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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/06/2025 has been entered.
Response to Arguments
Applicant's arguments filed 7/03/2025 have been fully considered but they are considered moot because a rejection with a different combination of references has been made below.
Response to Amendment
Regarding the rejections under 35 USC §103, a rejection has been made below.
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.
Claims 1, 3, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Saito et al. (U.S. Patent Application Publication No. 2021/0130012 A1; hereinafter Saito) in view of Liu et al. (“Use of Artificial Neural Networks for Helicopter Load Monitoring”; see reference U on PTO-892; hereinafter Liu) and further in view of Cheung et al. (“Helicopter Loads Synthesis using a Neural Network”; see reference V on PTO-892; hereinafter Cheung).
Regarding claim 1, Saito discloses:
a rotorcraft component (fuselage of an aircraft, see at least [0010]);
a plurality of sensors (acceleration sensor, strain gauge, and the like are installed in aircraft 1, see at least [0035]);
one or more non-transitory computer storage media storing programming for execution by one or more processors (read only memory for storing programs executed by CPU, see at least [0040]), the programming comprising instructions to:
access actual operating condition information for operating condition parameters of the rotorcraft, the actual operating condition information corresponding to sensor measurements from the plurality of sensors and associated with the rotorcraft component (operation data is information recorded in flight recorder and includes history information such as altitude, temperature, and wind speed, see at least [0032]; structural health monitoring (SMH) obtains structural condition data such as damage occurrence, strain condition, and degree of corrosion, see at least [0035]);
analyze, the actual operating condition information to generate initial predicted load information for the rotorcraft component (operation data and monitoring data such as strain is acquired to estimate load, see at least [0065] and Fig. 4)
determine, according to the predicted load information, an estimated fatigue life for the rotorcraft component that is individualized for the rotorcraft component (fatigue life is predicted based on the damage rule, estimated stress, and estimated load, see at least [0066] and Fig. 4).
Saito does not explicitly disclose:
A rotorcraft, comprising:
using an artificial intelligence model
the initial predicted load information comprising a first output data signal in a frequency domain
convert the initial predicted load information to a time domain to generate predicted load information for the rotorcraft component by applying a transform operation to the first output data signal to generate a second output data signal in a time domain
However, Liu teaches:
A rotorcraft (helicopter, see title)
analyze, using an artificial intelligence model, the actual operating condition information to generate initial predicted load information for the rotorcraft component (utilizing multi-layer artificial neural networks (ANNs) to determine airframe loads at fixed locations from flight state and control system parameters obtained during a Black Hawk flight load survey, see at least abstract; ANNs trained using data from steady state flight condition, see at least page 7, paragraph 3)
initial predicted load information to a time domain to generate predicted load information for the rotorcraft (control system parameters analyzed in time domain, see at least page 3, paragraph 4)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito by adding the neural networks taught by Liu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “the strong potential for artificial neural networks to reliably estimate airframe loads on rotary-wing aircraft” (see page 13, paragraph 1).
Additionally, Cheung teaches:
using an artificial intelligence model (ANN output see at least page 11),
the initial predicted load information comprising a first output data signal in a frequency domain (training result showing ANN output in frequency domain, see at least Fig. 9)
convert the initial predicted load information to a time domain to generate predicted load information for the rotorcraft component by applying a transform operation to the first output data signal to generate a second output data signal in a time domain (ANN output transformed back to time domain, see at least Fig. 10)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito and the neural networks and analysis in time domain taught by Liu by adding the domain conversions taught by Cheung with a reasonable expectation of success. Although the ANN outputs are in the frequency domain in Cheung, the analysis of Liu is done in the time domain. The transformation of data from a frequency domain to a time domain (and vice versa) using a Fast Fourier Transform (FFT) function can be done as a design choice or for specific purposes of the data being observed. One of ordinary skill in the art would have been motivated to make this modification in order “to verify the outputs from the network” (see page 10, paragraph 2).
Regarding claim 3, the combination of Saito, Liu, and Cheung teaches the elements above and Saito further discloses:
the rotorcraft component comprises: a pitch link of the rotorcraft; a fuselage of the rotorcraft (fuselage of an aircraft, see at least [0010]); or one or more rotor blades of the rotorcraft.
Regarding claim 6, the combination of Saito, Liu, and Cheung teaches the elements above and Saito further discloses:
the programming further comprises instructions to generate, according to the estimated fatigue life for the rotorcraft component, a maintenance plan that comprises an individualized maintenance recommendation for the rotorcraft component (repair method corresponding to the current degree of damage is presented, see at least [0068]).
Claims 7, 9, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Saito in view of Liu.
Regarding claim 7, Saito discloses:
A method, comprising: accessing, by a processing device (programs executed by CPU, see at least [0040]), actual operating condition information for an operating condition parameter associated with actual operation of a vehicle, the actual operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle (operation data is information recorded in flight recorder and includes history information such as altitude, temperature, and wind speed, see at least [0032]; structural health monitoring (SMH) obtains structural condition data such as damage occurrence, strain condition, and degree of corrosion, see at least [0035]);
analyzing, by the processing device, the actual operating condition information to generate predicted load information for the vehicle component (operation data and monitoring data such as strain is acquired to estimate load, see at least [0065] and Fig. 4); and
determining, according to the predicted load information, an estimated fatigue life for the vehicle component that is individualized for the vehicle component (fatigue life is predicted based on the damage rule, estimated stress, and estimated load, see at least [0066] and Fig. 4).
Saito does not explicitly disclose:
using an artificial intelligence model
However, Liu teaches:
analyze, using an artificial intelligence model, the actual operating condition information to generate initial predicted load information for the rotorcraft component (utilizing multi-layer artificial neural networks (ANNs) to determine airframe loads at fixed locations from flight state and control system parameters obtained during a Black Hawk flight load survey, see at least abstract; ANNs trained using data from steady state flight condition, see at least page 7, paragraph 3)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito by adding the neural networks taught by Liu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “the strong potential for artificial neural networks to reliably estimate airframe loads on rotary-wing aircraft” (see page 13, paragraph 1).
Regarding claim 9, the combination of Saito and Dempsey teaches the elements above and Saito further discloses:
the vehicle is an aircraft (aircraft, see at least [0010]);
the operating condition parameter comprises a flight parameter associated with an actual flight of the aircraft (operation data is information recorded in flight recorder and includes history information such as altitude, temperature, and wind speed, see at least [0032]); and
the actual operating condition information associated with the operating condition parameter comprises a data signal for the flight parameter (structural health monitoring (SMH) obtains structural condition data such as damage occurrence, strain condition, and degree of corrosion, see at least [0035]).
Regarding claim 16, the combination of Saito and Dempsey teaches the elements above and Saito further discloses:
generating, according to the estimated fatigue life for the vehicle component, a maintenance plan that comprises an individualized maintenance recommendation for the vehicle component (repair method corresponding to the current degree of damage is presented, see at least [0068])
Regarding claim 17, Saito discloses:
A computer system, comprising: one or more processing units; and one or more non-transitory computer-readable storage media storing programming for execution by the one or more processing units (read only memory for storing programs executed by CPU, see at least [0040]), the programming comprising instructions to:
access actual operating condition information for an operating condition parameter associated with actual operation of a vehicle, the actual operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle (operation data is information recorded in flight recorder and includes history information such as altitude, temperature, and wind speed, see at least [0032]; structural health monitoring (SMH) obtains structural condition data such as damage occurrence, strain condition, and degree of corrosion, see at least [0035]);
analyze, the actual operating condition information to generate predicted load information for the vehicle component (operation data and monitoring data such as strain is acquired to estimate load, see at least [0065] and Fig. 4); and
determine, according to the predicted load information, an estimated fatigue life for the vehicle component that is individualized for the vehicle component (fatigue life is predicted based on the damage rule, estimated stress, and estimated load, see at least [0066] and Fig. 4)
Saito does not explicitly disclose:
using an artificial intelligence model configured to determine predicted structural loads from operating condition information based on training using test operating condition information and corresponding test load information
However, Liu teaches:
using an artificial intelligence model configured to determine predicted structural loads from operating condition information based on training using test operating condition information and corresponding test load information (utilizing multi-layer artificial neural networks (ANNs) to determine airframe loads at fixed locations from flight state and control system parameters obtained during a Black Hawk flight load survey, see at least abstract; ANNs trained using data from steady state flight condition, see at least page 7, paragraph 3)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito by adding the neural networks taught by Liu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “the strong potential for artificial neural networks to reliably estimate airframe loads on rotary-wing aircraft” (see page 13, paragraph 1).
Regarding claim 18, the combination of Saito and Liu teaches the elements above and Saito further discloses:
the computer system is located on board the vehicle (aircraft management device 10 for aircraft 1, see at least [0029])
Regarding claim 20, the combination of Saito and Liu teaches the elements above and Saito further discloses:
the programming further includes instructions to generate, according to the estimated fatigue life for the vehicle component, a maintenance plan that comprises an individualized maintenance recommendation for the vehicle component (repair method corresponding to the current degree of damage is presented, see at least [0068])
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Saito in view of Liu as applied to claim 7 above and further in view of Cheung.
Regarding claim 8, the combination of Saito and Liu teaches the elements above and Saito further discloses:
the sensor measurements are part of a data signal received from a sensor associated with the vehicle component (acceleration sensor, strain gauge, and the like are installed in aircraft 1, see at least [0035]) (acceleration sensor, strain gauge, and the like are installed in aircraft 1, see at least [0035])
Saito does not explicitly disclose:
the method further comprises performing pre-processing on the data signal to generate the actual operating condition information
However, Dempsey teaches:
the method further comprises performing pre-processing on the data signal to generate the actual operating condition information (parameter signal were transformed from time domain to frequency domain, see at least page 5, paragraph 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito by adding the domain conversions taught by Cheung with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification “to perform the analyses in the frequency domain” because “the parameter signals are periodic” (see page 5, paragraph 2).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Saito in view of Liu and Cheung as applied to claim 1, and further in view of Huang et al. (“Convolutional Networks with Dense Connectivity”; see reference V in PTO-892; hereinafter Huang).
Regarding claim 2, the combination of Saito, Liu, and Cheung teaches the elements above but does not teach:
the artificial intelligence model comprises a plurality of layers arranged in a processing sequence, the plurality of layers comprising: at least one first dense layer; at least one second dense layer; and
between the at least one first dense layer and the at least one second dense layer: at least one convolutional layer; at least one normalization layer; and at least one pooling layer
However, Huang teaches:
the artificial intelligence model comprises a plurality of layers arranged in a processing sequence, the plurality of layers comprising: at least one first dense layer; at least one second dense layer; and between the at least one first dense layer and the at least one second dense layer: at least one convolutional layer; at least one normalization layer; and at least one pooling layer (deep DenseNet comprises a dense block 1 with convolution and pooling layers in succession, then a dense block 2, see at least Fig. 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito, the neural networks taught by Liu, and the domain conversions taught by Cheung by adding the deep DenseNet taught by Huang with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification “To further improve the information flow between layers” (see page 3, col. 2, para. 2) and to “substantially improve parameter efficiency” (see abstract).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Saito in view of Liu as applied to claim 7 above, and further in view of Huang.
Regarding claim 10, the combination of Saito and Liu teaches the elements above but does not teach:
the artificial intelligence model comprises a plurality of layers arranged in a processing sequence, the plurality of layers comprising: at least one first dense layer; at least one second dense layer; and between the at least one first dense layer and the at least one second dense layer: at least one convolutional layer; at least one normalization layer; and at least one pooling layer.
However, Huang teaches:
the artificial intelligence model comprises a plurality of layers arranged in a processing sequence, the plurality of layers comprising: at least one first dense layer; at least one second dense layer; and between the at least one first dense layer and the at least one second dense layer: at least one convolutional layer; at least one normalization layer; and at least one pooling layer (deep DenseNet comprises a dense block 1 with convolution and pooling layers in succession, then a dense block 2, see at least Fig. 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito and the neural networks taught by Liu by adding the deep DenseNet taught by Huang with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification “To further improve the information flow between layers” (see page 3, col. 2, para. 2) and to “substantially improve parameter efficiency” (see abstract).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Saito in view of Liu and Cheung as applied to claim 1 above, and further in view of Polanco (“Estimation of Structural Component Loads in Helicopters: A Review of Current Methodologies”; already made of record on IDS dated 10/26/2023).
Regarding claim 4, the combination of Saito, Liu, and Cheung teaches the elements above but does not teach:
the artificial intelligence model has been trained according to a training phase that comprises: accessing training data for a time window, the training data comprising: test operating condition information for a test operating condition of a test rotorcraft, the test operating condition information corresponding to sensor measurements associated with a rotorcraft component of the rotorcraft, the test operating condition corresponding to the operating condition parameter of the rotorcraft; and test load information for a training operating condition of one or more test rotorcraft; and training the artificial intelligence model using the training data.
However, Liu teaches:
the artificial intelligence model has been trained according to a training phase (training stage in ANN simulation, see at least page 4, paragraph 3) that comprises:
test operating condition information for a test operating condition of a test vehicle (trained using data from steady state flight condition for Black Hawk, see at least page 7, Fig. 3 and paragraph 3), the test operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle (strain data gathered from strain gauges, and other sensors captured flight state and control system parameters for test data, see at least page 3, paragraphs 3-6), the test operating condition corresponding to the operating condition parameter of the vehicle (flight state and control system parameters, see at least page 3, paragraph 3); and test load information for a training operating condition of one or more test vehicle (data obtained from flight loads survey for Black Hawk, see at least page 3, paragraph 5); and
training the artificial intelligence model using the training data (training the ANNs was carried out until validation error reached a minimum, see at least page 4, paragraph 4 and page 6, paragraph 3).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito by adding the neural networks taught by Liu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “the strong potential for artificial neural networks to reliably estimate airframe loads on rotary-wing aircraft” (see page 13, paragraph 1).
Additionally, Polanco teaches:
the artificial intelligence model has been trained according to a training phase that comprises: accessing training data for a time window (database generated from helicopter consisting of time histories of 24 flight parameters and 17 loads, see at least page 19, para. 2), the training data comprising:
test operating condition information for a test operating condition of a test rotorcraft, the test operating condition information corresponding to sensor measurements associated with a rotorcraft component of the rotorcraft, the test operating condition corresponding to the operating condition parameter of the rotorcraft (chosen parameters were pitch, roll, and yaw rates, vertical, lateral, and longitudinal accelerations, and longitudinal and lateral control positions, see at least page 19, para. 2, estimate time-varying oscillation of tailboom bending load and pitch link load, see at least page 19, para. 4); and
test load information for a training operating condition of one or more test rotorcraft (testing was conducted on data from seventh maneuver to test estimation accuracy, see at least page 19, para. 4); and
training the artificial intelligence model using the training data (load and flight parameters used to determining weighting coefficients of the neural network, see at least page 18, para. 3; network was trained, see at least page 18, para. 4).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito, the neural networks taught by Liu, and the domain conversions taught by Cheung by adding the neural network trained using a database taught by Polanco with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “accuracy in the neural network identification of mean and oscillatory components” (see page 18, para. 3).
Regarding claim 5, the combination of Saito, Liu, Cheung, and Polanco teaches the elements above but Saito does not disclose:
the training data for the time window is in a time domain; and the training phase further comprises converting, prior to training the artificial intelligence model using the training data, at least a portion of the training data to a frequency domain, such that the artificial intelligence model is trained in the frequency domain.
However, Cheung teaches:
the training data for the time window is in a time domain (parameter signals transformed from time domain, see at least page 5, paragraph 2); and
the training phase further comprises converting, prior to training the artificial intelligence model using the training data, at least a portion of the training data to a frequency domain, such that the artificial intelligence model is trained in the frequency domain (parameter signals transformed from time domain to frequency domain using FFT function, see at least page 5, paragraph 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito and the neural networks taught by Liu by adding the domain conversions taught by Cheung with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification “since the parameter signals are periodic… to perform the analyses in the frequency domain” (see page 2, paragraph 2).
Claims 11, 13, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Saito in view of Liu as applied to claims 7 and 17 above, and further in view of Polanco (“Estimation of Structural Component Loads in Helicopters: A Review of Current Methodologies”; already made of record on IDS dated 10/26/2023)
Regarding claim 11, the combination of Saito and Liu teaches the elements above but Saito does not disclose:
the artificial intelligence model has been trained according to training data that comprises: test operating condition information associated with a test operating condition of a test vehicle, the test operating condition information corresponding to sensor measurements associated with a vehicle component of the test vehicle under the test operating condition; and one or more test load signals
However, Liu teaches:
the artificial intelligence model has been trained according to training data (training stage in ANN simulation, see at least page 4, paragraph 3) that comprises:
test operating condition information associated with a test operating condition of a test vehicle (trained using data from steady state flight condition for Black Hawk, see at least page 7, Fig. 3 and paragraph 3), the test operating condition information corresponding to sensor measurements associated with a vehicle component of the test vehicle under the test operating condition (strain data gathered from strain gauges, and other sensors captured flight state and control system parameters for test data, see at least page 3, paragraphs 3-6); and one or more test load signals (data obtained from flight loads survey for Black Hawk, see at least page 3, paragraph 5); and
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito by adding the neural networks taught by Liu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “the strong potential for artificial neural networks to reliably estimate airframe loads on rotary-wing aircraft” (see page 13, paragraph 1).
Additionally, Polanco teaches:
the artificial intelligence model has been trained according to training data (load and flight parameters used to determining weighting coefficients of the neural network, see at least page 18, para. 3; network was trained, see at least page 18, para. 4) that comprises:
test operating condition information associated with a test operating condition of a test vehicle, the test operating condition information corresponding to sensor measurements associated with a vehicle component of the test vehicle under the test operating condition; and one or more test load signals (chosen parameters were pitch, roll, and yaw rates, vertical, lateral, and longitudinal accelerations, and longitudinal and lateral control positions, see at least page 19, para. 2, estimate time-varying oscillation of tailboom bending load and pitch link load, see at least page 19, para. 4; testing was conducted on data from seventh maneuver to test estimation accuracy, see at least page 19, para. 4)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito and the neural networks taught by Liu by adding the neural network trained using a database taught by Polanco with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “accuracy in the neural network identification of mean and oscillatory components” (see page 18, para. 3).
Regarding claim 13, the combination of Saito and Liu teaches the elements above but Saito does not disclose:
executing a training phase for training the artificial intelligence model, the training phase comprising: accessing training data for a time window, the training data comprising: test operating condition information for a test operating condition of a test vehicle, the test operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle, the test operating condition corresponding to the actual operating condition of the vehicle; and test load information for the time window; and training the artificial intelligence model using the training data.
However, Liu teaches:
executing a training phase for training the artificial intelligence model, the training phase comprising (training stage in ANN simulation, see at least page 4, paragraph 3):
test operating condition information for a test operating condition of a test vehicle (trained using data from steady state flight condition for Black Hawk, see at least page 7, Fig. 3 and paragraph 3), the test operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle (strain data gathered from strain gauges, and other sensors captured flight state and control system parameters for test data, see at least page 3, paragraphs 3-6), the test operating condition corresponding to the actual operating condition of the vehicle (flight state and control system parameters, see at least page 3, paragraph 3); test load information (data obtained from flight loads survey for Black Hawk, see at least page 3, paragraph 5); and
training the artificial intelligence model using the training data (training the ANNs was carried out until validation error reached a minimum, see at least page 4, paragraph 4 and page 6, paragraph 3).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito by adding the neural networks taught by Liu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “the strong potential for artificial neural networks to reliably estimate airframe loads on rotary-wing aircraft” (see page 13, paragraph 1).
Additionally, Polanco teaches:
executing a training phase for training the artificial intelligence model, the training phase comprising: accessing training data for a time window (database generated from helicopter consisting of time histories of 24 flight parameters and 17 loads, see at least page 19, para. 2), the training data comprising:
test operating condition information for a test operating condition of a test vehicle, the test operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle, the test operating condition corresponding to the actual operating condition of the vehicle (chosen parameters were pitch, roll, and yaw rates, vertical, lateral, and longitudinal accelerations, and longitudinal and lateral control positions, see at least page 19, para. 2, estimate time-varying oscillation of tailboom bending load and pitch link load, see at least page 19, para. 4); and
test load information for the time window (testing was conducted on data from seventh maneuver to test estimation accuracy, see at least page 19, para. 4); and
training the artificial intelligence model using the training data (load and flight parameters used to determining weighting coefficients of the neural network, see at least page 18, para. 3; network was trained, see at least page 18, para. 4).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito and the neural networks taught by Liu by adding the neural network trained using a database taught by Polanco with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “accuracy in the neural network identification of mean and oscillatory components” (see page 18, para. 3).
Regarding claim 15, the combination of Saito, Liu, and Polanco teaches the elements above but Saito does not disclose:
the training data is collected from a plurality of test vehicle operations, the test vehicle operations being actual vehicle operations or simulated vehicle operations.
However, Polanco teaches:
the training data is collected from a plurality of test vehicle operations, the test vehicle operations being actual vehicle operations or simulated vehicle operations (database generated from helicopter consisting of time histories of 24 flight parameters and 17 loads, see at least page 19, para. 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito and the neural networks taught by Liu by adding the neural network trained using a database taught by Polanco with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “accuracy in the neural network identification of mean and oscillatory components” (see page 18, para. 3).
Regarding claim 19, the combination of Saito and Liu teaches the elements above but Saito does not disclose:
the artificial intelligence model has been trained according to a training phase that comprises: accessing training data for a time window, the training data comprising: the test operating condition information, the test operating condition information being for a test operating condition of a test vehicle, the test operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle, the test operating condition corresponding to the operating condition parameter of the vehicle; and the test load information, the test load information being for a training operating condition of one or more test vehicle; and training the artificial intelligence model using the training data.
However, Liu teaches:
the artificial intelligence model has been trained according to a training phase (training stage in ANN simulation, see at least page 4, paragraph 3) that comprises:
the test operating condition information, the test operating condition information being for a test operating condition of a test vehicle (trained using data from steady state flight condition for Black Hawk, see at least page 7, Fig. 3 and paragraph 3), the test operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle (strain data gathered from strain gauges, and other sensors captured flight state and control system parameters for test data, see at least page 3, paragraphs 3-6), the test operating condition corresponding to the operating condition parameter of the vehicle (flight state and control system parameters, see at least page 3, paragraph 3); and the test load information, the test load information being for a training operating condition of one or more test vehicle (data obtained from flight loads survey for Black Hawk, see at least page 3, paragraph 5); and
training the artificial intelligence model using the training data (training the ANNs was carried out until validation error reached a minimum, see at least page 4, paragraph 4 and page 6, paragraph 3).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito by adding the neural networks taught by Liu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “the strong potential for artificial neural networks to reliably estimate airframe loads on rotary-wing aircraft” (see page 13, paragraph 1).
Additionally, Polanco teaches:
the artificial intelligence model has been trained according to a training phase that comprises: accessing training data for a time window (database generated from helicopter consisting of time histories of 24 flight parameters and 17 loads, see at least page 19, para. 2), the training data comprising:
the test operating condition information, the test operating condition information being for a test operating condition of a test vehicle, the test operating condition information corresponding to sensor measurements associated with a vehicle component of the vehicle, the test operating condition corresponding to the operating condition parameter of the vehicle (chosen parameters were pitch, roll, and yaw rates, vertical, lateral, and longitudinal accelerations, and longitudinal and lateral control positions, see at least page 19, para. 2, estimate time-varying oscillation of tailboom bending load and pitch link load, see at least page 19, para. 4); and
the test load information, the test load information being for a training operating condition of one or more test vehicle (testing was conducted on data from seventh maneuver to test estimation accuracy, see at least page 19, para. 4); and
training the artificial intelligence model using the training data (load and flight parameters used to determining weighting coefficients of the neural network, see at least page 18, para. 3; network was trained, see at least page 18, para. 4).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito and the neural networks taught by Liu by adding the neural network trained using a database taught by Polanco with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “accuracy in the neural network identification of mean and oscillatory components” (see page 18, para. 3).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Saito in view of Liu and Polanco as applied to claim 13 above, and further in view of Cheung.
Regarding claim 14, the combination of Saito, Liu, and Polanco teaches the elements above but Saito does not disclose:
the training data for the time window is in a time domain; and the method further comprises converting, prior to training the artificial intelligence model using the training data, at least a portion of the training data to a frequency domain, such that the artificial intelligence model is trained in the frequency domain.
the training data for the time window is in a time domain (parameter signals transformed from time domain, see at least page 5, paragraph 2); and
the training phase further comprises converting, prior to training the artificial intelligence model using the training data, at least a portion of the training data to a frequency domain, such that the artificial intelligence model is trained in the frequency domain (parameter signals transformed from time domain to frequency domain using FFT function, see at least page 5, paragraph 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito and the neural networks taught by Liu by adding the domain conversions taught by Cheung with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification “since the parameter signals are periodic… to perform the analyses in the frequency domain” (see page 2, paragraph 2).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Saito in view of Liu and Polanco as applied to claim 11 above, and further in view of Cheung and Giering et al. (U.S. Patent Application Publication No. 2018/0217585 A1; hereinafter Giering).
Regarding claim 12, the combination of Saito, Liu, and Polanco teaches the elements above and Saito further discloses:
analyzing, the actual operating condition information to generate the predicted load information for the vehicle component comprises generating, by the processing device (operation data and monitoring data such as strain is acquired to estimate load, see at least [0065] and Fig. 4)
Saito does not disclose:
the artificial intelligence model has been trained in a frequency domain;, initial predicted load information in the frequency domain; and the method further comprises converting, by the processing device, the initial predicted load information to a time domain.
However, Cheung teaches:
initial predicted load information in the frequency domain (training result showing ANN output in frequency domain, see at least Fig. 9); and the method further comprises converting, by the processing device, the initial predicted load information to a time domain (ANN output transformed back to time domain, see at least Fig. 10)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito, the neural networks taught by Liu, and the neural network trained using a database taught by Polanco by adding the domain conversions taught by Cheung with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification in order “to verify the outputs from the network” (see page 10, paragraph 2).
Additionally, Giering teaches:
the artificial intelligence model has been trained in a frequency domain (prognostic and health monitoring (PHM) sensors are converted into frequency domain data and are labeled to use as PHM training indicators, see at least [0003]);
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fatigue estimation disclosed by Saito, the neural networks taught by Liu, the neural network trained using a database taught by Polanco, and the domain conversions taught by Cheung by adding the training using frequency domain data taught by Giering with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for “creation and use of monitoring models from multiple sensor inputs for health and prognostic monitoring” (see [0014]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Valdes et al. (“Towards Conservative Helicopter Loads Prediction using Computational Intelligence Techniques”; see reference W on PTO-892) teaches hybrid models using particle swarm optimization and Levenberg-Marquardt learning for accurate predictions for main rotor loads in flight conditions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HANA LEE whose telephone number is (571)272-5277. The examiner can normally be reached Monday-Friday: 7:30AM-4:30PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at (571) 270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.L./Examiner, Art Unit 3662
/DALE W HILGENDORF/Primary Examiner, Art Unit 3662