DETAILED ACTION
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
The amendment received on 21 October 2025 has been acknowledged and entered.
Claims 1, 5, 11, 14, 17, and 19-20 have been amended.
Claims 10 and 18 have been canceled. No new claims have been added.
Claims 1-9, 11-17, and 19-20 are currently pending.
Response to Amendments and Arguments
Applicant's arguments filed 21 October 2021 with respect to the rejection of claims 1-9, 11-17, and 19-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (in Remarks, pages 14-15 of 17) that Applicant has amended claim 1 to incorporate all limitations of claim 10, as well as the limitation of claim 11 that recites that the trained machine learning model is a counterfactual recurrent network (CRN). The current amendment to claim 1 further recites the feature "the trained machine learning model is trained using training data that includes historical vehicle health signal data and historical maintenance event data," which is supported at least by FIG. 4B and Paras. [0053]-[0056] of the subject application…The rejection of claim 1 in the current Office action argues that each of the limitations of claim 1 is directed to a mental process, a method of organizing human activity, or insignificant solution activity. The rejection further states that the features of claim 1 are not integrated into a practical application and do not provide significantly more than a judicial exception. Claim 1 as currently amended recites a trained machine learning model that has a CRN architecture and is configured to determine numbers of vehicles that have respective maintenance-free operating periods (MFOPs) shorter than a duration of the active mission phase. In addition, the trained machine learning model is trained using historical vehicle health signal data and historical maintenance event data. This training data allows the machine learning model to predict potential maintenance events (e.g., due to a component failing) from vehicle health signal data measured at inferencing time.
As disclosed, for example, in Para. [0075], the logistic plan and maintenance plan are generated in a manner that balances efficient resource usage with robustness to adverse events. The logistic plan and maintenance plan therefore allow higher numbers of vehicles to remain operable during an active mission phase. The increase in the number of vehicles that remain operable during the active mission phase is achieved at least in part by executing the trained machine learning model recited in claim 1. Claim 1 as currently amended recites specific features of the machine learning model (its architecture and training data) that allow the trained machine learning model to achieve these advantages.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, even with the recent amendment, the claims as a whole recite a method of organizing human activity. The limitations are processes that, under their broadest reasonable interpretation, may be interpreted as at least as a “Mental Process” for the steps pertaining to determining and/or computing. Further, the steps are directed to teaching, and following rules or instructions. The fact that the claims are directed to a trained machine learning model that has a CRN architecture and is configured to determine numbers of vehicles that have respective maintenance-free operating periods (MFOPs) shorter than a duration of the active mission phase, does not take the claims out of the Method of organizing human activity grouping. Lastly, the courts determined 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)); and the courts also determined 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." Therefore, the Examiner maintains the claims are patent ineligible and do not integrate the judicial exception into practical application.
Applicant argues further (in Remarks, pages 15-16 of 17) that as recognized in Ex parted Desjardins (Appeal 2024- 000567, ARP Sept. 26, 2025), AI-related patent claims that are directed to improving how the machine learning model itself operates are not generic implementations but rather technological improvements to computer functionality. In Desjardins, the Appeals Review Panel vacated a § 101 rejection because the claims improved "how the machine-learning model operates." The Desjardins decision cautioned against equating any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components" without adequate explanation. The features of claim 1 related to training the machine learning model recite a specific improvement to the operation of the machine learning model. The trained machine learning model of claim 1 is accordingly not a generic computer component.
The trained CRN, as recited in claim 1 as currently amended, could not be instantiated in the human mind due to the differences in the structure and activity of artificial and biological neural networks. Executing the trained machine learning model is also not an instance of insignificant extra-solution activity, since the increase in the number of vehicles that remain operable during the active mission phase is achieved at least in part by executing the trained machine learning model. Claim 1 as currently amended therefore recites a specific structure and functionality of the trained machine learning model that distinguish the training machine learning model from operations that could be performed in the human mind or by organizing human activity, as well as distinguishing the trained machine learning model from extra-solution activity or generic computer components. Applicant respectfully submits that the trained machine learning model, in combination with the other features of claim 1, integrates any abstract ideas that may be recited in claim 1 into a practical application. Thus, claim 1 with the proposed amendment is directed to eligible subject matter at Step 2A Prong 2 of the Alice/Mayo subject matter eligibility test. Applicant also respectfully submits that the trained machine learning model, in combination with the other features of claim 1 as currently amended, amounts to significantly more than an abstract idea.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, Applicant appears to be referencing a business solution to a business problem. For instance, executing the trained machine learning model to “increase in the number of vehicles that remain operable during the active mission phase” appears to be a business solution to business problem by use of the machine learning model, and not a technical solution to a technical problem. It is suggested that Applicant provides the mechanism which provides an overall improvement to the system, processor, or models in the claims to provide significantly more. Secondly, the claims fail to show how the “trained model” was initially trained and amount to no more than mere instructions to apply the exception using a generic computer component. Lastly, the courts determined 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)); and "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." Because courts have consistently held that claims simply placing an abstract idea into a new field of use do not transform it into a patent-eligible invention, the Examiner maintains the claims are patent ineligible and do not integrate the judicial exception into practical application.
Applicant argues (in Remarks, page 16 of 17) that Applicant has amended independent claims 14 and 20 similarly to claim 1. For the reasons provided above with reference to claim 1, Applicant respectfully submits that claims 14 and 20 as currently amended are directed to eligible subject matter. In addition, Since all features of claims 10 and 18, and some of the features of claims 11 and 19, have been incorporated into their respective independent claims, Applicant has canceled claims 10 and 18 without prejudice and has amended claims 11 and 19 to reflect the amendments to claims 1 and 14. Applicant has also amended claims 5 and 17 to reflect the amendments to claims 1 and 14. Applicant respectfully requests the withdrawal of the rejection under 35 U.S.C. 101.
In response to Applicant’s argument, the Examiner respectfully disagrees for reasons stated above regarding the rejection of claims 1
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 1-9, 11-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1-9 and 11-13 are directed to a computing system (i.e., a machine); Claims 14-17 and 19 are directed to a method (i.e., a process); and Claim 20 is directed to a computing system (i.e., a machine). Therefore, Claims 1-9, 11-17, and 19-20 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1
Independent claims 1, 14, and 20 substantially recite:
generate/generating/generate a graphical model of a plurality of locations, a plurality of vehicles, and a plurality of vehicle maintenance resources;
receive/receiving/receiving, for each vehicle of the plurality of vehicles, respective vehicle health signal data and maintenance event data;
compute/computing/computing a logistic plan based at least in part on the graphical model, wherein:
the logistic plan includes an assignment of the vehicles and the vehicle maintenance resources among the plurality of locations; and
computing the logistic plan includes determining/determining/determining that the assignment of the vehicle maintenance resources satisfies a vulnerability assessment constraint;
over a plurality of maintenance plan generating iterations, compute/computing/computing a maintenance plan for the plurality of vehicles that minimizes or maximizes a maintenance plan objective function, wherein each of the maintenance plan generating iterations includes:
based at least in part on the vehicle health signal data, the maintenance event data, and the logistic plan, generating/generating/generating a plurality of candidate maintenance plans for the plurality of vehicles, wherein the candidate maintenance plans are associated with an active mission phase of a mission that utilizes the plurality of vehicles;
for each of the candidate maintenance plans, determining/determining/determining whether performing the maintenance plan when the vehicle maintenance resources have the assignment indicated in the logistic plan would violate the vulnerability assessment constraint, at least in part by:
at a trained machine learning model, for each of a plurality of simulated adverse events, determining/determining/determining a number of the vehicles that have respective maintenance-free operating periods (MFOPs) shorter than a duration of the active mission phase, wherein:
the trained machine learning model is trained/trained/trained using training data that includes historical vehicle health signal data and historical maintenance event data; and
computing/computing/computing over the plurality of simulated adverse events, an expected value of the number of vehicles that have MFOPs shorter than the duration;
selecting/selecting/selecting, as the maintenance plan, a candidate maintenance plan of the plurality of candidate maintenance plans that has a lowest expected value of the number of vehicles that have MFOPs shorter than the duration; and
if performing the maintenance plan would violate the vulnerability assessment constraint, modifying/modifying/modifying the logistic plan; and
output/outputting/outputting the logistic plan and the maintenance plan.
As per Independent claims 1, 14, and 20, the limitations as a whole recite a method of organizing human activity. The aforementioned limitations as drafted, are processes that, under their broadest reasonable interpretation, may be interpreted as at least as a “Mental Process” (concepts performed in the human mind) which includes observations, evaluations, judgments, and opinions and/or “Managing Personal Behavior or Relationships or Interactions Between People” which includes social activities, teaching, and following rules or instructions. Nothing in the claim elements preclude the step from practically being performed by the human mind (i.e. compute/computing/compute; computing/computing/ computing; determining/determining/determining; determining/determining/determining; trained/trained/trained; computing/computing/computing); Managing personal behavior or relationships or interactions between people (i.e. generate/generating/generate; receive/receiving/receive; compute/computing/compute; determining/determining/determining; compute/computing/compute; generating/generating/generating; determining/determining/determining; determining/determining/determining; trained/trained/trained; computing/computing/computing; selecting/selecting/selecting; modifying/modifying/modifying; and outputting/outputting/outputting).
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements (e.g. “a computing system,” “one or more processing devices,” and “a counterfactual recurrent network (CRN)”; claim 14 recites the additional element (e.g. “a computing system” and “a counterfactual recurrent network (CRN)”); and claim 20 recites the additional element (e.g. “one or more processing devices,” “a plurality of nodes,” “a plurality of location nodes,” “a plurality of location transition nodes,” ”a plurality of maintenance transition nodes,” ”a plurality of edges,” “a plurality of tokens,” “a plurality of vehicle tokens,” “a plurality of resource tokens” and “a counterfactual recurrent network (CRN)”)– using the “computing system” and/or “one or more processing devices” to perform the generate/generating/generate; receive/receiving/receive; compute/computing/compute; determining/determining/determining; compute/computing/compute; generating/generating/generating; determining/determining/determining; determining/determining/determining; trained/trained/trained; computing/computing/computing; selecting/selecting/selecting; modifying/modifying/modifying; and outputting/outputting/outputting step in claims 1, 14, and 20. The “one or more processing devices” in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of generate/generating/generate; receive/receiving/receive; compute/computing/compute; determining/determining/determining; compute/computing/compute; generating/generating/generating; determining/determining/determining; determining/determining/determining; trained/trained/trained; computing/computing/computing; selecting/selecting/selecting; modifying/modifying/modifying; and outputting/outputting/outputting in claims 1, 14, and 20) such that it amounts no more than mere instructions to “apply” the exception using a generic computer component. That is, the aforementioned limitations merely invoke the generic components as a tool to perform the abstract idea, e.g. see MPEP 2106.05(f).
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
Independent claims 1, 14, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the
“computing system,” “one or more processing devices,” and “counterfactual recurrent network (CRN)” in claim 1; the “computing system” and “counterfactual recurrent network (CRN)” in claim 14; and the “one or more processing devices,” “plurality of nodes,” “plurality of location nodes,” “plurality of location transition nodes,” ”plurality of maintenance transition nodes,” ”plurality of edges,” “plurality of tokens,” “plurality of vehicle tokens,” “plurality of resource tokens” and “counterfactual recurrent network (CRN)” to perform the generate/generating/generate; receive/receiving/receive; compute/computing/compute; determining/determining/determining; compute/computing/compute; generating/generating/generating; determining/determining/determining; determining/determining/determining; trained/trained/trained; computing/computing/computing; selecting/selecting/selecting; modifying/modifying/modifying; and outputting/outputting/outputting steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e. inventive concept) to the abstract idea. The claims are ineligible.
As per dependent claims 2, 3, 4, 7 , 15, and 16 the limitations merely narrow the previously recited abstract idea limitations. Dependent claims 2 and 15 recite the graphical model includes: a plurality of nodes including a plurality of location nodes that indicate the locations; a plurality of edges between the nodes; and a plurality of tokens including a plurality of vehicle tokens that represent the vehicles and a plurality of resource tokens that represent the vehicle maintenance resources. Dependent claims 3 and 16 recite the graphical model is a petrinet. Dependent claim 4 recite the vulnerability assessment constraint is: a connectedness constraint on the graphical model; a minimum cut constraint on the graphical model; or a resource replaceability constraint on the assignment of the vehicle maintenance resources. Dependent claim 7 recites removal of at least one edge from the graphical model; removal of at least one node from the graphical model; removal of at least one vehicle maintenance resource token; an increase in the weighting coefficient of at least one vehicle maintenance resource; or damage to at least one vehicle.
For the reasons described above with respect to claims 8 and 9, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 5 and 17, the recitations, “compute the logistics plan at least in part by…”; “applying a simulated adverse event of the plurality of simulated adverse events to the graphical model to generate a perturbed graphical model”; “determining whether the perturbed graphical model violates the vulnerability assessment constraint when the vehicle maintenance resources are assigned as indicated in the logistic plan”; and “if the perturbed graphical model violates the vulnerability assessment constraint: “computing a minimal transition set of one or more modifications to the logistic plan that resolve the violation of the vulnerability assessment constraint”; and “applying the minimal transition set to the logistic plan” are further directed to a method of organizing human activity and/or a mental process as described in claim 1. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 6, the recitations, “receive a respective plurality of weighting coefficients…”; and “generate the logistic plan based at least in part on the weighting coefficients” are further directed to a method of organizing human activity as described in claim 1. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 8, the recitation, ”compute the logistic plan at least in part by minimizing or maximizing a logistic plan objective function” is further directed to a method of organizing human activity and/or a mental process as described in claim 1. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 9, the recitation, “compute the logistic plan objective function based at least in part on: respective numbers of the plurality of vehicle maintenance resources located at each of the locations; the respective weighting coefficients associated with the plurality of vehicle maintenance resources; and a total number of resource relocation events specified by the logistic plan” is further directed to a method of organizing human activity and/or a mental process as described in claim 1. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 11 and 19, the recitation, “compute predicted vehicle health signal data based at least in part on the vehicle health signal data and the maintenance event data” .
Dependent Claims 2-9, 11-13, 15-17, and 19 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent Claims 2-9, 11-13, 15-17, and 19, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. The claims are not patent eligible.
Prior Art Discussion
As per independent claims 1, 14, and 20, the best prior art :
1) Zivelin et al. (US PG Pub. 2014/0278713 A1) discloses asset forecasting in asset intensive enterprises wherein each asset has an asset-specific scheduled maintenance plan includes activities pertaining to the asset-specific scheduled maintenance plan. Observations are made and events are recorded to generate a series of observations are put into a learning model which is then used to predict a future demand or a forecast for items in quantities that are not given in the asset-specific scheduled maintenance plan.
However, Zivelin et al. fails to disclose or fairly teach:
over a plurality of maintenance plan generating iterations,
compute a maintenance plan for the plurality of vehicles that minimizes or maximizes a maintenance plan objective function, wherein each of the maintenance plan generating iterations includes:
if performing the maintenance plan would violate the vulnerability assessment constraint, modifying the logistic plan
As per independent claims 1, 14, and 20, the best Foreign prior art :
1) Francino et al. (DE 102011051671 A1) discloses an optimization system using an iterative expert engine.
However, Francino et al. fails to disclose or fairly teach:
over a plurality of maintenance plan generating iterations,
compute a maintenance plan for the plurality of vehicles that minimizes or maximizes a maintenance plan objective function, wherein each of the maintenance plan generating iterations includes:
if performing the maintenance plan would violate the vulnerability assessment constraint, modifying the logistic plan
As per independent claims 1, 14, and 20, the best NPL prior art :
1) Daniel Riera et al., “PN to CSP Methodology: Improved Bounds”, : CCIA 2002, LNAI 2504, pp. 145–158, 2002.discloses an improvement to a methodology in the area of Knowledge Based Systems (KBS) which generate automatically Constraint Satisfaction Problems (CSP), using Petri-nets (PN) to model the problem and Constraint Programming (CP) in the solution by combining the modelling power of PN to represent both manufacturing architecture and production logistics, together with the optimization performance given by CP.
2) Jingyu Sheng and Darren Prescott, “A coloured Petri net framework for modelling aircraft fleet maintenance”, 2019, Reliability Engineering and System Safety 189 (2019) 67–88 discloses a variety of CPN (coloured Petri nets) models are established to represent fleet maintenance activities and maintenance management, as well as the factors that have a significant impact on fleet maintenance including fleet operation, aircraft failure logic and component failure processes.
3) Hoi-Lam Ma et al., “Tackling uncertainties in aircraft maintenance routing: A review of emerging technologies”, August 2022; Transportation Research Part E: Logistics and Transportation Review Volume 164, 12 pages discloses using modelling approaches to provide opportunities in the route to allow maintenance checks to take place or to model the regulations using constraints. For example, opportunity restrictions may force an aircraft to stay at a qualified station overnight for adequate daily-check hours, while regulation constraints require that a maintenance activity must be conducted before an aircraft reaches the maximum flying hours, takeoffs, and/or elapsed time.
However, Daniel Riera et al., Jingyu Sheng and Darren Prescott, and Hoi-Lam Ma et al. fails to disclose or fairly teach:
over a plurality of maintenance plan generating iterations,
compute a maintenance plan for the plurality of vehicles that minimizes or maximizes a maintenance plan objective function, wherein each of the maintenance plan generating iterations includes:
if performing the maintenance plan would violate the vulnerability assessment constraint, modifying the logistic plan
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Waagen et al. (US PG Pub. 20230394889 A1) discloses vehicle health management using a counterfactual machine learning model to optimize maintenance parameters.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/ Supervisory Patent Examiner, Art Unit 3628