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, 10, and 23 have been amended. Claims 3, 6, 11, 12, 14, and 21 have been amended. Claim 24 is added as a new claim. Claims 1, 2, 4, 5, 7-10, 13, 15-20, and 22-24.
Response to Arguments
Applicant's arguments filed 04/16/2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
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. The italicized claim limitations describe an abstract idea that is set forth or described in the claim as required by the Step 2A Prong One analysis. Additional elements are properly considered under Step 2A Prong Two and Step 2B.
It is clear from above, 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 that the training and retraining of models, and determining a compatibility distance cannot be practically performed in the human mind, and is not a commercial or business interaction is unpersuasive. Applicant appears to be conflating whether claims are directed to mental processes (observation, evaluation, judgment, opinion), certain method of organizing human activity, or mathematical concepts and whether the claims retie something more (beyond) the abstract idea. The claims describe the observation and evaluation of data corresponding to project and budget management, and making a decision (judgment/opinion) based on the observed and evaluated data. The receiving if the various inputs for projects as indicated in the cited limitations also correspond to the observation and evaluation of data, and is data gathering even if being performed on a computer. The same is true for monitoring the plurality of projects to determine unspent resources. 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. Further, as indicated above in the claim limitations and the portions of the cited specification, the claim limitations also recite 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 retraining and training of the machine learning model is a mathematical optimization process. The fact that it may be complex does not change the character of the claim limitation. The claims recite an abstract idea.
Under Step 2A Prong Two, applicant argues that their claims are integrated into a practical application. Examiner disagrees. Step 2A Prong Two evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. An example of limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include an improvement in the functioning of a computer, or an improvement to other technology or technical field. The courts have also identified limitations that did not integrate a judicial exception into a practical application, which include "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; generally linking the use of a judicial exception to a particular technological environment or field of use; adding insignificant extra-solution activity to the judicial exception. In the applicant’s claim 1, 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). The neuroevolutionary machine learning models also amount to insignificant extra-solution activity. The use of the neuroevolutionary machine learning models is nominally or tangentially related to the invention. For instance, the applicant’s specification indicates in [0013] that that a deep learning model may also be used as the machine learning model. 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 mentions that the training and evolving of the neuroevolutionary machine learning models 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 that expand genome size", that "[w]henever a new gene appears (e.g., through structural mutation), a global innovation number may be incremented and assigned to that gene, and "structural mutations may occur in two ways. Each mutation may expand the size of the genome by adding gene(s). In the add connection mutation, a single new connection gene with a random weight may be added connecting two previously unconnected nodes. Alternatively, in the add node mutation, an existing connection may be split and the new node may be placed where the old connection used to be." Applicant argues that these represent specific improvements to the architecture and operation of the machine learning models themselves, not a new data environment. Examiner disagrees. What applicant is describing in their specification and argument is fundamentally how Neuroevolution of Augmenting Topologies (“NEAT”) works, not how NEAT improves computers or technology. The innovation number, add-connection, and add-node mutations are defined feature of the NEAT algorithm which was developed by Kenneth Stanley and Risto Miikkulainen in 2002. Citation to their MIT published article , “Evolving Neural Networks Through Augmenting Topologies” (2002), is provided in the cited references document. In fact, the cited portions of the specification. The published article discloses in section 3 in the description of Fig. 3 (pg. 106):
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“Figure 3: The two types of structural mutation in NEAT. Both types, adding a connection and adding a node, are illustrated with the connection genes of a network shown above their phenotypes. The top number in each genome is the innovation number of that gene. The innovation numbers are historical markers that identify the original historical ancestor of each gene. New genes are assigned new increasingly higher numbers. In adding a connection, a single new connection gene is added to the end of the
genome and given the next available innovation number. In adding a new node, the
connection gene being split is disabled, and two new connection genes are added to the
end the genome. The new node is between the two new connections. A new node gene
(not depicted) representing this new node is added to the genome as well.”
The published article, also discloses, similar to the applicant’s specification and argument that:
“Whenever a new gene appears (through structural mutation), a global innovation number is incremented and assigned to that gene. The innovation numbers thus represent a chronology of the appearance of every gene in the system. As an example, let us say the two mutations in Figure 3 occurred one after another in the system. The new connection gene created in the first mutation is assigned the number 7, and the two new connection genes added during the new node mutation are assigned the numbers 8 and 9. In the future, whenever these genomes mate, the offspring will inherit the same innovation numbers on each gene; innovation numbers are never changed. Thus, the historical origin of every gene in the system is known throughout evolution.” (pg. 108, Section 3.2)
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. The algorithm would further correspond to mathematical equations, as the retraining and training of the machine learning model is a mathematical optimization process. Add-connection and add-node mutations describe mathematical or logic operations because adding a connection gene with a weight between nodes, or splitting an existing connection by inserting a node is mathematical restructuring operations on a graph similar to that of Fig. 5 of the applicant’s disclosure which correspond to the cited portions of the specification that applicant uses for their argument, and Fig. 3 of the NEAT publication. The graph representing a neural network or neuroevolutionary model does not take the operations out of the abstract idea grouping, and as indicated earlier, the machine learning models amount to “apply it” and generally linking the judicial exception to a particular field of use (applied to budget management). 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. Further, the said innovation numbers appear to be used to solve a problem related to budget prediction which further indicates “apply it" or merely using a computer (that uses the machine learning model) as a tool to implement the judicial expectation. 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. The innovation number, structural mutations, etc. 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 further argues that the limitation to “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 species groups reduces processing overhead by limiting crossover operations to genomes within the same species group to enable faster real-time monitoring of the budget plan" specifies a concrete technical outcome including reduced processing overhead and faster real-time monitoring of the budget plan that results directly from the species- grouped retraining architecture, and is an improvement to computer performance. Examiner disagrees. At issue first is the applicant claiming the benefit or using an already invented machine learning algorithm as their improvement. The processing advantage the applicant is describing is from NEAT’s topology expansion and is a characteristic of the NEAT algorithm. Applicant is claiming the benefit of Stanley and Miikkulainen’s innovation. The alleged efficiency is inherent to NEAT’s design implemented via computer, not to the claims. This argument is one in computational or computing overhead, and is not persuasive. Computing overhead is merely a combination of excess computation time, usage, or memory required to perform the specific task, which further indicates that the alleged improvement is an improvement in the business process (being performed via computer) rather than an improvement in the actual computer itself. Similarly, "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. Technical improvement focuses on enhancing the tools, software, or machinery, while business process improvement focuses on streamlining the steps, workflows, and methodologies people use to do their work. Applicant’s claims describe the latter. This is further indicated in applicant’s argument that they’re addressing the specific problems that “conventional budget management systems suffer from being ‘challenging, time- consuming, and inefficient’ with ‘no way to simulate potential solutions’ and ‘no current technology solution capable of integrating the vast and complex data from procurement, finance, and information technology operations.’” It is clear that the alleged improvement is an improvement in the business process that corresponds to certain methods of organizing human activity, mental processes, mathematical concepts, and not an improvement to computers or technology. Examiner also notes in response to the “retaining” argument 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. Further, as stated by the court in Recentive "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.
Applicant provides another argument that 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 the compatibility threshold" is a specific algorithmic mechanism representing a defined computational operation applied to the genome data structures of the neuroevolutionary machine learning models themselves, which governs how the model population is internally organized and processed and is an improvement to the machine learning models themselves. Examiner disagrees. Again, there is issue with applicant taking their argument on compatibility distance straight from the innovators of NEAT. Stanley and Miikkulainen’s publication recites:
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This is further proof that applicant is claiming a pre-existing mechanism and algorithm executed via computer and applying it in the context of budget managemet, or “apply it” ore merely using a computer as a tool to implement the judicial exception, as well as generally linking the judicial exception to a particular field of use (budget management).
Under Step 2B, applicant argues that the amended claims recite an inventive concept that amounts to significantly more than any alleged exception. Specifically, that “the ordered combination of the recited claim elements is unconventional. The specific arrangement of neuroevolutionary machine learning models employing the combination of features including at least: (1) linear genome representations with connection genes, node genes, and innovation numbers as chronological markers for gene appearance, (2) adding connection mutations and adding node mutations that expand genome size, (3) compatibility distance determinations to assign projects to species groups according to a compatibility threshold, and (4) species-grouped independent retraining that reduces processing overhead by limiting crossover operations to genomes within the same species group, is most certainly not well-understood, routine, or conventional (WURC) in the art”. Applicant’s assertion that these features are not WURC in the art is difficult to reconcile being that the features, as indicated in the previous sections, are drawn nearly verbatim from the very prior art that established NEAT. The features of the NEAT algorithm was developed by Kenneth Stanley and Risto Miikkulainen in 2002. Citation to their MIT published article , “Evolving Neural Networks Through Augmenting Topologies” (2002) is provided in the cited reference document. Additionally, 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, and they absolutely amount to well-understood, routine, and conventional activity under Step 2B. 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.
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, 15-20, and 22-24 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 22-24 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, 15-20, and 22-24 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; 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 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 processing overhead by limiting crossover operations to genomes within a same species group to enable faster real-time monitoring of a budget plan; 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 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 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). Additionally, the neuroevolutionary machine learning models also amount to insignificant extra-solution activity. The use of the neuroevolutionary machine learning models is nominally or tangentially related to the invention. For instance, the applicant’s specification indicates in [0013] that that a deep learning model may also be used as the machine learning model. 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. Additionally, the use of the neuroevolutionary machine learning models, neural networks using genetic algorithms, based on Neuroevolution of Augmenting Topologies (NEAT) amounts to well-understood, routine, and conventional activity (see Stanley KO, Miikkulainen R. Evolving neural networks through augmenting topologies. Evol Comput. 2002 Summer;10(2):99-127; see also Levin WO 2022005527, ¶0223 disclosing that “Similarity to other architecture search algorithms. As mutations not only tune the parameters of an existing (parent) network, but can also add and remove genotype network structure (edges and vertices), the evolutionary algorithm is performing what is known as “architecture search” (40). Many evolutionary approaches to architecture search exist, such as NEAT (NeuroEvolution of Augmenting Topologies; (41))” and that “NEAT evolves artificial neural networks”; see also Harkiewicz 2022/0253757, ¶0064 “Learning from the structured data can be accomplished using a variety of optimization routines, including but not limited to, Bayesian Optimization strategy, and N.E.A.T. ML (Neuroevolution of Augmenting Topologies) to learn from the output of optimization routine.”). 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; 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 processing overhead by limiting crossover operations to genomes within a same species group to enable 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). Additionally, the neuroevolutionary machine learning models also amount to insignificant extra-solution activity. The use of the neuroevolutionary machine learning models is nominally or tangentially related to the invention. For instance, the applicant’s specification indicates in [0013] that that a deep learning model may also be used as the machine learning model. 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. Additionally, the use of the neuroevolutionary machine learning models, neural networks using genetic algorithms, based on Neuroevolution of Augmenting Topologies (NEAT) amounts to well-understood, routine, and conventional activity (see Stanley KO, Miikkulainen R. Evolving neural networks through augmenting topologies. Evol Comput. 2002 Summer;10(2):99-127; see also Levin WO 2022005527, ¶0223 disclosing that “Similarity to other architecture search algorithms. As mutations not only tune the parameters of an existing (parent) network, but can also add and remove genotype network structure (edges and vertices), the evolutionary algorithm is performing what is known as “architecture search” (40). Many evolutionary approaches to architecture search exist, such as NEAT (NeuroEvolution of Augmenting Topologies; (41))” and that “NEAT evolves artificial neural networks”; see also Harkiewicz 2022/0253757, ¶0064 “Learning from the structured data can be accomplished using a variety of optimization routines, including but not limited to, Bayesian Optimization strategy, and N.E.A.T. ML (Neuroevolution of Augmenting Topologies) to learn from the output of optimization routine.”). 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, 20, 22, 23, and 24 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, 23, and 24 are also rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1, 2, 4, 5, 7-10, 13, 15-20, and 22-24 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 amended 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 reference do not appear to disclose the detailed 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3629
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