MACHINE LEARNING BASED SYSTEM AND METHOD FOR BUDGET MANAGEMENT

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 and 10 are amended. Claims 3, 6, 7, 11, 12, 14, 21, and 22 are canceled. Claims 25-28 are added as new claims. Response to Arguments Applicant's arguments filed 07/20/2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive. The Claims Recite A Judicial Exception Under Step 2A Prong One Applicant argues that their claims are not directed to an abstract idea under Step 2A Prong One. Examiner disagrees. Step 2A Prong One evaluates whether an 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 focuses on an abstract idea, and not on an improvement to technology and/or a technical field. Applicant’s specification recites, in several locations, concrete indication that the character of the claims as a whole are directed to budget management for organizations that merely utilizes machine learning methods for budget management. Consider the following: The Specification is titled “Machine Learning System and Method for Budget Management”, and the Abstract further describes that the machine learning system is based on Neuroevolution of Augmenting Topologies (NEAT) to provide efficient budget management. The Background describes: [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. The Specification also notes that “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” (Spec. [0005]), and “the present disclosure describes machine learning (ML) based systems and methods for budget management’ (Spec. [0006]). Other notable areas of the Specification include [0013] which discloses “Although the ML models described herein can be a deep learning model, a neuroevolutionary model may instead be utilized. A pretrained simulated model representing projects may be controlled by modulated inputs to determine how well the results correspond to a predicted output as presented by a budget plan. The computing device is configured to use the results of a simulation feedback of the simulation module for budget management.” Additionally, [0055] “In some embodiments, the system 200 may manage a budget of one or more enterprises (e.g., businesses, companies, projects, firms, organizations, etc.). The system 200 may also continuously monitor budget spend execution, and identify and propose improved execution paths to save costs, resources, and time associated with a number of projects (and/or processes defined by the projects) in the budget. The system 200 may represent an ML system that is based on a neuroevolution of augmenting topologies. The system 200 may further include or execute natural language processing to analyze inputs and to automate dynamic creation and automation of an ML model that may execute on a particular budget to assess, plan, and replan expenses/costs when a budget does not match a planned expense/spend.” Next consider the claim limitations such as: 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…, determine, for each pair of the linear genome representations, a compatibility distance based on excess genes, disjoint genes, and matching genes of the pair, and assign each of the plurality of projects to a species group according to a compatibility threshold; predict, using the trained and evolved one or more neuroevolutionary machine learning models, a project state indicating project results; monitor, over a time period, and using the one or more neuroevolutionary machine learning models, 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; and retrain the one or more neuroevolutionary machine learning models within each species group independently based on the predicted project state and the redirection of the at least one partially unspent resource…, and receive contracts and subcontracts and generate the budget plan for at least one of the plurality of projects; execute the budget plan; an receive details associated with procured assets and services corresponding to executing the budget plan; receive details related to asset status and service status from the asset and service management module; monitor a real-time execution of the budget plan; 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, and provide fund authorization and fund status. It is clear from above emphasized citations from the specification, that the claims considered in light of the specification indicates that the character of the claims as a whole is directed to excluded subject matter, specifically, the claim 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. Applicant’s argument insinuating that the claims are not being considered based on what the claims actually recite is invalid. The 101 section explicitly recites the claim limitations that correspond to the judicial exception groupings. The specification is mentioned to show that the claims considered in light of the specification indicates that the character of the claims as a whole is directed to excluded subject matter, which is proper. Applicant’s argument regarding the mathematical equations judicial exception grouping is unpersuasive. The claim recites limitations describing the use of neuroevolutionary machine learning models which is a technique that uses genetic algorithms to build and optimize neural networks. Further, the machine learning system is based on Neuroevolution of Augmenting Topologies (NEAT) which is fundamentally a mathematical and computational optimization algorithm. The claims correspond to mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations). Applicant’s argument regarding mental processes is unpersuasive. The claim limitations 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. Applicant argues that the limitations cannot be practically performed un the human mind because they’re computational operations applied to defined data at a scale and speed that is machine dependent This argument is unpersuasive. The mental steps inquiry under Step 2A Prong One is not “could a human do this at the same speed/precision.” It’s whether the claim limitation, under broadest reasonable interpretation, covers performance in the mind (observation, evaluation, judgment, opinion) but for the recitation of generic computer components. MPEP §2106.04(a)(2) is explicit that a claim can recite a mental process even though it’s performed with the assistance of, or implemented on, a computer. The question is the underlying nature of the step, not whether a human could literally replicate the computer’s exact arithmetic or functionality that is unique to computer processing. Applicant’s argument regarding certain methods of organizing human activity is unpersuasive. 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 indicating project results, monitoring the projects, determining unspent resources and redirecting unspent resources, and receiving contracts and generating budget plans. Additionally, the claims when viewed in light of the specification further supports this conclusion. Applicant proceeds to recite that determining compatibility distance between pairs of linear genome representations, assigning projects to species groups according to a compatibility threshold, and retraining neuroevolutionary machine learning models within species groups to reduce performance effort on the system does not constitute a fundamental economic practice, a commercial or legal interaction, the management of personal behavior or relationships, or the following of rules or instructions, but that is not the proper analysis, nor is that what the examiner’s analysis stated. The judicial exception of certain methods of organizing human activity is set forth or described the claims. The claims detail various aspects of budget management, including but not limited to: receive, for the budget, input for a plurality of projects in the budget; predict, using the trained and evolved one or more neuroevolutionary machine learning models, a project state indicating project results; monitor, over a time period, and using the one or more neuroevolutionary machine learning models, 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; a budget formulation module configured to receive contracts and subcontracts and generate the budget plan for at least one of the plurality of projects; a budget execution module configured to execute the budget plan; an asset and service management module configured to receive details associated with procured assets and services corresponding to executing the budget plan; receive details related to asset status and service status from the asset and service management module; 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 a fund management module configured to track available funds of each project in the plurality of projects, receive funding plans from the spend management module, receive cost estimates and funding requests from a cost recovery module, provide fund status to the spend management module, and provide fund authorization and fund status to the budget execution module, all of which directly correspond to certain methods of organizing human activity (business relations, commercial interactions, managing personal behaviors, following rules or instructions). Cherry-picking limitations that are analyzed as additional elements which are properly analyzed under Step 2A Prong Two (e.g., retraining neuroevolutionary machine learning models) and limitations that correspond to mental processes and mathematical equations (e.g., determining compatibility distance between pairs of linear genome representations (see [0089]-[0090] of the specification), assigning projects to species groups according to a compatibility threshold) to assert that because those limited limitations may not correspond to certain methods of organizing human activity, that the claims themselves don’t correspond to certain methods of organizing human activity is not a proper analysis under Step 2A Prong Two, particularly when the claims should be considered as a whole. For the reasons set forth above, the claims recite an abstract idea under Step 2A Prong One. The Judicial Exception Is Not Integrated Into A Practical Application Under Step 2A Prong Two Applicant argues that the claims integrate the judicial exception into a practical application. Examiner disagrees. The courts have identified limitations that did not integrate a judicial exception into a practical application: (see MPEP §2106.04(d)) Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea; Adding insignificant extra-solution activity to the judicial exception; Generally linking the use of a judicial exception to a particular technological environment or field of use; In the applicant’s claimed invention, the judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: 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. Applicant argues that their claims achieve an improvement in technology because their claims recite “specific computational steps, including ‘determine, for each pair of the linear genome representations, a compatibility distance based on excess genes, disjoint genes, and matching genes of the pair, and assign each of the plurality of projects to a species group according to a compatibility threshold’ and ‘retrain the one or more neuroevolutionary machine learning models within each species group independently...wherein retraining within each species group reduces performance effort on the system by limiting crossover operations to genomes within a same species group, enabling faster real-time monitoring of a budget plan,’ each of which governs how the neuroevolutionary machine learning models themselves are internally organized and retrained” and further asserts that these are specific steps through which the machine learning technology achieves a technical improvement similar to what Recentive Analytics requires. Examiner disagrees. To start, the limitation “determine, for each pair of the linear genome representations, a compatibility distance based on excess genes, disjoint genes, and matching genes of the pair, and assign each of the plurality of projects to a species group according to a compatibility threshold” is further directed to the judicial exception. It is important to note, the judicial exception alone cannot provide the improvement (see MPEP §2106.05(a)). Regarding the limitation “retrain the one or more neuroevolutionary machine learning models within each species group independently...wherein retraining within each species group reduces performance effort on the system by limiting crossover operations to genomes within a same species group, enabling faster real-time monitoring of a budget plan,” examiner notes that “enabling faster real-time monitoring of a budget plan” is not an improvement in computers or technology, but instead, at best, and improvement in the judicial exception itself—the business process budget management that correspond to observation and evaluation of data, etc. Applicant’s alleged improvement is not a technical improvement in computers or technology as required under Step 2A Prong Two of the Alice/Mayo framework. Technical improvements to computers or computer functionality 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 claims fall in the latter. Applicant presents no evidence showing an improvement to computer technology or functionality. The computer(s) utilized in the applicant’s invention and claims are performing standard computer processing operations: collecting, analyzing/processing, formatting, and outputting processed data. Under Step 2A Prong Two, the question is whether the claims reflects an improvement to the functioning of a computer or other technology. The claimed steps of the applicant’s invention are the speciation mechanism of the NEAT algorithm: a compatibility distance computed from excess, disjoint, and matching genes, grouping by a threshold, and restricting crossover within the species. The speciation mechanism that the applicant argues is found in K. O. Stanley and R. Miikkulainen, "Evolving Neural Networks through Augmenting Topologies," in Evolutionary Computation, vol. 10, no. 2, pp. 99-127, June 2002. This is a known technique and applicant presents no evidence in the claims of the speciation to modify this technique. Therefore, applicant is applying an existing machine learning technique to a new data environment, that new environment being budget management. The claims do not improve the machine learning model nor does it improve the machine learning algorithm. Under Step 2A Prong Two, this is also considered generally linking the judicial exception to a particular field of use, which examiner indicated in the previous office action. This is the situation in Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025). In Recentive Analytics, the court held 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." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). Regarding the argument that the claim limitation enables faster real-time monitoring of a budget plan, applicant is reminded that the court in Recentive Analytics also held that 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). Additionally, the court held 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). Likewise, retraining models on updated data and organizing them into species are inherent to neuroevolution and do not amount to a technological improvement. The claimed speciation steps are disclosed in prior art publication Stanley and Miikkulainen publication which describes the steps applicant argues. Because these steps and their asserted benefit were already known in the NEAT algorithm, they are not an improvement to machine learning technology that applicant has contributed. Applicant merely applies the known NEAT technique to budget management. Describing the features, mechanics, or structure of a known algorithm in the claim language, specification, or arguments do not transform the claims into a patent eligible improvement. Examiner notes that the genome expression is simply a data representation and not a technical improvement. Expanding the data representation or genes is just adding elements to a data structure. Applicant describing this in biological terminology does not change what is actually happening computationally. Neuroevolutionary models are known to use or mimic biological natural selection to evolve a network's connection weights, structure, or both. Examiner also importantly notes that, even if the describe NEAT architecture represented an improvement to machine learning (which examiner argues it does not), the claims do not capture the alleged improvements in a way that it is integrated with the budget management system. For instance, the budget management system of the applicant’s invention may be performed using deep learning model as the machine learning model according to applicant’s specification [0013]. Examiner also notes that the alleged improvement is stated in a conclusory manner without providing sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology (MPEP §2106.04(d)(1)). Any alleged improvement in the specification is tied to improving the judicial exception itself, but there is no evidence or sufficient details indicating an improvement in machine learning. Applicant is using an already known machine learning model to apply to their business process related to budget management. 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. In conclusion regarding NEAT, applicant conflates describing a known algorithm’s defining characteristics and expressions with an improvement in machine learning, which it is not. The compatibility distance computed from excess, disjoint, and matching genes, grouping by a threshold, and restricting crossover within the species is known in NEAT, and applicant is not providing details to an improvement in machine learning, but instead the claims are directed to a judicial exception (budget management) with an instruction to use NEAT. Applicant’s argument alleging an improper read of the specification into the claims is invalid. Applicant is conflating two distinct doctrines. The “reading limitations into the claims” prohibition is in the context of claim construction where it is improper to import unclaimed features from a preferred embodiment to narrow or expand the scope of a claim term for the purpose of validity analysis over prior art (e.g., infringement analysis). That is not what is occurring here under the 101 analysis. Under the “directed to” inquiry, looking to the specification to determine whether claims are directed to an improvement in computer functionality, or whether it is process that itself qualifies as an "abstract idea" for which computers are invoked merely as a tool is proper (see Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016)). The use of the specification in this context does not add limitations to the claims, the claims are still evaluated exactly as recited with no additional claim elements imported. Rather, the specification is used as evidence of what problem the claim invention claims to solve, what the applicant identifies as the advance over the prior art, and how the claim language should be understood in context, all of which informs (not alters) the scope of what is actually claimed. Applicant’s characterization of the analysis as an improper due to importing the specification into the claims is not supported by case law and is traversed. The claims being considered as a whole and in light of the specification’s own description of the problem to be solved and the character of the claims remain directed to certain methods of organizing human activity and mental processes. Applicant’s argument regarding reducing performance effort on the on the system is also unpersuasive. Applicant’s amended language that the species-restricted retaining “reduces performance effort on the system” is the same computational-overhead argument that applicant previously advanced, just restated in different words. In the response filed 04/16/2026, applicant argued that “The linear genome encoding, compatibility distance determination, and species-grouped independent retaining collectively define a specific architecture that improves the operation of the machine learning models themselves and produces measurable improvements to copter performance in the form of reduced processing overhead and faster real-time monitoring of the budget plan.” (see pg. 13 of the Response filed 04/16/2026). Replacing “overhead” with “performance effort” does not change the substance. Both describe fewer computational operations and faster processing. This is not an improvement to the functioning of a computer or to machine learning technology. Changing the label on the same benefit does not make it a technological improvement. An efficiency gain that results from performing an abstract idea or a known algorithm on a computer, rather than a change to the computer or algorithm, does not show a technological improvement. "Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015); see also MPEP 2106.05(f). Faster monitoring of a budget plan is related to the judicial exception and is at best an abstract benefit, not a technical improvement. Retraining and real-time monitoring are improvements to budget management outcomes, but not improvements to how the computer itself functions or operates. Limiting crossover to genomes within the same species is how the known NEAT algorithm operates (Stanley & Miikkulainen, 2002). Any reduction in computation flows from applying that known technique, not from a claimed modification to the model, the genome representation, or the training process. Thus, there is no improvement in machine learning technology not the computer functionality itself. Lastly, applicant’s argument that the compatibility distance determination and species-groped retraining is an improvement because it “governs how the neuroevolutionary machine learning models are structured and retained" is unpersuasive. The argument is directed towards an improvement in the judicial exception itself and not an improvement in computers or technology. The compatibility distance determination is a mathematical calculation (see [0089]-[0090] which also directly corresponds with formula 1 on pg. 110 of Stanley & Miikkulainen), and grouping genomes into a species by comparing that value to a threshold is a mathematical grouping or evaluation that corresponds to mental processes. That the models are “internally structured and retrained” describes the output of the mathematical process and not a technical improvement. For the reasons set forth above, the judicial exception is not integrated into a practical application under Step 2A Prong Two. The Claims Do Not Include Additional Elements That Are Sufficient To Amount To Significantly More Than The Judicial Exception Under Step 2B Applicant argues that the claims include additional elements that amount to significantly more than the abstract idea. Examiner disagrees. 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 (budget management). 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. Applicant’s argument that the Office has not provided sufficient evidentiary support that that the ordered combination of (1) linear genome representations with connection genes, node genes, and innovation numbers as chronological markers for gene appearance, (2) add connection mutations and add node mutations that expand genome size, (3) compatibility distance determination based on excess genes, disjoint genes, and matching genes to assign projects to species groups according to a compatibility threshold, and (4) species-grouped independent retraining that reduces performance effort on the system by limiting crossover operations to genomes within the same species group, is well-understood, routine, or conventional (WURC) in the art, is unpersuasive under Step 2B. Those limitations have already been identified as corresponding to the judicial exception groupings. They are not considered “additional elements” for the purpose of the Step 2B analysis. Stanley and Miikkulainen (2002) is cited to establish that NEAT is a known algorithm, and that the algorithm is merely applied in a specific data environment (budget management). Thus, the use of the neuroevolutionary machine learning models, neural networks using genetic algorithms, based on Neuroevolution of Augmenting Topologies (NEAT) amounts to generally linking the judicial exception to a particular field of use, which the courts have found to not sufficient to amount to significantly more than the judicial exception. Applicant is reminded that, under step 2B, whether the additional elements are well-understood, routine, and conventional activity is only one consideration under Step 2B. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception also include: (see MPEP §2106.05) Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer; and Generally linking the use of the judicial exception to a particular technological environment or field of use; Thus, since the additional elements amount to no more than mere instructions to apply the exception using a computer, and generally linking the judicial exception to a particular field of use, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Applicant’s argument that “Stanley and Miikkulainen do not disclose, and the Office has not cited any reference disclosing, the specific ordered combination of NEAT-based neuroevolutionary machine learning models integrated with a multi-module budget management architecture comprising a simulation module, budget formulation module, budget execution module, asset and service management module, spend management module, and fund management module, wherein the models are retrained within species groups to reduce performance effort on the system and enable faster real-time monitoring of a budget plan” is also an invalid argument because applicant is arguing novelty or obviousness. Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. There appears to have been confusion on why the Stanley & Miikkulainen reference (and other references) were cited. The cited references were to show that NEAT is a known machine learning technique, and is merely being applied to budget management here (field of use). To eliminate confusion, the 101 rejection has been simplified to reflect that conclusion, and the Stanley & Miikkulainen reference is only cited in the response to arguments to support examiner’s finding that NEAT is a known machine learning technique that is merely being applied to a budget management environment or application, which still amounts to generally linking the judicial exception to a particular field of use and “apply it” or merely implement the judicial exception via computer, both of which results in the claims being ineligible under Step 2B. For the reasons set forth above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. 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, 2, 4, 5, 8-10, 13, 15-20, and 23-28 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, 8-9, and 23-28 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, 8-10, 13, 15-20, and 23-28 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; determine, for each pair of the linear genome representations, a compatibility distance based on excess genes, disjoint genes, and matching genes of the pair, and assign each of the plurality of projects to a species group according to a compatibility threshold; predict, using the [trained and evolved one or more neuroevolutionary machine learning models], a project state indicating project results; monitor, over a time period, and using the one or more [neuroevolutionary machine learning models], 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; and retrain the one or more [neuroevolutionary machine learning models] within each species group independently based on the predicted project state and the redirection of the at least one partially unspent resource, wherein retraining within each species group reduces performance effort on the system by limiting crossover operations to genomes within a same species group enabling faster real-time monitoring of a budget plan; and a [budget formulation module] configured to receive contracts and subcontracts and generate the budget plan for at least one of the plurality of projects; a [budget execution module] configured to execute the budget plan; an [asset and service management module] configured to receive details associated with procured assets and services corresponding to executing the budget plan; a [spend management module] in communication with the [budget execution module], the [budget formulation module], and the [asset and service management module], wherein the [spend management module] is configured to: receive details related to asset status and service status from the [asset and service management module]; 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 a [fund management module] configured to track available funds of each project in the plurality of projects, receive funding plans from the [spend management module], receive cost estimates and funding requests from a [cost recovery module], 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 indicating project results, monitoring the projects, determining unspent resources and redirecting unspent resources, and 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 claim also recites limitations or features that correspond to mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) as evidenced by the claims describing the use of neuroevolutionary machine learning models which is a technique that uses genetic algorithms to build and optimize neural networks. Further, the machine learning system is based on Neuroevolution of Augmenting Topologies (NEAT) which is fundamentally a mathematical and computational optimization algorithm. 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 simply because the claims recite the additional elements of: 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 (budget management). 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 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, 5, and 23-28 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, 5, and 23-28 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; determining, for each pair of the linear genome representations, a compatibility distance based on excess genes, disjoint genes, and matching genes of the pair, and assigning each of the one or more projects to a species group according to a compatibility threshold; 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 one or more [neuroevolutionary machine learning models] within each species group independently based on the predicted project state and the redirection of the at least one partially unspent resource, wherein retraining within each species group reduces performance effort on the system by limiting crossover operations to genomes within a same species group enabling faster real-time monitoring of a budget plan generating the 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 claim also recites limitations or features that correspond to mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) as evidenced by the claims describing the use of neuroevolutionary machine learning models which is a technique that uses genetic algorithms to build and optimize neural networks. Further, the machine learning system is based on Neuroevolution of Augmenting Topologies (NEAT) which is fundamentally a mathematical and computational optimization algorithm. 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 claims 13, 17, 18, and 20 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, and 20 are also rejected under 35 U.S.C. 101. Allowable Subject Matter Claims 1, 2, 4, 5, 8-10, 13, 15-20, and 23-28 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. Additionally, Chandra (2014/0019370) discloses a system and method for transforming project management application representations into business process models. An aspect provides for receiving at least one portion of a project management application representation, and generating at least one business process by applying at least one transformation pattern to the at least one project management representation. The references do not appear to disclose the detailed limitations of the applicant’s claims. The claims appear to 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. Another relevant non-patent literature prior art reference found that is relevant to the applicant’s invention is “Evolving neural networks through augmenting topologies” (Stanley and Miikkulainen; 2002) which discloses the innovation of NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. The references do not appear to disclose the amended limitations of the applicant’s claims, specifically with applying NEAT in a budget management environment. The claims appear to 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.. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. DIONE N. SIMPSON Primary Examiner Art Unit 3628 /DIONE N. SIMPSON/Primary Examiner, Art Unit 3629