DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is in response to application 18/491,712 filed on 02/26/2026. Claims 1, 12 and 17 have been amended. Claim 6 has been canceled. Claims 1-5 and 7-21 are currently pending and examined in the instant office action. The rejections are as stated below.
Response to Arguments
Applicant’s arguments, filed 02/26/2026 with respect to the rejection(s) of claim(s) 1-5 and 7-21 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Amarnathan et al., US 20220057473 A1, in view of Yamashita et al., US 20170372601 A1, in view of Akkaram et al., US 20190146470 A1, in view of Sebastian Wloch, US 20210216688A1 and in view of Winger et al., US 20220149650 A1.
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 1-5, 9-10, 12-15 and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Amarnathan et al., US 20220057473 A1, in view of Yamashita et al., US 20170372601 A1, in view of Akkaram et al., US 20190146470 A1, in view of Sebastian Wloch, US 20210216688 A1, and in view of Winger et al., US 20220149650 A1, hereinafter referred to as Amarnathan, Yamashita, Akkaram, Wloch and Winger, respectively.
Regarding claim 1, Amarnathan discloses a method, comprising:
identifying a chain of operations for one or more systems associated with a vehicle, the chain of operations comprising a plurality of consecutive operations for the one or more systems (As described in detail herein, aircraft may perform one or more of the aforementioned operations of supplemental navigation program using the components of system. Supplemental navigation program may provide system with positional data and/or directional data for controlling a flight path of aircraft a by changing a speed, heading, altitude (e.g., manually, semi-autonomously, autonomously) in accordance with flight control program based on the location information received from nearby terminals in lieu of sensors – See at least ¶44).
Amarnathan fails to disclose clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations; wherein the sensor data is obtained from one or more sensors devices of the vehicle; computing statistical values for one or more parameters across the clustered operation phases; identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data; computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-system devices in the one or more systems, using the sensor data with the one or more outlier parameters excluded.
However, Yamashita teaches:
clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations (Meanwhile, the server divides a plurality of the sensor nodes in the wireless sensor network system into a plurality of “clusters” each including at least one partial cluster, based on the number of the sensor nodes. For example, the server sets the number of the sensor nodes to be included in a single cluster to (hereinafter, the number may be referred to as a “cluster size limit”) – See at least ¶51);
wherein the sensor data is obtained from one or more sensors devices of the vehicle (a processor configured to: control calibration of sensing values in a sensor node group formed of sensor nodes each including a sensor and a wireless device and being capable of performing multi-hop communication – See at least ¶9);
computing statistical values for one or more parameters across the clustered operation phases (Then, the server calculates a “calibration reference value” based on an average of sensing values of the partial cluster included in a cluster, for each cluster. For example, if a certain cluster includes a plurality of partial clusters, the server calculates the “calibration reference value” of the cluster by obtaining a weighted average based on a plurality of averages of sensing values corresponding to the respective partial clusters – See at least ¶52);
identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data (A second embodiment relates to an embodiment in which a sensing value determined as an “abnormal value (i.e., “outlier”) is excluded from “use target values” to be used for calculating the calibration reference value – See at least ¶105);
computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-systems in the one or more systems, using the sensor data with the one or more outlier parameters excluded (Incidentally, if an “outlier” is included in the sensing values collected for a certain cluster, the processor in the server of the second embodiment calculates a “calibration reference value” while excluding the sensing value corresponding to the outlier. For example, the processor calculates an average excluding the outlier by using “Trim mean” based on the sensing value of each of the sensor nodes included in a certain cluster, and uses the calculated average as the “calibration reference value”. “Trim mean” is an algorithm conventionally used to calculate an average excluding an abnormal value – See at least ¶114); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Amarnathan and include the features of clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations; wherein the sensor data is obtained from one or more sensors devices of the vehicle; computing statistical values for one or more parameters across the clustered operation phases; identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data; computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-systems in the one or more systems, using the sensor data with the one or more outlier parameters excluded, as taught by Yamashita, to detect possible mechanical faults in an aircraft system to ensure safety and airline service efficiency.
The combination of Amarnathan and Yamashita fail to disclose determining a physics based parameter that is computed based on the sensor data, wherein the physics based parameter is associated with a physical parameter associated with the first sub-system.
However, Akkaram teaches:
determining a physics based parameter that is computed based on the sensor data (In some examples, a workscope effect calculator (WEC) apparatus generates predictive asset health quantifiers by identifying leading indicators by comparing asset monitoring information (e.g., asset sensor data, etc.) to data obtained from executing one or more models (e.g., physics-based models, fleet average regression models, historical trend models, etc.) and identifying deviations of specific engines and/or operators based on the comparison – See at least ¶45),
wherein the physics based parameter is associated with a physical parameter associated with the first sub-system (In some examples, the physics-based model is a digital twin model of the engine. For example, the digital twin model can simulate physics behavior, a thermodynamic health, a performance health, etc., of the engine using asset monitoring information based on the inputs, information stored in the database, the workscope results from the workscope quantifier analyzer, etc. For example, the physics-based model can simulate inputs and outputs of the sensors of FIGS. 1-2 of the engine to generate an AHQ based on the workscope results. For example, the physics-based model can be a digital twin model of the engine of FIG. 1 that can calculate an asset health quantifier (AHQ) of the booster compressor of FIG. 1 such as a quantity of TOW hours based on (1) a completed workscope on the booster compressor (e.g., one or more components of the booster compressor have been replaced, serviced, etc.) and (2) a severity factor. For example, the digital twin model can generate the AHQ with improved accuracy based on more accurately characterizing a health of the booster compressor as a result of the completed workscope on the booster compressor – See at least ¶126).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan and Yamashita and include the features of determining a physics based parameter that is computed based on the sensor data, wherein the physics based parameters is associated with a physical parameter associated with the first sub-system, as taught by Akkaram, to generate a predictive asset health quantifier of a turbine engine (See at least ¶1 of Akkaram).
The combination of Amarnathan, Yamashita and Akkaram fail to disclose combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data, wherein the digital twin value is generated using a modeled version of the first sub-system; and detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values.
However, Wloch teaches:
combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data (Sub-module 610 takes in model data 604 and conditions 608, (i.e. corresponds to sensor data representative of the physical subsystem), processes those inputs to generate simulated aerodynamic forces (i.e. corresponding to the claimed physics-based parameter), and sub-module 614 combines the simulated aerodynamic forces to generate model aerodynamic properties/performance 616 (i.e. corresponding to the claimed digital twin value generated from the modeled subsystem). Likewise, simulated aerodynamic performance is calculated by combining the simulated aerodynamic forces of the model surface portions – See at least ¶60, 77 and 84),
wherein the digital twin value is generated using a modeled version of the first sub-system (A three-dimensional model includes model surface portions having associated surface portion parameters from which simulated aerodynamic forces and simulated aerodynamic performance are calculated, wherein the simulated aerodynamic performance is generated from the modeled aircraft (i.e. corresponds to a digital twin value generated using a modeled version of the first sub-system – See at least ¶23-24, 60, 77 and 84); and
detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values (The simulated aerodynamic performance is compared with reference data and target/current normalization parameters to determine an error/discrepancy, and the identified error is used to adjust the modeled aircraft (i.e. corresponds to detecting an anomaly by comparing generated data with expected (i.e. nominal) values – See at least ¶38, 48-49, 61, 77 and 84).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita and Akkaram and include the features of combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data, wherein the digital twin value is generated using a modeled version of the first sub-system; and detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values, as taught by Wloch, to enable one to cause and understand aerodynamic behavior in a virtual object (e.g., a plane or automobile), and to tune various parameters of the model to produce desired aerodynamic behavior in simulation (See at least ¶12 of Wloch).
The combination of Amarnathan, Yamashita, Akkaram and Wloch fail to disclose isolating the first sub-system based on detecting the anomaly; and performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system.
However, Winger teaches:
isolating the first sub-system based on detecting the anomaly (FIG. 2 shows an example of the prognostics system that has the ability to isolate misbehaving subsystems to prevent power loss and to provide redundant power to safety subsystems using the single battery – See at least ¶9); and
performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system (control determines if a failure due to the detected abnormality is imminent. If a failure due to the detected abnormality is not imminent, control issues a warning to the driver of the vehicle (e.g., turns on a service indicator). If a failure due to the detected abnormality is imminent, control disables the vehicle operation – See at least ¶103).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita, Akkaram and Wloch and include the features of isolating the first sub-system based on detecting the anomaly; and performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system, as taught by Winger, to ensure the safety of occupants of the vehicle (See at least ¶4).
Regarding claim 2, Amarnathan, as modified, discloses:
wherein the vehicle is associated with one or more aircraft (Any one of the aircrafts may be connected to another and/or to one or more hubs over a communication network using a vehicle management computer corresponding to each of the aircraft or hub – See at least ¶22) and
wherein the chain of operations comprises a chain of flight legs for the one or more aircraft, and wherein the operating conditions comprise operating conditions for the aircraft (Airspace may accommodate various types of aircraft flying at various altitudes and along various routes, (i.e. flight legs) – See at least ¶21. It should be appreciated that supplemental navigation program, when executed by CPU, may continuously evaluate the operating state of radar sensor throughout an active flight of aircraft a until sensor(s) is deactivated at the conclusion of the flight. In this instance, system may obtain directional data for use by sensor(s) to determine a location of aircraft a relative to other terminals via radar sensor – See at least ¶65).
Regarding claim 3, Amarnathan, as modified, discloses wherein the one or more aircraft comprise a specific aircraft tail and wherein the chain of flight legs comprises a chain of flight legs for the specific aircraft tail (Airspace may accommodate various types of aircraft flying at various altitudes and along various routes, (i.e. flight legs) – See at least ¶21. Generally, GPS may be a navigation system configured to determine positional data orientation, heading – See at least ¶36).
Regarding claim 4, Amarnathan, as modified, discloses wherein the one or more aircraft comprise a plurality of aircraft in a fleet of aircraft, and wherein the chain of flight legs comprises a chain of flight legs for the plurality of aircraft (receiving directional data of the terminal relative to the vehicle; determining a first location of the vehicle relative to the terminal based on the directional data; and determining a second location of the vehicle relative to an operating environment of the vehicle based on the first location and the positional data – See at least ¶7. The present disclosure is directed to overcoming one or more of the challenges discussed above in the BACKGROUND. As UAM vehicles generally operate near other vehicles, such as, for example, alongside other UAM vehicles in a fleet – See at least ¶17).
Regarding claim 5, Amarnathan as modified teaches the method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Amarnathan as modified does not teach wherein the statistical values comprise at least one of a minimum, maximum, or mean value.
However, Yamashita teaches wherein the statistical values comprise at least one of a minimum, maximum, or mean value (Subsequently, the sensor node calculates an “average of sensing values” in own “partial cluster”. Specifically, the sensor node calculates an average of a sensing value of own sensor and the sensing values respectively reported from the sensor nodes – See at least ¶43).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Amarnathan and include the feature of wherein the statistical values comprise at least one of a minimum, maximum, or mean value, as taught by Yamashita, to detect possible mechanical faults in an aircraft system to ensure safety and airline service efficiency.
Regarding claim 9, Amarnathan as modified teaches the method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Amarnathan as modified does not teach identifying the component at least partially responsible for the anomaly in the first sub-system.
However, Yamashita teaches identifying the component at least partially responsible for the anomaly in the first sub-system (The processor corrects the sensing value by adding the calculated “error value” to the sensing value of the sensor, and reports the corrected sensing value to the server via the parent node of the subject sensor node for each “collection period” – See at least ¶97).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Amarnathan and include the feature of identifying the component at least partially responsible for the anomaly in the first sub-system, as taught by Yamashita, to detect possible mechanical faults in an aircraft system to ensure safety and airline service efficiency.
Regarding claim 10, Amarnathan as modified teaches the method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Amarnathan as modified does not teach wherein identifying the component at least partially responsible for the anomaly in the first sub-system further comprises: using a reasoning algorithm to identify the first component, based on the anomaly.
However, Yamashita teaches wherein identifying the component at least partially responsible for the anomaly in the first sub-system further comprises: using a reasoning algorithm to identify the first component, based on the anomaly (For example, the processor calculates an average excluding the outlier by using “Trim mean” based on the sensing value of each of the sensor nodes included in a certain cluster, and uses the calculated average as the “calibration reference value”. “Trim mean” is an algorithm conventionally used to calculate an average excluding an abnormal value – See at least ¶114).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Amarnathan and include the feature of identifying the component at least partially responsible for the anomaly in the first sub-system further comprises: using a reasoning algorithm to identify the first component, based on the anomaly, as taught by Yamashita, to detect possible mechanical faults in an aircraft system to ensure safety and airline service efficiency.
Regarding claim 12, Amarnathan discloses a system, comprising:
one or more processors (processor – See at least ¶9); and
one or more memories storing a program, which, when executed on any combination of the one or more processors, performs an operation (Examples of the processor include a CPU, a Digital Signal Processor (DSP), and a Field Programmable Gate Array (FPGA). Furthermore, examples of the memory include a Random Access Memory (RAM) such as a Synchronous Dynamic Random Access Memory (SDRAM), a Read Only Memory (ROM), and a flash memory. Various processing functions executed by the processor are implemented by recording a program corresponding to each of the various processing functions in the memory and causing the processor to execute the programs – See at least ¶57), the operation comprising:
identifying a chain of operations for one or more systems associated with a vehicle, the chain of operations comprising a plurality of consecutive operations for the one or more systems (As described in detail herein, aircraft may perform one or more of the aforementioned operations of supplemental navigation program using the components of system. Supplemental navigation program may provide system with positional data and/or directional data for controlling a flight path of aircraft a by changing a speed, heading, altitude (e.g., manually, semi-autonomously, autonomously) in accordance with flight control program based on the location information received from nearby terminals in lieu of sensors – See at least ¶44).
Amarnathan fails to disclose clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations; wherein the sensor data is obtained from one or more sensor devices of the vehicle; computing statistical values for one or more parameters across the clustered operation phases; identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data; computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-systems in the one or more systems, using the sensor data with the one or more outlier parameters excluded.
However, Yamashita teaches:
clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations (Meanwhile, the server divides a plurality of the sensor nodes in the wireless sensor network system into a plurality of “clusters” each including at least one partial cluster, based on the number of the sensor nodes. For example, the server sets the number of the sensor nodes to be included in a single cluster to (hereinafter, the number may be referred to as a “cluster size limit”) – See at least ¶51);
wherein the sensor data is obtained from one or more sensor devices of the vehicle (a processor configured to: control calibration of sensing values in a sensor node group formed of sensor nodes each including a sensor and a wireless device and being capable of performing multi-hop communication – See at least ¶9);
computing statistical values for one or more parameters across the clustered operation phases (Then, the server calculates a “calibration reference value” based on an average of sensing values of the partial cluster included in a cluster, for each cluster. For example, if a certain cluster includes a plurality of partial clusters, the server calculates the “calibration reference value” of the cluster by obtaining a weighted average based on a plurality of averages of sensing values corresponding to the respective partial clusters – See at least ¶52);
identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data (A second embodiment relates to an embodiment in which a sensing value determined as an “abnormal value (i.e., “outlier”) is excluded from “use target values” to be used for calculating the calibration reference value – See at least ¶105);
computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-system devices in the one or more systems, using the sensor data with the one or more outlier parameters excluded (Incidentally, if an “outlier” is included in the sensing values collected for a certain cluster, the processor in the server of the second embodiment calculates a “calibration reference value” while excluding the sensing value corresponding to the outlier. For example, the processor calculates an average excluding the outlier by using “Trim mean” based on the sensing value of each of the sensor nodes included in a certain cluster, and uses the calculated average as the “calibration reference value”. “Trim mean” is an algorithm conventionally used to calculate an average excluding an abnormal value – See at least ¶114);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Amarnathan and include the feature of clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations; computing statistical values for one or more parameters across the clustered operation phases; identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data; computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-systems in the one or more systems, using the sensor data with the one or more outlier parameters excluded, as taught by Yamashita, to detect possible mechanical faults in an aircraft system to ensure safety and airline service efficiency.
The combination of Amarnathan and Yamashita fail to disclose determining a physics based parameter that is computed based on the sensor data, wherein the physics based parameter is associated with a physical parameter associated with the first sub-system.
However, Akkaram teaches:
determining a physics based parameter that is computed based on the sensor data (In some examples, a workscope effect calculator (WEC) apparatus generates predictive asset health quantifiers by identifying leading indicators by comparing asset monitoring information (e.g., asset sensor data, etc.) to data obtained from executing one or more models (e.g., physics-based models, fleet average regression models, historical trend models, etc.) and identifying deviations of specific engines and/or operators based on the comparison – See at least ¶45),
wherein the physics based parameter is associated with a physical parameter associated with the first sub-system (In some examples, the physics-based model is a digital twin model of the engine. For example, the digital twin model can simulate physics behavior, a thermodynamic health, a performance health, etc., of the engine using asset monitoring information based on the inputs, information stored in the database, the workscope results from the workscope quantifier analyzer, etc. For example, the physics-based model can simulate inputs and outputs of the sensors of FIGS. 1-2 of the engine to generate an AHQ based on the workscope results. For example, the physics-based model can be a digital twin model of the engine of FIG. 1 that can calculate an asset health quantifier (AHQ) of the booster compressor of FIG. 1 such as a quantity of TOW hours based on (1) a completed workscope on the booster compressor (e.g., one or more components of the booster compressor have been replaced, serviced, etc.) and (2) a severity factor. For example, the digital twin model can generate the AHQ with improved accuracy based on more accurately characterizing a health of the booster compressor as a result of the completed workscope on the booster compressor – See at least ¶126).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan and Yamashita and include the feature of determining a physics based parameter that is computed based on the sensor data, wherein the physics based parameters is associated with a physical parameter associated with the first sub-system, as taught by Akkaram, to generate a predictive asset health quantifier of a turbine engine (See at least ¶1 of Akkaram).
The combination of Amarnathan, Yamashita and Akkaram fail to disclose combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data, wherein the digital twin value is generated using a modeled version of the first sub-system; and detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values.
However, Wloch teaches:
combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data (Sub-module 610 takes in model data 604 and conditions 608, (i.e. corresponds to sensor data representative of the physical subsystem), processes those inputs to generate simulated aerodynamic forces (i.e. corresponding to the claimed physics-based parameter), and sub-module 614 combines the simulated aerodynamic forces to generate model aerodynamic properties/performance 616 (i.e. corresponding to the claimed digital twin value generated from the modeled subsystem). Likewise, simulated aerodynamic performance is calculated by combining the simulated aerodynamic forces of the model surface portions – See at least ¶60, 77 and 84),
wherein the digital twin value is generated using a modeled version of the first sub-system (A three-dimensional model includes model surface portions having associated surface portion parameters from which simulated aerodynamic forces and simulated aerodynamic performance are calculated, wherein the simulated aerodynamic performance is generated from the modeled aircraft (i.e. corresponds to a digital twin value generated using a modeled version of the first sub-system – See at least ¶23-24, 60, 77 and 84); and
detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values (The simulated aerodynamic performance is compared with reference data and target/current normalization parameters to determine an error/discrepancy, and the identified error is used to adjust the modeled aircraft (i.e. corresponds to detecting an anomaly by comparing generated data with expected (i.e. nominal) values – See at least ¶38, 48-49, 61, 77 and 84).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita and Akkaram and include the features of combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data, wherein the digital twin value is generated using a modeled version of the first sub-system; and detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values, as taught by Wloch, to enable one to cause and understand aerodynamic behavior in a virtual object (e.g., a plane or automobile), and to tune various parameters of the model to produce desired aerodynamic behavior in simulation (See at least ¶12 of Wloch).
The combination of Amarnathan, Yamashita, Akkaram and Wloch fail to disclose isolating the first sub-system based on detecting the anomaly; and performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system.
However, Winger teaches:
isolating the first sub-system based on detecting the anomaly (FIG. 2 shows an example of the prognostics system that has the ability to isolate misbehaving subsystems to prevent power loss and to provide redundant power to safety subsystems using the single battery – See at least ¶9); and
performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system (control determines if a failure due to the detected abnormality is imminent. If a failure due to the detected abnormality is not imminent, control issues a warning to the driver of the vehicle (e.g., turns on a service indicator). If a failure due to the detected abnormality is imminent, control disables the vehicle operation – See at least ¶103).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita, Akkaram and Wloch and include the features of isolating the first sub-system based on detecting the anomaly; and performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system, as taught by Winger, to ensure the safety of occupants of the vehicle (See at least ¶4).
Regarding claim 13, Amarnathan, as modified, discloses:
wherein the vehicle is associated with one or more aircraft (Any one of the aircrafts may be connected to another and/or to one or more hubs over a communication network using a vehicle management computer corresponding to each of the aircraft or hub – See at least ¶22), and
wherein the chain of operations comprises a chain of flight legs for the one or more aircraft, and wherein the operating conditions comprise operating conditions for the aircraft (Airspace may accommodate various types of aircraft flying at various altitudes and along various routes, (i.e. flight legs) – See at least ¶21. It should be appreciated that supplemental navigation program, when executed by CPU, may continuously evaluate the operating state of radar sensor throughout an active flight of aircraft a until sensor(s) is deactivated at the conclusion of the flight. In this instance, system may obtain directional data for use by sensor(s) to determine a location of aircraft a relative to other terminals via radar sensor – See at least ¶65).
Regarding claim 14, Amarnathan, as modified, discloses wherein the one or more aircraft comprise a specific aircraft tail and wherein the chain of flight legs comprises a chain of flight legs for the specific aircraft tail (Airspace may accommodate various types of aircraft flying at various altitudes and along various routes, (i.e. flight legs) – See at least ¶21. Generally, GPS may be a navigation system configured to determine positional data orientation, heading – See at least ¶36).
Regarding claim 15, Amarnathan, as modified, discloses wherein the one or more aircraft comprise a plurality of aircraft in a fleet of aircraft, and wherein the chain of flight legs comprises a chain of flight legs for the plurality of aircraft (receiving directional data of the terminal relative to the vehicle; determining a first location of the vehicle relative to the terminal based on the directional data; and determining a second location of the vehicle relative to an operating environment of the vehicle based on the first location and the positional data – See at least ¶7. The present disclosure is directed to overcoming one or more of the challenges discussed above in the BACKGROUND. As UAM vehicles generally operate near other vehicles, such as, for example, alongside other UAM vehicles in a fleet – See at least ¶17).
Regarding claim 17, Amarnathan discloses a computer program product comprising: a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation (Examples of the processor include a CPU, a Digital Signal Processor (DSP), and a Field Programmable Gate Array (FPGA). Furthermore, examples of the memory include a Random Access Memory (RAM) such as a Synchronous Dynamic Random Access Memory (SDRAM), a Read Only Memory (ROM), and a flash memory. Various processing functions executed by the processor are implemented by recording a program corresponding to each of the various processing functions in the memory and causing the processor to execute the programs – See at least ¶57), the operation comprising:
identifying a chain of operations for one or more systems associated with a vehicle, the chain of operations comprising a plurality of consecutive operations for the one or more systems (As described in detail herein, aircraft may perform one or more of the aforementioned operations of supplemental navigation program using the components of system. Supplemental navigation program may provide system with positional data and/or directional data for controlling a flight path of aircraft a by changing a speed, heading, altitude (e.g., manually, semi-autonomously, autonomously) in accordance with flight control program based on the location information received from nearby terminals in lieu of sensors – See at least ¶44).
Amarnathan fails to disclose clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations; wherein the sensor data is obtained from one or more sensor devices of the vehicle; computing statistical values for one or more parameters across the clustered operation phases; identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data; computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-systems in the one or more systems, using the sensor data with the one or more outlier parameters excluded.
However, Yamashita teaches:
clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations (Meanwhile, the server divides a plurality of the sensor nodes in the wireless sensor network system into a plurality of “clusters” each including at least one partial cluster, based on the number of the sensor nodes. For example, the server sets the number of the sensor nodes to be included in a single cluster to (hereinafter, the number may be referred to as a “cluster size limit”) – See at least ¶51);
wherein the sensor data is obtained from one or more sensor devices of the vehicle (a processor configured to: control calibration of sensing values in a sensor node group formed of sensor nodes each including a sensor and a wireless device and being capable of performing multi-hop communication – See at least ¶9);
computing statistical values for one or more parameters across the clustered operation phases (Then, the server calculates a “calibration reference value” based on an average of sensing values of the partial cluster included in a cluster, for each cluster. For example, if a certain cluster includes a plurality of partial clusters, the server calculates the “calibration reference value” of the cluster by obtaining a weighted average based on a plurality of averages of sensing values corresponding to the respective partial clusters – See at least ¶52);
identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data (A second embodiment relates to an embodiment in which a sensing value determined as an “abnormal value (i.e., “outlier”) is excluded from “use target values” to be used for calculating the calibration reference value – See at least ¶105);
computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-system devices in the one or more systems, using the sensor data with the one or more outlier parameters excluded (Incidentally, if an “outlier” is included in the sensing values collected for a certain cluster, the processor in the server of the second embodiment calculates a “calibration reference value” while excluding the sensing value corresponding to the outlier. For example, the processor calculates an average excluding the outlier by using “Trim mean” based on the sensing value of each of the sensor nodes included in a certain cluster, and uses the calculated average as the “calibration reference value”. “Trim mean” is an algorithm conventionally used to calculate an average excluding an abnormal value – See at least ¶114);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Amarnathan and include the feature of clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations; computing statistical values for one or more parameters across the clustered operation phases; identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data; computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-systems in the one or more systems, using the sensor data with the one or more outlier parameters excluded, as taught by Yamashita, to detect possible mechanical faults in an aircraft system to ensure safety and airline service efficiency.
The combination of Amarnathan and Yamashita fail to disclose determining a physics based parameter that is computed based on the sensor data, wherein the physics based parameters is associated with a physical parameter associated with the first sub-system.
However, Akkaram teaches:
determining a physics based parameter that is computed based on the sensor data (In some examples, a workscope effect calculator (WEC) apparatus generates predictive asset health quantifiers by identifying leading indicators by comparing asset monitoring information (e.g., asset sensor data, etc.) to data obtained from executing one or more models (e.g., physics-based models, fleet average regression models, historical trend models, etc.) and identifying deviations of specific engines and/or operators based on the comparison – See at least ¶45),
wherein the physics based parameters is associated with a physical parameter associated with the first sub-system (In some examples, the physics-based model is a digital twin model of the engine. For example, the digital twin model can simulate physics behavior, a thermodynamic health, a performance health, etc., of the engine using asset monitoring information based on the inputs, information stored in the database, the workscope results from the workscope quantifier analyzer, etc. For example, the physics-based model can simulate inputs and outputs of the sensors of FIGS. 1-2 of the engine to generate an AHQ based on the workscope results. For example, the physics-based model can be a digital twin model of the engine of FIG. 1 that can calculate an asset health quantifier (AHQ) of the booster compressor of FIG. 1 such as a quantity of TOW hours based on (1) a completed workscope on the booster compressor (e.g., one or more components of the booster compressor have been replaced, serviced, etc.) and (2) a severity factor. For example, the digital twin model can generate the AHQ with improved accuracy based on more accurately characterizing a health of the booster compressor as a result of the completed workscope on the booster compressor – See at least ¶126).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan and Yamashita and include the feature of determining a physics based parameter that is computed based on the sensor data, wherein the physics based parameters is associated with a physical parameter associated with the first sub-system, as taught by Akkaram, to generate a predictive asset health quantifier of a turbine engine (See at least ¶1 of Akkaram).
The combination of Amarnathan, Yamashita and Akkaram fail to disclose combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data, wherein the digital twin value is generated using a modeled version of the first sub-system; and detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values.
However, Wloch teaches:
combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data (Sub-module 610 takes in model data 604 and conditions 608, (i.e. corresponds to sensor data representative of the physical subsystem), processes those inputs to generate simulated aerodynamic forces (i.e. corresponding to the claimed physics-based parameter), and sub-module 614 combines the simulated aerodynamic forces to generate model aerodynamic properties/performance 616 (i.e. corresponding to the claimed digital twin value generated from the modeled subsystem). Likewise, simulated aerodynamic performance is calculated by combining the simulated aerodynamic forces of the model surface portions – See at least ¶60, 77 and 84),
wherein the digital twin value is generated using a modeled version of the first sub-system (A three-dimensional model includes model surface portions having associated surface portion parameters from which simulated aerodynamic forces and simulated aerodynamic performance are calculated, wherein the simulated aerodynamic performance is generated from the modeled aircraft (i.e. corresponds to a digital twin value generated using a modeled version of the first sub-system – See at least ¶23-24, 60, 77 and 84); and
detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values (The simulated aerodynamic performance is compared with reference data and target/current normalization parameters to determine an error/discrepancy, and the identified error is used to adjust the modeled aircraft (i.e. corresponds to detecting an anomaly by comparing generated data with expected (i.e. nominal) values – See at least ¶38, 48-49, 61, 77 and 84).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita and Akkaram and include the feature of combining the sensor data, the determined physics based parameter, and a digital twin value, to generate combined data, wherein the digital twin value is generated using a modeled version of the first sub-system; and detecting an anomaly in the first sub-system based on comparing the combined data and the computed one or more nominal values, as taught by Wloch, to enable one to cause and understand aerodynamic behavior in a virtual object (e.g., a plane or automobile), and to tune various parameters of the model to produce desired aerodynamic behavior in simulation (See at least ¶12 of Wloch).
The combination of Amarnathan, Yamashita, Akkaram and Wloch fail to disclose isolating the first sub-system based on detecting the anomaly; and performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system.
However, Winger teaches:
isolating the first sub-system based on detecting the anomaly (FIG. 2 shows an example of the prognostics system that has the ability to isolate misbehaving subsystems to prevent power loss and to provide redundant power to safety subsystems using the single battery – See at least ¶9); and
performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system (control determines if a failure due to the detected abnormality is imminent. If a failure due to the detected abnormality is not imminent, control issues a warning to the driver of the vehicle (e.g., turns on a service indicator). If a failure due to the detected abnormality is imminent, control disables the vehicle operation – See at least ¶103).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita, Akkaram and Wloch and include the features of isolating the first sub-system based on detecting the anomaly; and performing, based on isolating the first sub-system, at least one of repairing or disabling a component of the first sub-system, as taught by Winger, to ensure the safety of occupants of the vehicle (See at least ¶4).
Regarding claim 18, Amarnathan, as modified, discloses:
wherein the vehicle is associated with one or more aircraft (Any one of the aircrafts may be connected to another and/or to one or more hubs over a communication network using a vehicle management computer corresponding to each of the aircraft or hub – See at least ¶22) and
wherein the chain of operations comprises a chain of flight legs for the one or more aircraft, and wherein the operating conditions comprise operating conditions for the aircraft (Airspace may accommodate various types of aircraft flying at various altitudes and along various routes, (i.e. flight legs) – See at least ¶21. It should be appreciated that supplemental navigation program, when executed by CPU, may continuously evaluate the operating state of radar sensor throughout an active flight of aircraft a until sensor(s) is deactivated at the conclusion of the flight. In this instance, system may obtain directional data for use by sensor(s) to determine a location of aircraft a relative to other terminals via radar sensor – See at least ¶65).
Regarding claim 19, Amarnathan, as modified, discloses wherein the one or more aircraft comprise a specific aircraft tail and wherein the chain of flight legs comprises a chain of flight legs for the specific aircraft tail (Airspace may accommodate various types of aircraft flying at various altitudes and along various routes, (i.e. flight legs) – See at least ¶21. Generally, GPS may be a navigation system configured to determine positional data orientation, heading – See at least ¶36).
Regarding claim 20, Amarnathan, as modified, discloses wherein the one or more aircraft comprise a plurality of aircraft in a fleet of aircraft, and wherein the chain of flight legs comprises a chain of flight legs for the plurality of aircraft (receiving directional data of the terminal relative to the vehicle; determining a first location of the vehicle relative to the terminal based on the directional data; and determining a second location of the vehicle relative to an operating environment of the vehicle based on the first location and the positional data – See at least ¶7. The present disclosure is directed to overcoming one or more of the challenges discussed above in the BACKGROUND. As UAM vehicles generally operate near other vehicles, such as, for example, alongside other UAM vehicles in a fleet – See at least ¶17).
Regarding claim 21, the combination of Amarnathan, Yamashita, Akkaram and Wloch fail to disclose wherein repairing the component comprises: repairing the component via automated software or hardware corrections.
However, Winger teaches wherein repairing the component comprises: repairing the component via automated software or hardware corrections (For example, control may initially simply restart (i.e., initialize or reboot) the misbehaving subsystem. If that does not solve the problem, control may perform a hardware reset on the misbehaving subsystem by disconnecting power to the misbehaving subsystem and after waiting for a period of time reconnecting the power to the misbehaving system – See at least ¶90).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita, Akkaram and Wloch and include the feature of wherein repairing the component comprises: repairing the component via automated software or hardware corrections, as taught by Winger, to ensure the safety of occupants of the vehicle (See at least ¶4).
Claim(s) 7-8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Amarnathan et al., US 20220057473 A1, in view of Yamashita et al., US 20170372601 A1, in view of Akkaram et al., US 20190146470 A1, in view of Sebastian Wloch, US 20210216688 A1, in view of Winger et al., US 20220149650 A1, as applied to claims 2 and 13 above and further in view of Xiaokun et al., CN 110162746A, hereinafter referred to as Amarnathan, Yamashita, Akkaram, Wloch, Winger and Xiaokun, respectively.
Regarding claim 7, the combination of Amarnathan, Yamashita, Akkaram, Wloch and Winger fail to disclose determining a health index for the first sub-system based on calculating at least one of a cosine similarity or Euclidian distance for a plurality of parameters in the sub-system compared with the computed one or more nominal values.
However, Xiaokun teaches determining a health index for the first sub-system based on calculating at least one of a cosine similarity or Euclidian distance for a plurality of parameters in the sub-system compared with the computed one or more nominal values (Although the standardized method and the weighting method are improved, there is still a phenomenon that cannot be completely separated. The weighted cosine similarity degree is the best, and the improved weighted cosine similarity is used in the present invention for health index calculation and fault diagnosis – See at least Detailed Ways Paragraph section, lines 28-31).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita, Akkaram, Wloch and Winger and include the feature of determining a health index for the first sub-system based on calculating at least one of a cosine similarity or Euclidian distance for a plurality of parameters in the sub-system compared with the computed one or more nominal values, as taught by Xiaokun, to provide a health warning and fault diagnosis for an aircraft.
Regarding claim 8, the combination of Amarnathan, Yamashita, Akkaram, Wloch and Winger fail to disclose wherein determining the health index for the first sub-system is based on calculating both the cosine similarity and the Euclidian distance for the plurality of parameters in the sub-system compared with the computed one or more nominal values.
However, Xiaokun teaches wherein determining the health index for the first sub-system is based on calculating both the cosine similarity and the Euclidian distance for the plurality of parameters in the sub-system compared with the computed one or more nominal values (Although the standardized method and the weighting method are improved, there is still a phenomenon that cannot be completely separated. The weighted cosine similarity degree is the best, and the improved weighted cosine similarity is used in the present invention for health index calculation and fault diagnosis – See at least Detailed Ways Paragraph section, lines 28-31).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita, Akkaram, Wloch and Winger and include the feature of wherein determining the health index for the first sub-system is based on calculating both the cosine similarity and the Euclidian distance for the plurality of parameters in the sub-system compared with the computed one or more nominal values, as taught by Xiaokun, to provide a health warning and fault diagnosis for an aircraft.
Regarding claim 16, the combination of Amarnathan, Yamashita, Akkaram, Wloch and Winger fail to disclose determining a health index for the first sub-system based on calculating at least one of a cosine similarity or Euclidian distance for a plurality of parameters in the sub-system compared with the computed one or more nominal values.
However, Xiaokun teaches determining a health index for the first sub-system based on calculating at least one of a cosine similarity or Euclidian distance for a plurality of parameters in the sub-system compared with the computed one or more nominal values (Although the standardized method and the weighting method are improved, there is still a phenomenon that cannot be completely separated. The weighted cosine similarity degree is the best, and the improved weighted cosine similarity is used in the present invention for health index calculation and fault diagnosis – See at least Detailed Ways Paragraph section, lines 28-31).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita, Winger and Akkaram and include the feature of determining a health index for the first sub-system based on calculating at least one of a cosine similarity or Euclidian distance for a plurality of parameters in the sub-system compared with the computed one or more nominal values, as taught by Xiaokun, to provide a health warning and fault diagnosis for an aircraft.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Amarnathan et al., US 20220057473 A1, in view of Yamashita et al., US 20170372601 A1, in view of Akkaram et al., US 20190146470 A1, in view of Sebastian Wloch, US 20210216688 A1, in view of Winger et al., US 20220149650 A1, as applied to claim 10 above and further in view of Meng et al., CN 112396105B, hereinafter referred to as Amarnathan, Yamashita, Akkaram, Wloch, Winger, and Meng, respectively.
Regarding claim 11, the combination of Amarnathan, Yamashita, Akkaram, Wloch and Winger fail to disclose wherein the reasoning algorithm is a Bayesian network trained based on historical data relating to operation of the aircraft.
However, Meng teaches wherein the reasoning algorithm is a Bayesian network trained based on historical data relating to operation of the aircraft (the present invention provides a method for intelligently generating flight training subjects based on Bayesian network – See at least Contents of the invention paragraph, lines 1-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amarnathan, Yamashita, Akkaram, Wloch and Winger and include the feature of wherein the reasoning algorithm is a Bayesian network trained based on historical data relating to operation of the aircraft, as taught by Meng, for intelligently generating flight training subjects based on Bayesian network (See at least Content of Invention paragraph).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Stefan Hainzl, (US 20130332779 A1) discloses automatic monitoring of at least one component of a physical system, includes checking data of a data record for errors caused by a preceding data processing, checking the data in the physical context of the at least one sensor for errors resulting from infringements of the assumptions of physical and/or system-related factors in elements of the measurement chain.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHMOUD M KAZIMI whose telephone number is (571)272-3436. The examiner can normally be reached M-F 7am-5pm.
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, Erin Bishop can be reached at 5712703713. 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.
RESPECTFULLY SUBMITTED
/MAHMOUD M KAZIMI/Examiner, Art Unit 3665
Prosecution Insights