039], [0042], in the process for forecasting transaction volume levels according to an embodiment of the present invention, step 280 forecasts the volume per location per time interval is by dividing the forecasted daily transaction volume is divided into time intervals (i.e., based on the calculated average for each date), [0031], [0033], [0038], when determining the FTE requirements, the system uses the enhanced forecast of transaction volumes, wherein in step 200, the transaction volume per transaction type per time interval per location is forecasted (i.e., based on the calculated average), in step 210 an enhanced forecast (per location or per transaction per location) is created, and step 220 determines FTE requirements to meet the targeted service level);
receive optimization inputs relating to scheduling constraints for full-time shifts, fixed part-time shifts, and …, wherein the optimization inputs are received … of an application executed on an electronic device ([0024], [0029], the resource planning server 20 comprises a queuing module 130 configured to analyze predefined target service levels, the correlated transaction and service activity times, and the enhanced transaction volume per location to create full time equivalent requirements that identifies the FTE requirements for each position at a particular location, [0038], when the FTE requirements are optimized, the system utilizes the forecasted FTE requirements, human resource constraints, cost considerations to determine the optimum number of resources required, the optimum full-time and part-time mix of resources, [0085]-[0086], Fig. 9, in process for optimizing staff levels and work schedules the process begins at step 500 where a representative full time equivalent profile is determined for a particular location (or for each location if there are multiple), wherein data that is analyzed in this step in order to make the determination includes the FTE requirements for each time interval (and for each location, as appropriate), step 510 executes the FTE requirement optimization model (per location) in order to identify the number of required resources with different shift templates in step 515, wherein data that is utilized in the execution of the FTE requirement model in step 510 includes the resource requirement profile and predetermined shift templates that are determined based upon human resource data);
generate, by executing the first optimization model using the … demand estimate and the optimization inputs, the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules ([0091], in the optimization process, the workforce requirements management system employs an integer linear programming ("ILP") model divided into two models, wherein in the first model, the requirement model, the optimum number and type of the resource schedule templates are determined (i.e., optimal shift patterns for full-time employee schedules and fixed part-time employee schedules) to cover the FTE requirements of the organization for transaction volume (i.e., using the demand output), wherein resources of each type are, e.g., full time, part time, etc., [0085]-[0087], after a representative FTE profile is determined in 500 by analyzing the FTE requirements (i.e., using the demand output), in step 510 the system then executes the FTE requirement optimization model (per location) in order to in step 515 identify the number of required resources with different shift templates, optimal shift patterns for full-time employee schedules and fixed part-time employee schedules), and in the next step, the planning optimization model (per location) is executed to meet the forecasted transaction); and
generate, by executing the second optimization model using the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules from the first optimization model and the optimization inputs, headcounts and the … shifts ([0091], in the optimization process, the workforce requirements management system employs an integer linear programming ("ILP") model divided into two models, wherein in the second model, the planning model, the number of resources (i.e., headcounts) of each type (e.g., full time, part time, etc.) (i.e., full-time and part-time employee) and their weekly working schedules are determined based on human resource management rules, [0086]-[0087], step 520 executes the planning optimization model (per location) to determine in step 525 the number of full time equivalents and part time equivalents needed to meet the forecasted transaction (i.e., headcounts), and step 530 determines an optimized FTE and PTE work schedule (per location) that presents the most efficient combination of FTE and PTE employees to meet the forecast transaction); and
generate a shift schedule comprising the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules, the headcounts, and the variable part-time shifts ([0087], step 530 determines an optimized FTE and PTE work schedule (per location) that presents the most efficient combination of FTE and PTE employees to meet the forecast transaction); and
causing the shift schedule to be [output on] (Abstract, outputs generated by the workforce requirements management system include a resource forecast, a resource plan, and a resource schedule, [0087], in step 530 the system determines an appropriate and optimized FTE and PTE work schedule (per location) that presents the most efficient combination of FTE and PTE employees to meet the forecast transaction) the application executed on the electronic device ([0095]-[0100], fig. 10).
While Kiran discloses all of the above, including a memory configured to store ([0005], [0095]-[0100], fig. 10) a first optimization model and a second optimization model, wherein the first optimization model when executed determines optimal shift patterns for full-time employee schedules and fixed part-time employee schedules over a period of time, and wherein the second optimization model when executed determines headcounts …; …
generate a … demand estimate based on the calculated average forecasted demand;
receive optimization inputs relating to scheduling constraints for full-time shifts, fixed part-time shifts, and …, wherein the optimization inputs are received … of an application executed on an electronic device;
generate, by executing the first optimization model using the … demand estimate and the optimization inputs, the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules; and
generate, by executing the second optimization model using the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules from the first optimization model and the optimization inputs, headcounts and the … shifts; and
generate a shift schedule comprising the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules, the headcounts, and the variable part-time shifts; and
causing the shift schedule to be [output on] the application executed on the electronic device (as above), Kiran does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Putcha.
Putcha teaches an optimization model when executed determines headcounts and variable part-time shifts ([0017], the transportation staffing system takes as input a number of hours of demand and generates an optimal number of full time, part time for each forecasted day of demand, [0034], [0037], fig. 2, at step 208, the staffing module 130 generates an optimal transportation workload for the distribution center to deliver inventory to the stores, wherein driver and vehicle assignment may take place, wherein an example, the transportation staffing system generates an optimal number of transportation resources, the transportation staffing system may also calculate the number of flex or part-time drivers, [0048], at step 210, the user interface module 140 generates a user interface displaying the optimal transportation workload, e.g., in fig. 7, [0076], fig. 7 depicts the optimal workload generated by the transportation staffing system including the number of drivers per program, number of full-time drivers, number of part-time drivers, flex time drivers);
generate a one-week demand estimate … ([0022], [0032], the forecast module 110 may forecast the amount of transportation miles needed to deliver inventory from the distribution center to stores serviced by the distribution center based on a sales forecast for a defined time period, [0075], the number of weeks within a period can change);
receive optimization inputs relating to scheduling constraints for full-time shifts, fixed part-time shifts, and variable part-time shifts, wherein the optimization inputs are received via … of an application executed on an electronic device ([0016]-[0017], the transportation staffing system described herein generates transportation workload based on driver staffing requirements and constraints for each store served by a distribution center that optimizes at least the following: vehicle utilization, driver utilization and cost, wherein the transportation staffing system into consideration part-time drivers and third-party/contract drivers to help fulfill peak or variable transportation demands, and takes as input a daily number of hours of demand to generate a number of full time, part time and third party drivers for each day, [0037], the transportation staffing system generates an optimal number of transportation resources from a private fleet and an optimal number of transportation resources from a third-party fleet, the number of flex or part-time drivers, taking into consideration the percentage of full-time drivers that are on paid-time-off (PTO) or leave-of-absence (LOA) for a distribution center, the minimum and maximum mileage constraints for flex or part-time drivers, and the number of flex or part-time drivers needed to maintain a certain percentage buffer (e.g., 3% buffer), [0045], constraints are built into the mixed integer programming model organized into groups, e.g., the transportation staffing scheduled cannot include more than 20% of 5-day drivers, [0074], the optimal workload in miles is determined based budget constraints and financial profitability);
generate, by executing the … optimization model using the one-week demand estimate and the optimization inputs, the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules; and
generate, by executing the … optimization model using the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules from the first optimization model and the optimization inputs, headcounts and the variable part-time shifts ([0017], the transportation staffing system takes as input a number of hours of demand and generates an optimal number of full time, part time for each forecasted day of demand, [0029], [0034], [0037], fig. 2, in a method 200 for generating a transportation staffing schedule, at step 208, the staffing module 130 generates an optimal transportation workload for the distribution center to deliver inventory to the stores, and then, driver and vehicle assignment may take place, wherein an example, the transportation staffing system generates an optimal number of transportation resources, the transportation staffing system may also calculate the number of flex or part-time drivers, [0048], at step 210, the user interface module 140 generates a user interface displaying the optimal transportation workload, e.g., in figs. 7-9, [0076], fig. 7 depicts the optimal workload generated by the transportation staffing system including the number of drivers per program, number of full-time drivers, number of part-time drivers, flex time drivers, and [0078], fig. 9, displays a daily view of an optimal workload and optimal number of drivers for a week (e.g., week 7)); and
generate a shift schedule comprising the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules, the headcounts, and the variable part-time shifts ([0029], [0034], [0037], fig. 2, in a method 200 for generating a transportation staffing schedule, at step 208, the staffing module 130 generates an optimal transportation workload for the distribution center to deliver inventory to the stores, and then, driver and vehicle assignment may take place, wherein an example, the transportation staffing system generates an optimal number of transportation resources, the transportation staffing system may also calculate the number of flex or part-time drivers, [0076], fig. 7 depicts the optimal workload generated by the transportation staffing system including the number of drivers per program, number of full-time drivers, number of part-time drivers, flex time drivers); and
causing the shift schedule to be displayed on a second user interface of the application executed on the electronic device. ([0048], at step 210, the user interface module 140 generates a user interface displaying the optimal transportation workload, e.g., described in connection with FIGS. 7, 8 and 9, [0076], fig. 7 depicts the optimal workload generated by the transportation staffing system including the number of drivers per program, number of full-time drivers, number of part-time drivers, flex time drivers, and [0078], fig. 9, displays a daily view of an optimal workload and optimal number of drivers for a week (e.g., week 7)).
Kiran and Putcha are analogous fields of invention because both address the problem of determining optimal staffing plans. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Kiran the ability to generate a one-week demand estimate, execute an optimization model using the one-week demand estimate to output the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules, the headcounts, and the variable part-time shifts, and display staffing recommendation levels, as taught by Putcha, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of generating a one-week demand estimate, executing a first and second optimization model using the one-week demand estimate to output the optimal shift patterns for the full-time employee schedules and the fixed part-time employee schedules, the headcounts, and the variable part-time shifts, and displaying staffing recommendation levels, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Kiran with the aforementioned teachings of Putcha in order to produce the added benefit of addressing variable demand and work schedules, helping fulfill peak or variable transportation demands reducing the risk of under-utilized workers. [0002], [0016], [0073].
Further, while the combined teachings of Kiran and Putcha teach receive optimization inputs relating to scheduling constraints for full-time shifts, fixed part-time shifts, and variable part-time shifts, wherein the optimization inputs are received via … of an application executed on an electronic device (as above), Kiran and Putcha do not teach receiving such optimization inputs are received via a first user interface, which however, is taught by further teachings in Ogawa.
Ogawa teaches receive optimization inputs relating to scheduling constraints for shifts, wherein the optimization inputs are received via a first user interface of an application executed on an electronic device ([0083], [0087], Fig. 5 is a diagram illustrating an example of an indicator list screen 200 for a certain optimization template 5 for an optimization template 5 for a work shift of a part-time job at a certain store, which includes a constraint list 220 including a plurality of constraints and each constraint is provided for each item to be optimized, that is, each indicator of an optimization problem to be solved, e.g., an operating cost such as an employee allowance, total working hours of all employees, an attendance preference achievement rate of employees, a customer satisfaction expectation value, and an expected number of customers in a case of a retail store, [0112], [0116], FIG. 11 is a diagram illustrating an example of an import screen 500, example of a data file imported in FIG. 11 when an optimization template 5 for work shift optimization (part-time job management) is selected include shift record data 510 (FIG. 12(a)), vacation/work preference data 512 (FIG. 12(b)), and an employee master 514 (FIG. 12(c))).
Kiran and Ogawa are analogous fields of invention because both address the problem of determining optimal staffing plans. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Kiran the ability to receive optimization inputs relating to scheduling constraints for shifts via a first user interface of an application executed on an electronic device, as taught by Ogawa, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of receiving optimization inputs relating to scheduling constraints for shifts via a first user interface of an application executed on an electronic device, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Kiran with the aforementioned teachings of Ogawa in order to produce the added benefit of efficiently and effectively solve a complex optimization problem. [0015].
Regarding claim 2, the combined teachings of Kiran, Putcha, and Ogawa teach the system of claim 1 (as above). Further, while Kiran discloses all of the above and the facility ([0018], embodiments manage workforce requirements in a service industry organization by identifying the workforce (both FTE and PTE) requirements for the location and create a future workforce plan for the location), Kiran does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Putcha.
Putcha teaches wherein the facility comprises a retail store, a fulfillment center, and a distribution center ([0022], [0032], the forecast module 110 may forecast the amount of transportation needed to deliver inventory from the distribution center to stores serviced by the distribution center).
Kiran and Putcha are analogous fields of invention because both address the problem of determining optimal staffing plans. Further, it would have been obvious to one of ordinary skill in the art to have modified Kiran with the aforementioned teachings of Putcha in order to produce the added benefit of addressing variable demand and work schedules, helping fulfill peak or variable transportation demands reducing the risk of under-utilized workers. [0002], [0016], [0073].
Regarding claim 3, the combined teachings of Kiran, Putcha, and Ogawa teach the system of claim 1 (as above). Further, Kiran discloses all of the above and wherein the first optimization model comprises a set of valid full-time shifts, a set of valid fixed part-time shifts, and a set of day within a week ([0091], in the first model, the requirement model, the optimum number and type of the resource schedule templates are determined, wherein these schedules are optimized to ensure that there are enough employee resources to cover the FTE requirements of the organization for transaction volume, and the resources of each type are e.g., full time, part time, etc., [0042]-[0043], [0045] step 280 forecasts the volume per location per time interval is by dividing the forecasted daily transaction volume is divided into time intervals, wherein in an example deployment of the above described process, for each (1) day of the week; (2) transaction type; and (3) location, the total daily transaction levels are plotted for each day of the month to determine the total transaction volume forecast for the particular day, [0090], the above described process determines the best full time and part time resource mix and schedule those resources to fit the intra-day and intra-week fluctuations identified by a queuing model).
Regarding claim 4, the combined teachings of Kiran, Putcha, and Ogawa teach the system of claim 3 (as above). Further, Kiran discloses all of the above and wherein the second optimization model comprises the set of valid full-time shifts, the set of valid fixed part-time shifts, and the set of day within a week ([0087], [0091], after the planning model in step 520, step 530 determines an appropriate and optimized FTE and PTE work schedule with the most efficient combination of FTE and PTE employees to meet the forecast transaction, wherein in the optimization process above, in the second model, the planning model, the number of resources of each type (e.g., full time, part time, etc.) and their weekly working schedules are determined based on human resource management rules, [0042]-[0043], [0045] step 280 forecasts the volume per location per time interval is by dividing the forecasted daily transaction volume is divided into time intervals, wherein in an example deployment of the above described process, for each (1) day of the week; (2) transaction type; and (3) location, the total daily transaction levels are plotted for each day of the month to determine the total transaction volume forecast for the particular day, [0090], the above described process determines the best full time and part time resource mix and schedule those resources to fit the intra-day and intra-week fluctuations identified by a queuing model).
Regarding claim 5, the combined teachings of Kiran, Putcha, and Ogawa teach the system of claim 1 (as above). Further, while Kiran discloses all of the above and wherein the first optimization model defines a first set of parameters, first decision variables, and first …, and wherein the second optimization model defines a second set of parameters, second decision variables, and second constraints ([0086]-[0087], in step 510 the system then executes the FTE requirement optimization model (per location) (i.e., first model) in order to identify the number of required resources (i.e., first decision variables) with different shift templates in step 515 using data including the resource requirement profile and predetermined shift templates that are determined based upon human resource data (i.e., first parameters), step 520 executes the planning optimization model (per location) (i.e., second model) to determine in step 525 the number of full time equivalents and part time equivalents (i.e., second decision variables) needed to meet the forecasted transaction using data including the different shift templates and cost coefficients derived from human resource data (i.e., second parameters) and employee work schedule constraints derived from human resource data (i.e., second constraints), [0091], in the optimization process, the workforce requirements management system employs an integer linear programming ("ILP") model divided into two models, wherein in the second model, the planning model, the number of resources (i.e., headcounts) of each type (e.g., full time, part time, etc.) (i.e., full-time and part-time employee) and their weekly working schedules are determined based on human resource management rules), Kiran does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Putcha.
Putcha teaches the optimization model defines a … set of parameters, … decision variables, and … constraints ([0037]-[0045], the transportation staffing system generates an optimal number of resources and the number of flex or part-time drivers considers the minimum and maximum mileage constraints for flex or part-time drivers, and the number of flex or part-time drivers, in an example, the optimal transportation workload using mixed integer programming by creating decision variables, building objective functions, such as if minimize the number of drivers scheduled, then objective is min Σid Xid, and if minimize the number of cost, then objective is Σid Cid Xid, and constraints are built into the mixed integer programming model, e.g., the transportation staffing scheduled cannot include more than 20% of 5-day drivers, and this can be represented as the equation: Σid:i is 5-day driver Xid ≤ 0.2 × Σid Xid).
Kiran and Putcha are analogous fields of invention because both address the problem of determining optimal staffing plans. Further, it would have been obvious to one of ordinary skill in the art to have modified Kiran with the aforementioned teachings of Putcha in order to produce the added benefit of addressing variable demand and work schedules, helping fulfill peak or variable transportation demands reducing the risk of under-utilized workers. [0002], [0016], [0073].
Regarding claim 6, the combined teachings of Kiran, Putcha, and Ogawa teach the system of claim 5 (as above). Further, Kiran discloses all of the above and wherein the first set of parameters and the second set of parameters are the same ([0086]-[0087], in step 510 the system then executes the FTE requirement optimization model (per location) (i.e., first model) in order to identify the number of required resources with different shift templates in step 515 using data including the resource requirement profile and predetermined shift templates that are determined based upon human resource data (i.e., shift templates and human resource data - first parameters), step 520 executes the planning optimization model (per location) (i.e., second model) to determine in step 525 the number of full time equivalents and part time equivalents (i.e., second decision variables) needed to meet the forecasted transaction using data including the different shift templates and cost coefficients derived from human resource data (i.e., shift templates and human resource data - second parameters are the same as the first)).
Regarding claim 9, the combined teachings of Kiran, Putcha, and Ogawa teach the system of claim 1 (as above). Further, while Kiran discloses all of the above and wherein the second optimization model when executed is configured to: provide first headcounts for full-time shift and part-time shift; ([0091], in the optimization process, the workforce requirements management system employs an integer linear programming ("ILP") model divided into two models, wherein in the second model, the planning model, the number of resources (i.e., headcounts) of each type (e.g., full time, part time, etc.) (i.e., full-time and part-time employee) and their weekly working schedules are determined based on human resource management rules, [0086]-[0087], step 520 executes the planning optimization model (per location) to determine in step 525 the number of full time equivalents and part time equivalents needed to meet the forecasted transaction (i.e., headcounts), and step 530 determines an optimized FTE and PTE work schedule (per location) that presents the most efficient combination of FTE and PTE employees to meet the forecast transaction), Kiran does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Putcha.
Putcha teaches the optimization model when executed is configured to: provide first headcounts for full-time shift and part-time shift; select optimal variable shift patterns; and assign second headcounts to each optimal variable shift pattern ([0017], the transportation staffing system takes as input a number of hours of demand and generates an optimal number of full time, part time for each forecasted day of demand, [0029], [0034], [0037], fig. 2, in a method 200 for generating a transportation staffing schedule, at step 208, the staffing module 130 generates an optimal transportation workload for the distribution center to deliver inventory to the stores, and then, driver and vehicle assignment may take place, wherein an example, the transportation staffing system generates an optimal number of transportation resources, the transportation staffing system may also calculate the number of flex or part-time drivers, [0048], at step 210, the user interface module 140 generates a user interface displaying the optimal transportation workload, e.g., in figs. 7-9, [0076], fig. 7 depicts the optimal workload generated by the transportation staffing system including the number of drivers per program, number of full-time drivers, number of part-time drivers, flex time drivers, and [0078], fig. 9, displays a daily view of an optimal workload and optimal number of drivers for a week (e.g., week 7)).
Kiran and Putcha are analogous fields of invention because both address the problem of determining optimal staffing plans. Further, it would have been obvious to one of ordinary skill in the art to have modified Kiran with the aforementioned teachings of Putcha in order to produce the added benefit of addressing variable demand and work schedules, helping fulfill peak or variable transportation demands reducing the risk of under-utilized workers. [0002], [0016], [0073].
Regarding claims 11-16 & 19, these claims are substantially similar to claims 1-6 & 9, and are, therefore, rejected on the same basis. While claims 11-16 & 19 are directed toward a method, Kiran discloses a method as claimed. Abstract.
Claims 8 & 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kiran, et al. (US 20050240465 A1), hereinafter Kiran, in view of Putcha, et al. (US 20190295204 A1), hereinafter Putcha, and in further view of Ogawa, et al. (US 20250078102 A1), hereinafter Ogawa, and Sager, et al. (US 20230206148 A1), hereinafter Sager.
Regarding claim 8, the combined teachings of Kiran, Putcha, and Ogawa teach the system of claim 1 (as above). Further, while Kiran discloses all of the above and wherein the second optimization model comprises a … models covering the period of time ([0091], in the optimization process, the workforce requirements management system employs an integer linear programming ("ILP") model divided into two models, wherein in the second model, the planning model, the number of resources (i.e., headcounts) of each type (e.g., full time, part time, etc.) (i.e., full-time and part-time employee) and their weekly working schedules are determined based on human resource management rules, [0086]-[0087], step 520 executes the planning optimization model (per location) to determine in step 525 the number of full time equivalents and part time equivalents needed to meet the forecasted transaction (i.e., headcounts), and step 530 determines an optimized FTE and PTE work schedule (per location) that presents the most efficient combination of FTE and PTE employees to meet the forecast transaction), Kiran does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Sager.
Sager teaches wherein the second optimization model comprises a plurality of models covering the period of time in a sequential manner ([0020], [0034], staff planner 110 may generate long-range staff plans, short-term schedules, wherein long-range staff planning problems and short-term scheduling problems may be expressed using MIP models, each comprise unique models and other models that are not MIP models, wherein short-term schedules may be created for shorter periods, such as, one or two weeks, [0054]-[0056], [0059], [0083], at activity 304, long-range staff planning engine 200 creates a long-range staff planning model, wherein creating a long-range staff planning model spawns various other models, including MIP models, wherein the long-range staff planning engine 200 accesses data from the start of a current week to the end of the plan duration, and at 308, long-range staff planning engine 200 modifies the long-range staff planning model before being executed the second time, [0057]-[0058], [0064], [0072], wherein a scheduling model and MIP model are created by long-range staff planning engine 200 in method of fig. 4 including long-range staff planning engine 200 populates a base scheduling model 502 (FIG. 5) with any fixed schedule items of model values 504 and one or more goals are defined that creates MIP model 510).
Kiran and Sager are analogous fields of invention because both address the problem of determining optimal staffing plans. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Kiran the ability for a model to comprise a plurality of models covering the period of time in a sequential manner, as taught by Sager, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of the second model comprising a plurality of models covering the period of time in a sequential manner, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Kiran with the aforementioned teachings of Sager in order to produce the added benefit of improving labor demand coverage. [0085].
Regarding claim 18, this claim is substantially similar to claim 8, and is, therefore, rejected on the same basis. While claim 18 is directed toward a method, Kiran discloses a method as claimed. Abstract.
Claims 10 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kiran, et al. (US 20050240465 A1), hereinafter Kiran, in view of Putcha, et al. (US 20190295204 A1), hereinafter Putcha, and in further view of Ogawa, et al. (US 20250078102 A1), hereinafter Ogawa, and Singh, et al. (US 20220067632 A1), hereinafter Singh.
Regarding claim 10, the combined teachings of Kiran, Putcha, and Ogawa teach the system of claim 1 (as above). Further, while Kiran discloses all of the above and wherein the first optimization model and the second optimization model are … Integer Linear Programming ([]ILP) model based algorithms ([0091], in the optimization process, the workforce requirements management system employs an integer linear programming ("ILP") model divided into two models, wherein in the second model, the planning model, the number of resources (i.e., headcounts) of each type (e.g., full time, part time, etc.) (i.e., full-time and part-time employee) and their weekly working schedules are determined based on human resource management rules, [0086]-[0087], step 520 executes the planning optimization model (per location) to determine in step 525 the number of full time equivalents and part time equivalents needed to meet the forecasted transaction (i.e., headcounts), and step 530 determines an optimized FTE and PTE work schedule (per location) that presents the most efficient combination of FTE and PTE employees to meet the forecast transaction), Kiran does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Singh.
Singh teaches wherein the … optimization model are Mixed Integer Linear Programming (MILP) model based algorithm ([0057], wherein the objective function of a mixed integer linear programming (MILP) is formulated such that it captures the candidates' preference for schedules in order to maximize the likelihood of fulfillment of labor order data, Abstract, in a device described for scheduling optimization, an optimized set of schedules may be determined by solving an optimization problem and proposed schedules for candidate workers are selected).
Kiran and Singh are analogous fields of invention because both address the problem of determining optimal work schedules. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Kiran the ability for the optimization model to be a Mixed Integer Linear Programming (MILP) model based algorithm, as taught by Singh, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of the first and second optimization models are Mixed Integer Linear Programming (MILP) model based algorithms, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Kiran with the aforementioned teachings of Singh in order to produce the added benefit of meeting business constraints, human resource compliance constraints, regulatory constraints, and/or schedule preferences of employees to improve the likelihood of successful fulfillment of the labor order. [0014].
Regarding claim 20, this claim is substantially similar to claim 10, and is, therefore, rejected on the same basis. While claim 20 is directed toward a method, Kiran discloses a method as claimed. Abstract.
Conclusion
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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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623
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