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 .
Status of the Claims
Claims 1, 4, 5, 7-10 13, 15, and 16 are amended. Claims 3, 6, 11, 12, and 14 are canceled. Claims 21-23 are added as new claims. Claims 1, 2, 4, 5, 7-10, 13, and 15-23 are pending.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/24/2025 was filed before the mailing of this action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments, see pg. 11, filed 10/24/2025, with respect to 35 U.S.C. 112(b) have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection has been withdrawn.
Applicant's arguments filed 10/24/2025 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that the claimed subject matter encompasses AI/ML model in a way that cannot be practically performed in the human mind and provides a technical solution to a technical problem. Examiner disagrees. An ML model is used to perform limitations relating to budget management including, but not limited to receiving input for a plurality of projects, training a machine learning model of the projects in the budget based on the input, predicting a project state indicating results, monitoring the projects for variations over a period of time, determining at least one partially unsent resource associated with the project, etc. The limitations that are being used by the ML model directly correspond to certain methods of organizing human activity (business relations, commercial interactions, managing personal behaviors, following rules or instructions), and also directly correspond to mental processes (observation, evaluation, judgment, opinion) since the claims describe the observation and evaluation of data corresponding to judgment management, and making a decision (judgment/opinion) based on the observed and evaluated data. The claims recite an abstract idea. The use of ML models does not take the claims out of the judicial exception groupings. The court in Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) found that "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Further, the court stated "Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15. Similarly to what the court found in Recentive the only thing the claims disclose about the use of machine learning is that machine learning is used in a certain environment; budget management. Beyond this point on machine learning, 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 the claims focus on an abstract idea, and not on an improvement to technology and/or a technical field. The specification recites: [0003] Conventional budget formulation and execution are challenging, time-consuming, and inefficient. Such conventional budget formulation often introduces human error and provides no way to simulate potential solutions to impact the set budget strategy. The human process may include setting an unchanging strategy and deciding on actions and activities without any recourse or ability to change the budget or execution of the budget. [0004] Chief Information Officers (CIOs) in the world today are faced with the paradox of accelerating business change while experiencing delays and uncertainty in information collection, integration, and reporting. The CIOs are asked to provide accurate data to answer pressing business questions instantaneously. For example, questions such as how many personnel should be laid off given a financial situation are challenging to address. [0005] Conventional budget management techniques do not address the following problems: manual and ad hoc data management of data sources, organizational and/or budget information being sparse or unavailable from a definable source, and unreliable systems security and deployment. Excluding the use of massive contract staff to sift data in spreadsheets of budget data, there is no current technology solution capable of integrating the vast and complex data from procurement, finance, and information technology operations. This conventional approach still results in untimely and inaccurate decisions with respect to budget formulation and execution. Therefore, there is a need for a system and method for budget management that allows an organization to make instantaneous decisions and efficiently support a direct correlation between funds and execution. The claims, considered in light of the specification, further indicates that the claims are drawn towards the abstract idea grouping of certain methods of organizing human activity.
Applicant also argues that the claimed subject matter provides a technical improvement including a feedback loop that improves resource allocation and that the interconnected module system improves computer capabilities for budget management software. Examiner disagrees. Improving resource allocation via utilizing an interconnected module system does not constitute an improvement in computers or technology, but at best, an improvement in the judicial exception itself. Improving resource allocation is an improvement in the business process related to applicant’s budget management plan and indicates an improvement in commercial interactions, business relations, managing personal behaviors. ). It is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology (emphasis added). 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. Also, applicant’s statement that monitoring a real-time execution of a budget plan cannot be performed by a human user in real time is not a valid statement. Regarding the applicant’s argument about the feedback loop, Examiner reminds applicant that "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12.
Applicant further argues that the claims do not recite mental processes. Examiner disagrees. The claim limitations also directly correspond to mental processes (observation, evaluation, judgment, opinion) since the claims describe the observation and evaluation of data corresponding to judgment management, and making a decision (judgment/opinion) based on the observed and evaluated data. Further, 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. This is the case in the applicant’s invention.
Regarding applicant’s assertion that the claims recite an improvement in computers or technology in Step 2A Prong Two and Step 2B, as indicated in the previous section, the alleged “improvement” is at best an improvement in the judicial exception itself instead of an improvement in computers or technology. The judicial exception is not integrated into a practical application at least one database, one or more processors, at least one computing device comprising memory storing a set of program modules that include: a simulation module, neuroevolutionary machine learning models, a budget formulation module, a budget execution module, an asset and service management module, a spend management module, and a fund management module. The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the neuroevolutionary machine learning models amount to generally linking the judicial exception to a particular field of use (budget management). 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 claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements 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 claim is not patent eligible.
The 35 U.S.C. 101 rejection is maintained.
Applicant’s arguments, see pg. 22, filed 10/24/2025, with respect to 35 U.S.C. 103 have been fully considered and are persuasive. The 35 U.S.C. 103 rejection of has been withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 4, 5, 7-10, 13, and 15-23 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, 2, 4, 5, 7-9, and 21-23 recite a system (i.e. machine), and claims 10, 13, and 15-20 recite a method (i.e. process). Therefore claims 1, 2, 4, 5, 7-10, 13, and 15-23 fall within one of the four statutory categories of invention.
Independent claim 1 recites the limitations: receive, for the budget, input for a plurality of projects in the budget; train and evolve one or more [neuroevolutionary machine learning models] for each of the plurality of projects in the budget based at least in part on the input using linear genome representations of neural network connectivity with connection genes and node genes, wherein each connection gene includes an innovation number representing a chronological marker for gene appearance, and wherein structural mutations include add connection mutations and add node mutations that expand genome size, predicting a project state indicating project results; monitor, over a time period, the plurality of projects and variations in the project results; determine, based on the monitoring, at least one partially unspent resource associated with the project results monitored over the time period; redirect the at least one partially unspent resource to one or more other projects in the plurality of projects; retrain the system based on the predicted project state and the redirection of the at least one partially unspent resource; receive contracts and subcontracts and generate at least one budget plan for at least one of the plurality of projects; execute the at least one budget plan; receive details associated with procured assets and services corresponding to executing the budget plan; receive details related to asset status and service; monitor a real-time execution of the budget plan, and update the budget plan based on determined budget predictions generated by the one or more [neuroevolutionary machine learning models] using a baseline version of the budget plan; and track available funds of each project in the plurality of projects; receive funding plans, receive cost estimates and funding requests, provide fund status to the [spend management module], and provide fund authorization and fund status to the [budget execution module]. The invention is drawn towards machine learning methods and systems for budget management, and recites limitations that directly correspond to certain methods of organizing human activity (business relations, commercial interactions, managing personal behaviors, following rules or instructions), as evidenced by limitations detailing receiving inputs for the budget and training a machine learning model based on the inputs, predicting a project state indicting result, monitoring the projects, determining unspent resources and redirecting unspent resources and also receiving contracts and generating budget plans. The claim limitations also directly correspond to mental processes (observation, evaluation, judgment, opinion) since the claims describe the observation and evaluation of data corresponding to judgment management, and making a decision (judgment/opinion) based on the observed and evaluated data. The claims recite an abstract idea.
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 at least one database, one or more processors, at least one computing device comprising memory storing a set of program modules that include: a simulation module, neuroevolutionary machine learning models, a budget formulation module, a budget execution module, an asset and service management module, a spend management module, and a fund management module. The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the neuroevolutionary machine learning models amount to generally linking the judicial exception to a particular field of use (budget management). 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 claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements 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 claim is not patent eligible.
Dependent claim 2 recites the limitation that the system is a digital twin system. The claim recites the additional element of the digital twin system which amounts to “apply it” or merely using a computer as a tool to implement the abstract idea. 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 7 recites the limitations: receive a cost recovery plan from the spend management module, receive asset and service usage and determine whether budgeted costs are being recovered at or above a predefined rate. The limitations are further directed to the abstract idea analyzed above. The claim also recites the additional elements of the cost recovery module, a cost recovery plan from the spend management module, and the cost management module. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception. 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 8 recites the limitations: actual costs from invoices and estimated costs per plan, receive asset status and service status, receive cost priorities and targets, receive approved asset and service acquisitions and approved acquisitions strategy, and provide cost projections. The limitations are further directed to the abstract idea analyzed above. The claim also recites the additional elements of a cost management module, the asset and service management module, the spend management module, and the budget execution module. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception. 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 9 recite the limitations: information related to contracts to procure and charge for authorized assets and services, receive actual costs and procurement status, provide cost projections, and determine whether actions related to the budget are valid within a predefined time period. The limitations are further directed to the abstract idea analyzed above. The claim also recites the additional elements of a contract management module, the cost management module, and the spend management module. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception. 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claims 4 and 5 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 4 and 5 are also rejected under 35 U.S.C. 101.
Independent claim 10 recites the limitations of: training and evolving one or more [neuroevolutionary machine learning models] for one or more projects associated with the budget, wherein the one or more projects is configured to generate project results according to budget input received for the one or more projects, wherein the training and evolving uses linear genome representations of neural network connectivity with connection genes and node genes, wherein each connection gene includes an innovation number representing a chronological marker for gene appearance, and wherein structural mutations include add connection mutations and add node mutations; predicting a project state indicating project results; monitoring variations in the generated project results over time; determining, based on the monitoring, at least one partially unspent resource associated with the generated project results; redirecting the at least one partially unspent resource to one or more other projects associated with the budget; and retraining the system based on the redirection of the at least one partially unspent resource; generating at least one budget plan for the one or more projects; executing the budget plan, and receiving details associated with procured assets and services corresponding to executing the budget plan; receiving details related to asset status and service status from the [asset and service management module]; monitoring a real-time execution of the budget plan; updating the budget plan based on determined budget predictions generated by the one or more [neuroevolutionary machine learning models] using a baseline version of the budget plan; tracking available funds corresponding to the one or more projects, and receiving funding plans for the one or more projects. The invention is drawn towards machine learning methods and systems for budget management, and recites limitations that directly correspond to certain methods of organizing human activity (business relations, commercial interactions, managing personal behaviors, following rules or instructions), as evidenced by limitations detailing training and evolving one or more [machine learning models] for one or more projects associated with the budget, wherein the one or more projects is configured to generate project results according to budget input received for the one or more projects; monitoring variations in the generated project results over time; determining, based on the monitoring, at least one partially unspent resource associated with the generated project results; redirecting the at least one partially unspent resource to one or more other projects associated with the budget, etc. The claim limitations also directly correspond to mental processes (observation, evaluation, judgment, opinion) since the claims describe the observation and evaluation of data corresponding to judgment management, and making a decision (judgment/opinion) based on the observed and evaluated data. The claims recite an abstract idea.
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 at least one database, one or more processors, at least one computing device comprising memory storing a set of program modules comprising a simulation module, one or more neuroevolutionary machine learning models, a budget formulation module, a budget execution module, an asset and service management module, a spend management module, and a fund management module. The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the one or more neuroevolutionary machine learning models amount to generally linking the judicial exception to a particular field of use (budget management). 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 claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements 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 claim is not patent eligible.
Dependent claim 15 recites the limitations of: receiving cost estimates and funding requests, and providing fund status, and fund authorization and fund status to the budget execution module. The limitations are further directed to the abstract idea analyzed above. The claim also recites the additional elements of the fund management module, the computing device, a cost recovery module, budget execution module, and the spend management module. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception. 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 16 recites the limitations of: receiving asset status and service status, actual costs from invoices and estimated costs per plan, and receiving cost priorities and targets. The limitations are further directed to the abstract idea analyzed above. The claim also recites the additional elements of the cost management module, the computing device, asset and service module, and the spend management module. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception. 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 19 recites the limitations of: receiving actual costs and procurement status, and information associated with contracts to procure and charge for authorized assets and services. The limitations are further directed to the abstract idea analyzed above. The claim also recites the additional elements of a contract management module, the computing device, and the cost management module. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception. 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 21 recites the limitations: develop a [neural network model] for each project in the plurality of projects, wherein each project and program resource in the budget defining inputs and outputs for the budget; modulate one or more inputs of the [neuroevolutionary model], receive output data for the modulated inputs; determine if the received output corresponds to a predicted output; provide the modulated inputs into the [neuroevolutionary model] if the output data is different from the predicted output; and update the [neuroevolutionary] model using the output data. The limitations are further directed to the abstract idea analyzed above. The claim also recites the additional elements of a neural network model and neuroevolutionary model. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception, and generally linking the judicial exception to a particular field of use. 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claims 13, 17, 18, 20, 22, and 23 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 13, 17, 18, 20, 22, and 23 are also rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1, 2, 4, 5, 7-10, 13, and 15-23 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 Laliberte (2024/0220887) which discloses determining project attribute range values for at least one project attribute, such as a project cost and/or a project schedule, of at least one new project include receiving historical data related to at least one previous performance of a same or similar project as the at least one new project, the historical data including historical project attribute values of the at least one project attribute, generating multiple respective machine learning models using different sets of training data determined from the received historical data, each of the different sets of the training data being used to train a respective one of the machine learning models, and determining a range of values for the at least one project attribute of the at least one new project by applying the multiple respective machine learning models to the at least one project attribute of the new project. The reference does not disclose the detailed amended limitations of the applicant’s claims. The claims overcome the prior art.
The closest non-patent literature prior art reference found that is relevant to applicant’s invention includes the publication “Towards the Neuroevolution of Low-level artificial general intelligence” (Pontes-Filho, et. al.; 2022) which discloses the evaluation of a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence that allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. The reference does not disclose the detailed amended limitations of the applicant’s claims. The claims overcome the prior art.
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.
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 3628