DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is in response to applicant’s filing date 12/03/2025. Claims 1, 11 and 13 have been amended. Claim 21 is a new claim. 1-21 currently pending.
Priority
Acknowledgment is made for applicant’s filing for foreign application #IN202311018133 filed on 03/17/2023.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/03/2025 has been entered.
Response to Arguments
Applicant’s arguments submitted 12/03/2025, with respect to the previous 35 U.S.C. 101 of claims 1-21 have been fully considered and are unpersuasive.
With respect to the previous 35 U.S.C. 101 of claim 1, Applicant argues:
The claims Satisfy Step 2A because they are not directed to a Judicial Exception.
Examiner respectfully disagrees. The 2019 Revised Guidance explains that “mental processes” include acts that people can perform in their minds or using pen and paper, even if the claim recites that a generic computer component performs the acts. The claims are directed to a method of forecasting operation data of an aircraft. The claims disclose the steps of determining forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes and determining, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure. Hence, examiner has indicated that these identified limitations are directed to “mental process” and has provided a justification for why these limitations fall within one of the enumerated groupings of abstract ideas. Furthermore, using a computer as a tool to implement the abstract ideas does not make it less abstract. This is sufficient under the guidelines of the 2019 PEG and October 2019 Update as cited above. Accordingly, it seems reasonable to examiner to group the abstract idea under “mental process” as enumerated in Section I of the 2019 PEG.
Under the 2019 PEG, Step 2A, prong two, applicant submits that the claims integrate the alleged judicial exception into a practical application. Examiner respectfully disagrees. Integration into a practical application requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Limitations that are not indicative of integration into a practical application are those that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. -see MPEP 2106.05(f). A claim may be found to be eligible if it integrates a judicial exception into a practical application as cited by Applicant. Claiming improved data processing efficiency inherent with applying any improvement to the judicial exception itself on a computer does not provide an inventive concept. The claims do not integrate the judicial exception into a practical application.
Applicant argues:
(B) The claims satisfy Step 2B of the Alice/Mayo Framework because each of the claims as a whole amounts to significantly more than any of the judicial exceptions.
Examiner respectfully disagrees. The courts found that “… if a patent’s recitation of a computer amounts to a mere instruction to ‘implement[t]’ an abstract idea ‘on . . . a computer,’ that addition cannot impart patent eligibility.” Alice Corp., 134 S.Ct. at 2358. The claimed invention does not indicate that specialized computer hardware is necessary to implement the claimed systems, similar to the claims at issue in Alice Corp. See Alice Corp., 134 S.Ct. at 2360 (determining that the hardware recited in the claims was “purely functional and generic,” and did not “offer a meaningful limitation beyond generally linking the use of the [method] to a particular technological environment, that is, implementation via computers”). The claims here are not directed to a specific improvement to computer functionality nor an inventive solution to any computer specific problem. Also, limiting the use of an abstract idea “‘to a particular technological environment’ does not confer patent eligibility as this cannot be considered an improvement to computer or technology and so cannot be “significantly more.” Examiner notes that the computer limitations and the claim as a whole do not add significantly more than the abstract idea itself, because the claim does not amount to an improvement to the functioning of a computer itself; and the claim does not move beyond a general link of the use of an abstract idea to a particular technological environment. A generic recitation of a processor performing its generic computer functions does not make the claims less abstract.
(C) Applicant argues the claims recite subject matter that is patent eligible.
Examiner respectfully disagrees. Examiner notes the claims do not integrate the judicial exception into a practical application and therefore the 35 U.S.C. 101 rejection of claims 1-20 are maintained.
Applicant’s arguments submitted on 12/03/2025, with respect to the previous 35 U.S.C. 103 rejection of claims 1-21 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Durant et al., WO2020053778 A1, in view of Subramaniyan et al., US 20170193381 A1, and in view of Cogill, et al., US 20160267391 A1, hereinafter referred to as Durant, Subramaniyan and Cogill, respectively.
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.
The determination of whether a claim recites patent ineligible subject matter is a two-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP 2106 (III)
Claim 1. A method of forecasting operation data of an aircraft, the method comprising:
receiving, by a computer system, historical flight data of the aircraft, the historical flight data including historical departure airports, historical arrival airports, and historical period of flight occurrence: [pre-solution activity (data gathering) using generic sensors]; and
calculating, by the computer system, at least one matrix based on the historical flight data [post insignificant extra solution activity];
calculating, by the computer system, a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports based on the at least one matrix [post insignificant extra solution activity];
determining, by the computer system, based on the transition probability matrix, using a hidden Markov model (HMM) or a sequential Long Short-Term Memory (LSTM) procedure [post insignificant extra solution activity], forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes [mental process/step];
receiving, by the computer system, past aircraft sensor parameters comprising past engine parameters [pre-solution activity (data gathering) using generic sensors];
calculating, by the computer system, using a bootstrapping procedure or the sequential LSTM procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes, forecast future aircraft operational data comprising future engine parameters [post insignificant extra solution activity]; and
determining, by the computer system [post insignificant extra solution activity], based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure, the one or more systems comprising the engine [mental process/step].
101 Analysis – Step 1: Statutory Category – Yes
The claim recites a method. The claim falls within one of the four statutory categories. See MPEP 2106.03.
Step 2A Prong one evaluation: Judicial Exception – Yes- Mental processes
In Step 2A, Prong one of the 2019 PEG, a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper.” See MPEP 2106.04(a)(2)(III)
The claim recites the limitations of determining forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes and determining, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure, the one or more systems comprising the engine. These limitations, as drafted, are simple processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “by the computer system.” That is, other than reciting “by the computer system” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the ““by the computer system,” language, the claim encompasses a person looking at data collected and forming a simple judgement. The mere nominal recitation of by a computer does not take the claim limitations out of the mental process grouping.
Thus, the claim recites a mental process.
Step 2A Prong two evaluation: Practical Application – No
In Step 2A, prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recited additional elements or steps of by the computer system and sensors. In particular, the sensors are recited at a high level of generality (i.e. as a general means of generating data) and amounts to mere pre-solution activity, which is a form of insignificant extra-solution activity. Lastly, the “by the computer system …” limitation is recited at a high-level generality (i.e., a computer performing generic computer function) such that it amounts to no more than mere instructions to “apply” the exception using a generic computer component.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B evaluation: Inventive concept – No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e. whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong two, the additional elements in the claim amount to no more than the mere instructions to apply the exception using a generic computer component. The same analysis applies here in Step 2B, i.e. mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an invention concept in Step 2B. See MPEP 2106.05(f).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the “sensor system and a computer device were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine and conventional activity in the field.
The specification recites that “the operational data includes day-to-day flight data and recorded sensor information that captures various operational and environmental parameters of the aircraft in three phases, e.g., takeoff, climb, and cruise” (See ¶23), and further does not provide any indication that the computer device is anything other than conventional computer element (See ¶55 of applicant’s specification). See MPEP 2106.05(d)(II)
Independent claim 11 recites similar limitations performed by the method of claim 1. Therefore, claim 11 is rejected under the same rationales used in the rejections of claim 1 as outlined above.
Independent claim 13 recites similar limitations performed by the method of claim 1. Therefore, claim 13 is rejected under the same rationales used in the rejections of claim 1 as outlined above.
Dependent claims 2-10, 12 and 14-21 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-10, 12 and 14-20 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1
Therefore, claims 1-21 are ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Durant et al., WO2020053778 A1, in view of Subramaniyan et al., US 20170193381 A1, and in view of Cogill, et al., US 20160267391 A1, hereinafter referred to as Durant, Subramaniyan and Cogill, respectively.
Regarding claim 1, Durant discloses a method of forecasting operation data of an aircraft, the method comprising:
receiving, by a computer system, historical flight data of the aircraft, the historical flight data including historical departure airports, historical arrival airports, and historical period of flight occurrence (Furthermore, information from the carrier provided schedules (e.g. specifically landing and departure timestamps and airports) optionally allows for the contaminant data to be queried through a service provider – See at least page 12, lines 1-10. derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time – See at least page 16, lines 1-10);
calculating, by the computer system, at least one matrix based on the historical flight data (training data is provided to the machine learning to configure the machine learning, for example by using historical data obtained from previous aircraft flights and previous weather conditions - See at least page 6, lines 19-23);
receiving, by the computer system, past aircraft sensor parameters comprising past engine parameters (Historical data potentially comprises ground-based sensors, sensors mounted on aircraft, for events or time points in the past – See at least page 28, lines 28-31).
Durant fails to explicitly disclose calculating, by the computer system, using a bootstrapping procedure or the sequential LSTM procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes, forecast future aircraft operational data comprising future engine parameters; and determining, by the computer system, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure, the one or more systems comprising the engine.
However, Subramaniyan teaches:
calculating, by the computer system, using a bootstrapping procedure or the sequential LSTM procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes, forecast future aircraft operational data comprising future engine parameters (The DEF computing device uses a variety of techniques, alone or in combination, to estimate missing data. The techniques used include, without limitation, probabilistic principal component analysis (PPCA), Markov Process modeling, use of correlated distributions (bootstrapping) – See at least ¶24); and
determining, by the computer system, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure, the one or more systems comprising the engine (The systems and methods are able to estimate missing data and forecast future data without specific information about all variables of the component. The claimed systems and methods enable key commercial advantages such as building of accurate predictive models for design improvements, performance management, condition-based maintenance – See at least ¶57).
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 invention of Durant and include the feature of calculating, by the computer system, using a bootstrapping procedure or the sequential LSTM procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes, forecast future aircraft operational data; and determining, by the computer system, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure, as taught by Subramaniyan, to predict future data for an aircraft.
The combination of Durant and Subramaniyan fail to disclose calculating, by the computer system, a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports based on the at least one matrix; determining, by the computer system, based on the transition probability matrix, using a hidden Markov model (HMM) or a sequential Long Short Term Memory (LSTM) procedure, forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes.
However, Cogill teaches calculating, by the computer system, a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports based on the at least one matrix; determining, by the computer system, based on the transition probability matrix, using a hidden Markov model (HMM) or a sequential Long Short Term Memory (LSTM) procedure, forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes (The present invention relates to system and method for forecasting a probabilistic distribution of time delays added to a scheduled start time and a scheduled end time of a task. Specific applications include forecasting probabilistic distributions of arrival times and departure times for an item such as a transport or a work piece in a process – See at least ¶2. In the next three paragraphs, a Markov model and a hidden Markov model for use herein are discussed in general. Markov models are one of the most basic stochastic models, which are fully determined by a set of states transition probabilities from state Xi to state Xj. The transition probabilities can be seen as a matrix or a probability distribution of the next state, conditional on the current state. FIG. 4 illustrates one example of a Markov model – See at least ¶32).
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 combination of Durant and Subramaniyan and include the feature of calculating, by the computer system, a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports based on the at least one matrix; determining, by the computer system, based on the transition probability matrix, using a hidden Markov model (HMM) or a sequential Long Short Term Memory (LSTM) procedure, forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes, as taught by Cogill, to predict future data for an aircraft.
Regarding claim 2, Durant discloses wherein the calculating, by the computer system, the transition probability matrix based on the historical flight data comprises calculating the transition probability matrix for a plurality of matrices, each matrix being computed for a selected period of time during a one-year period (The system optionally produces a three-dimensional (3D) matrix for each contaminant separately and store or combine them to obtain four dimensional (4D) weather model using time. In one embodiment, the system calculates at least one modified aircraft flight plan for the at least one aircraft flight, an estimated contamination risk for the modified aircraft flight plan being generated for comparison with the estimated contamination for at least one aircraft flight plan – See at least page 23, lines 47-50).
Regarding claim 3, Durant discloses wherein the calculating, by the computer system, the transition probability matrix based on the historical flight data comprises computing elements of the transition probability matrix based on elements of a matrix representing a frequency of usage of the historical departure airports of the aircraft (The system optionally produces a three-dimensional (3D) matrix for each contaminant separately and store or combine them to obtain four dimensional (4D) weather model using time. In one embodiment, the system calculates at least one modified aircraft flight plan for the at least one aircraft flight, an estimated contamination risk for the modified aircraft flight plan being generated for comparison with the estimated contamination for at least one aircraft flight plan – See at least page 23, lines 47-50).
Regarding claim 4, Durant discloses arranging, by the computer system, the historical flight data of the aircraft and the forecasted sequence of future routes in a time sequence (Furthermore, information from the carrier provided schedules (e.g. specifically landing and departure timestamps and airports) optionally allows for the contaminant data to be queried through a service provider – See at least page 12, lines 1-10. derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time – See at least page 16, lines 1-10).
Regarding claim 5, Durant discloses receiving, by the computer system, the past aircraft sensor parameters comprises receiving past aircraft sensor parameters associated with one or more engines configuration used in the aircraft (Historical data potentially comprises ground-based sensors, sensors mounted on aircraft, for events or time points in the past – See at least page 28, lines 28-31).
Regarding claim 6, Durant fails to explicitly disclose wherein the calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises using, by the bootstrapping procedure, forecasted sequence of routes to fetch a relevant record from the historical flight data that maps aircraft route, aircraft configuration and a time period of forecast.
However, Subramaniyan teaches wherein the calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises using, by the bootstrapping procedure, forecasted sequence of routes to fetch a relevant record from the historical flight data that maps aircraft route, aircraft configuration and a time period of forecast (The systems and methods are able to estimate missing data and forecast future data without specific information about all variables of the component. The claimed systems and methods enable key commercial advantages such as building of accurate predictive models for design improvements, performance management, condition-based maintenance – See at least ¶57).
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 invention of Durant and include the feature of wherein the calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises using, by the bootstrapping procedure, forecasted sequence of routes to fetch a relevant record from the historical flight data that maps aircraft route, aircraft configuration and a time period of forecast, as taught by Subramaniyan, to predict future data for an aircraft.
Regarding claim 7, Durant discloses plotting the past aircraft sensor parameters and the future aircraft operational data versus time (Historical data potentially comprises ground-based sensors, sensors mounted on aircraft, for events or time points in the past – See at least page 28, lines 28-31).
Regarding claim 8, Durant fails to explicitly disclose calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises applying, by the bootstrapping procedure, sampling rules to the historical flight data.
However, Subramaniyan teaches calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises applying, by the bootstrapping procedure, sampling rules to the historical flight data (The systems and methods are able to estimate missing data and forecast future data without specific information about all variables of the component. The claimed systems and methods enable key commercial advantages such as building of accurate predictive models for design improvements, performance management, condition-based maintenance – See at least ¶57).
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 invention of Durant and include the feature of calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises applying, by the bootstrapping procedure, sampling rules to the historical flight data, as taught by Subramaniyan, to predict future data for an aircraft.
Regarding claim 9, Durant fails to explicitly disclose wherein the calculating, using the bootstrapping procedure, sampling rules on the historical flight data comprises searching, by the computer system, a database storage unit in communication with the computer system for a record having a string of parameters including an identifier of the aircraft, an engine on the aircraft, a departure airport, an arrival airport, and a time of flight departure.
However, Subramaniyan teaches wherein the calculating, using the bootstrapping procedure, sampling rules on the historical flight data comprises searching, by the computer system, a database storage unit in communication with the computer system for a record having a string of parameters including an identifier of the aircraft, an engine on the aircraft, a departure airport, an arrival airport, and a time of flight departure (The systems and methods are able to estimate missing data and forecast future data without specific information about all variables of the component. The claimed systems and methods enable key commercial advantages such as building of accurate predictive models for design improvements, performance management, condition-based maintenance – See at least ¶57).
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 invention of Durant and include the feature of wherein the calculating, using the bootstrapping procedure, sampling rules on the historical flight data comprises searching, by the computer system, a database storage unit in communication with the computer system for a record having a string of parameters including an identifier of the aircraft, an engine on the aircraft, a departure airport, an arrival airport, and a time of flight departure, as taught by Subramaniyan, to predict future data for an aircraft.
Regarding claim 10, Durant discloses wherein, when the record having the string of parameters including the identifier of the aircraft, the engine on the aircraft, the departure airport, the arrival airport, and the time of flight departure is found, further comprising associating the historical flight data with the record and forecasting the future aircraft operational data (Furthermore, information from the carrier provided schedules (e.g. specifically landing and departure timestamps and airports) optionally allows for the contaminant data to be queried through a service provider – See at least page 12, lines 1-10. derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time – See at least page 16, lines 1-10).
Regarding claim 11, Durant discloses a method of forecasting operation data of an aircraft, the method comprising:
receiving, by a computer system, operational flight data of the aircraft that includes a plurality of aircraft flight sensor parameters from one or more components of the aircraft, the plurality of aircraft flight sensor parameters being associated with a plurality of flight phases of the aircraft, the plurality of aircraft sensor parameters including past engine parameters (Furthermore, information from the carrier provided schedules (e.g. specifically landing and departure timestamps and airports) optionally allows for the contaminant data to be queried through a service provider – See at least page 12, lines 1-10. derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time – See at least page 16, lines 1-10);
training, by the computer system, a sequential Long Short Term Memory (LSTM) procedure using the operational flight data for the plurality of flight phases of the aircraft (Embodiments of the present disclosure beneficially analyze variations by time of day and year, and long-term trends, and enables airlines to make changes such as scheduling take-offs at times of day when contaminants are lower to reduce the impact or costs of contaminant exposure – See at least page 24, lines 6-15);
outputting, by the computer system, multi-output data for each of the plurality of aircraft flight sensor parameters associated with a corresponding flight phase of the aircraft (Historical data potentially comprises ground-based sensors, sensors mounted on aircraft, for events or time points in the past – See at least page 28, lines 28-31);
Durant fails to explicitly disclose determining, by the computer system, based on the multi-output data correlated with the forecasted route data structure, a plurality of forecast aircraft flight sensor parameters from the one or more components of the aircraft to repair the one or more components to prevent failure of the one or more components or to replace the one or more components prior to failure, the plurality of forecast aircraft flight sensor parameters including future engine parameters.
However, Subramaniyan teaches:
determining, by the computer system, based on the multi-output data correlated with the forecasted route data structure, a plurality of forecast aircraft flight sensor parameters from the one or more components of the aircraft to repair the one or more components to prevent failure of the one or more components or to replace the one or more components prior to failure, the plurality of forecast aircraft flight sensor parameters including future engine parameters (The systems and methods are able to estimate missing data and forecast future data without specific information about all variables of the component. The claimed systems and methods enable key commercial advantages such as building of accurate predictive models for design improvements, performance management, condition-based maintenance – See at least ¶57).
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 invention of Durant and include the feature of determining, by the computer system, based on the multi-output data correlated with the forecasted route data structure, a plurality of forecast aircraft flight sensor parameters from the one or more components of the aircraft to repair the one or more components to prevent failure of the one or more components or to replace the one or more components prior to failure, as taught by Subramaniyan, to predict future data for an aircraft.
The combination of Durant and Subramaniyan fail to disclose grouping, by the computer system, the multi-output data for each of the plurality of aircraft flight sensor parameters with a respective flight record based on forecasted route data structure forecasted based on a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports by a Hidden Markov Model (HMM) or the sequential LSTM procedure to correlate between the multi-output data with the forecasted route data structure.
However, Cogill teaches grouping, by the computer system, the multi-output data for each of the plurality of aircraft flight sensor parameters with a respective flight record based on forecasted route data structure forecasted based on a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports by a Hidden Markov Model (HMM) or the sequential LSTM procedure to correlate between the multi-output data with the forecasted route data structure (The present invention relates to system and method for forecasting a probabilistic distribution of time delays added to a scheduled start time and a scheduled end time of a task. Specific applications include forecasting probabilistic distributions of arrival times and departure times for an item such as a transport or a work piece in a process – See at least ¶2. In the next three paragraphs, a Markov model and a hidden Markov model for use herein are discussed in general. Markov models are one of the most basic stochastic models, which are fully determined by a set of states transition probabilities from state Xi to state Xj. The transition probabilities can be seen as a matrix or a probability distribution of the next state, conditional on the current state. FIG. 4 illustrates one example of a Markov model – See at least ¶32).
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 combination of Durant and Subramaniyan and include the feature of grouping, by the computer system, the multi-output data for each of the plurality of aircraft flight sensor parameters with a respective flight record based on forecasted route data structure forecasted based on a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports by a Hidden Markov Model (HMM) or the sequential LSTM procedure to correlate between the multi-output data with the forecasted route data structure, as taught by Cogill, to predict future data for an aircraft.
Regarding claim 12, Durant discloses wherein the operational flight data of the aircraft comprises aircraft flight parameters and environmental factors (Furthermore, information from the carrier provided schedules (e.g. specifically landing and departure timestamps and airports) optionally allows for the contaminant data to be queried through a service provider – See at least page 12, lines 1-10. derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time – See at least page 16, lines 1-10).
Regarding claim 13, Durant discloses a non-transitory computer-readable medium storing a computer-executable code that when executed by a computer system, causes the computer system to perform a method of forecasting operation data of an aircraft, the method comprising:
receiving, by a computer system, historical flight data of the aircraft, the historical flight data including historical departure airports, historical arrival airports, and historical period of flight occurrence (Furthermore, information from the carrier provided schedules (e.g. specifically landing and departure timestamps and airports) optionally allows for the contaminant data to be queried through a service provider – See at least page 12, lines 1-10. derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time – See at least page 16, lines 1-10);
calculating, by the computer system, at least one matrix based on the historical flight data (training data is provided to the machine learning to configure the machine learning, for example by using historical data obtained from previous aircraft flights and previous weather conditions - See at least page 6, lines 19-23);
receiving, by the computer system, past aircraft sensor parameters comprising past engine parameters (Historical data potentially comprises ground-based sensors, sensors mounted on aircraft, for events or time points in the past – See at least page 28, lines 28-31).
Durant fails to explicitly disclose calculating, by the computer system, using a bootstrapping procedure or the sequential LSTM procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes, forecast future aircraft operational data comprising future engine sensor parameters; and determining, by the computer system, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure, the one or more systems comprising the engine.
However, Subramaniyan teaches:
calculating, by the computer system, using a bootstrapping procedure or the sequential LSTM procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes, forecast future aircraft operational data comprising future engine sensor parameters (The DEF computing device uses a variety of techniques, alone or in combination, to estimate missing data. The techniques used include, without limitation, probabilistic principal component analysis (PPCA), Markov Process modeling, use of correlated distributions (bootstrapping) – See at least ¶24); and
determining, by the computer system, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure, the one or more systems comprising the engine (The systems and methods are able to estimate missing data and forecast future data without specific information about all variables of the component. The claimed systems and methods enable key commercial advantages such as building of accurate predictive models for design improvements, performance management, condition-based maintenance – See at least ¶57).
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 invention of Durant and include the feature of calculating, by the computer system, using a bootstrapping procedure or the sequential LSTM procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes, forecast future aircraft operational data comprising future engine sensor parameters; and determining, by the computer system, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to repair the one or more systems to prevent failure of the one or more systems or to replace the one or more systems prior to failure, the one or more systems comprising the engine, as taught by Subramaniyan, to predict future data for an aircraft.
The combination of Durant and Subramaniyan fail to disclose calculating, by the computer system, a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports based on the at least one matrix; determining, by the computer system, based on the transition probability matrix, using a hidden Markov model (HMM) or a sequential Long Short Term Memory (LSTM) procedure, forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes.
However, Cogill teaches calculating, by the computer system, a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports based on the at least one matrix; determining, by the computer system, based on the transition probability matrix, using a hidden Markov model (HMM) or a sequential Long Short Term Memory (LSTM) procedure, forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes (The present invention relates to system and method for forecasting a probabilistic distribution of time delays added to a scheduled start time and a scheduled end time of a task. Specific applications include forecasting probabilistic distributions of arrival times and departure times for an item such as a transport or a work piece in a process – See at least ¶2. In the next three paragraphs, a Markov model and a hidden Markov model for use herein are discussed in general. Markov models are one of the most basic stochastic models, which are fully determined by a set of states transition probabilities from state Xi to state Xj. The transition probabilities can be seen as a matrix or a probability distribution of the next state, conditional on the current state. FIG. 4 illustrates one example of a Markov model – See at least ¶32).
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 combination of Durant and Subramaniyan and include the feature of calculating, by the computer system, a transition probability matrix capturing a pattern of the historical departure airports and the historical arrival airports based on the at least one matrix; determining, by the computer system, based on the transition probability matrix, using a hidden Markov model (HMM) or a sequential Long Short Term Memory (LSTM) procedure, forecasted departure airports, forecasted arrival airports, and time of arrival of the aircraft to the forecasted arrival airports to build a forecasted sequence of future routes, as taught by Cogill, to predict future data for an aircraft.
Regarding claim 14, Durant discloses wherein the calculating, by the computer system, the transition probability matrix based on the historical flight data comprises calculating the transition probability matrix for a plurality of matrices, each matrix being computed for a selected period of time during a one-year period (The system optionally produces a three-dimensional (3D) matrix for each contaminant separately and store or combine them to obtain four dimensional (4D) weather model using time. In one embodiment, the system calculates at least one modified aircraft flight plan for the at least one aircraft flight, an estimated contamination risk for the modified aircraft flight plan being generated for comparison with the estimated contamination for at least one aircraft flight plan – See at least page 23, lines 47-50).
Regarding claim 15, Durant discloses wherein the calculating, by the computer system, the transition probability matrix based on the historical flight data comprises computing elements of the transition probability matrix based on elements of a matrix representing a frequency of usage of the historical departure airports of the aircraft (The system optionally produces a three-dimensional (3D) matrix for each contaminant separately and store or combine them to obtain four dimensional (4D) weather model using time. In one embodiment, the system calculates at least one modified aircraft flight plan for the at least one aircraft flight, an estimated contamination risk for the modified aircraft flight plan being generated for comparison with the estimated contamination for at least one aircraft flight plan – See at least page 23, lines 47-50).
Regarding claim 16, Durant discloses arranging, by the computer system, the historical flight data of the aircraft and the forecasted sequence of future routes in a time sequence (Furthermore, information from the carrier provided schedules (e.g. specifically landing and departure timestamps and airports) optionally allows for the contaminant data to be queried through a service provider – See at least page 12, lines 1-10. derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time – See at least page 16, lines 1-10).
Regarding claim 17, Durant discloses receiving, by the computer system, the past aircraft sensor parameters comprises receiving past aircraft sensor parameters associated with one or more engines configuration used in the aircraft (Historical data potentially comprises ground-based sensors, sensors mounted on aircraft, for events or time points in the past – See at least page 28, lines 28-31).
Regarding claim 18, Durant fails to explicitly disclose calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises applying, by the bootstrapping procedure, sampling rules to the historical flight data.
However, Subramaniyan teaches calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises applying, by the bootstrapping procedure, sampling rules to the historical flight data (The systems and methods are able to estimate missing data and forecast future data without specific information about all variables of the component. The claimed systems and methods enable key commercial advantages such as building of accurate predictive models for design improvements, performance management, condition-based maintenance – See at least ¶57).
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 invention of Durant and include the feature of calculating, by the computer system, using the bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes to forecast the future aircraft operational data comprises applying, by the bootstrapping procedure, sampling rules to the historical flight data, as taught by Subramaniyan, to predict future data for an aircraft.
Regarding claim 19, Durant fails to explicitly disclose wherein the calculating, using the bootstrapping procedure, sampling rules on the historical flight data comprises searching, by the computer system, a database storage unit in communication with the computer system for a record having a string of parameters including an identifier of the aircraft, an engine on the aircraft, a departure airport, an arrival airport, and a time of flight departure.
However, Subramaniyan teaches wherein the calculating, using the bootstrapping procedure, sampling rules on the historical flight data comprises searching, by the computer system, a database storage unit in communication with the computer system for a record having a string of parameters including an identifier of the aircraft, an engine on the aircraft, a departure airport, an arrival airport, and a time of flight departure (The systems and methods are able to estimate missing data and forecast future data without specific information about all variables of the component. The claimed systems and methods enable key commercial advantages such as building of accurate predictive models for design improvements, performance management, condition-based maintenance – See at least ¶57).
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 invention of Durant and include the feature of wherein the calculating, using the bootstrapping procedure, sampling rules on the historical flight data comprises searching, by the computer system, a database storage unit in communication with the computer system for a record having a string of parameters including an identifier of the aircraft, an engine on the aircraft, a departure airport, an arrival airport, and a time of flight departure, as taught by Subramaniyan, to predict future data for an aircraft.
Regarding claim 20, Durant discloses wherein, when the record having the string of parameters including the identifier of the aircraft, the engine on the aircraft, the departure airport, the arrival airport, and the time of flight departure is found, further comprising associating the historical flight data with the record and forecasting the future aircraft operational data (Furthermore, information from the carrier provided schedules (e.g. specifically landing and departure timestamps and airports) optionally allows for the contaminant data to be queried through a service provider – See at least page 12, lines 1-10. derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time – See at least page 16, lines 1-10).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Durant et al., WO2020053778 A1, in view of Subramaniyan et al., US 20170193381 A1, in view of Cogill, et al., US 20160267391 A1, as applied to claim 1 above and further in view of Delaye et al., US 20120221193 A1, hereinafter referred to as Durant, Subramaniyan Cogill and Delaye, respectively.
Regarding claim 21, the combination of Durant, Subramaniyan and Cogill fail to disclose wherein to repair the one or more systems or to replace the one or more systems comprises removing the engine.
However, Delaye teaches wherein to repair the one or more systems or to replace the one or more systems comprises removing the engine (The method according to the invention can model the behaviour of a complete fleet of engines producing a prediction of the number of engine removals and the workscopes on the different engines as a function of the different input data concerning the fleet recorded in the database. This method also manages technical history of the engines taking account of their aging in the determination of removal plans – See at least ¶35).
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 invention of Durant and include the feature of wherein to repair the one or more systems or to replace the one or more systems comprises removing the engine, as taught by Delaye, to predict maintenance operations on a current aircraft engine.
Conclusion
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/M.M.K./Examiner, Art Unit 3665
/DONALD J WALLACE/Primary Examiner, Art Unit 3665