Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/26/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/26/2026 was filed before the mailing of this action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claims 1, 3-7, 13, 17, 18, 20, and 21 have been amended. Claim 8 is canceled. Claims 1-7 and 19-21 are pending.
Response to Arguments
Applicant's arguments filed 03/26/2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant appears to argue that their claims are similar to Desjardins since the applicant references a Subject Matter Eligibility Declaration from December 2025 which is specifically directed towards the Desjardins appeal review panel case. Examiner disagrees. Generating a “reduced noise” dataset (which is merely a filtered dataset per applicant’s specification [0019] and how the dataset is described in the claims) that is generated based on applying rule mining and relationship rules, is not an improvement in computers (operational) or technology. Amended the claim to change the terminology from “a second dataset” to a “reduced noise dataset” does not change the scope of the claims. More on how the applicant’s claims and invention are not similar to Desjardins is provided in the final paragraph of this section.
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)). Here, it is clear from the Specification (including the claim language) that claim 1 (and independent claims 13 and 20) focuses on an abstract idea, and not on an improvement to technology and/or a technical field. The Specification discloses: [0003] The airline industry applies operations research methods and tools for the planning and scheduling of resources. Optimization-based decision support systems enable efficient and cost effective scheduling of aircraft and crew, and enable handling of short-term re-scheduling problems where modifications to initial plans are required before final schedules can be executed. [0004] On each day of operation, planned crew and aircraft schedules can become infeasible due to external situations and internal situations. The external situations and internal situations are identified by the optimization-based decision support systems as alerts. To date, no planning tools have been able to cope with the complexity of characterizing alerts and providing transactions to resolve the alerts. Despite the increasing power of hardware and sophisticated solution methods, there is still a gap between the reality faced in airlines' operations control and the decision support offered by the systems targeting the recovery of aircraft, crew, and passenger itineraries in one integrated system. [0005] Accordingly, there is a need for a method and system configured to characterize alerts and to predict a transaction to resolve an alert. [0019] Aspects disclosed herein present systems and methods for characterizing alerts and predicting transactions to resolve an alert. The system uses historical flight operator data associated with operation of an aircraft fleet and performs data mining techniques on the historical flight operator data. For example, association rule mining can be used, in order to characterize the transactions most frequently applied by an operator in order to solve an alert that causes an operational conflict in the schedules and/or crew rosters. The system can also use entity relationship rules which matches rules between an operational conflict and the applied transaction strategies, so that the common information between them (e.g., what resource is affected by the alert and what resource has its schedule or roster modified by the applied transaction) has a match. Application of associated rule mining and entity relationship rules allows for generation of a noise reduced dataset (e.g., a filtered dataset) that removes residual or noisy information from both elements that may be exogenous to the core information to be analyzed. [0021] The techniques and systems described herein provide the technical advantage of leveraging datasets containing airline disruption management information (e.g., previous alerts and transactions to resolve those alerts) from an analytical perspective. The described data mining techniques characterize the strategies and transactions most frequently applied by operators in order to solve alerts that cause operational conflicts in the aircraft schedules and/or crew rosters. These portions of the specification, along with the claim set as which defines the scope of the claimed invention, when considered as whole clearly discloses that the invention and claims are drawn towards characterizing alerts and predicting a transaction to resolve an alert in aircraft operations that affect the crew and passenger itineraries, and the claims directly correspond to certain methods of organizing human activity (following rules or instructions; managing personal interactions or behavior) as evidenced by the claim limitations performing steps of performing classification, analyzing data, determinizing statistical significance values, determining a strategy that solves the alert, and estimating strategies to solve disruptions in schedules, operational rules, or both, related to an airline. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) as evidenced by the limitations detailing estimating strategies to solve disruptions in scheduled and/or operational rules, evaluating or analyzing data and rules, performing classification operations corresponding to the rules and/or operations, and making a decision or determination (judgment/opinion) based on the observed or evaluated data. The limitations recite an abstract idea.
Applicant attempts to argue that their claims are similar to that of manipulating computer data structures (e.g., the pixels of a digital image and two-dimensional array known as a mask) and output of a modified computer data structure (a halftoned image), but applicant’s claims are in no way similar to the very technical aspects of manipulating the pixels of a digital image and two-dimensional array and outputting a modified halftoned image. Applicant’s claims merely describe the observation ana evaluation of data relating to the disruption of schedules, operation, rules, etc., filtering the dataset, removing alters based on significance value thresholds, determining a strategy to solve an alert, and sending the strategy to a device for display. This is a business process. At best, the alleged improvement is an improvement in the judicial exception itself and not an improvement in computers or technology. It is important to keep in mind that an improvement in the judicial exception itself is not an improvement in technology (emphasis added). For example, in Trading Technologies Int’l v. IBG LLC, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Similarly, the Applicant’s claim recitations are an improvement in the judicial exception, not an improvement in technology.
Step 2A Prong two evaluates whether the claim recites additional elements that integrate the judicial exception into a practical application. The courts have identified limitations that did not integrate a judicial exception into a practical application which include 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. Taking claims 1 and 13 for example, the judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a computing device, a display device (claim 1,) one or more memories and one or more processors (claim 13). 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. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Furthermore, just to address the recitation of a machine learning model in the claims, it is noted that applicant’s use or leverage of a generic machine learning model does not integrate the judicial exception into a practical application, nor makes it analogous to Desjardins. In Desjardins, the application is directed to the field of machine learning techniques, and the claims are aimed at specific and technically detailed ways of performing machine learning. Therefore, the claims fall into the category of an improvement to a computer/technology because of the specific technical nature of the claims. This is not the case with the applicant’s claims or invention. Applicant merely using generic machine learning and applies it to its business process/steps. There is no improvement in machine learning techniques in applicant’s claims. "[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). Also, as quoted from 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.
The 35 U.S.C. 101 rejection is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 and 9-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 13, and 20 recite the amended limitation(s) “generating a reduced noise dataset by: determining one or more statistical significance values associated with historical implementation of one or more of the previously selected transactions; and removing one or more residual alerts from the set of alerts and one or more residual transactions from the sets of transactions when the one or more statistical significance values is less than a threshold.” There does not appear to be support in the applicant’s specification for the amended underlined limitation. Applicant indicates in their remarks that support for the claim amendments can be found in the specification at least at paragraph [0030]-[0031]. After reviewing the referenced portions of the specification, as well as other areas of the specification relating to removing alerts, there was no support found for removing one or more residual alerts from the set of alerts and one or more residual transactions from the sets of transactions when the one or more statistical significance values is less than a threshold. Thus, the amended limitation is considered new matter.
Dependent claims 2-7, 9-12, 14-19, and 21 are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, due to their dependency on the rejected independent claims indicated above.
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 1-7 and 9-21 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 1-7, 9-12, 20, and 21 recite a method (i.e. process), 13-19 recite a system (i.e. machine). Therefore claims 1-7 and 9-21 fall within one of the four statutory categories of invention.
Independent claims 1 and 13 recites the limitations: retrieving, at [a computing device] configured to estimate strategies to solve disruptions in schedules, operational rules, or both, related to an airline based on previously selected strategies and previously selected transactions, historical flight operator data associated with operation of an aircraft fleet; performing classification operations to generate a first dataset, wherein the first dataset includes a set of alerts, corresponding to one or more of the disruptions in the schedules, the operational rules, or both, and sets of transactions, corresponding to one or more of the previously selected transactions, for each alert; analyzing the first dataset to determine association rules and entity relationship rules; generating a reduced noise dataset by: determining one or more statistical significance values associated with historical implementation of one or more of the previously selected transactions; and removing one or more residual alerts from the set of alerts and one or more residual transactions from the sets of transactions when the one or more statistical significance values is less than a threshold; determining a strategy to be used to solve an alert based on the reduced noise dataset; and sending output to a [display device] to display the strategy (claim 1). The invention and claims are drawn towards characterizing alerts and predicting a transaction to resolve an alert in aircraft operations that affect the crew and passenger itineraries, and the claims directly correspond to certain methods of organizing human activity (following rules or instructions; managing personal interactions or behavior) as evidenced by the claim limitations performing steps of performing classification, analyzing data, determinizing statistical significance values, determining a strategy that solves the alert, and estimating strategies to solve disruptions in schedules, operational rules, or both, related to an airline. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) as evidenced by the limitations detailing estimating strategies to solve disruptions in scheduled and/or operational rules, evaluating or analyzing data and rules, performing classification operations corresponding to the rules and/or operations, and making a decision or determination (judgment/opinion) based on the observed or evaluated data. The limitations 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: a computing device, a display device (claim 1,) one or more memories and one or more processors (claim 13). 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. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 10 and 14 recite the limitations that determining the strategy to be used to solve the alert further comprises determining, using a first machine learning model, that a manual transaction should be performed. The claim limitations are further directed to the abstract idea analyzed above. The machine learning model, being a computation program or algorithm, corresponds to mathematical concepts (mathematical formulas or equations, mathematical calculation). The claims along with the additional elements identified above in the analysis of the independent claims, amount to no more than mere instructions to apply the judicial exception using a generic computer. Accordingly, in combination, the 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 claims are not patent eligible.
The machine learning model corresponding to mathematical concepts also applies to other recitations of a machine learning model in the claim set as well (a second model and third machine leaning model in claims 11, 12, 15, 16)
Independent claim 20 recite the limitations: obtaining, at a [computing device] configured to estimate strategies to solve disruptions in schedules, operational rules, or both, related to an airline based on previously selected strategies and previously selected transactions, a first dataset that includes a set of alerts and sets of transactions for each alert of the set of alerts; generating a reduced noise dataset by: determining one or more statistical significance values associated with historical implementation of one or more of the previously selected transactions; and removing one or more residual alerts from the set of alerts and one or more residual transactions from the sets of transactions, when the one or more statistical significance values associated with the one or more residual alerts is less than a threshold value; determining that an alert has occurred; determining, using a first machine learning model, that a manual transaction should be performed; determining, using a second machine learning model, a type of strategy to be used to solve the alert based on the reduced noise dataset; determining, using a third machine learning model, one or more transactions associated with the type of strategy to be used to solve the alert based on the reduced noise dataset; and sending an indication of the one or more transactions associated with the type of strategy to be used to solve the alert to [a device]. The invention and claims are drawn towards characterizing alerts and predicting a transaction to resolve an alert in aircraft operations that affect the crew and passenger itineraries, and the claims directly correspond to certain methods of organizing human activity (following rules or instructions; managing personal interactions or behavior) as evidenced by the claim limitations estimating strategies to solve disruptions in schedules, operational rules, or both, related to an airline and performing steps of determining an alert has occurred; determining that a manual transaction should be performed; determining a type of strategy to be used to solve the alert; determining one or more transactions associated with the type of strategy to be used to solve the alert; and sending indication the one or more transactions associated with the type of strategy to be used to solve the alert. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) as evidenced by the limitations detailing evaluating or analyzing data, and making a decision or determination (judgment/opinion) based on the observed or evaluated data. The claim also generally recites machine learning models, the models being a computation program or algorithm, which corresponds to mathematical concepts (mathematical formulas or equations, mathematical calculation). The limitations 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 claim recites the additional elements of: a computing device and a device. 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. 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. 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 claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claims 2-7, 9, 11, 12, 15-19, and 21 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 2-7, 9, 11, 12, 15-19, and 21 are also rejected under 35 U.S.C. 101. The claims are not patent eligible.
Allowable Subject Matter
Claims 1-7 and 9-21 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 Small (2008/0010005) which discloses a method for providing situational awareness for conditions related to aircrafts. The process includes receiving data related to one or more events that have the potential to affect conditions related to an aircraft's departure from a plurality of enterprise related systems, correlating the received data in accordance with one or more business rules, generating an aircraft departure situational awareness data set from the correlated data, processing the aircraft departure situational awareness data set in view of at least one user profile, and providing at least one recommendation, each recommendation associated with one user profile, directed to addressing the conditions related to aircraft departure. The reference does not appear to explicitly disclose the amended limitations of the applicant’s claims, particularly the removal of one or more residual alerts from the set of alerts and one or more residual transactions from the sets of transactions when the one or more statistical significance values is less than a threshold. The claims appear to overcome the prior art.
The closest non-patent literature prior art reference found that is relevant to the applicant’s invention includes the publication “Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview” (Fu, Avdelidis); 2023) which provides an analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The publication does not appear to disclose the amended limitations of the applicant’s claims, particularly the removal of one or more residual alerts from the set of alerts and one or more residual transactions from the sets of transactions when the one or more statistical significance values is less than a threshold. 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 3628
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