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 Applicant's Amendment and Remarks filed on 4/8/2026. This Action is made FINAL.
Claims 1-20 are pending for examination.
Response to Arguments
(A) Applicant's arguments filed “The Office Action asserts that these claims set forth a "mental process" and/or "certain methods of organizing human activity." See, e.g., page 4. However, the Applicant submits that the pending claims set forth neither mental processes nor methods of organizing human activity. Rather, the pending claims set forth interaction between disparate systems to transform location and environmental data into index values that are then used with an analytical model to process sensor and/or control input data from the aircraft to generate a health assessment. Such transformation, processing, and generation between multiple disparate systems (aircraft, first computer system, second computer system, sensor/control, etc.) cannot be a mental process and also cannot represent an organization of human activity. Rather, it is a new and improved system of devices exchanging and transforming data to determine a remaining useful life of one or more engines of the aircraft. The indication of remaining life is output trigger maintenance and/or aggregation of one or more engines with similar remaining life” on 4/8/2026 have been fully considered but they are not persuasive.
As to point (A), the examiner respectfully disagrees. The examiner notes transform location and environmental data into index values and generate a health assessment indicated an abstract idea. The examiner further notes “first computer system, second computer system, sensor/control” are merely using a computer to perform the process (MPEP § 2106.05). Regarding the additional limitations of “outputting, by the second computer system, the remaining useful life to trigger at least one of maintenance or aggregation of the one or more engines”, 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, the claim limitation recited outputting the remaining useful life to trigger which does not indicate direct control of initiating the maintenance or aggregation.
(B) Applicant's arguments filed “Claim 1 sets forth a method involving, inter alia, a certain interface and interaction between a first computer system, a remote sensor, an aircraft, a second computer system, and at least one of a sensor or control input to hide localized environmental data while generating, using a model, a remaining useful life of one or more engines to trigger at least one of maintenance or aggregation of the engine(s). None of the cited references teaches or suggests such a method. As acknowledged in the Office Action, Chiaramonte fails to teach or suggest elements now set forth in claim 1. While not relied upon to reject claim 1, Martinez is similarly deficient, instead identifying the nearest engines in a database for further assessment. See, e.g., para. 17-18 of Martinez. For at least these reasons, the rejection of claim 1, and all claims depending therefrom, should be withdrawn. Claim 1, and all claims depending therefrom, is in condition for allowance, and such action is respectfully requested” and similar arguments for claims 19 and 20 on 4/8/2026 have been fully considered but they are not persuasive.
As to point (B), the examiner respectfully disagrees. The examiner further notes the amended limitations would be fully encompassed by the cited reference.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/6/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-20 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 1-18 are directed to A method of generating an engine health assessment (i.e., a process). Therefore, claims 1-18 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 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for reminder of the 101 rejection. Claim 1 recites:
A method of generating an engine health assessment, the method comprising:
receiving, by a first computer system, a first set of data including environmental data from a remote sensor;
receiving, by the first computer system interfacing with an aircraft, a second set of data including operating data having an operating time and location data from the aircraft having one or more engines;
calculating, by the first computer system, localized environmental data from the first set of data and the second set of data;
generating, by the first computer system, one or more index values from the localized environmental data;
receiving, by a second computer system, the one or more index values from the first computer system, the one or more index values hiding the localized environmental data from the second computer system;
receiving, from the aircraft by the second computer system, a third set of data comprising operational data from at least one of a sensor or a control input of the one or more engines;
performing engine health assessment, by the second computer system, using an analytical model, the analytical model to process based the third set of data according to the one or more index values to estimate a health of the one or more engines;
determining, by the second computer system, a remaining useful life of the one or more engines based on the estimated health of the one or more engines; and
outputting, by the second computer system, the remaining useful life to trigger at least one of maintenance or aggregation of the one or more engines.
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, “calculating…localized environmental data from the first set of data and the second set of data” in the context of this claim encompasses the user mentally calculating data. Similarly, the limitation of " generating…one or more index values from the localized environmental data " in the context of this claim encompasses the user mentally calculating index values. Furthermore, the limitation of “performing engine health assessment … to process based the third set of data according to the one or more index values to estimate a health of the one or more engines” in the context of this claim encompasses the user mentally estimating engine health based on gathered data. Lastly, the limitation of “determining … a remaining useful life of the one or more engines based on the estimated health of the one or more engines” in the context of this claim encompasses the user mentally predicting remaining useful life based on the estimated health. 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 method of generating an engine health assessment, the method comprising:
receiving, by a first computer system, a first set of data including environmental data from a remote sensor;
receiving, by the first computer system interfacing with an aircraft, a second set of data including operating data having an operating time and location data from the aircraft having one or more engines;
calculating, by the first computer system, localized environmental data from the first set of data and the second set of data;
generating, by the first computer system, one or more index values from the localized environmental data;
receiving, by a second computer system, the one or more index values from the first computer system, the one or more index values hiding the localized environmental data from the second computer system;
receiving, from the aircraft by the second computer system, a third set of data comprising operational data from at least one of a sensor or a control input of the one or more engines;
performing engine health assessment, by the second computer system, using an analytical model, the analytical model to process based the third set of data according to the one or more index values to estimate a health of the one or more engines;
determining, by the second computer system, a remaining useful life of the one or more engines based on the estimated health of the one or more engines; and
outputting, by the second computer system, the remaining useful life to trigger at least one of maintenance or aggregation of the one or more engines.
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 “receiving…a first set of data including environmental data from a remote sensor”; “receiving…a second set of data including operating data having an operating time and location data from the aircraft having one or more engines”; “receiving…the one or more index values from the first computer system, the one or more index values hiding the localized environmental data from the second computer system”; “receiving, from the aircraft … a third set of data comprising operational data from at least one of a sensor or a control input of the one or more engines”, 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, “receiving… data/values” 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 a first computer system”; “by the first computer system interfacing with an aircraft”; “by the second computer system”; “using an analytical 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, first computer system recited at a high-level of generality (i.e., as computer performing a generic computer function of gathering and processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Second computer system recited at a high-level of generality (i.e., as computer performing a generic computer function of gathering and processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Analytical model recited at a high-level of generality (i.e., as computer model performing a generic computer function of analyzing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Regarding the additional limitations of “outputting … the remaining useful life to trigger at least one of maintenance or aggregation of the one or more engines”, 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, “outputting … the remaining useful life” 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 1 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 a first computer system”; “by the first computer system interfacing with an aircraft”; “by the second computer system”; “using an analytical 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 “receiving…a first set of data including environmental data from a remote sensor”; “receiving…a second set of data including operating data having an operating time and location data from the aircraft having one or more engines”; “receiving…the one or more index values from the first computer system, the one or more index values hiding the localized environmental data from the second computer system”; “receiving, from the aircraft … a third set of data comprising operational data from at least one of a sensor or a control input of the one or more engines”; “outputting … the remaining useful life to trigger at least one of maintenance or aggregation of the one or more engines”, 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) 1 is/are ineligible under 35 U.S.C. 101.
Regarding Claim 2, the claim recites further narrowing limitation on the “the environmental data” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 4, the claim recites further narrowing limitation on the “recommending actions” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 5, the claim recites further narrowing limitation on the “operating time” and “location data” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 6, the claim recites further narrowing limitation on the “environmental data” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 7, the claim recites further narrowing limitation on the “environmental data” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 8, the claim recites further narrowing limitation on the “operational data” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 9, the claim recites “the analytical model is part of a health management system for the one or more engines” 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 10, the claim recites “evaluate a current health or condition of the one or more engines” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application and the claim recites “the analytical 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 11, the claim recites “the first computer system is distinct and separate from the second computer system” 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 12, the claim recites “normalizing the localized environmental data; and generating the one or more index values” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
Regarding Claim 13, the claim recites “performing the engine health assessment” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application and the claim recites “by the second computer system, using the analytical 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 14, the claim recites “using the remaining amount of useful life of the one or more engines to match the one or more engines with other engines having a similar remaining amount of useful life” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
Regarding Claim 15, the claim recites “performing the engine health assessment” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application and the claim recites “by the second computer system, using the analytical 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 16, the claim recites “estimating a time horizon of a useful life of the one or more engines” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
Regarding Claim 17, the claim recites “the first computer system is associated with a first entity and the second computer system is associated with a second entity different from the first entity” 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 18, the claim recites further narrowing limitation on the “provides the one or more index values” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
As per claim 19, it recites A method of generating an engine health assessment having limitations similar to those of claim 1 and therefore is rejected on the same basis.
As per claim 20, it recites A non-transitory computer-readable medium storing a computer-executable code that, when executed by a second computer system, causes the second computer system to perform a method of generating an engine health assessment having limitations similar to those of claim 1 and therefore is rejected on the same basis.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-13, 15-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chiaramonte (US20190147412A1).
Regarding claim 1, Chiaramonte teaches A method of generating an engine health assessment, the method comprising:
receiving, by a first computer system, a first set of data including environmental data from a remote sensor(Chiaramonte Para 173 “the collection engine 400 obtains the asset sensor data 430 to determine operating conditions experienced by the engine 102 of FIG. 1. In some examples, the collection engine 400 obtains the asset sensor data 430 from the database 345 of FIG. 3. In some examples, the asset sensor data 430 corresponds to sensor data obtained from the sensors 144, 146 of FIG. 2. For example, the asset sensor data 430 can include sensor measurements such as a speed of a rotor, a pressure, a temperature, a vibration, etc., experienced by the engine 102 of FIG. 1. In some examples, the asset sensor data 430 includes a range of sensor measurements for an environmental parameter (e.g., a pressure, a temperature, etc.) measured by an environmental parameter sensor (e.g., a pressure sensor, a temperature sensor, etc.)”; Para 174 “the collection engine 400 obtains the asset environmental data 432 to determine environmental conditions experienced by the engine 102. In some examples, the collection engine 400 obtains the asset environmental data 432 from the database 345 of FIG. 3. In some examples, the asset environmental data 432 includes a range of environmental condition parameters experienced by the engine 102. For example, the asset environmental data 432 can include a range of ambient temperatures (e.g., a range of 10-40 degrees Celsius, etc.), precipitation amounts, salt atmosphere percentages (e.g., a range of 5-55% salt atmosphere, etc.), a range of airborne particulate matter sizes (e.g., a size of a man-made airborne particulate matter, a size of a naturally occurring airborne particulate matter, etc.), humidity percentages (e.g., a range of 40-95% humidity, etc.), etc., experienced by the engine 102”);
receiving, by the first computer system interfacing with an aircraft, a second set of data including operating data having an operating time and location data from the aircraft having one or more engines(Chiaramonte Para 175 “the collection engine 400 obtains the operational/utilization data 434 to determine a usage of the engine 102. In some examples, the operational/utilization data 434 includes a utilization plan of the engine 102. For example, the operational/utilization data 434 can include a number of cycles (e.g., flight cycles, operation cycles, etc.), a number of hours in operation, types of flight routes (e.g., flights from a first destination to a second destination, etc.), a number of flight legs (e.g., a number of hours from a first destination to a second destination, etc.), etc., completed by the engine 102. In some examples, the operational/utilization data 434 includes operating behavior of one or more assets by one or more airline operators. For example, the operational/utilization data 434 can include operating rating information corresponding to an operation of the engine 102 compared to a rated maximum capacity of the turbine engine. For example, the operational/utilization data 434 can include average takeoff de-rate information, average climb de-rate information, etc. In another example, the operational/utilization data 434 can include an average thrust parameter of the engine 102, a percentage indicating how often the engine 102 is at full power during one or more cycles, etc”);
calculating, by the first computer system, localized environmental data from the first set of data and the second set of data(Chiaramonte: Fig. 5 Element 504 and 506; Para 201 “the collection engine 400 can select the engine 102 of FIG. 1 to process. At block 504, the example asset health calculator 300 obtains asset monitoring information. For example, the collection engine 400 can obtain the inputs 325 of FIGS. 3-4. An example process that can be used to implement block 504 is described below in connection with FIG. 6”; Para 202 “At block 506, the example asset health calculator 300 executes model(s) to generate actual asset health quantifiers. For example, the health quantifier generator 410 can generate an actual AHQ of the engine 102, the booster compressor 114 of the engine 102, etc.);
generating, by the first computer system, one or more index values from the localized environmental data(Chiaramonte: Fig. 5 Element 510; Para 203 “At block 510, the example asset health calculator 300 aggregates actual and projected asset health quantifiers. For example, the health quantifier generator 410 can aggregate a plurality of the actual and the projected AHQ for the engine 102”; Para 168 “the AWGS 220 includes the database 345 to record data (e.g., asset health quantifiers, workscope quantifiers, the inputs 325, the model inputs 335, the requirements 340, the task information 350, etc.)… The example database 345 can respond to queries for information related to data in the database 345. For example, the database 345 can respond to queries for additional data by providing the additional data (e.g., the one or more data points), by providing an index associated with the additional data in the database 345, etc”; Para 318 “the analytics 2620 can be implemented using a health state transfer function 2705 to process asset health quantifier output from the asset health quantifier 300 to generate an asset health state 2640 to provide to the workscope task generator 2605. The example health state transfer function 2705 leverages the overall and component asset health quantifiers provided by the asset health quantifier 300 as well as forecast utilization and environmental information for the target asset, etc.”);
receiving, by a second computer system, the one or more index values from the first computer system, the one or more index values hiding the localized environmental data from the second computer system(Chiaramonte: Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”; Para 318 “the analytics 2620 can be implemented using a health state transfer function 2705 to process asset health quantifier output from the asset health quantifier 300 to generate an asset health state 2640 to provide to the workscope task generator 2605. The example health state transfer function 2705 leverages the overall and component asset health quantifiers provided by the asset health quantifier 300 as well as forecast utilization and environmental information for the target asset, etc.”; i.e. using a health state transfer function 2705 to process asset health quantifier output from the asset health quantifier 300(the one or more index values hiding the localized environmental data from the second computer system) to generate an asset health state 2640 (the one or more index values from the first computer system));
receiving, from the aircraft by the second computer system, a third set of data comprising operational data from at least one of a sensor or a control input of the one or more engines(Chiaramonte: Para 318 “the analytics 2620 can be implemented using a health state transfer function 2705 to process asset health quantifier output from the asset health quantifier 300 to generate an asset health state 2640 to provide to the workscope task generator 2605. The example health state transfer function 2705 leverages the overall and component asset health quantifiers provided by the asset health quantifier 300 as well as forecast utilization and environmental information for the target asset, etc.”; Para 173 “the collection engine 400 obtains the asset sensor data 430 to determine operating conditions experienced by the engine 102 of FIG. 1. In some examples, the collection engine 400 obtains the asset sensor data 430 from the database 345 of FIG. 3. In some examples, the asset sensor data 430 corresponds to sensor data obtained from the sensors 144, 146 of FIG. 2. For example, the asset sensor data 430 can include sensor measurements such as a speed of a rotor, a pressure, a temperature, a vibration, etc., experienced by the engine 102 of FIG. 1. In some examples, the asset sensor data 430 includes a range of sensor measurements for an environmental parameter (e.g., a pressure, a temperature, etc.) measured by an environmental parameter sensor (e.g., a pressure sensor, a temperature sensor, etc.)” );
performing engine health assessment, by the second computer system, using an analytical model, the analytical model to process based the third set of data according to the one or more index values to estimate a health of the one or more engines(Chiaramonte: Para 318 “the analytics 2620 can be implemented using a health state transfer function 2705 to process asset health quantifier output from the asset health quantifier 300 to generate an asset health state 2640 to provide to the workscope task generator 2605. The example health state transfer function 2705 leverages the overall and component asset health quantifiers provided by the asset health quantifier 300 as well as forecast utilization and environmental information for the target asset, etc.”; Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”);
determining, by the second computer system, a remaining useful life of the one or more engines based on the estimated health of the one or more engines(Chiaramonte: Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”); and
outputting, by the second computer system, the remaining useful life to trigger at least one of maintenance or aggregation of the one or more engines(Chiaramonte: Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”; Para 345 “At block 3108, tasks for maintenance of the target asset are generated by the workscope task generator 2605 using a workscope transfer function (e.g., defined by Equation 2, etc.) applied to the health, non-health, and mission information. For example, given the target asset health level, non-health constraints, and next mission requirements, the workscope task generator 2605 applies a workscope transfer function and evaluates whether the health level of the target asset will enable the target asset to function at a certain level for the next mission. If not, then the workscope task generator 2605 determines a workscope of tasks to repair, replace, and/or otherwise maintain the asset for the next mission (e.g., to raise a health level of the target asset to a level of capability, durability, operability, performance, etc., sufficient for the next mission and/or another subsequent mission, etc.)”).
Regarding claim 2, Chiaramonte teaches The method of claim 1, wherein the environmental data includes data from satellite remote sensing or air quality databases, or any combination thereof(Chiaramonte: Para 174 “the collection engine 400 obtains the asset environmental data 432 to determine environmental conditions experienced by the engine 102. In some examples, the collection engine 400 obtains the asset environmental data 432 from the database 345 of FIG. 3. In some examples, the asset environmental data 432 includes a range of environmental condition parameters experienced by the engine 102. For example, the asset environmental data 432 can include a range of ambient temperatures (e.g., a range of 10-40 degrees Celsius, etc.), precipitation amounts, salt atmosphere percentages (e.g., a range of 5-55% salt atmosphere, etc.), a range of airborne particulate matter sizes (e.g., a size of a man-made airborne particulate matter, a size of a naturally occurring airborne particulate matter, etc.), humidity percentages (e.g., a range of 40-95% humidity, etc.), etc., experienced by the engine 102. In some examples, the asset environmental data 432 includes a duration of environmental condition parameters experienced by the engine 102. For example, the asset environmental data 432 can include an amount of time the engine 102 experienced a salt atmosphere of 30%, 40%, 50%, etc”).
Regarding claim 3, Chiaramonte teaches The method of claim 1, further comprising adjusting operating parameters of the one or more engines or performing preventive maintenance on the one or more engines in order to extend remaining useful life or to avoid reliability issues based on the remaining useful life of the one or more engines(Chiaramonte: Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”; Para 345 “At block 3108, tasks for maintenance of the target asset are generated by the workscope task generator 2605 using a workscope transfer function (e.g., defined by Equation 2, etc.) applied to the health, non-health, and mission information. For example, given the target asset health level, non-health constraints, and next mission requirements, the workscope task generator 2605 applies a workscope transfer function and evaluates whether the health level of the target asset will enable the target asset to function at a certain level for the next mission. If not, then the workscope task generator 2605 determines a workscope of tasks to repair, replace, and/or otherwise maintain the asset for the next mission (e.g., to raise a health level of the target asset to a level of capability, durability, operability, performance, etc., sufficient for the next mission and/or another subsequent mission, etc.)”).
Regarding claim 4, Chiaramonte teaches The method of claim 1, wherein recommending actions to perform maintenance of the one or more engines comprises removing components of the one or more engines, repairing components of the one or more engines, replacing components of the one or more engines, or any combination thereof(Chiaramonte: Para 345 “At block 3108, tasks for maintenance of the target asset are generated by the workscope task generator 2605 using a workscope transfer function (e.g., defined by Equation 2, etc.) applied to the health, non-health, and mission information. For example, given the target asset health level, non-health constraints, and next mission requirements, the workscope task generator 2605 applies a workscope transfer function and evaluates whether the health level of the target asset will enable the target asset to function at a certain level for the next mission. If not, then the workscope task generator 2605 determines a workscope of tasks to repair, replace, and/or otherwise maintain the asset for the next mission (e.g., to raise a health level of the target asset to a level of capability, durability, operability, performance, etc., sufficient for the next mission and/or another subsequent mission, etc.)”).
Regarding claim 5, Chiaramonte teaches The method of claim 1, wherein the operating time comprises a date or a time, or both, of operation of the one or more engines, and the location data comprises a latitude location and a longitude location of the aircraft having the one or more engines(Chiaramonte: Para 175 “the collection engine 400 obtains the operational/utilization data 434 to determine a usage of the engine 102. In some examples, the operational/utilization data 434 includes a utilization plan of the engine 102. For example, the operational/utilization data 434 can include a number of cycles (e.g., flight cycles, operation cycles, etc.), a number of hours in operation, types of flight routes (e.g., flights from a first destination to a second destination, etc.), a number of flight legs (e.g., a number of hours from a first destination to a second destination, etc.), etc., completed by the engine 102.”).
Regarding claim 6, Chiaramonte teaches The method of claim 1, wherein the environmental data is associated with a given location of the one or more engines and a given time or environmental exposure induced locally by the one or more engines, and the environmental data is obtained from satellite remote sensing, or air quality databases, or any combination thereof(Chiaramonte: Para 174 “the collection engine 400 obtains the asset environmental data 432 to determine environmental conditions experienced by the engine 102. In some examples, the collection engine 400 obtains the asset environmental data 432 from the database 345 of FIG. 3. In some examples, the asset environmental data 432 includes a range of environmental condition parameters experienced by the engine 102. For example, the asset environmental data 432 can include a range of ambient temperatures (e.g., a range of 10-40 degrees Celsius, etc.), precipitation amounts, salt atmosphere percentages (e.g., a range of 5-55% salt atmosphere, etc.), a range of airborne particulate matter sizes (e.g., a size of a man-made airborne particulate matter, a size of a naturally occurring airborne particulate matter, etc.), humidity percentages (e.g., a range of 40-95% humidity, etc.), etc., experienced by the engine 102. In some examples, the asset environmental data 432 includes a duration of environmental condition parameters experienced by the engine 102. For example, the asset environmental data 432 can include an amount of time the engine 102 experienced a salt atmosphere of 30%, 40%, 50%, etc”).
Regarding claim 7, Chiaramonte teaches The method of claim 1, wherein the environmental data comprises physical or chemical elements or contaminants, dust, or sand, or any combination thereof(Chiaramonte: Para 174 “the collection engine 400 obtains the asset environmental data 432 to determine environmental conditions experienced by the engine 102. In some examples, the collection engine 400 obtains the asset environmental data 432 from the database 345 of FIG. 3. In some examples, the asset environmental data 432 includes a range of environmental condition parameters experienced by the engine 102. For example, the asset environmental data 432 can include a range of ambient temperatures (e.g., a range of 10-40 degrees Celsius, etc.), precipitation amounts, salt atmosphere percentages (e.g., a range of 5-55% salt atmosphere, etc.), a range of airborne particulate matter sizes (e.g., a size of a man-made airborne particulate matter, a size of a naturally occurring airborne particulate matter, etc.), humidity percentages (e.g., a range of 40-95% humidity, etc.), etc., experienced by the engine 102. In some examples, the asset environmental data 432 includes a duration of environmental condition parameters experienced by the engine 102. For example, the asset environmental data 432 can include an amount of time the engine 102 experienced a salt atmosphere of 30%, 40%, 50%, etc”).
Regarding claim 8, Chiaramonte teaches The method of claim 1, wherein the operational data of the one or more engines comprises physical sensors, control inputs, or any combination thereof(Chiaramonte: Para 452 “the collection engine 4900 obtains the asset sensor data 430 to determine operating conditions experienced by the engine 102 of FIG. 1. In some examples, the asset sensor data 430 corresponds to inputs to the engine 102. For example, the asset sensor data 430 can include an engine command (e.g., a thrust control input, a de-rate control input, etc.), an engine input, etc. For example, the asset sensor data 430 can correspond to information obtained from a closed loop control module included in the turbine engine controller 100 of FIGS. 1-2. For example, the asset sensor data 430 can include parameters generated by an algorithm executed by the turbine engine controller 100 in response to an engine control input, an environmental factor, etc.”).
Regarding claim 9, Chiaramonte teaches The method of claim 1, wherein the analytical model is part of a health management system for the one or more engines(Chiaramonte: Fig. 26B Element 2620; Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”).
Regarding claim 10, Chiaramonte teaches The method of claim 1, wherein the analytical model is configured to evaluate a current health or condition of the one or more engines(Chiaramonte: Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”).
Regarding claim 11, Chiaramonte teaches The method of claim 1, wherein the first computer system is distinct (Chiaramonte: Fig. 3 Element 300) and separate from the second computer system(Chiaramonte: Fig. 3 Element 305).
Regarding claim 12, Chiaramonte teaches The method of claim 1, further comprising normalizing the localized environmental data; and generating the one or more index values after normalizing of the localized environmental data(Chiaramonte: Fig. 55; Para 541 “In the illustrated example of FIG. 55, the de-rate option A current with overrides (Taper X) bars 5510 represent the operators A and B currently using a Taper X schedule and allowing pilots to override the engine de-rate option A and normalizing the operators A and B to a baseline TOW severity ratio of 1.00. In the illustrated example of FIG. 55, the de-rate option A with no overrides (Taper X) bars 5520 represent the operators A and B using the Taper X schedule and executing the de-rate option A except without allowing pilots to override the de-ration option A. As depicted in FIG. 55, the operator A previously allowed more overrides compared to operator B and, thus, benefited from a higher TOW severity ratio by not allowing pilots to override the de-rate option A engine behavior”).
Regarding claim 13, Chiaramonte teaches The method of claim 1, wherein performing the engine health assessment, by the second computer system, using the analytical model to execute at least one of a machine learning algorithm or a data analytics algorithm, based on the one or more index values, and the third set of data to estimate the health of the one or more engines comprises determining a remaining amount of useful life or a remaining usable service capability of the one or more engines without revealing the localized environmental data(Chiaramonte: Para 318 “the analytics 2620 can be implemented using a health state transfer function 2705 to process asset health quantifier output from the asset health quantifier 300 to generate an asset health state 2640 to provide to the workscope task generator 2605. The example health state transfer function 2705 leverages the overall and component asset health quantifiers provided by the asset health quantifier 300 as well as forecast utilization and environmental information for the target asset, etc.”); Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”; Para 319 “the health state transfer function 2705 utilizes available input data to model and/or otherwise quantify a current asset health state and an estimated or projected ULR for the target asset from a current point in time to a target point in time for asset removal (e.g., a contract end of life, etc.)”).
Regarding claim 15, Chiaramonte teaches The method of claim 1, wherein performing the engine health assessment, by the second computer system, using the analytical model execute at least one of a machine learning algorithm or a data analytics algorithm, based on the one or more index values, and the third set of data to estimate the health of the one or more engines comprises predicting, using the analytical model, a future health or a condition of the one or more engines(Chiaramonte: Para 318 “the analytics 2620 can be implemented using a health state transfer function 2705 to process asset health quantifier output from the asset health quantifier 300 to generate an asset health state 2640 to provide to the workscope task generator 2605. The example health state transfer function 2705 leverages the overall and component asset health quantifiers provided by the asset health quantifier 300 as well as forecast utilization and environmental information for the target asset, etc.”); Para 312 “the workscope task generator 2605 processes analytics 2620 generated as a result of the asset health calculation, including asset health rank, etc., from the asset health calculator 300. Asset health can be modeled using a physics-based model (e.g., a digital twin, neural network, etc.) of the asset (e.g., a turbine engine, diesel engine, other electromechanical device, etc.), for example. Asset health can be modeled while the asset is still fielded (e.g., the engine is still on-wing, etc.), for example In addition to modeling current asset health, a future health or life expectancy for the asset can be determined by the model and/or other analytics 2620. The health analytics 2620 generate an asset health state 2640 (e.g., a health level and/or life expectancy, etc.) for the asset, which is provided to the workscope task generator 2605”).
Regarding claim 16, Chiaramonte teaches The method of claim 15, wherein predicting the future health or the condition of the one or more engines comprises estimating a time horizon of a useful life of the one or more engines(Chiaramonte: Para 247 “The example method begins at block 1202 at which the example asset health calculator 300 generates a planning horizon value. For example, the removal scheduler 420 can generate a planning horizon value 1900, shown in the example of FIG. 19, of two years” ).
Regarding claim 17, Chiaramonte teaches The method of claim 1, wherein the first computer system is associated with a first entity(Chiaramonte: Fig. 3 Element 300) and the second computer system is associated with a second entity different from the first entity(Chiaramonte: Fig. 3 Element 305).
Regarding claim 18, Chiaramonte teaches The method of claim 17, wherein the first entity provides the one or more index values to the second entity without the first entity needing to reveal location information of the one or more engines to the second entity(Chiaramonte: Para 168 “the AWGS 220 includes the database 345 to record data (e.g., asset health quantifiers, workscope quantifiers, the inputs 325, the model inputs 335, the requirements 340, the task information 350, etc.). In the illustrated example, the database 345 is communicatively coupled to the asset health calculator 300, the task generator 305, the task optimizer 310, the workscope effect calculator 315, and the FAHA 320 (e.g., when communicatively coupled to the network 330, etc.). The example database 345 can respond to queries for information related to data in the database 345. For example, the database 345 can respond to queries for additional data by providing the additional data (e.g., the one or more data points), by providing an index associated with the additional data in the database 345, etc”; i.e. asset health quantifiers received from asset health calculator 300(the first entity) by the task generator 305(the second entity) would not include the location data).
As per claim 19, it recites A method of generating an engine health assessment based on one or more index values associated with environmental factors and location information having limitations similar to those of claim 1 and therefore is rejected on the same basis.
As per claim 20, it recites A non-transitory computer-readable medium storing a computer-executable code that, when executed by a second computer system, causes the second computer system to perform a method of generating an engine health assessment based on one or more index values associated with environmental factors and location information having limitations similar to those of claim 1 and therefore is rejected on the same basis. Chiaramonte further teaches A non-transitory computer-readable medium storing a computer-executable code (Chiaramonte: Para 63 “a module, unit, or system may include a computer processor, controller, and/or other logic-based device that performs operations based on instructions stored on a tangible and non-transitory computer readable storage medium, such as a computer memory”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chiaramonte (US20190147412A1) in view of Martinez (US20160177856A1).
In regards to claim 14, Chiaramonte teaches The method of claim 13
Yet Chiaramonte do not explicitly teach using the remaining amount of useful life of the one or more engines to match the one or more engines with other engines having a similar remaining amount of useful life, for engine aggregation.
However, in the same field of endeavor, Martinez teaches using the remaining amount of useful life of the one or more engines to match the one or more engines with other engines having a similar remaining amount of useful life, for engine aggregation (Martinez: Para 17 “the string of fuzzy terms is compared to a comprehensive database comprising the characteristic strings of engines with different RULs. The nearest engines in the database are identified for further assessment within the IFPU”; Para 18 “in dependence of the nearest engines resulting from the comparisons carried out in the ESMU, predicts the evolution of the deterioration of the engine and estimates the RUL of the engine under test. If the nearest engines found in the matching unit have similar RULs, the predicted value is the average of the RULs of these nearest engines”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify The method of Chiaramonte with the feature of using the remaining amount of useful life of the one or more engines to match the one or more engines with other engines having a similar remaining amount of useful life, for engine aggregation disclosed by Martinez. One would be motivated to do so for the benefit of “understanding the level of deterioration (the first associated to direct damage and the second to deterioration due to its utilization) and therefore the costs associated to future maintenance” (Martinez: Para 12).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Beygi (US20200042919A1) disclosed The functions further include, based on (i) the first probability, (ii) the second probability, (iii) a first operation history of the first retired aircraft, and (iv) a second operation history of the second unretired aircraft, generating a theoretical hazard function that includes a baseline hazard factor that is dependent on a current service lifetime of the unretired representative aircraft of the identified type and a proportional hazard factor that is dependent on an operation history of the unretired representative aircraft of the identified type. The functions further include, based on (i) the theoretical hazard function, (ii) the second service lifetime, and (iii) the second operation history, calculating a quantity of a component to maintain in inventory, where the component is configured for use on the second unretired aircraft. The functions further include initiating a process to maintain the calculated quantity of the component in inventory.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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
6/29/26
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