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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/01/2026 has been entered.
Status of the Claims
Claims 1, 13, and 20 have been amended. Claims 5, 8, 9, and 17 have been canceled. Claims 1-4, 6, 7, 10-16, and 18-20 are pending.
Response to Arguments
Applicant's arguments filed 04/27/2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that claim 1 is directed to more than an abstract idea because the claim recites a particular solving architecture carried out by computer components on graph data structures. Applicant’s argument is unpersuasive. Under Step 2A Prong One of the Alice/Mayo framework, the evaluation is whether the claim recites a judicial exception, i.e., whether abstract idea is set forth or described in the claim. The Federal Circuit has explained that "the 'directed to' inquiry applies a stage-one filter to claims, considered in light of the specification, based on whether 'their character as a whole is directed to excluded subject matter."' Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the claims is on a specific improvement in relevant technology or on a process that itself qualifies as an "abstract idea" for which computers are invoked merely as a tool. Here, it is clear from the Specification (including the claim language) that claim 1 (and similar claims 13 and 20) focuses on an abstract idea. Applicant’s specification discloses in [0017] “Systems and techniques are described herein supportive of field workforce management and dispatching. The systems and techniques described herein support determining an optimal set of routes for a fleet of personnel (e.g., crews, vehicles, and the like) to serve a given set of customers, each with specific demands, while minimizing the total cost. The systems and techniques support features capable of effectively modeling real-world constraints common to real life enterprises (e.g., modeling of real-life constraints). Non-limiting examples of the constraints include multiple time windows (e.g., time availabilities, operating hours, or the like), multiple depots (e.g., distribution centers) with heterogeneous vehicles, multiple commodities/crafts, synchronized visits, personnel availability (e.g., crew open shifts), and the like”. Also, in [0045]…“NP-hard problems may involve a significant amount of computational resources to solve according to some other approaches, and aspects of the MILP solver 215 support effective solving of NP-hard problems including a reduction in computational resources, reduced processing time, increased processing efficiency, and higher accuracy”. The invention and claims are drawn towards workforce management (e.g., the dispatch of personnel to locations to complete tasks) utilizing mixed-integer linear programming and the claims recite limitations that directly correspond to certain methods of organizing human activity (managing personal interactions, behavior, relationships; business relations) as evidenced by limitations detailing generating a graph with a set of workers, customers, tasks associated with the customers based on customer information associated with the tasks and resource and budget information associated with the customers; a set of operation and business rules associated with the workers, customers, tasks used to generate a graph, and generating solutions associated with the managing and dispatching the set of workers. The claim limitations also directly correspond to mental processes (observation, evaluation, judgment, opinion) as shown by the limitations generating outcomes based on observed and evaluated data, including the amended limitations describing generating the one or more solutions is based on processing an input comprising an indication of one or more of: a target prediction accuracy associated with generating the one or more solutions; and a target processing time associated with generating the one or more solutions; and generating output data indicating how far the one or more solutions are from a second solution for solving the MILP problem, wherein the second solution differs from the one or more solutions with respect to the target prediction accuracy, the target processing time, or both (note that these limitations also fall into certain methods of organizing human activity as well). Lastly, the claim limitations correspond to mathematical concepts (mathematical calculations, mathematical formulas or equations, mathematical relationships), as evidenced by generating based on the graph a mixed-integer linear program (MILP) problem associated with completing the set of tasks, generating one or more solutions associated with dispatching and managing the set of workers in association with solving the MILP problem; generating an initial solution for solving the MILP problem based on processing, by a [solving engine], input data over a target temporal duration; generating the one or more solutions is based on processing, by the [MILP solver engine], at least a portion of the initial solution; and generating the one or more solutions is based on solving a set of linear equations comprised in the MILP problem. The claims recite an abstract idea.
Applicant’s argument that claim 1 is directed to more than an abstract idea because the claim recites a particular solving architecture carried out by computer components on graph data structures is unpersuasive. Constructing a graph representation of a workforce scheduling problem and constraining it is simply a mathematical or conceptual organization of the data. Applying the judicial exception to a particular data structure does not transform the claim into patent eligible subject matter. The graphs are being used for abstract idea of workforce scheduling optimization (certain methods of organizing human activity). Applicant stating that the claimed features (relating to constructing the graph(s)) are being carried out by a computer further indicates that the invention is directed to an abstract idea for which computers are merely invoked as a tool to implement the abstract idea. Running a mixed-integer linear program (“MILP”) on a graph data structure is a computational step. The claim does not describe how the computer itself solved the problem in any technologically improved way, and only describes the problem to be solved (dispatching and managing the set of works in association with solving the MILP). Claims can recite a mental process even if they are claimed as being performed on a computer. If the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept, the claim is considered to recite a mental process (MPEP §2106.04(a)(2)). Generating an extended knowledge graph or directed graph to represent scheduling is a data structure decision, not a technical improvement to computers or technology. The graphs represent relationships between workers, tasks, locations and exist to make the scheduling traceable. When considered as a whole, the focus of the claim and invention according to the specification is achieving an optimized workforce dispatch which is a business and/or organizational outcome. Any alleged improvement, at best, is an improvement to the judicial exception itself. It is important to keep in mind that an improvement in the judicial exception itself is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG LLC, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Similarly, the Applicant’s claim recitations are an improvement in the judicial exception, not an improvement in technology. Technical improvement focuses on enhancing the tools, software, or machinery, while business process improvement focuses on streamlining the steps, workflows, and methodologies people use to do their work. Applicant’s invention and claims focus on business process workflow efficiency and optimization, which is an improvement to the judicial exception itself.
Applicant argues that under Step 2A Prong Two, the claim integrates the judicial exception into a practical application, reciting again the limitations of generating the directed and extended knowledge graphs. Examiner disagrees. The generation and use of these graphs have been addressed in the above section, and are being used for abstract idea of workforce scheduling optimization (certain methods of organizing human activity). Applying the judicial exception to a particular data structure does not transform the claim into patent eligible subject matter. Generating an extended knowledge graph or directed graph to represent scheduling is a data structure decision, not a technical improvement to computers or technology. The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a directed graph, an extended knowledge graph, a solving engine and a MILP solver engine, a memory having one or more processors (claim 13), and a computer readable storage medium and processor (claim 20). The additional elements of a solving engine and a MILP solver engine, a memory having one or more processors (claim 13), and a computer readable storage medium and processor (claim 20) are computer components recited at a high-level of generality performing the above-mentioned limitations, and amounts to no more than mere instructions to apply the judicial exception using a generic computer. The graphs amount to generally linking the judicial exception to a particular field of use (field workforce management and dispatch). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Applicant argues under Step 2B that the claim recites significantly more than the judicial exception because the claim provides a specific graph-conditioning, model-generation, and solver-control workflow. Examiner disagrees. Those specific claim limitations are further directed to the judicial exception itself. The claims do 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 amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
The 35 U.S.C. 101 rejection is maintained.
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 1-4, 6, 7, 10-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-4, 6, 7, and 10-12 recite a method (i.e. process), claims 13-16, 18, and 19 recite a system (i.e. machine), and claim 20 recites a computer program product (i.e. machine or article of manufacture). Therefore claims 1-4, 6, 7, 10-16, and 18-20 fall within one of the four statutory categories of invention.
Independent claims 1, 13, and 20 recite the limitations: generating a [directed graph] associated with a set of workers, a set of customers, and a set of tasks associated with the set of customers, based on: customer information associated with the set of tasks; and resource and budget information associated with the set of customers; generating an [extended knowledge graph] based on the [directed graph] and a set of operation and business rules, by preprocessing directed arcs comprised in the directed graph based on the set of operation and business rules, wherein the set of operation and business rules are associated with the set of workers, the set of customers, the set of tasks, and one or more facilities associated with the set of tasks, wherein the preprocessing comprises: identifying moves in the [directed graph] based on one or more of: precedence criteria, technician-task matching criteria, a criteria not allowing travel between depots, and lack of available time to travel between tasks; and removing, from the directed graph, directed arcs respectively corresponding to the identified moves; tracking movement of one or more workers of the set of workers to a first task among the set of tasks, tracking movement of the one or more workers between the first task and at least one other task of the set of tasks, or both based on the [extended knowledge graph]; generating, based on the [extended knowledge graph], a mixed-integer linear program (MILP) problem associated with completing the set of tasks; generating an initial solution for solving the MILP problem based on processing, by a [solving engine], input data over a target temporal duration; generating, by an [MILP solver engine], one or more solutions associated with dispatching and managing the set of workers in association with solving the MILP problem, wherein: generating the one or more solutions is based on processing, by the [MILP solver engine], at least a portion of the initial solution; generating the one or more solutions is based on solving a set of linear equations comprised in the MILP problem; and generating the one or more solutions is based on processing, by the [MILP solver engine], an input comprising an indication of one or more of: a target prediction accuracy associated with generating the one or more solutions; and a target processing time associated with generating the one or more solutions; and generating, by the [MILP solver engine], output data indicating how far the one or more solutions are from a second solution for solving the MILP problem, wherein the second solution differs from the one or more solutions with respect to the target prediction accuracy, the target processing time, or both. The invention and claims are drawn towards workforce management (e.g., the dispatch of personnel to locations to complete tasks) utilizing mixed-integer linear programming and the claims recite limitations that directly correspond to certain methods of organizing human activity (managing personal interactions, behavior, relationships; business relations) as evidenced by limitations detailing generating a graph with a set of workers, customers, tasks associated with the customers based on customer information associated with the tasks and resource and budget information associated with the customers; a set of operation and business rules associated with the workers, customers, tasks used to generate a graph, and generating solutions associated with the managing and dispatching the set of workers. The claim limitations also directly correspond to mental processes (observation, evaluation, judgment, opinion) as shown by the limitations generating outcomes based on observed and evaluated data, including the amended limitations describing generating the one or more solutions is based on processing an input comprising an indication of one or more of: a target prediction accuracy associated with generating the one or more solutions; and a target processing time associated with generating the one or more solutions; and generating output data indicating how far the one or more solutions are from a second solution for solving the MILP problem, wherein the second solution differs from the one or more solutions with respect to the target prediction accuracy, the target processing time, or both (note that these limitations also fall into certain methods of organizing human activity as well). Lastly, the claim limitations correspond to mathematical concepts (mathematical calculations, mathematical formulas or equations, mathematical relationships), as evidenced by generating based on the graph a mixed-integer linear program (MILP) problem associated with completing the set of tasks, generating one or more solutions associated with dispatching and managing the set of workers in association with solving the MILP problem; generating an initial solution for solving the MILP problem based on processing, by a [solving engine], input data over a target temporal duration; generating the one or more solutions is based on processing, by the [MILP solver engine], at least a portion of the initial solution; and generating the one or more solutions is based on solving a set of linear equations comprised in the MILP problem.
Note: The features or elements in brackets in the above section are inserted for reading clarity, but are analyzed as “additional elements” under step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a directed graph, an extended knowledge graph, a solving engine and a MILP solver engine, a memory having one or more processors (claim 13), and a computer readable storage medium and processor (claim 20). The additional elements of a solving engine and a MILP solver engine, a memory having one or more processors (claim 13), and a computer readable storage medium and processor (claim 20) are computer components recited at a high-level of generality performing the above-mentioned limitations, and amounts to no more than mere instructions to apply the judicial exception using a generic computer. The graphs amount to generally linking the judicial exception to a particular field of use (field workforce management and dispatch). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do 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 amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 6 and 18 recite the limitations the MILP problem comprises a model comprising the set of workers, the set of customers, the set of tasks, the customer information, the resource and budget information, and the set of operation and business rules; and generating the one or more solutions is based on the model. The limitations are further directed to the abstract idea analyzed above, and the model specifically corresponds to mathematical concepts. The claims are not patent eligible.
Dependent claims 2-4, 7, 10-12, 14-16, and 19 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above, and/or additional elements that have been analyzed in the rejected claims above. Thus, claims 2-4, 7, 10-12, 14-16, and 19 are also rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-4, 6, 7, 10-16, and 18-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The closest patent or patent application prior art reference found that is relevant to the applicant’s invention includes Phan (2022/0058590) which disclose a method for maintaining equipment in a geo-distributed system that includes a selection of quantities to optimize when adjusting a maintenance schedule of the geo-distributed system that includes multiple pieces of equipment that are spread over a geographical region, and wherein the maintenance schedule identifies when a set of maintenance tasks are executed at a first equipment from the geo-distributed system over a predetermined duration. The process further includes generating, by the processor, a mixed-integer linear program for optimizing the maintenance schedule using a set of predetermined constraints, executing the mixed-integer linear program via a mixed-integer linear program solver, and adjusting the maintenance schedule by selecting only a subset of the maintenance tasks. The prior art reference does not appear to disclose the limitations of the applicant’s invention that includes tracking movement of one or more workers of the set of workers to a first task among the set of tasks, tracking movement of the one or more workers between the first task and at least one other task of the set of tasks, or both based on the extended knowledge graph, nor the currently amended limitations. The claims appear to overcome the prior art.
The closest non-patent literature prior art reference found that is relevant to the applicant’s invention includes the publication “A Crowd-Sensing Framework for Allocation of Time-Constrained and Location-Based Tasks” (Estrada, et. al.; 2020) which explores a service computing framework for time constrained-task allocation in location based crowd-sensing systems by relying on a recruitment algorithm that implements a multi-objective task allocation algorithm based on Particle Swarm Optimization, queuing schemes to handle efficiently the incoming sensing tasks in the server side and at the end-user side, a task delegation mechanism to avoid delaying or declining the sensing requests due to unforeseen user context, and a reputation management component to manage the reputation of users based on their sensing activities and task delegation. The prior art reference does not appear to disclose the amended limitations of the applicant’s invention that includes tracking movement of one or more workers of the set of workers to a first task among the set of tasks, tracking movement of the one or more workers between the first task and at least one other task of the set of tasks, or both based on the extended knowledge graph, nor the currently amended limitations of the claims. The claims appear to overcome the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3629
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