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 .
Response to Amendment
This action is in response to amendments and remarks filed on 10/29/2025. Claims 1-20 are pending. Claims 1, 6, and 14 have been amended. The 35 U.S.C. 101 rejections have been withdrawn in light of the instant amendments. This action is made final, as necessitated by amendment.
Response to Arguments
Applicant’s arguments appear to be directed solely to the amended subject matter which have been considered and addressed as detailed below under Claim Rejections.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 09/10/2025 has been acknowledged.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1, 3-10, 13-14, 16-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carvalho (US 20200309810) in view of Anderson (US 20170158347).
Regarding claim 1, Carvalho teaches a method for real-time estimating synthetic air data, comprising:
receiving, via a first stage of a synthetic air data (SyAD) system (par. 22, “The technology herein provides a trustworthy and advantageous method to calculate an independent source of air data that addresses the problems discussed above, i.e., it is designed to be totally independent of traditional air data sensors and it uses other sensors that are already present in modern aircraft systems”) configured to execute on at least one processor (par. 41, onboard computer 27) aboard an aircraft, the first stage including at least one machine learning (ML) algorithm (par. 52 Fig. 2, neural network 23), a plurality of absolute aircraft parameters sensed by an absolute position sensor of the aircraft, the plurality of absolute aircraft parameters including absolute aircraft position data (par. 60 Fig. 3, "Geometric Altitude (H): normally measured by aircraft's Global Positioning System (GPS)”) and aircraft ground speed data (par. 59 Fig. 3, "Ground Speed (GS): normally measured by aircraft's Inertial Reference Units (IRU) (e.g., accelerometers and/or gyro sensors) or Global Positioning System (GPS)”);
receiving, via the first stage, a plurality of inertial aircraft parameters sensed by an inertial reference unit (IRU) of the aircraft (par. 59, aircraft's Inertial Reference Units (IRU)), the inertial aircraft parameters including aircraft attitude data (par. 63 Fig. 3, "Angle of Attitude (Theta): normally measured by aircraft's Inertial Reference Units (IRU)”), aircraft angular rate data (par. 61 Fig. 3, "Angle of Trajectory (Gamma): normally measured by aircraft's Inertial Reference Units (IRU)"), and aircraft linear acceleration data (par. 64-66 Fig. 3 Longitudinal (Nx), vertical (Nz), and lateral acceleration load factor (Ny));
receiving, via the first stage, a plurality of aircraft component parameters, each aircraft component parameter associated with a component or a subsystem of the aircraft (par. 58 Fig. 3, "Weight (W): Estimated aircraft weight"; par. 62 Fig. 3, " Engine's Magnitude of Thrust (Thrust)”);
estimating, during at least one flight phase and via the first stage, a first-stage synthetic air data (SyAD) set based on at least the plurality of absolute aircraft parameters, the plurality of aircraft inertial parameters, and the plurality of aircraft component parameters (see Fig. 3, inputs 31 and 32 are used to estimate the air data 35), the first-stage SyAD set comprising: an initial true airspeed (VTAS) of the aircraft; an initial angle of attack (AoA, α) of the aircraft; and an initial sideslip angle (AoS, β) of the aircraft (Fig. 3, estimated air data; par. 25-27, Angle of Attack, angle of sideslip, and airspeed);
receiving, via a second stage of the SyAD system, the second stage configured for implementation of at least one non-linear Kalman filter (par. 52, "The neural network estimated air data output may be filtered (24) using some Low-pass, Kalman or Complementary filter"—though a non-linear Kalman filter is not explicitly used, the use of an extended Kalman filter is mentioned (par. 20) and would be an obvious conclusion), a subset of the plurality of absolute aircraft parameters, of the plurality of inertial aircraft parameters, and of the plurality of aircraft component parameters (Fig. 3, inputs 31 and 32); receiving, via the second stage, the first-stage SyAD set (Fig 3, Estimated Air Data);
estimating, during at least one flight phase and via the second stage, a blended SyAD set based on 1) the subset of the plurality of absolute aircraft parameters, of the plurality of aircraft inertial parameters, and of the plurality of aircraft component parameters and 2) the first-stage SyAD set (par. 52, “a neural network estimating airspeed may have the noisy behavior of its signal reasonably removed by merging the neural network airspeed estimation with measured aircraft longitudinal acceleration in a Kalman or Complementary filter. The function of such filtering in some implementations may be to “fuse” the estimated air data with other input data to provide a more accurate, reliable and/or robust output”), the blended SyAD set comprising: a blended true airspeed of the aircraft; a blended AoA of the aircraft; and a blended AoS of the aircraft (Fig. 3, filtered estimated air data 36).
Carvalho fails to teach forwarding the blended SyAD set to a redundant air data system (ADS) for use as at least one of: an additional standby signal to add an additional laver of redundancy, an indicator of fault detection, or for tiebreaking in the event of disagreeing inertial aircraft parameters, wherein the redundant ADS includes at least one of a triple-redundant configuration or a dual-input voter configuration; and forwarding the blended SyAD set from the redundant ADS to an avionics system of the aircraft.
However, Anderson teaches forwarding the blended SyAD set to a redundant air data system (ADS) for use as at least one of: an additional standby signal to add an additional laver of redundancy, an indicator of fault detection, or for tiebreaking in the event of disagreeing inertial aircraft parameters (par. 29, “synthetic air data system 12 can provide a redundant (e.g., backup) air data system that generates air data output values usable for operation of aircraft 12 when one or more pneumatic-based air data output values are determined to be unreliable”), wherein the redundant ADS includes at least one of a triple-redundant configuration or a dual-input voter configuration (par. 3, “many aircraft incorporate multiple (e.g., two, three, four, or more) pneumatic air data probes, certain of which are designated as backup systems for use when a primary system is deemed unreliable”); and forwarding the blended SyAD set from the redundant ADS to an avionics system of the aircraft (par. 3, “aircraft manufacturers typically incorporate redundant (e.g., backup) systems that can provide outputs to consuming systems”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carvalho to incorporate the teachings of Anderson to increase system reliability in the event a primary system fails (par. 3). Using synthetic air data as a backup system appears to be well-known in the art.
Regarding claim 3, the combination of Carvalho in view of Anderson teaches the method of Claim 1. Carvalho further teaches the at least one non-linear Kalman filter is selected from a group including a Particle Filter, an Unscented Kalman Filter (UKF), and an Extended Kalman Filter (EKF) (par. 52, "The neural network estimated air data output may be filtered (24) using some Low-pass, Kalman or Complementary filter"—though an Extended Kalman filter is not explicitly used, the use of an extended Kalman filter is mentioned (par. 20) and would be an obvious conclusion).
Regarding claim 4, the combination of Carvalho in view of Anderson teaches the method of Claim 1. Carvalho further teaches the aircraft angular rate data includes a three-axis angular rate of the aircraft, comprising a pitch rate, a roll rate, and a yaw rate (par. 75-77 Fig. 3, pitch (Q), roll (P), and yaw (R) rate); the aircraft linear acceleration data includes a three-axis linear acceleration of the aircraft (par. 64-66 Fig. 3 Longitudinal (Nx), vertical (Nz), and lateral acceleration load factor (Ny)); and the aircraft attitude data includes a three-axis attitude estimation of the aircraft comprising a roll angle, a pitch angle, and a heading angle (par. 63, “Angle of Attitude (Theta): normally measured by aircraft's Inertial Reference Units (IRU), but might also be supplied by other systems in some particular implementations. Corresponds to one of the Euler Angles, measured between the fuselage center axis and the Earth's plane”).
Regarding claim 5, the combination of Carvalho in view of Anderson teaches the method of Claim 1. Carvalho further teaches the aircraft absolute position data includes a latitude, a longitude, and an altitude of the aircraft (par. 60 Fig. 3, “Geometric Altitude (H): normally measured by aircraft's Global Positioning System (GPS)”—although latitude and longitude aren’t explicitly stated, the use of GPS would suggest that the latitude and longitude would be measured); and wherein the aircraft ground speed data includes a three-axis ground speed vector of the aircraft and a ground track of the aircraft (par. 59 Fig. 3, “Ground Speed (GS): normally measured by aircraft's Inertial Reference Units (IRU) (e.g., accelerometers and/or gyro sensors) or Global Positioning System (GPS), but might also be supplied by other systems in some particular implementations. Corresponds to the aircraft's speed relative to the ground, considered as an inertial reference”).
Regarding claim 6, the combination of Carvalho in view of Anderson teaches the method of Claim 1. Carvalho further teaches the at least one ML algorithm includes at least one artificial neural network (ANN) configured for estimation of the first-stage SyAD set (par. 52 Fig. 2, neural network 23).
Regarding claim 7, the combination of Carvalho in view of Anderson teaches the method of Claim 1. Carvalho further teaches the plurality of aircraft component parameters includes at least one of: aircraft engine data (par. 62, " Engine's Magnitude of Thrust (Thrust): a plurality of parameters may be used as indicatives of how much thrust the engines are providing to the aircraft at or during some instant of time. For example, the following may be used: Fan Speed (N1), or Thrust Lever Angle (TLA), or Engine Pressure Ratio (EPR) or others); aircraft control surface data (par. 70, "Elevator Position (Elev): normally supplied by sensors belonging to Primary Flight Control Systems"); or aircraft mass data (par. 58 Fig. 3, "Weight (W): Estimated aircraft weight").
Regarding claim 8, the combination of Carvalho in view of Anderson teaches the method of Claim 7. Carvalho further teaches the aircraft engine data includes at least one of: a fuel burn rate associated with an engine of the aircraft; an engine speed associated with an engine of the aircraft; a throttle lever position; or a pressure ratio associated with an engine of the aircraft (par. 62, " Engine's Magnitude of Thrust (Thrust): a plurality of parameters may be used as indicatives of how much thrust the engines are providing to the aircraft at or during some instant of time. For example, the following may be used: Fan Speed (N1), or Thrust Lever Angle (TLA), or Engine Pressure Ratio (EPR) or others”).
Regarding claim 9, the combination of Carvalho in view of Anderson teaches the method of Claim 7. Carvalho further teaches the aircraft control surface data includes at least one of: an aileron position; an elevator position (par. 70, "Elevator Position (Elev): normally supplied by sensors belonging to Primary Flight Control Systems"); a rudder position; a stabilizer position (par. 69, "Horizontal Stabilizer Position (H-Stab)); a spoiler position; a flap position; a slat position; or a gear position associated with landing gear of the aircraft.
Regarding claim 10, the combination of Carvalho in view of Anderson teaches the method of Claim 7. Carvalho further teaches the aircraft mass data includes at least one of: a weight of the aircraft (par. 58 Fig. 3, "Weight (W): Estimated aircraft weight"); or a center of gravity (CG) of the aircraft (par. 71, “Center of Gravity (CG): Estimated aircraft center of gravity”).
Regarding claim 13, the combination of Carvalho in view of Anderson teaches the method of Claim 1. Carvalho further teaches forwarding the blended SyAD set to at least one of: an avionics system of the aircraft; or a redundant air data system (ADS) configuration of the aircraft (par 53, “The filtered estimated air data may be used by some onboard computer such as the Flight Control Computer (25) or may be used to display a synthetic air data (such as airspeed) to pilots (26). Some logics (10) that may consume the neural network's output (unfiltered or filtered, as previously described) are Flight Control System Logic (25) (for example Control Laws or Common Design Error Monitors) and Flight Crew Indications (26) (like an indication of estimated airspeed to the pilots through avionics displays)”).
Regarding claim 14, Carvalho teaches an aircraft-based synthetic air data (SyAD) system (par. 22, “The technology herein provides a trustworthy and advantageous method to calculate an independent source of air data that addresses the problems discussed above, i.e., it is designed to be totally independent of traditional air data sensors and it uses other sensors that are already present in modern aircraft systems”), comprising: one or more processors (par. 41, onboard computer 27); non-transitory computer-readable memory (par. 41, “non-transitory memory device”) encoded with instructions which, when executed by the one or more processors, cause the SyAD system to:
receive a plurality of absolute aircraft parameters sensed by at least one absolute position sensor of an aircraft, the plurality of absolute aircraft parameters including absolute position data (par. 60 Fig. 3, "Geometric Altitude (H): normally measured by aircraft's Global Positioning System (GPS)”) and ground speed data of the aircraft (par. 59 Fig. 3, "Ground Speed (GS): normally measured by aircraft's Inertial Reference Units (IRU) (e.g., accelerometers and/or gyro sensors) or Global Positioning System (GPS)”);
receive a plurality of inertial aircraft parameters from at least one inertial reference unit (IRU) of the aircraft (par. 59, aircraft's Inertial Reference Units (IRU)), the plurality of inertial aircraft parameters including aircraft attitude data (par. 63 Fig. 3, "Angle of Attitude (Theta): normally measured by aircraft's Inertial Reference Units (IRU)”), aircraft angular rate data (par. 61 Fig. 3, "Angle of Trajectory (Gamma): normally measured by aircraft's Inertial Reference Units (IRU)"), and aircraft linear acceleration data (par. 64-66 Fig. 3 Longitudinal (Nx), vertical (Nz), and lateral acceleration load factor (Ny));
receive a plurality of aircraft component parameters from at least one of a component or a subsystem of the aircraft, the plurality of aircraft component parameters including at least one of aircraft engine data (par. 62, " Engine's Magnitude of Thrust (Thrust): a plurality of parameters may be used as indicatives of how much thrust the engines are providing to the aircraft at or during some instant of time. For example, the following may be used: Fan Speed (N1), or Thrust Lever Angle (TLA), or Engine Pressure Ratio (EPR) or others), aircraft control surface data (par. 70, "Elevator Position (Elev): normally supplied by sensors belonging to Primary Flight Control Systems") or aircraft mass data (par. 58 Fig. 3, "Weight (W): Estimated aircraft weight");
estimate, during at least one flight phase and via a first stage comprising at least one machine learning (ML) algorithm trained according to a flight envelope of the aircraft (par. 52 Fig. 2, neural network 23), a first- stage SyAD set based on the plurality of absolute aircraft parameters, the plurality of inertial aircraft parameters, and the plurality of aircraft component parameters (see Fig. 3, inputs 31 and 32 are used to estimate the air data 35), the first-stage SyAD set comprising: an initial true airspeed (VTAS) of the aircraft; an initial angle of attack (AoA, α) of the aircraft; and an initial sideslip angle (AoS, β) of the aircraft (Fig. 3, estimated air data; par. 25-27, Angle of Attack, angle of sideslip, and airspeed);
receive, via a second stage configured for implementation of at least one non-linear Kalman filter (par. 52, "The neural network estimated air data output may be filtered (24) using some Low-pass, Kalman or Complementary filter"—though a non-linear Kalman filter is not explicitly used, the use of an extended Kalman filter is mentioned (par. 20) and would be an obvious conclusion), the estimated first-stage SyAD set (Fig 3, Estimated Air Data);
estimate, during at least one flight phase and via the second stage, a blended SyAD set based on the first-stage SyAD set and a subset of the plurality of absolute aircraft parameters, the plurality of inertial aircraft parameters, and the plurality of aircraft component parameters (par. 52, “a neural network estimating airspeed may have the noisy behavior of its signal reasonably removed by merging the neural network airspeed estimation with measured aircraft longitudinal acceleration in a Kalman or Complementary filter. The function of such filtering in some implementations may be to “fuse” the estimated air data with other input data to provide a more accurate, reliable and/or robust output”), the blended SyAD set comprising: a blended true airspeed of the aircraft; a blended AoA of the aircraft; and a blended AoS of the aircraft (Fig. 3, filtered estimated air data 36).
Carvalho fails to teach to forward the blended SyAD set to a redundant air data system (ADS) for use as at least one of: an additional standby signal to add an additional layer of redundancy, an indicator of fault detection, or for tiebreaking in the event of disagreeing inertial aircraft parameters, wherein the redundant ADS includes at least one of a triple-redundant configuration or a dual-input voter configuration; and forward the blended SyAD set from the redundant ADS to an avionics system of the aircraft.
However, Anderson teaches to forward the blended SyAD set to a redundant air data system (ADS) for use as at least one of: an additional standby signal to add an additional layer of redundancy, an indicator of fault detection, or for tiebreaking in the event of disagreeing inertial aircraft parameters (par. 29, “synthetic air data system 12 can provide a redundant (e.g., backup) air data system that generates air data output values usable for operation of aircraft 12 when one or more pneumatic-based air data output values are determined to be unreliable”), wherein the redundant ADS includes at least one of a triple-redundant configuration or a dual-input voter configuration (par. 3, “many aircraft incorporate multiple (e.g., two, three, four, or more) pneumatic air data probes, certain of which are designated as backup systems for use when a primary system is deemed unreliable”); and forward the blended SyAD set from the redundant ADS to an avionics system of the aircraft (par. 3, “aircraft manufacturers typically incorporate redundant (e.g., backup) systems that can provide outputs to consuming systems”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carvalho to incorporate the teachings of Anderson to increase system reliability in the event a primary system fails (par. 3). Using synthetic air data as a backup system appears to be well-known in the art.
Regarding claim 16, the combination of Carvalho in view of Anderson teaches the aircraft-based SyAD system of Claim 14. Carvalho further teaches the at least one non-linear Kalman filter is selected from a group including a Particle Filter, an Unscented Kalman Filter (UKF), and an Extended Kalman Filter (EKF) (par. 52, "The neural network estimated air data output may be filtered (24) using some Low-pass, Kalman or Complementary filter"—though an Extended Kalman filter is not explicitly used, the use of an extended Kalman filter is mentioned (par. 20) and would be an obvious conclusion).
Regarding claim 17, the combination of Carvalho in view of Anderson teaches the aircraft-based SyAD system of Claim 14. Carvalho further teaches the at least one ML algorithm includes at least one artificial neural network (ANN) trained according to a flight envelope of the aircraft (par. 52 Fig. 2, neural network 23).
Regarding claim 20, the combination of Carvalho in view of Anderson teaches the aircraft-based SyAD system of Claim 14. Carvalho further teaches the encoded instructions further cause the SyAD system to forward the blended SyAD set to at least one of a flight control system of the aircraft or a redundant air data system (ADS) configuration of the aircraft (par 53, “The filtered estimated air data may be used by some onboard computer such as the Flight Control Computer (25) or may be used to display a synthetic air data (such as airspeed) to pilots (26). Some logics (10) that may consume the neural network's output (unfiltered or filtered, as previously described) are Flight Control System Logic (25) (for example Control Laws or Common Design Error Monitors) and Flight Crew Indications (26) (like an indication of estimated airspeed to the pilots through avionics displays)”).
Claim(s) 2 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Carvalho in view of Anderson, and further in view of Park (Park, Y. G., & Park, C. G. (2016). Wind velocity estimation without an air speed sensor using Kalman filter under the colored measurement noise. In 30th Congress of the International Council of the Aeronautical Sciences, ICAS 2016 (30th Congress of the International Council of the Aeronautical Sciences, ICAS 2016). International Council of the Aeronautical Sciences.).
Regarding claim 2, the combination of Carvalho in view of Anderson teaches the method of Claim 1. Both Carvalho and Anderson fail to teach the at least one non-linear Kalman filter includes a stochastic wind model; and wherein the blended SyAD set includes an estimated wind speed local to the aircraft.
However, Park teaches the at least one non-linear Kalman filter includes a stochastic wind model; and wherein the blended SyAD set includes an estimated wind speed local to the aircraft (page 2 column 2 line 22, “In this paper, a six degree-of-freedom model of aircraft is used for system model of extended Kalman. Its aerodynamic coefficients are nonlinear functions of position, air velocity, attitude, rotation rates and control input and the wind can be modeled random walk model whose variance is changed depending on altitude”).
Carvalho and Park are analogous art because both relate to estimating parameters for an aircraft.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Carvalho in view of Anderson to incorporate the teachings of Park. Park states “The gliding and control performance of an unpowered air vehicle is affected by the wind velocity. In order to maximize its gliding distance, the gliding vehicle has to fly with the velocity that minimizes the path angle. As a result, the velocity can be described to a function of wind speed. Therefore, the wind velocity is one of the most important components of the unpowered gliding vehicle to achieve an appropriate control and gliding performance.” (page 1 column 1 line 22). Wind affects the movement of an aircraft, and therefore an air data system should take into account the wind speed and direction.
Regarding claim 15, the combination of Carvalho in view of Anderson teaches the aircraft-based SyAD system of Claim 14. Both Carvalho and Anderson fail to teach the at least one non-linear Kalman filter includes a stochastic wind model; and the blended SyAD set includes an estimated wind speed local to the aircraft.
However, Park teaches the aircraft-based SyAD system of Claim 14. Carvalho fails to teach the at least one non-linear Kalman filter includes a stochastic wind model; and the blended SyAD set includes an estimated wind speed local to the aircraft (page 2 column 2 line 22, “In this paper, a six degree-of-freedom model of aircraft is used for system model of extended Kalman. Its aerodynamic coefficients are nonlinear functions of position, air velocity, attitude, rotation rates and control input and the wind can be modeled random walk model whose variance is changed depending on altitude”).
Carvalho and Park are analogous art because both relate to estimating parameters for an aircraft.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Carvalho in view of Anderson to incorporate the teachings of Park. Park states “The gliding and control performance of an unpowered air vehicle is affected by the wind velocity. In order to maximize its gliding distance, the gliding vehicle has to fly with the velocity that minimizes the path angle. As a result, the velocity can be described to a function of wind speed. Therefore, the wind velocity is one of the most important components of the unpowered gliding vehicle to achieve an appropriate control and gliding performance.” (page 1 column 1 line 22). Wind affects the movement of an aircraft, and therefore an air data system should take into account the wind speed and direction.
Claim(s) 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Carvalho in view of Anderson, and further in view of Sly (US 20200018776).
Regarding claim 11, the combination of Carvalho in view of Anderson teaches method of Claim 1. Both Carvalho and Anderson fail to teach determining at least one residual error associated with the SyAD system, the residual error based on a difference between the first-stage SyAD set and the blended SyAD set; and when the residual error meets or exceeds a threshold level: generating an alert associated with the SyAD system; and invalidating at least one of the first-stage SyAD set or the blended SyAD set.
However, Sly teaches determining at least one residual error associated with the SyAD system, the residual error based on a difference between the first-stage SyAD set and the blended SyAD set (par. 30, “In some examples, one or more of consuming systems 54 can identify the presence of a failure condition in one or more of the independent air data systems based on a comparison of the independent air data parameter outputs); and when the residual error meets or exceeds a threshold level (par. 30, “within a threshold deviation”): generating an alert associated with the SyAD system (par. 30, “consuming systems 54 can, in certain examples, store, annunciate, or otherwise indicate the presence of the failure condition in the identified air data system”); and invalidating at least one of the first-stage SyAD set or the blended SyAD set (par. 30, “Consuming systems 54 can refrain from utilizing air data parameter outputs from the identified air data system having the failure condition”).
Carvalho and Sly are analogous art because both relate to air data systems.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Carvalho in view of Anderson to incorporate the teachings of Sly. Sly states that determining faulty systems lead to “increasing integrity of the air data parameter outputs utilized for, e.g., flight control functions of aircraft” and “facilitating maintenance operations on components of the identified air data system having the failure condition” (par. 30). Invalidating data that deviates greatly is already well known in the art and can be seen in many air data systems.
Regarding claim 18, the combination of Carvalho in view of Anderson teaches the aircraft-based SyAD system of Claim 14. Both Carvalho and Anderson fail to teach the encoded instructions further cause the SyAD system to: determine at least one residual error associated with the SyAD system, the residual error based on a difference between the first-stage SyAD set and the blended SyAD set; and when the residual error meets or exceeds a threshold level: generating an alert associated with the SyAD system; and invalidating at least one of the first-stage SyAD set or the blended SyAD set.
However, Sly teaches the aircraft-based SyAD system of Claim 14, wherein the encoded instructions further cause the SyAD system to: determine at least one residual error associated with the SyAD system, the residual error based on a difference between the first-stage SyAD set and the blended SyAD set (par. 30, “In some examples, one or more of consuming systems 54 can identify the presence of a failure condition in one or more of the independent air data systems based on a comparison of the independent air data parameter outputs); and when the residual error meets or exceeds a threshold level (par. 30, “include parameter output values that are within a threshold deviation”); and when the residual error meets or exceeds a threshold level (par. 30, “within a threshold deviation”): generating an alert associated with the SyAD system (par. 30, “consuming systems 54 can, in certain examples, store, annunciate, or otherwise indicate the presence of the failure condition in the identified air data system”); and invalidating at least one of the first-stage SyAD set or the blended SyAD set (par. 30, “Consuming systems 54 can refrain from utilizing air data parameter outputs from the identified air data system having the failure condition”).
Carvalho and Sly are analogous art because both relate to air data systems.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Carvalho in view of Anderson to incorporate the teachings of Sly. Sly states that determining faulty systems lead to “increasing integrity of the air data parameter outputs utilized for, e.g., flight control functions of aircraft” and “facilitating maintenance operations on components of the identified air data system having the failure condition” (par. 30). Invalidating data that deviates greatly is already well known in the art and can be seen in many air data systems.
Claim(s) 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Carvalho in view of Anderson and further in view of Fang (US 12061277)
Regarding claim 12, the combination of Carvalho in view of Anderson teaches the method of Claim 1. Carvalho further teaches estimating, via the second stage, a blended SyAD set includes: determining, via a measurement covariance matrix of the second stage, a suitability of the first stage with respect to a flight envelope of the aircraft based on one or more of the plurality of absolute aircraft parameters, the plurality of inertial aircraft parameters, or the plurality of aircraft component parameters (par. 81, “Another kind of preprocessing (28) (33) associated with weight and center of gravity estimations is related to some interlocks that might freeze the estimations if the aircraft has assumed abnormal attitudes. They could be formed by thresholds defining a flight envelope outside of which the estimations would return big errors”).
Both Carvalho and Anderson fail to teach adjusting the at least one non-linear Kalman filter based on the determined suitability.
However, Fang teaches adjusting the at least one non-linear Kalman filter based on the determined suitability (column 14 line 6, “The omnisource adaptive Kalman filtering algorithm provided by the present application dynamically adjusts the weight of each navigation source according to the error covariance matrix output by the Kalman filter, thus ensuring the reliability of the positioning result”).
Carvalho and Fang are analogous art because both relate to collecting parameters to estimate data in an aircraft.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Carvalho in view of Anderson to incorporate the teachings of Fang. Fang states that doing so would optimize the algorithm and lead to “generating the optimal fusion factor for each navigation source and improving the performance and reliability of the whole navigation system” (column 14 line 6). Adjusting the Kalman filter gain depending on reliability is already well known in the art and can be seen in many air data systems.
Regarding claim 19, the combination of Carvalho in view of Anderson teaches the aircraft-based SyAD system of Claim 14. Carvalho further teaches the encoded instructions further cause the SyAD system to: determine, via a measurement covariance matrix of the second stage, a suitability of the first stage with respect to the flight envelope based on one or more of the plurality of absolute aircraft parameters, the plurality of inertial aircraft parameters, or the plurality of aircraft component parameters (par. 81, “Another kind of preprocessing (28) (33) associated with weight and center of gravity estimations is related to some interlocks that might freeze the estimations if the aircraft has assumed abnormal attitudes. They could be formed by thresholds defining a flight envelope outside of which the estimations would return big errors”).
Both Carvalho and Anderson fail to teach to adjust the at least one non-linear Kalman filter based on the determined suitability.
However, Sly teaches to adjust the at least one non-linear Kalman filter based on the determined suitability (column 14 line 6, “The omnisource adaptive Kalman filtering algorithm provided by the present application dynamically adjusts the weight of each navigation source according to the error covariance matrix output by the Kalman filter, thus ensuring the reliability of the positioning result”).
Carvalho and Fang are analogous art because both relate to collecting parameters to estimate data in an aircraft.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Carvalho in view of Anderson to incorporate the teachings of Fang. Fang states that doing so would optimize the algorithm and lead to “generating the optimal fusion factor for each navigation source and improving the performance and reliability of the whole navigation system” (column 14 line 6). Adjusting the Kalman filter gain depending on reliability is already well known in the art and can be seen in many air data systems.
Conclusion
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 MINATO LEE HORNER whose telephone number is (571)272-5425. The examiner can normally be reached M-F 8-5.
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, Christian Chace can be reached at (571) 272-4190. 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.
/M.L.H./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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