DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to RCE and Amendment filed on 6/2/2026.
Claims 1-11 are withdrawn.
Claim 16 is canceled.
Claims 12-15, 17-26 are pending for examination.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/2/2026 has been entered.
Response to Arguments
(A) Applicant's arguments filed “The applicant respectfully traverses the rejections of claims 12-21 under 35 U.S.C. §101 as allegedly being directed to an abstract idea because the claimed subject matter is not directed to an abstract idea, and because the claims integrate any alleged abstract idea into a practical application. Claims 12, 19, and 21 are amended to include a control step, as discussed in the interview. Advantages of the control step include, for example, predicting failure without any direct sensor measurements, and predicting failure more than thirty cycles before failure occurs, which allows for efficient operational scheduling of repairs. See e.g., paragraph [0033]. For this reason, the applicant respectfully requests withdrawal of the rejections of claims 12-21.” on 12/10/2025 have been fully considered but they are not persuasive.
As to point (A), the examiner respectfully disagrees. The examiner further notes the limitations of “generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action”, the examiner submits that these limitations are mere data outputting in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “generating an electronic maintenance action request” and “sending the electronic maintenance action request” indicate post-solution activity such that it amounts no more than a step of outputting data for use in a claimed process. The examiner further notes that the amendment failed to indicate a control step. A control step would indicate a control operation of the components such as adjustment of the operation of the components or termination of the operation of the component.
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 12-15, 17-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis-Step 1
Claims 12-15, 17-18, 20, 22-23 are directed to A computer-implemented method of using machine learning for failure prediction in a cabin air compressor (CAC) system (i.e., a process). Therefore, claims 12-20 are within at least one of the four statutory categories.
101 Analysis-Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 12 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for reminder of the 101 rejection. Claim 12 recites:
A computer-implemented method of using machine learning for failure prediction in a cabin air compressor (CAC) system, the computer-implemented method comprising:
collecting, at one or more processors, streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation;
providing, by the one or more processors, the collected CAC feature data to a machine learning model, the machine learning model filtering out background noise and/or other signal anomalies from the streams of CAC data, the machine learning model being trained using time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score; and
generating, using the machine learning model, the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer;
the failure pattern analyzer, by the one or more processors, applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction; and
generating a failure prediction report containing the CAC failure prediction, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting,
generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action,
wherein the physical characteristics data comprises CAC speed, change in CAC speed over time, CAC mass flow rate, change in CAC mass flow rate over time, CAC outlet pressure, change in CAC outlet temperature over time, CAC outlet temperature, and change in CAC outlet temperature over time; the calculated CAC operation data comprises work of a CAC system, energy change of a CAC compressor, rolling data of the work of the CAC, and rolling data of the energy change of the CAC; and the CAC feature data comprises a CAC efficiency and a rolling CAC efficiency rolling over time.
The examiner submits that the foregoing bolded limitation(s) constitute a "mental process" and/or “certain methods of organizing human activity” because under its broadest reasonable interpretation, the claim covers performance of the limitation by a user or in the human mind. For example, “filtering out background noise and/or other signal anomalies from the streams of CAC data” in the context of this claim encompasses the user mentally filtering data. “generating…the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer” in the context of this claim encompasses the user mentally predicting a failure score. Similarly, the limitation of " the failure pattern analyzer … applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction " in the context of this claim encompasses the user mentally predicting a failure. Accordingly, the claim recites at least one abstract idea.
101 Analysis-Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim as a whole, integrates the abstract into a partial application. As noted in the 2019 PEG, it must be determined whether there are any additional elements recited in the claim beyond the judicial exception(s), and whether those additional elements integrate the exception into a practical application of the exception.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A computer-implemented method of using machine learning for failure prediction in a cabin air compressor (CAC) system, the computer-implemented method comprising:
collecting, at one or more processors, streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation;
providing, by the one or more processors, the collected CAC feature data to a machine learning model, the machine learning model filtering out background noise and/or other signal anomalies from the streams of CAC data, the machine learning model being trained using time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score; and
generating, using the machine learning model, the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer;
the failure pattern analyzer, by the one or more processors, applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction; and
generating a failure prediction report containing the CAC failure prediction, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting,
generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action,
wherein the physical characteristics data comprises CAC speed, change in CAC speed over time, CAC mass flow rate, change in CAC mass flow rate over time, CAC outlet pressure, change in CAC outlet temperature over time, CAC outlet temperature, and change in CAC outlet temperature over time; the calculated CAC operation data comprises work of a CAC system, energy change of a CAC compressor, rolling data of the work of the CAC, and rolling data of the energy change of the CAC; and the CAC feature data comprises a CAC efficiency and a rolling CAC efficiency rolling over time.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “collecting … streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation”, “providing … the collected CAC feature data to a machine learning model”, and “wherein the physical characteristics data comprises CAC speed, change in CAC speed over time, CAC mass flow rate, change in CAC mass flow rate over time, CAC outlet pressure, change in CAC outlet temperature over time, CAC outlet temperature, and change in CAC outlet temperature over time; the calculated CAC operation data comprises work of a CAC system, energy change of a CAC compressor, rolling data of the work of the CAC, and rolling data of the energy change of the CAC; and the CAC feature data comprises a CAC efficiency and a rolling CAC efficiency rolling over time” the examiner submits that these limitations are mere data gathering in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “collecting … CAC feature data”, “providing … the collected CAC feature data”, and “the physical characteristics data comprises” indicate pre-solution activity such that it amounts no more than a step of gathering data for use in a claimed process.
Regarding the additional limitations of “at one or more processors”, “by the one or more processors”, “the machine learning model being trained using time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score”, “using the machine learning model”, “by the one or more processors”, the examiner submits that these limitations are mere instructions to apply the above noted abstract idea by merely using a computer to perform the process (MPEP § 2106.05). In particular, processor recited at a high-level of generality (i.e., as processor performing a generic computer function of computing and organizing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Furthermore, machine learning model recited at a high-level of generality (i.e., as algorithm performing a generic computer function of computing result) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Regarding the additional limitations of “generating a failure prediction report containing the CAC failure prediction, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting” and “generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action”, the examiner submits that these limitations are mere data outputting in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “generating a failure prediction report”, “storing, transmitting, and/or displaying the failure prediction reporting”, “generating an electronic maintenance action request”, and “sending the electronic maintenance action request” indicate post-solution activity such that it amounts no more than a step of outputting data for use in a claimed process.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add no thing that is nor already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2 106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis-Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 12 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “at one or more processors”, “by the one or more processors”, “the machine learning model being trained using time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score”, “using the machine learning model”, “by the one or more processors” amounts to nothing more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component cannot provide an inventive concept.
Furthermore, regarding the additional limitation of “collecting … streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation”, “providing … the collected CAC feature data to a machine learning model”, “wherein the physical characteristics data comprises CAC speed, change in CACspeed over time, CAC mass flow rate, change in CAC mass flow rate over time, CAC outlet pressure, change in CAC outlet temperature over time, CAC outlet temperature, and change in CAC outlet temperature over time; the calculated CAC operation data comprises work of a CAC system, energy change of a CAC compressor, rolling data of the work of the CAC, and rolling data of the energy change of the CAC; and the CAC feature data comprises a CAC efficiency and a rolling CAC efficiency rolling over time”, “generating a failure prediction report containing the CAC failure prediction, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting”, and “generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action”, the examiner submits that the limitation merely adds insignificant extra-solution activity to the at least one abstract idea as previously discussed.
Hence the claim is not patent eligible.
Therefore, claim(s) 12 is/are ineligible under 35 U.S.C. 101.
Regarding Claim 13, the claim recites further narrowing limitation on the “storing, transmitting, and/or displaying the failure prediction report” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 14, the claim recites further narrowing limitation on the “storing, transmitting, and/or displaying the failure prediction report” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 15, the claim recites further narrowing limitation on the “storing, transmitting, and/or displaying the failure prediction report” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 17, the claim recites further narrowing limitation on the “the heuristic is selected from the group consisting of: a prediction score threshold value, an average prediction score over time, a prediction score pattern over time, and a rate of change in prediction score” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 18, the claim recites further narrowing limitation on the “obtaining the time series CAC feature training data from one or more data stores and providing the time series CAC feature training data to train the machine learning model” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 20, the claim recites “applying the obtained time series CAC feature training data to a feature selection pipeline that performs SHapley Additive exPLanations (SHAP) reducing a total number of CAC features in the time series CAC feature training data before providing the time series CAC feature training data to train the machine learning model” which is mere instructions to apply the exception using a generic computer component and fail to integrate the abstract idea into a practical application.
Regarding Claim 22, the claim recites “transmitting, by the one or more processors, the failure prediction report to a maintenance server” which is mere instructions which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 23, the claim recites “wherein the network interface is a wireless interface” which is mere instructions to apply the exception using a generic computer component and fail to integrate the abstract idea into a practical application.
Regarding Claim 24, the claim recites further narrowing limitation on the “conducting…an automated analysis based on the electronic maintenance action” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application. The claim recites “by a computer system disposed on an airplane” which is mere instructions to apply the exception using a generic computer component and fail to integrate the abstract idea into a practical application.
Regarding Claim 25, the claim recites further narrowing limitation on the “generating an electronic maintenance action further includes flagging a CAC system for maintenance action” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
101 Analysis-Step 1
Claims 19 are directed to A computer-implemented method of using machine learning for failure prediction in a cabin air compressor (CAC) system (i.e., a process). Therefore, claims 19 is within at least one of the four statutory categories.
101 Analysis-Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 19 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for reminder of the 101 rejection. Claim 19 recites:
A computer-implemented method of using machine learning for failure prediction in a cabin air compressor (CAC) system, the computer-implemented method comprising:
collecting, at one or more processors, streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation;
providing, by the one or more processors, the collected CAC feature data to a machine learning model, the machine learning model filtering out background noise and/or other signal anomalies from the streams of CAC data, the machine learning model being trained using time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score; and
generating, using the machine learning model, the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer;
the failure pattern analyzer, by the one or more processors, applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction;
generating a failure prediction report containing the CAC failure prediction, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting; and
generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action,
wherein the machine learning model comprises a gradient boosting model.
The examiner submits that the foregoing bolded limitation(s) constitute a "mental process" and/or “certain methods of organizing human activity” because under its broadest reasonable interpretation, the claim covers performance of the limitation by a user or in the human mind. For example, “filtering out background noise and/or other signal anomalies from the streams of CAC data” in the context of this claim encompasses the user mentally filtering data. “generating…the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer” in the context of this claim encompasses the user mentally predicting a failure score. Similarly, the limitation of " the failure pattern analyzer … applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction " in the context of this claim encompasses the user mentally predicting a failure. Accordingly, the claim recites at least one abstract idea.
101 Analysis-Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim as a whole, integrates the abstract into a partial application. As noted in the 2019 PEG, it must be determined whether there are any additional elements recited in the claim beyond the judicial exception(s), and whether those additional elements integrate the exception into a practical application of the exception.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A computer-implemented method of using machine learning for failure prediction in a cabin air compressor (CAC) system, the computer-implemented method comprising:
collecting, at one or more processors, streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation;
providing, by the one or more processors, the collected CAC feature data to a machine learning model, the machine learning model filtering out background noise and/or other signal anomalies from the streams of CAC data, the machine learning model being trained using time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score; and
generating, using the machine learning model, the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer;
the failure pattern analyzer, by the one or more processors, applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction; and
generating a failure prediction report containing the CAC failure prediction, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting; and
generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action,
wherein the machine learning model comprises a gradient boosting model.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “collecting … streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation”, “providing … the collected CAC feature data to a machine learning model” the examiner submits that these limitations are mere data gathering in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “collecting … CAC feature data”, “providing … the collected CAC feature data”, and “the physical characteristics data comprises” indicate pre-solution activity such that it amounts no more than a step of gathering data for use in a claimed process.
Regarding the additional limitations of “at one or more processors”, “by the one or more processors”, “the machine learning model being trained using time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score”, “using the machine learning model”, “by the one or more processors”, “wherein the machine learning model comprises a gradient boosting model” the examiner submits that these limitations are mere instructions to apply the above noted abstract idea by merely using a computer to perform the process (MPEP § 2106.05). In particular, processor recited at a high-level of generality (i.e., as processor performing a generic computer function of computing and organizing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Furthermore, machine learning model recited at a high-level of generality (i.e., as algorithm performing a generic computer function of computing result) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Regarding the additional limitations of “generating a failure prediction report containing the CAC failure prediction, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting” and “generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action”, the examiner submits that these limitations are mere data outputting in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “generating a failure prediction report”, “storing, transmitting, and/or displaying the failure prediction reporting”, “generating an electronic maintenance action request”, and “sending the electronic maintenance action request” indicate post-solution activity such that it amounts no more than a step of outputting data for use in a claimed process.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add no thing that is nor already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2 106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis-Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 12 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “at one or more processors”, “by the one or more processors”, “the machine learning model being trained using time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score”, “using the machine learning model”, “by the one or more processors”, “wherein the machine learning model comprises a gradient boosting model” amounts to nothing more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component cannot provide an inventive concept.
Furthermore, regarding the additional limitation of “collecting … streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation”, “providing … the collected CAC feature data to a machine learning model”, “generating a failure prediction report containing the CAC failure prediction, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting”, and “generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action” the examiner submits that the limitation merely adds insignificant extra-solution activity to the at least one abstract idea as previously discussed.
Hence the claim is not patent eligible.
Therefore, claim(s) 19 is/are ineligible under 35 U.S.C. 101.
Regarding Claim 26, the claim recites further narrowing limitation on the “scheduling one or more of a leak check, a system debris check, a CAC motor power supply quality check, and/or a breakaway torque check” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
101 Analysis-Step 1
Claims 21 are directed to A computer-implemented method of using machine learning for failure prediction in a cabin air compressor (CAC) system (i.e., a process). Therefore, claims 21 is within at least one of the four statutory categories.
101 Analysis-Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 21 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for reminder of the 101 rejection. Claim 21 recites:
A non-transitory computer-readable medium, having stored thereon computer-executable instructions that, when executed by one or more processors, cause a computer to:
collect streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation;
provide the collected CAC feature data to a machine learning model, the machine learning model filtering out background noise and/or other signal anomalies from the streams of CAC data, the machine learning model being trained using time series CAC feature training data obtained from one or more data stores, the time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score, wherein the time series CAC feature training data is applied to a feature selection pipeline that performs SHapley Additive exPLanations (SHAP) reducing a total number of CAC features in the time series CAC feature training data before providing the time series CAC feature training data to train the machine learning model;
generate, using the machine learning model, the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer;
by the failure pattern analyzer, by the one or more processors, applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction;
generate a failure prediction report containing the CAC failure prediction; and
generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action,
wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting.
The examiner submits that the foregoing bolded limitation(s) constitute a "mental process" and/or “certain methods of organizing human activity” because under its broadest reasonable interpretation, the claim covers performance of the limitation by a user or in the human mind. For example, “filtering out background noise and/or other signal anomalies from the streams of CAC data” in the context of this claim encompasses the user mentally filtering data. “generate…the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer” in the context of this claim encompasses the user mentally predicting a failure score. Similarly, the limitation of "by the failure pattern analyzer … applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction " in the context of this claim encompasses the user mentally predicting a failure. Accordingly, the claim recites at least one abstract idea.
101 Analysis-Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim as a whole, integrates the abstract into a partial application. As noted in the 2019 PEG, it must be determined whether there are any additional elements recited in the claim beyond the judicial exception(s), and whether those additional elements integrate the exception into a practical application of the exception.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A non-transitory computer-readable medium, having stored thereon computer-executable instructions that, when executed by one or more processors, cause a computer to:
collect streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation;
provide the collected CAC feature data to a machine learning model, the machine learning model filtering out background noise and/or other signal anomalies from the streams of CAC data, the machine learning model being trained using time series CAC feature training data obtained from one or more data stores, the time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score, wherein the time series CAC feature training data is applied to a feature selection pipeline that performs SHapley Additive exPLanations (SHAP) reducing a total number of CAC features in the time series CAC feature training data before providing the time series CAC feature training data to train the machine learning model;
generate, using the machine learning model, the CAC failure prediction score and providing the CAC failure prediction score to a failure pattern analyzer;
by the failure pattern analyzer, by the one or more processors, applying a heuristic to the CAC failure prediction score to determine a CAC failure prediction;
generate a failure prediction report containing the CAC failure prediction; and
generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action,
wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “collect streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation”, “provide the collected CAC feature data to a machine learning model”, “wherein the time series CAC feature training data is applied to a feature selection pipeline that performs SHapley Additive exPLanations (SHAP) reducing a total number of CAC features in the time series CAC feature training data before providing the time series CAC feature training data to train the machine learning model” the examiner submits that these limitations are mere data gathering in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “collect streams of CAC data”, “provide the collected CAC feature data”, and “the time series CAC feature training data is applied to a feature selection pipeline that performs SHapley Additive exPLanations (SHAP) reducing a total number of CAC features in the time series CAC feature training data before providing the time series CAC feature training data to train the machine learning model” indicate pre-solution activity such that it amounts no more than a step of gathering data for use in a claimed process.
Regarding the additional limitations of “by the one or more processors”, “the machine learning model being trained using time series CAC feature training data obtained from one or more data stores, the time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score”, “using the machine learning model” the examiner submits that these limitations are mere instructions to apply the above noted abstract idea by merely using a computer to perform the process (MPEP § 2106.05). In particular, processor recited at a high-level of generality (i.e., as processor performing a generic computer function of computing and organizing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Furthermore, machine learning model recited at a high-level of generality (i.e., as algorithm performing a generic computer function of computing result) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Regarding the additional limitations of “generate a failure prediction report containing the CAC failure prediction; and generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting”, the examiner submits that these limitations are mere data outputting in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “generate a failure prediction report”, “generating an electronic maintenance action request”, “sending the electronic maintenance action request”, and “storing, transmitting, and/or displaying the failure prediction reporting” indicate post-solution activity such that it amounts no more than a step of outputting data for use in a claimed process.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add no thing that is nor already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2 106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis-Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 12 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “by the one or more processors”, “the machine learning model being trained using time series CAC feature training data obtained from one or more data stores, the time series CAC feature training data comprising time series physical characteristics training data, time series calculated CAC operation training data, and time series calculated CAC efficiency training data, at least a portion of the time series CAC feature training data corresponding to CAC failure events, CAC performance degradation, and CAC performance normal, the machine learning model being trained to generate a CAC failure prediction score”, “using the machine learning model” amounts to nothing more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component cannot provide an inventive concept.
Furthermore, regarding the additional limitation of “collect streams of CAC data from one or more CAC sensors, the streams of CAC data including CAC feature data comprising physical characteristics data of aircraft components of the CAC system, calculated CAC operation data, and/or calculated CAC efficiency data, the CAC feature data corresponding to aircraft runtime operation”, “provide the collected CAC feature data to a machine learning model”, “wherein the time series CAC feature training data is applied to a feature selection pipeline that performs SHapley Additive exPLanations (SHAP) reducing a total number of CAC features in the time series CAC feature training data before providing the time series CAC feature training data to train the machine learning model”, and “generate a failure prediction report containing the CAC failure prediction; and generating an electronic maintenance action request based on the failure prediction report and sending the electronic maintenance action request to a remote computer for initiating a maintenance action, wherein generating the failure prediction report comprises storing, transmitting, and/or displaying the failure prediction reporting”, the examiner submits that the limitation merely adds insignificant extra-solution activity to the at least one abstract idea as previously discussed.
Hence the claim is not patent eligible.
Therefore, claim(s) 21 is/are ineligible under 35 U.S.C. 101.
Allowable Subject Matter
Claims 12, 19, 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 features “the calculated CAC operation data comprises work of a CAC system, energy change of a CAC compressor, rolling data of the work of the CAC, and rolling data of the energy change of the CAC; and the CAC feature data comprises a CAC efficiency and a rolling CAC efficiency rolling over time” in claim 12; “the machine learning model comprises a gradient boosting model” in claim 19, “applying the obtained time series CAC feature training data to a feature selection pipeline that performs SHapley Additive exPLanations (SHAP) reducing a total number of CAC features in the time series CAC feature training data before providing the time series CAC feature training data to train the machine learning model” in claim 21 when taken in the context of the claims as a whole, were not uncovered in the prior art teachings.
Gautam (US20210118242A1) disclosed predicting a needed repair and/or maintenance activity for an aircraft system of this disclosure employs a plurality of sensors that continuously detect or sense operation signals or operation parameters of the aircraft system, such as a cabin air compressor. The plurality of sensors log the operation signals of the cabin air compressor sensed by the sensors to a computer system on the aircraft, such as a machine learning computer system.
TURETTA (US20230032571A1) disclosed an improved integration framework to organize and modify procedures according to the current context, and select between different intervention definition processes, using simulation models as references; thus allowing the implementation of multiple intervention definition paradigms in parallel and selecting the best one for each specific situation and context, and working as a “safety net” for non-deterministic processes such as artificial intelligence.
BEAVEN (US20170060125A1) disclosed a method of diagnosing a fault in an air-conditioning pack of an aircraft, wherein the air-conditioning pack includes one or more sensors. The method includes transmitting data from at least one of the sensors operably coupled to the air-conditioning pack during one of pre-flight, post-flight, or longest cruise, comparing the transmitted data to a predetermined threshold, diagnosing a fault in the air-conditioning pack based on the comparison, and providing an indication of the diagnosed fault.
In particular, the combination of operation data disclosed in claim 12, gradient boosting model disclosed in claim 19, and a feature selection pipeline that performs SHapley Additive exPLanations (SHAP) disclosed in claim 21 is not disclosed in the prior art.
Claims 13-15, 17-18, 20, 22-25 would be allowable based on the dependence on claim 12 therefor inheriting the allowable subject matter disclosed in claim 12.
Claim 26 would be allowable based on the dependence on claim 19 therefor inheriting the allowable subject matter disclosed in claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zywiak (US20170292872A1) disclosed a computer-implemented method for a prognostic for flow sensor BIT for a motor driven compressor is provided. The method includes receiving a first input, the first input related to an input power of a motor for driving a compressor, and receiving a second input, the second input related to a temperature differential of the compressor. The method also includes calculating an estimated airflow based on the first input and the second input, and exporting data associated with the first input, the second input, and the estimated airflow.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WENYUAN YANG whose telephone number is (571)272-5455. The examiner can normally be reached Monday - Thursday 9:00AM-5:00PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hitesh Patel can be reached at (571) 270-5442. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.Y./Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
6/15/26
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