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 Office Action is in response to Applicant’s response filed on November 14th, 2025. Claims 1-8, and 10-20 are presently pending and are presented for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 14th, 2025 has been entered.
Response to Amendment
In response to applicant’s amendment filed November 14th, 2025. Examiner withdraws the previous claim objection; withdraws the previous 35 USC § 103 prior art rejections.
Response to Arguments
Applicant’s arguments, filed November 14th, 2025, with respect to the rejection(s) of the claim(s) under 35 U.S.C. 103 have been fully considered.
Regarding the arguments provided for the rejection of claim 1 as put forth on page 8 of applicant’s remarks, the applicant’s arguments have been fully considered. Applicant argues “The Office Action acknowledges, on page 7, that Karpman fails to disclose the recitation of updating the AI model based on the plurality of parameter values in response to identifying the set of mismatched parameter values. In an attempt to remedy the deficiencies of Karpman, the Office Action cites paragraphs [0033] and [0035] of Adibhatla…the use of the sensing errors 416 to "tune the terms and parameters of the model 214" is directed to the generation of estimates 418 and 420 for the purpose of affecting resulting virtual sensor output estimate values 412, and is not directed to updating the model 214 itself The tracking filter 216 provides external compensation terms that are applied to inputs of the model 214, and the model 214 runs with the compensated input. Adibhatla lacks any incorporation of parameter data into the model 214 in order to update the model 214” (from remarks pg. 8)
As to point (a), Examiner partially agrees. In view of applicant’s amendment and arguments, examiner agrees that the prior art previously applied does not overcome the amendment. This newly added limitation is not taught by Karpman nor Adibhatla. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US-20200248622 (hereinafter, “Crowley”).
Additionally, regarding the arguments provided for the rejection of claim 1 as put forth on pages 8 and 9 of applicant’s remarks, the Applicant’s arguments have been fully considered. Applicant argues “Claim 1 also requires that the update occurs in response to the identification of mismatched parameter values. The feedback loop of FIG. 4 in Adibhatla is used to generate bias/shift estimates and is not "in response to identifying" a mismatched set. Adibhatla fails to disclose a comparison of values from a sensor and updating the model conditionally upon detection of a mismatched set of these values. Converting the compensation scheme of Adibhatla to the claimed model-updating routine requires structure redesign and hindsight to achieve the improved selection via model updating using redundant sensor ground-truth” (from remarks pg. 8-9).
As to point (b), Examiner partially agrees. In view of applicant’s amendment and arguments, examiner agrees that the prior art previously applied does not overcome the amendment. This newly added limitation is not taught by Karpman nor Adibhatla. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US-20200248622 (hereinafter, “Crowley”).
Regarding the arguments provided for the rejection of claim 12 and 20 as put forth on pages 9 of applicant’s remarks, the Applicant’s arguments have been fully considered. Applicant argues “Independent claims 12 and 20 recite subject matter similar to independent claim 1. For at least the foregoing reasons, Applicants respectfully submit that this rejection of independent claims 1, 12, and 20, and thus, the rejections of the dependent claims should be withdrawn.” (from remarks pg. 9).
As to point (c), see points (a) and (b).
Regarding the arguments provided for the rejection of claim 7, 8, and 16 as put forth on pages 9 of applicant’s remarks, the Applicant’s arguments have been fully considered. Applicant argues “Claims 7, 8 and 16 depend from independent claims 1 and 12, and Malta fails to provide any disclosure that remedies the deficiencies of Karpman and Adibhatla described above with respect to independent claim 1. Claims 7, 8, and 16 are patentable for at least the same reasons as independent claim 1. For at least those reasons, Applicants respectfully submit that this rejection should be withdrawn.” (from remarks pg. 9).
As to point (d), see points (a) and (b).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 9-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over US-20190226407 (hereinafter, “Karpman”) in view of US-20200248622 (hereinafter, “Crowley”).
Regarding claim 1 Karpman discloses a method (see at least Figs. 3A and 3B) for processing parameter values from a redundant sensor configured to sense a parameter, the sensed parameter used in the control of an aircraft engine (see at least [0030]; “the engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters associated with the engine 130”), the redundant sensor disposed within an aircraft (see at least [0001]; “Exemplary embodiments pertain to the art of electronic control systems, and more particularly, to engine control systems for aircraft vehicles”), comprising:
sensing a plurality of parameter values for a same parameter with a redundant sensor, and providing a plurality of sensed parameter values to a control unit (see at least [0027]; “the engine controller 150 may process output and/or input data for the data’s respective input/output destination. The input to the EPOS 110, provided by the engine controller 150, may be processed by FDA logic to detect range faults as well as in-range failures (e.g., rate-limit, cross-channel mismatch, etc.) and provide a reasonable input value along with a health status indication”), the plurality of sensed parameter values including a first parameter value and a second parameter value produced by the redundant sensor sensing the same parameter at a same time (see at least [0030]; “The engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters with the engine 130. In at least one embodiment, the engine sensors 12a and 126b are configured to output an engine measurement signal indicating a measured state of the engine,” and [0057]; “The method begins at operation 400, and at operation 402 various system parameters are obtained,” the various system parameters from sensors are obtained concurrently);
using the control unit to identify a set of mismatched parameter values from among the plurality of sensed parameter values, wherein said set of mismatched parameter values is identified when the first parameter value and the second parameter value do not equal one another (see at least [0059]; “When, however, a hard failure is not detected at operation 410, an engine sensor cross-channel diagnostic is performed at operation 414. In at least one embodiment, the engine sensor cross-channel diagnostic may include comparing the measured engine response output from a first engine sensor with the measured engine response output from a second engine sensor. In normal operating conditions YCRTR_T_Cha, should match YCRTR_T_Chb. Therefore, differences or deviations between YCRTR_T_Cha and YCRTR_T_Chb can indicate the presence of a possible engine sensor-in-range failure”);
producing a predicted parameter value for the identified set of mismatched parameter values…(see at least [0020-0023]; “The engine controller 150 includes an engine parameter on board synthesis (EPOS) module 110. The EPOS module 110 can be constructed as an electronic hardware controller that includes memory and a processor configured to execute various algorithms, software programs, and computer-readable program instructions stored in the memory…the engine controller 150 utilizes the EPOS module 110 an various control laws to generate and/or process control instructions for the engine 130…The EPOS module 110 can also generate estimated engine response parameters (Yest) of the engine 130 and synthesized engine response parameters (YCRTR) along with other non-measured signals to the control law module,” the synthesized engine response parameter is equivalent to Applicant’s predicted parameter value and the EPOS module corresponds to Applicant’s module)…wherein the trained…model produces the predicted parameter value using data input representative of engine conditions existing at a same time as the redundant sensor is sensing said parameter (see at least [0023]; “In at least one embodiment, a synthesized value of the engine actuator 124 can be generated. These estimated engine response parameters (Yest) and synthesized sensor response parameters (Ycrtr) are not measured values obtained directly from hardware sensors, but are instead estimated values that are computed based on a subset of control demands from the operator interface 140 (e.g. aircraft environmental control system bleed demand), boundary condition sensing (Ube) such as for example, ambient pressure and temperature, engine condition sensing (Ycrtr_t) such as pressure and temperature at high compressor inlet, and effecter sensing (Ufb) such as burner fuel flow and compressor variable vanes. The engine response estimated parameters (Yest) include, but are not limited to, low compressor exit pressure and temperature, burner exit pressure and temperature, turbo machinery torques, and engine core and duct flows. The synthesized sensor signals (Ycrtr,) include, but are not limited to, low and high spool shaft rotating speeds, low turbine exit temperature and high compressor exit pressure and temperature. These various synthesized sensor signals (Ycrtr) are generated independently from the measured engine operating parameters (Ycrtr_t),” the synthesized value is compared to the actual measured value based on the conditions in order to determine if there is an error in the sensing); and
operating the control unit to select between the first parameter value and the second parameter value among the set of mismatched parameter values for further processing as part of the control of the aircraft engine, using the predicted parameter value (see at least [0060]; “The single channel engine sensor in-range failure accommodation operations include, but are not limited to, deactivating the correcting mode of the EPOS module 110, isolating or disconnecting the faulty channel (i.e., the faulty engine sensor) using estimated engine response (YCRTR) as referee, setting selected value of engine response (YCRTR_T) to exclusively the normal operating channel,” YCRTR is used as the basis of determining which channel is faulty and is equivalent to Applicant’s predicted parameter, in response to the determination the control unit disconnects the fault channel).
Karpman does not disclose the model for predicting a parameter value being an artificial intelligence (AI) model that is trained using a stored database of parameter values representative of the sensed parameter …
…updating the AI model by incorporating, into the AI model, parameter data that is based on the plurality of parameter values from the redundant sensor, in response to identifying the set of mismatched parameter values.
Crowley, in the same field of endeavor teaches producing a predicted parameter value for the identified set of mismatched parameter values using an artificial intelligence (AI) model that is trained using a stored database of parameter values representative of the sensed parameter (see at least [0085]; “some of the blocks in FIGS. 2-8, 10, and 11 may be implemented, at least in part, using a neural network technique or apparatus,” and [0064]; “the estimate of the parameter may, in some cases, be generated by the fuzzy logic estimate calculator 180,” fuzzy logic as well as neural networks are considered under broadest reasonable interpretation to be artificial intelligence)…
…updating the AI model by incorporating, into the AI model, parameter data that is based on the plurality of parameter values…in response to identifying the set of mismatched parameter values (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the state of the sensor actuators includes errors in the sensing value which would include the values not matching the expected values, and [0058]; “If the difference between the measured data of the sensor 108 and the reference data of the actuator model 104 and/or engine model 102 is outside of a threshold value, the controller 106 may take various steps to address the difference including update the sensor and actuator model 104 with the data of the sensor 108,”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Regarding claim 2 Karpman in view of Crowley renders obvious all of the limitations of claim 1. Additionally, Karpman discloses wherein the redundant sensor has a plurality of channels and the first parameter value is produced by a first channel of the redundant sensor and said second parameter value is produced by a second channel of the redundant sensor (see at least [0030]; “the engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters associated with the engine 130”).
Regarding claim 3 Karpman in view of Crowley renders obvious all of the limitations of claim 1. Additionally, Crowley, in the same field of endeavor, teaches wherein the database of parameter values representative of the sensed parameter includes data representative of parameter values previously collected from the aircraft (see at least [0058]; “In operation, the sensor 108 monitors one or more engine operating parameter(s), such as temperature, pressure, position, and the like, and provides data corresponding to the parameter to the controller 106, which may store the data in memory 110,” and [0013]; “produce real-time model-based estimate engine parameters based on a previous iteration estimate of parameters inlet conditions, and based on engine control parameters,” the model is based in part on previous conditions detected).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Regarding claim 4 Karpman in view of Crowley renders obvious all of the limitations of claim 3. Additionally, Crowley, in the same field of endeavor teaches wherein the predicted parameter value is at least in part based on the data representative of parameter values previously collected from the aircraft included within the database (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the model is based on the states of the sensors that have been collected, since the parameter is predicted using the model, the predicted parameter is also based on the states of sensors collected).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Regarding claim 5 Karpman in view of Crowley renders obvious all of the limitations of claim 4. Additionally, Karpman discloses wherein the predicted parameter value is at least in part based on operational data values collected at the time the mismatched parameter values is identified (see at least [0023]; “In at least one embodiment, a synthesized value of the engine actuator 124 can be generated. These estimated engine response parameters (Yest) and synthesized sensor response parameters (Ycrtr) are not measured values obtained directly from hardware sensors, but are instead estimated values that are computed based on a subset of control demands from the operator interface 140 (e.g. aircraft environmental control system bleed demand), boundary condition sensing (Ubc) such as for example, ambient pressure and temperature, engine condition sensing (Ycrtr_t) such as pressure and temperature at high compressor inlet, and effecter sensing (Ufb) such as burner fuel flow and compressor variable vanes. The engine response estimated parameters (Yest) include, but are not limited to, low compressor exit pressure and temperature, burner exit pressure and temperature, turbo machinery torques, and engine core and duct flows. The synthesized sensor signals (Ycrtr,) include, but are not limited to, low and high spool shaft rotating speeds, low turbine exit temperature and high compressor exit pressure and temperature. These various synthesized sensor signals (Ycrtr) are generated independently from the measured engine operating parameters (Ycrtr_t),” and Fig. 3A03B and [0057]; “the method begin at operation 400, and at operation 402 various system parameters are obtained. The systems parameters include measured engine response parameters, estimated engine response parameters” the EPOS module uses various sensed parameters in order to estimate the desired parameter at the same time as obtaining the measured parameter values).
Regarding claim 6 Karpman in view of Crowley renders obvious all of the limitations of claim 1. Additionally, Crowley, in the same field of endeavor teaches further comprising storing the plurality of parameter values from the …sensor produced during a mission of the aircraft (see at least [0058]; “In operation, the sensor 108 monitors one or more engine operating parameter(s), such as temperature, pressure, position, and the like, and provides data corresponding to the parameter to the controller 106, which may store the data in memory 110”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Regarding claim 10 Karpman in view of Crowley renders obvious all of the limitations of claim 1. Additionally, Karpman discloses wherein the plurality of parameter values from the redundant sensor are within a range indicating that a sensed portion of the aircraft engine is operating properly (see at least [0044]; “the engine sensor FDA module 118 is configured to detect in-range fault conditions of the engine sensor 126a and 126b, single engine channel failures (i.e., a fault with a single engine sensor 126a or 126b), and/or dual engine channel failures (i.e., a fault with both engine sensors 126a and 126b),” the in-range conditions correspond to Applicants parameter vales within a range indicating that a sensed portion of the aircraft is operating properly).
Regarding claim 11 Karpman in view of Crowley renders obvious all of the limitations of claim 1. Additionally, Karpman discloses wherein the sensed parameter is one or more of pressure, temperature, aircraft altitude, speed, acceleration, power, torque, weight, or aircraft ambient conditions (see at least [0030]; “the engine sensors 126a and 126b that measure the working fluid pressure, temperature and fluid flow at various axial and radial locations in the flow path. The engine sensors 126a and 126b may comprise a variety of different sensing devices, including, but not limited to, temperature sensors, current sensors, voltage sensors, level sensors, altitude sensors, and/or blade tip sensors”).
Regarding claim 12 Karpman discloses a control system for an aircraft engine of an aircraft (see at least Fig. 1 and [0001]; “Exemplary embodiments pertain to the art of electronic control systems, and more particularly, to engine control systems of aircraft vehicles”), the control system comprising:
an electronic control unit (ECU) (see at least Fig. 1; engine controller 150);
a redundant sensor disposed within the aircraft and in communication with the ECU, the redundant sensor configured to sense a parameter, the sensed parameter used in the control of the aircraft engine, and to produce a plurality of parameter values including a first parameter value and a second parameter value by sensing the parameter at a same time (see at least [0030]; “The engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters with the engine 130. In at least one embodiment, the engine sensors 12a and 126b are configured to output an engine measurement signal indicating a measured state of the engine,” and [0057]; “The method begins at operation 400, and at operation 402 various system parameters are obtained,” the various system parameters from sensors are obtained concurrently); and
…wherein the ECU is configured to identify a set of mismatched parameter values received from the redundant sensor, wherein said set of mismatched parameter values is identified when the first parameter value and the second parameter value do not equal one another (see at least [0059]; “When, however, a hard failure is not detected at operation 410, an engine sensor cross-channel diagnostic is performed at operation 414. In at least one embodiment, the engine sensor cross-channel diagnostic may include comparing the measured engine response output from a first engine sensor with the measured engine response output from a second engine sensor. In normal operating conditions YCRTR_T_Cha, should match YCRTR_T_Chb. Therefore, differences or deviations between YCRTR_T_Cha and YCRTR_T_Chb can indicate the presence of a possible engine sensor-in-range failure”); and
…configured to produce a predicted parameter value for the identified set of mismatched parameter values …(see at least [0020-0023]; “The engine controller 150 includes an engine parameter on board synthesis (EPOS) module 110. The EPOS module 110 can be constructed as an electronic hardware controller that includes memory and a processor configured to execute various algorithms, software programs, and computer-readable program instructions stored in the memory…the engine controller 150 utilizes the EPOS module 110 an various control laws to generate and/or process control instructions for the engine 130…The EPOS module 110 can also generate estimated engine response parameters (Yest) of the engine 130 and synthesized engine response parameters (YCRTR) along with other non-measured signals to the control law module,” the synthesized engine response parameter is equivalent to Applicant’s predicted parameter value and the EPOS module corresponds to Applicant’s module)using the trained…model and data input representative of engine operating conditions as a same time as the redundant sensor is sensing said sensed parameter (see at least [0023]; “In at least one embodiment, a synthesized value of the engine actuator 124 can be generated. These estimated engine response parameters (Yest) and synthesized sensor response parameters (Ycrtr) are not measured values obtained directly from hardware sensors, but are instead estimated values that are computed based on a subset of control demands from the operator interface 140 (e.g. aircraft environmental control system bleed demand), boundary condition sensing (Ube) such as for example, ambient pressure and temperature, engine condition sensing (Ycrtr_t) such as pressure and temperature at high compressor inlet, and effecter sensing (Ufb) such as burner fuel flow and compressor variable vanes. The engine response estimated parameters (Yest) include, but are not limited to, low compressor exit pressure and temperature, burner exit pressure and temperature, turbo machinery torques, and engine core and duct flows. The synthesized sensor signals (Ycrtr,) include, but are not limited to, low and high spool shaft rotating speeds, low turbine exit temperature and high compressor exit pressure and temperature. These various synthesized sensor signals (Ycrtr) are generated independently from the measured engine operating parameters (Ycrtr_t),” the synthesized value is compared to the actual measured value based on the conditions in order to determine if there is an error in the sensing), and to selectively communicate the predicted parameter value to the ECU (see at least [0020-0023]; “The engine controller 150 includes an engine parameter on board synthesis (EPOS) module 110. The EPOS module 110 can be constructed as an electronic hardware controller that includes memory and a processor configured to execute various algorithms, software programs, and computer-readable program instructions stored in the memory…the engine controller 150 utilizes the EPOS module 110 an various control laws to generate and/or process control instructions for the engine 130…The EPOS module 110 can also generate estimated engine response parameters (Yest) of the engine 130 and synthesized engine response parameters (YCRTR) along with other non-measured signals to the control law module,” the synthesized engine response parameter is equivalent to Applicant’s predicted parameter value and the EPOS module corresponds to Applicant’s module); and
wherein the ECU is configured to select between the first parameter value and the second parameter value among the set of mismatched parameter value for further processing as part of the control of the aircraft engine, using the predicted parameter value (see at least [0060]; “The single channel engine sensor in-range failure accommodation operations include, but are not limited to, deactivating the correcting mode of the EPOS module 110, isolating or disconnecting the faulty channel (i.e., the faulty engine sensor) using estimated engine response (YCRTR) as referee, setting selected value of engine response (YCRTR_T) to exclusively the normal operating channel,” YCRTR is used as the basis of determining which channel is faulty and is equivalent to Applicant’s predicted parameter, in response to the determination the control unit disconnects the fault channel).
Karpman, does not disclose an engine data recorder (EDR) in communication with the ECU, the EDR having an artificial intelligence (AI) model that is trained using a stored database of parameter values representative of the sensed parameter;
…and wherein the EDR is configured to update the AI model by incorporating, into the AI model, parameter data that is based on the plurality of parameter values from the redundant sensor, in response to identifying the set of mismatched parameter values.
Crowley, in the same field of endeavor teaches an engine data recorder (EDR) in communication with the ECU, the EDR having an artificial intelligence (AI) model that is trained using a stored database of parameter values representative of the sensed parameter (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the model is trained based on sensor states and engine states);
…and wherein the EDR is configured to update the AI model by incorporating, into the AI model, parameter data that is based on the plurality of parameter values from the redundant sensor, in response to identifying the set of mismatched parameter values (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the state of the sensor actuators includes errors in the sensing value which would include the values not matching the expected values, and [0058]; “If the difference between the measured data of the sensor 108 and the reference data of the actuator model 104 and/or engine model 102 is outside of a threshold value, the controller 106 may take various steps to address the difference including update the sensor and actuator model 104 with the data of the sensor 108,”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Regarding claim 13 Karpman in view of Crowley renders obvious all of the limitations of claim 12. Additionally, Karpman discloses wherein the redundant sensor has a plurality of channels and the first parameter value is produced by a first channel of the redundant sensor and the second parameter value is produced by a second channel of the redundant sensor (see at least [0030]; “the engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters associated with the engine 130”).
Regarding claim 14 Karpman in view of Crowley renders obvious all of the limitations of claim 12. Additionally, Crowley, in the same field of endeavor, teaches wherein the database of parameter values representative of the sensed parameter includes data representative of parameter values previously collected from the aircraft (see at least [0058]; “In operation, the sensor 108 monitors one or more engine operating parameter(s), such as temperature, pressure, position, and the like, and provides data corresponding to the parameter to the controller 106, which may store the data in memory 110,” and [0013]; “produce real-time model-based estimate engine parameters based on a previous iteration estimate of parameters inlet conditions, and based on engine control parameters,” the model is based in part on previous conditions detected).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Regarding claim 15 Karpman in view of Crowley renders obvious all of the limitations of claim 14. Additionally, Crowley, in the same field of endeavor teaches wherein the predicted parameter value is at least in part based on the data representative of parameter values previously collected from the aircraft included within the database (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the model is based on the states of the sensors that have been collected, since the parameter is predicted using the model, the predicted parameter is also based on the states of sensors collected).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Regarding claim 17 Karpman in view of Crowley renders obvious all of the limitations of claim 12. Additionally, Crowley, in the same field of endeavor teaches wherein the EDR is configured to store the plurality of parameter values from the…sensor produced during a mission of the aircraft (see at least [0058]; “In operation, the sensor 108 monitors one or more engine operating parameter(s), such as temperature, pressure, position, and the like, and provides data corresponding to the parameter to the controller 106, which may store the data in memory 110”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Regarding claim 18 Karpman in view of Crowley renders obvious all of the limitations of claim 12. Additionally, Karpman discloses wherein the plurality of parameter values from the redundant sensor are within a range indicating that a sensed portion of the aircraft engine is operating properly (see at least [0044]; “the engine sensor FDA module 118 is configured to detect in-range fault conditions of the engine sensor 126a and 126b, single engine channel failures (i.e., a fault with a single engine sensor 126a or 126b), and/or dual engine channel failures (i.e., a fault with both engine sensors 126a and 126b),” the in-range conditions correspond to Applicants parameter vales within a range indicating that a sensed portion of the aircraft is operating properly).
Regarding claim 19 Karpman in view of Crowley renders obvious all of the limitations of claim 12. Additionally, Karpman discloses wherein the sensed parameter is one or more of pressure, temperature, aircraft altitude, speed, acceleration, power, torque, weight, or aircraft ambient conditions (see at least [0030]; “the engine sensors 126a and 126b that measure the working fluid pressure, temperature and fluid flow at various axial and radial locations in the flow path. The engine sensors 126a and 126b may comprise a variety of different sensing devices, including, but not limited to, temperature sensors, current sensors, voltage sensors, level sensors, altitude sensors, and/or blade tip sensors”).
Regarding claim 20 Karpman discloses a method (see at least Figs. 3A and 3B) for processing parameter values from redundant sensors configured to sense a parameter, the sensed parameter used in the control of an aircraft engine (see at least [0030]; “the engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters associated with the engine 130”), the redundant sensors disposed within an aircraft (see at least [0001]; “Exemplary embodiments pertain to the art of electronic control systems, and more particularly, to engine control systems for aircraft vehicles”), comprising:
producing a first parameter value from a first sensor configured to sense a parameter (see at least [0030]; “The engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters with the engine 130. In at least one embodiment, the engine sensors 12a and 126b are configured to output an engine measurement signal indicating a measured state of the engine,” and [0057]; “The method begins at operation 400, and at operation 402 various system parameters are obtained,” engine sensor 126a corresponds to Applicant’s first sensor);
producing a second parameter value from a second sensor configured to sense the parameter (see at least [0030]; “The engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters with the engine 130. In at least one embodiment, the engine sensors 12a and 126b are configured to output an engine measurement signal indicating a measured state of the engine,” and [0057]; “The method begins at operation 400, and at operation 402 various system parameters are obtained,” engine sensor 126b corresponds to Applicant’s second sensor);
wherein the first sensor and the second sensor are configured to be redundant, sensing the parameter at a same time (see at least [0030]; “The engine sensors 126a and 126b are in signal communication with the engine 130 via their respective channels. Multiple data channels are employed to provide sensor monitoring redundancy and improved robustness. Each engine sensor 126a and 126b may measure operational parameters with the engine 130. In at least one embodiment, the engine sensors 12a and 126b are configured to output an engine measurement signal indicating a measured state of the engine,” and [0057]; “The method begins at operation 400, and at operation 402 various system parameters are obtained,” the various system parameters from sensors are obtained concurrently);
identifying a set of mismatched parameter values among the first parameter value and the second parameter value, the set of mismatched parameter values identified when the first parameter value and the second parameter value produced by sensing the parameter at the same time do not equal one another (see at least [0059]; “When, however, a hard failure is not detected at operation 410, an engine sensor cross-channel diagnostic is performed at operation 414. In at least one embodiment, the engine sensor cross-channel diagnostic may include comparing the measured engine response output from a first engine sensor with the measured engine response output from a second engine sensor. In normal operating conditions YCRTR_T_Cha, should match YCRTR_T_Chb. Therefore, differences or deviations between YCRTR_T_Cha and YCRTR_T_Chb can indicate the presence of a possible engine sensor-in-range failure”)
producing a predicted parameter value for the identified set of mismatched parameter values…(see at least [0020-0023]; “The engine controller 150 includes an engine parameter on board synthesis (EPOS) module 110. The EPOS module 110 can be constructed as an electronic hardware controller that includes memory and a processor configured to execute various algorithms, software programs, and computer-readable program instructions stored in the memory…the engine controller 150 utilizes the EPOS module 110 an various control laws to generate and/or process control instructions for the engine 130…The EPOS module 110 can also generate estimated engine response parameters (Yest) of the engine 130 and synthesized engine response parameters (YCRTR) along with other non measured signals to the control law module,” the synthesized engine response parameter is equivalent to Applicant’s predicted parameter value and the EPOS module corresponds to Applicant’s module) )…wherein the trained…model produces the predicted parameter value using data input representative of engine conditions existing at a same time as the redundant sensor is sensing said parameter (see at least [0023]; “In at least one embodiment, a synthesized value of the engine actuator 124 can be generated. These estimated engine response parameters (Yest) and synthesized sensor response parameters (Ycrtr) are not measured values obtained directly from hardware sensors, but are instead estimated values that are computed based on a subset of control demands from the operator interface 140 (e.g. aircraft environmental control system bleed demand), boundary condition sensing (Ube) such as for example, ambient pressure and temperature, engine condition sensing (Ycrtr_t) such as pressure and temperature at high compressor inlet, and effecter sensing (Ufb) such as burner fuel flow and compressor variable vanes. The engine response estimated parameters (Yest) include, but are not limited to, low compressor exit pressure and temperature, burner exit pressure and temperature, turbo machinery torques, and engine core and duct flows. The synthesized sensor signals (Ycrtr,) include, but are not limited to, low and high spool shaft rotating speeds, low turbine exit temperature and high compressor exit pressure and temperature. These various synthesized sensor signals (Ycrtr) are generated independently from the measured engine operating parameters (Ycrtr_t),” the synthesized value is compared to the actual measured value based on the conditions in order to determine if there is an error in the sensing);
providing the predicted parameter value to a control unit (see at least [0027]; “the engine controller 150 may process output and/or input data for the data’s respective input/output destination. The input to the EPOS 110, provided by the engine controller 150, may be processed by FDA logic to detect range faults as well as in-range failures (e.g., rate-limit, cross-channel mismatch, etc.) and provide a reasonable input value along with a health status indication);
operating the control unit to select between the first parameter value and the second parameter value among the set of mismatched parameter values for further processing as part of the control of the aircraft engine, using the predicted parameter value (see at least [0060]; “The single channel engine sensor in-range failure accommodation operations include, but are not limited to, deactivating the correcting mode of the EPOS module 110, isolating or disconnecting the faulty channel (i.e., the faulty engine sensor) using estimated engine response (YCRTR) as referee, setting selected value of engine response (YCRTR_T) to exclusively the normal operating channel,” YCRTR is used as the basis of determining which channel is faulty and is equivalent to Applicant’s predicted parameter, in response to the determination the control unit disconnects the fault channel).
Karpman does not disclose the model for predicting a parameter value being an artificial intelligence (AI) model that is trained using a database of parameter values representative of the sensed parameter…
…and updating the AI model by incorporating, into the AI model, parameter data that is based on the first parameter value and the second parameter value, in response to identifying the set of mismatched parameter values.
Crowley, in the same field of endeavor teaches the model for predicting a parameter value being an artificial intelligence (AI) model (see at least [0085]; “some of the blocks in FIGS. 2-8, 10, and 11 may be implemented, at least in part, using a neural network technique or apparatus,” and [0064]; “the estimate of the parameter may, in some cases, be generated by the fuzzy logic estimate calculator 180,” fuzzy logic as well as neural networks are considered under broadest reasonable interpretation to be artificial intelligence) that is trained using a database of parameter values representative of the sensed parameter (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the model is trained based on sensor states and engine states)…
…and updating the AI model by incorporating, into the AI model, parameter data that is based on the first parameter value and the second parameter value, in response to identifying the set of mismatched parameter values (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the state of the sensor actuators includes errors in the sensing value which would include the values not matching the expected values, and [0058]; “If the difference between the measured data of the sensor 108 and the reference data of the actuator model 104 and/or engine model 102 is outside of a threshold value, the controller 106 may take various steps to address the difference including update the sensor and actuator model 104 with the data of the sensor 108,”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Claim(s) 7, 8, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Karpman in view of Crowley (as applied above) in further view of US-20170259944 (hereinafter, “Malta”).
Regarding claim 7 Karpman in view of Crowley renders obvious all of the limitations of claim 6. Additionally, Crowley, in the same field of endeavor teaches stored plurality of parameter values from the…sensor produced during the mission of the aircraft (see at least [0058]; “In operation, the sensor 108 monitors one or more engine operating parameter(s), such as temperature, pressure, position, and the like, and provides data corresponding to the parameter to the controller 106, which may store the data in memory 110”)… for processing into a form for updating the Al model (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the state of the sensor actuators includes errors in the sensing value which would include the values not matching the expected values, and [0058]; “If the difference between the measured data of the sensor 108 and the reference data of the actuator model 104 and/or engine model 102 is outside of a threshold value, the controller 106 may take various steps to address the difference including update the sensor and actuator model 104 with the data of the sensor 108,”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Karpman in view of Crowley does not teach downloading the stored plurality of parameter values from the…sensor produced during the mission of the aircraft to a remote system portion
Malta, in the same field of endeavor teaches downloading the stored plurality of parameter values from the…sensor produced during the mission of the aircraft to a remote system portion (see at least [0015]; “The probabilistic model platform 150 may store information into and / or retrieve information from various data sources, such as the aircraft data recorded during flight data source 110 and / or user platforms 170. The various data sources may be locally stored or reside remote from the probabilistic model platform 150”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman and Crowley with the remote system of Malta. One of ordinary skill in the art would have been motivated to make this modification for the benefit of easy access to the system via multiple devices (see at least Malta; [0015-0016]).
Regarding claim 8 Karpman in view of Crowley and Malta renders obvious all of the limitations of claim 7. Additionally, Malta, in the same field of endeavor teaches wherein the remote system portion is ground based, cloud based, or some combination thereof (see at least [0014]; “The various data sources may be locally stored or reside remote from the probabilistic model platform 150. Although a single probabilistic model platform 150 is shown in FIG. 1, any number of such devices may be included. Moreover, various devices described herein might be combined according to embodiments of the present invention. For example, in some embodiments, the probabilistic model platform 150 and one or more data sources might comprise a single apparatus. The probabilistic model platform 150 function may be performed by a constellation of networked apparatuses, in a distributed processing or cloud - based architecture.”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman and Crowley with the remote system of Malta. One of ordinary skill in the art would have been motivated to make this modification for the benefit of easy access to the system via multiple devices (see at least Malta; [0015-0016]).
Regarding claim 16 Karpman in view of Crowley renders obvious all of the limitations of claim 1. Additionally, Crowley, in the same field of endeavor teaches wherein the EDR is configured to store the plurality of parameter values from the…sensor produced during a mission of the aircraft (see at least [0058]; “In operation, the sensor 108 monitors one or more engine operating parameter(s), such as temperature, pressure, position, and the like, and provides data corresponding to the parameter to the controller 106, which may store the data in memory 110”)…for processing into a form for updating the Al model (see at least [0060]; “This accommodation is accomplished by updating the engine model 102, sensor and actuator models 104, and or the models of the adaptive controls 200 in the model-based control system 100 with information regarding the states of the engine 20, sensors actuators 114 and the like,” the state of the sensor actuators includes errors in the sensing value which would include the values not matching the expected values, and [0058]; “If the difference between the measured data of the sensor 108 and the reference data of the actuator model 104 and/or engine model 102 is outside of a threshold value, the controller 106 may take various steps to address the difference including update the sensor and actuator model 104 with the data of the sensor 108,”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman with the AI model of Crowley. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving synthesis techniques (see at least Crowley; [0071]).
Karpman in view of Crowley does not teach downloaded the stored plurality of parameter values from the…sensor to a remote system portion.
Malta, in the same field of endeavor teaches downloaded the stored plurality of parameter values from the…sensor to a remote system portion (see at least [0015]; “The probabilistic model platform 150 may store information into and / or retrieve information from various data sources, such as the aircraft data recorded during flight data source 110 and / or user platforms 170. The various data sources may be locally stored or reside remote from the probabilistic model platform 150”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor fault detection of Karpman and Crowley with the remote system of Malta. One of ordinary skill in the art would have been motivated to make this modification for the benefit of easy access to the system via multiple devices (see at least Malta; [0015-0016]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20050228619 teaches a method for estimating a parameter based on signals received from redundant sensors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEIGH NICOLE TURNBAUGH whose telephone number is (703)756-1982. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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/ASHLEIGH NICOLE TURNBAUGH/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
3/2/26