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 .
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Status of the Claims
This is a Final Office Action in response to Applicant’s amendment of 18 December 2025. Claims 1-20 are pending and have been considered as follows.
Response to Amendment and/or Argument
Applicant’s amendments and/or arguments with respect to the Claim Rejection of Claims 1, 16 and 20 under 35 USC 103 as being unpatentable over Jaime in view of Moeller as set forth in the office action of 26 September 2025 have been considered and are NOT persuasive. Specifically, Applicant argues (Page 7 of Applicant’s Remarks filed on 12/18/2025) that the newly amended claim limitation (namely, “analyze the audio signal to determine a first speed associated with the vehicle based on a comparison of the audio signal to an acoustic signature of a first plurality of acoustic signatures, wherein each acoustic signature of the first plurality of acoustic signatures is associated with the first surface and includes acoustic characteristics generated by the first surface;”) overcomes prior arts and is therefore allowable.
The Examiner’s Response:
Examiner has carefully considered Applicant’s Amendment and/or Argument and respectfully disagrees. During the interview conducted on 18 December 2025, Applicant proposed amendments to independent claim 1, and it was discussed that the proposed amendments appeared to overcome the rejection under 35 U.S.C. 103 over Jaime in view of Moeller, pending further searches and consideration. Applicant subsequently amended the claims accordingly. However, upon further consideration of the claims as amended, the rejection of claims 1, 16 and 20 under 35 U.S.C. 103 over Jaime in view of Moeller is maintained. It is determined that prior art Jaime (EP 3865880 A1) continues to teach or render obvious the newly amended limitation “analyze the audio signal to determine a first speed associated with the vehicle based on a comparison of the audio signal to an acoustic signature of a first plurality of acoustic signatures, wherein each acoustic signature of the first plurality of acoustic signatures is associated with the first surface and includes acoustic characteristics generated by the first surface”. Specifically, Prior art Jaime discloses acquiring acoustic signals generated by airflow interacting with aircraft surfaces (see at least Fig. 1 [0005-0007]), and analyzing spectral and amplitude characteristics of those signals to determine aircraft flight conditions including airspeed (see at least Fig. 1-2. 4-5 [0005-0008, 0014]). Jaime further teaches that the acoustic characteristics vary as a function of airflow speed over the surface (see at least Fig. 2 & 4 [0014-0016, 0025-0027]: FIG. 4 is a graph depicting magnitude/frequency relations of acoustic waves detected at various airspeeds…Acoustic-magnitude/frequency relations 54A-54J depict the relation between the magnitude and frequency of acoustic waves detected by acoustic transducers 14A-14D (depicted in FIG. 1 ). Acoustic-magnitude/frequency relations 54A-54J correspond to acoustic waves detected when aircraft 10 (depicted in FIG. 1 ) is flying at airspeeds of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 0.97 Mach, respectively. Each of acoustic-magnitude/frequency relations 54A-54J were detected in a wind tunnel replicating aircraft 10 flying at an altitude of approximately 1000 feet above sea level… Controller 16 can then determine airspeed based on the received signal and on the acoustic waves detected by acoustic transducers 14A-14D. Controller 16 of aircraft 10 can perform, for example, a Fast Fourier Transform (FFT) of an acoustic wave detected by acoustic transducers 14A-14D. Controller 14 can the select one or more of the data points of the FFT corresponding to the frequency range that indicates airspeed of aircraft 10 (e.g., between 20 and 100kHz in the depicted graph) and that such variations are used to determine the aircraft’s speed. The disclosed relationship between surface generated acoustic characteristics and speed corresponds to a plurality of acoustic signatures associated with the surface, as different speeds necessarily produce distinguishable acoustic patterns. Comparing a measured audio signal to such known or learned acoustic behavior, as taught by Jaime, under the broadest reasonable interpretation, teaches the newly amended limitation of “analyze the audio signal to determine a first speed associated with the vehicle based on a comparison of the audio signal to an acoustic signature of a first plurality of acoustic signatures, wherein each acoustic signature of the first plurality of acoustic signatures is associated with the first surface and includes acoustic characteristics generated by the first surface”. Accordingly, the rejections of claims 1, 16 and 20 under 35 U.S.C. 103 is therefore made final.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 8-11, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jaime et al. (EP 3865880 A1 hereinafter Jaime) in view of Moeller (US 2012/0173191 A1).
Regarding claim 1 (similarly claims 16 and 20), Jaime teaches A vehicle (see at least Fig. 1-5) comprising:
a body; (see at least Fig. 1-5)
a first microphone positioned in the body and configured to receive sound generated by movement of a first surface of the body relative to a fluid (see at least Fig. 1-5 [0005-0058]: An aircraft equipped with a system for determining aircraft flying conditions based on acoustics signals caused by airflows. Acoustic transducers are mounted to exterior surfaces of an aircraft and each of acoustic transducers is configured to detect acoustic waves caused by the airflow wherein the acoustic transducers can be microphones including MEMS microphones, condenser microphones, lasers, or other acoustic-to-electric transducers. These acoustic waves (e.g., acoustic characteristic) detected are indicative of or intrinsic to an airstream condition proximate the acoustic transducer. Such airstream conditions include altitude and airspeed, for example.);
one or more processors coupled to the first microphone (see at least Fig. 1-5), wherein the one or more processors are configured to:
receive an audio signal corresponding to sound received by the first microphone; (see at least Fig. 1-5 [0005-0058]: Each of acoustic transducers is configured to detect acoustic waves caused by the airflow. As the airflow passes over each of the acoustic transducers, acoustic characteristics of (e.g., acoustic waves caused by) the airflow are detected by the acoustic transducers.)
analyze the audio signal to determine a first speed associated with the vehicle based on a comparison of the audio signal to an acoustic signature of a first plurality of acoustic signatures, wherein each acoustic signature of the first plurality of acoustic signatures is associated with the first surface and includes acoustic characteristics generated by the first surface; and (see at least Fig. 1-5 [0005-0058]: Acoustic transducers are mounted to exterior surface of aircraft are configured to detect acoustic waves caused by the airflow. As the airflow passes over each of the acoustic transducers, acoustics characteristics of (e.g., acoustic waves caused by) the airflow are detected by the acoustic transducer wherein the acoustics waves are indicative of an airstream conditions (e.g., airspeed and altitude) proximate to the acoustic transducers. The airspeed of the aircraft can be known by various manners. Controller 16 can then determine airspeed based on the received signal and on the acoustic waves detected by acoustic transducers 14A-14D. Controller 16 of aircraft 10 can perform, for example, a Fast Fourier Transform (FFT) of an acoustic wave detected by acoustic transducers 14A-14D. Controller 14 can the select one or more of the data points of the FFT corresponding to the frequency range that indicates airspeed of aircraft 10 (e.g., between 20 and 100kHz in the depicted graph). Fig. 4 shows that acoustic magnitude/frequency relations 54a-54j are analyzed to determine aircraft speed. Each curve corresponds to a distinct airspeed (0.1-0.97 Mach) and represents the acoustic characteristics generated by the specific aircraft surface, forming a plurality of distinguishable surface-specific acoustic signatures. The system determines air speed by comparing a measured acoustic signal to these stored/learned magnitude/frequency relations, and the curves are substantially separated between 20-100 khz, allowing reliable differentiation of airspeed. The specification further notes that similar graphs can be constructed for other altitudes, reinforcing that the analysis relies on comparing the audio signal to a plurality of surface generated acoustic signatures to determine speed of the aircraft.)
based at least on the first speed, cause performance of one or more vehicle management operations. (see at least Fig. 1-5 [0005-0058]: Aircraft interface may be used to communicate information processed by system 12 (e.g. alert signals) and an aircraft including aircraft conditions, flying conditions, and/or atmospheric conditions.)
it may be alleged that Jaime does not explicitly teach a first microphone positioned in an interior of the body and configured to receive sound generated by movement of a first surface of the body relative to a fluid;
Moeller is directed to system and method for determining a velocity relevant to a body using sound waves, Moeller teaches a first microphone positioned in an interior of the body and configured to receive sound generated by movement of a first surface of the body relative to a fluid; (see at least Fig. 1-5 [0002-0105]: Microphones are placed at different location on an aircraft body. The microphones may be mounted on the fuselage exterior or interior to the cabin. The microphone receive noises which is then converted into digital form. The correlation between the noise pattens is used to determine airspeed (i.e. velocity of the body with respect to the surrounding air.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Jaime aircraft to incorporate the technique of mounting the microphones on the interior to the cabin as taught by Moeller with reasonable expectation of success because mounting the microphone interior to the cabin would be preferable for the purposes of protecting the microphones (Moeller [0028]).
Regarding claim 2, the combination of Jaime in view of Moeller teaches The vehicle of claim 1, Jaime further teaches wherein the one or more processors are further configured to transform the audio signal to a frequency domain signal. (see at least Fig. 1-5).
Regarding claim 3, the combination of Jaime in view of Moeller teaches The vehicle of claim 2, Jaime further teaches wherein the one or more processors transform the audio signal to the frequency domain signal by application of Fourier transform to the audio signal. (see at least Fig. 1-5 [0005-0058]: As the airflow passes over each of the acoustic transducers, acoustics characteristics of (e.g., acoustic waves caused by) the airflow are detected by the acoustic transducer wherein the acoustics waves are indicative of an airstream conditions (e.g., airspeed and altitude) proximate to the acoustic transducers. The airspeed of the aircraft can be known by various manners. Controller 16 can then determine airspeed based on the received signal and on the acoustic waves detected by acoustic transducers 14A-14D. Controller 16 of aircraft 10 can perform, for example, a Fast Fourier Transform (FFT) of an acoustic wave detected by acoustic transducers 14A-14D. Controller 14 can the select one or more of the data points of the FFT corresponding to the frequency range that indicates airspeed of aircraft 10 (e.g., between 20 and 100kHz in the depicted graph).)
Regarding claim 8 (similarly claim 17), the combination of Jaime in view of Moeller teaches The vehicle of claim 1 (similarly claim 16), Jaime further teaches wherein the one or more processors analyzing the audio signals includes:
obtain information associated with a plurality of frequencies of the audio signal; (see at least Fig. 1-5)
analyze a frequency intensity level associated with each of the plurality of frequencies; (see at least Fig. 1-5 [0005-0058]: As the airflow passes over each of the acoustic transducers, acoustics characteristics of (e.g., acoustic waves caused by) the airflow are detected by the acoustic transducer wherein the acoustics waves are indicative of an airstream conditions (e.g., airspeed and altitude) proximate to the acoustic transducers. The airspeed of the aircraft can be known by various manners. Controller 16 can then determine airspeed based on the received signal and on the acoustic waves detected by acoustic transducers 14A-14D. Controller 16 of aircraft 10 can perform, for example, a Fast Fourier Transform (FFT) of an acoustic wave detected by acoustic transducers 14A-14D. Controller 14 can the select one or more of the data points of the FFT corresponding to the frequency range that indicates airspeed of aircraft 10 (e.g., between 20 and 100kHz in the depicted graph).) and
based at least on the frequency intensity levels associated with each of the plurality of frequencies, determined the first speed. (see at least Fig. 1-5 [0005-0058]: As the airflow passes over each of the acoustic transducers, acoustics characteristics of (e.g., acoustic waves caused by) the airflow are detected by the acoustic transducer wherein the acoustics waves are indicative of an airstream conditions (e.g., airspeed and altitude) proximate to the acoustic transducers. The airspeed of the aircraft can be known by various manners. Controller 16 can then determine airspeed based on the received signal and on the acoustic waves detected by acoustic transducers 14A-14D. Controller 16 of aircraft 10 can perform, for example, a Fast Fourier Transform (FFT) of an acoustic wave detected by acoustic transducers 14A-14D. Controller 14 can the select one or more of the data points of the FFT corresponding to the frequency range that indicates airspeed of aircraft 10 (e.g., between 20 and 100kHz in the depicted graph).)
Regarding claim 9, the combination of Jaime in view of Moeller teaches The vehicle of claim 1, Jaime further teaches wherein the vehicle comprises a land vehicle, a water vehicle, an aircraft (see at least Fig. 1), a spacecraft, or a combination thereof.
Regarding claim 10, the combination of Jaime in view of Moeller teaches The vehicle of claim 1,
Jaime further teaches wherein the one or more vehicle management operations comprises display of an indication of the first speed (see at least Fig. 1-5 [0005-0058]: Aircraft interface may be used to communicate information processed by system 12 (e.g. alert signals) and an aircraft including aircraft conditions, flying conditions, and/or atmospheric conditions.), generation of an excessive speed signal, generation of an insufficient speed alert signal, alteration of the first speed, or some combination thereof.
Regarding claim 11 (similarly claim 18), the combination of Jaime in view of Moeller teaches The vehicle of claim 1 (similarly claim 16), wherein the one or more processors are further configured to:
Jaime further teaches receive a second audio signal corresponding to a second sound from a second microphone positioned in the vehicle; wherein the second microphone is configured to receive second sound generated by the movement of a second surface of the body relative to the fluid(see at least Fig. 1-5 [0005-0058]: Each of acoustic transducers is configured to detect acoustic waves caused by the airflow. As the airflow passes over each of the acoustic transducers, acoustic characteristics of (e.g., acoustic waves caused by) the airflow are detected by the acoustic transducers.); and
analyze the audio signal to determine a first speed associated with the vehicle based on a second comparison of the second audio signal to an acoustic signature of a second plurality of acoustic signatures, wherein each acoustic signature of the second plurality of acoustic signatures is associated with the second surface and includes acoustic characteristics generated by the second surface; and (see at least Fig. 1-5 [0005-0058]: Acoustic transducers are mounted to exterior surface of aircraft are configured to detect acoustic waves caused by the airflow. As the airflow passes over each of the acoustic transducers, acoustics characteristics of (e.g., acoustic waves caused by) the airflow are detected by the acoustic transducer wherein the acoustics waves are indicative of an airstream conditions (e.g., airspeed and altitude) proximate to the acoustic transducers. The airspeed of the aircraft can be known by various manners. Controller 16 can then determine airspeed based on the received signal and on the acoustic waves detected by acoustic transducers 14A-14D. Controller 16 of aircraft 10 can perform, for example, a Fast Fourier Transform (FFT) of an acoustic wave detected by acoustic transducers 14A-14D. Controller 14 can the select one or more of the data points of the FFT corresponding to the frequency range that indicates airspeed of aircraft 10 (e.g., between 20 and 100kHz in the depicted graph). Fig. 4 shows that acoustic magnitude/frequency relations 54a-54j are analyzed to determine aircraft speed. Each curve corresponds to a distinct airspeed (0.1-0.97 Mach) and represents the acoustic characteristics generated by the specific aircraft surface, forming a plurality of distinguishable surface-specific acoustic signatures. The system determines air speed by comparing a measured acoustic signal to these stored/learned magnitude/frequency relations, and the curves are substantially separated between 20-100 khz, allowing reliable differentiation of airspeed. The specification further notes that similar graphs can be constructed for other altitudes, reinforcing that the analysis relies on comparing the audio signal to a plurality of surface generated acoustic signatures to determine speed of the aircraft.)
it may be alleged that Jaime does not explicitly teach a second microphone positioned in an interior of the vehicle; wherein the second microphone is configured to receive sound generated by movement of a second surface of the body relative to the fluid;
Moeller is directed to system and method for determining a velocity relevant to a body using sound waves, Moeller teaches a second microphone positioned in an interior of the vehicle; wherein the second microphone is configured to receive sound generated by movement of a second surface of the body relative to the fluid; (see at least Fig. 1-5 [0002-0105]: Microphones are placed at different location on an aircraft body. The microphones may be mounted on the fuselage exterior or interior to the cabin. The microphone receive noises which is then converted into digital form. The correlation between the noise pattens is used to determine airspeed (i.e. velocity of the body with respect to the surrounding air.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Jaime aircraft to incorporate the technique of mounting the microphones on the interior to the cabin as taught by Moeller with reasonable expectation of success because mounting the microphone interior to the cabin would be preferable for the purposes of protecting the microphones (Moeller [0028]).
Regarding claim 15, the combination of Jaime in view of Moeller teaches The vehicle of claim 1,
Jaime further teaches wherein the acoustic signature is based at least on a location of the first surface on the vehicle, skin friction associated with the first surface, a shape of the first surface, or a combination thereof. (see at least Fig. 1-5 [0005-0058]: System 12 can be configured in various manners so as to determine airspeed and/or altitude of aircraft 10. In the embodiment depicted in FIG. 1 , system 12 is a passive air data sensing system, which is configured to determine airspeed and/or altitude of aircraft 10 based, at least in part, on the detected acoustic waves caused by the airflow. System 12 can be in communication with other air data sensing systems of aircraft 10. These other air data sensing systems can provide system 12 with signals indicative of various air data metrics and/or flight metrics, which can be used by system 12 in conjunction with the acoustic waves detected for determination of airspeed and/or altitude of aircraft 10. Calibration of system 12 can be performed so as to accommodate acoustic variations resulting from the local airflow variations that occur at different locations of the aircraft.)
Claim(s) 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Jaime in view of Moeller and Karnofski et al. (US 2020/0408106 A1 hereinafter Karnofski).
Regarding claim 4, the combination of Jaime in view of Moeller teaches The vehicle of claim 1,
the combination of Jaime in view of Moeller does not explicitly teach wherein the one or more processors analyzing the audio signal provides the audio signal to a trained machine learning model that determines the first speed.
Karnofski is directed to health monitoring system for health monitoring of turbomachinery on the aircraft, Karnofski teaches wherein said analyze the audio signal provides the audio signal to a trained machine learning model that determines the first speed. (see at least Claims 1-21 Fig. 2A- 7 [0067-0193]: receiving signals in response to acoustic waves transmitted from the engine system, identifying characteristics of the acoustic waves and analyzing the signals from the acoustic sensor using a computer-implemented machine learning model that determines the health status of the engine or engine subsystem wherein the sensors measuring parameters include aircraft altitude and aircraft speed.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Jaime and Moeller to incorporate the technique of providing the audio signal to a trained machine learning model that determines the first speed as taught by Karnofski with reasonable expectation of success because acoustic signal analysis often requires expert interpretation and trained machine learning model can automate this process and therefore reducing the need for manual analysis and enables real-time decision in critical applications such as fault detection.
Regarding claim 5, the combination of Jaime in view of Moeller and Karnofski teaches The vehicle of claim 4,
the combination of Jaime in view of Moeller does not explicitly teach wherein the trained machine learning model is trained using training data associated with frequency domain transformations of a training data audio signal.
Karnofski is directed to health monitoring system for health monitoring of turbomachinery on the aircraft, Karnofski teaches wherein the trained machine learning model is trained using training data associated with frequency domain transformations of a training data audio signal. (see at least Claims 1-21 Fig. 2A- 7 [0067-0193]: training the computer-implemented machine learning model using the signals comprising training signals, including: obtaining the training signals during different operating conditions of the engine or the engine subsystem; obtaining at least one property of the training signals as a function of the different operating conditions, the at least one property selected from one or more frequencies of the acoustic waves and one or more amplitudes of the acoustic waves: the one or more frequencies including one or more first frequencies and one or more second frequencies, and the one or more amplitudes including one or more first amplitudes and one or more second amplitudes; determining a plurality of health statuses of the engine or the engine subsystem as a function of the different operating conditions, the health statuses including: a first health status associated with the engine comprising a healthy engine or the engine subsystem comprising a healthy engine subsystem, and a second health status associated with the engine comprising an unhealthy engine or the engine subsystem comprising an unhealthy engine subsystem, associating the one or more first frequencies, the one or more first amplitudes, or the one or more first frequencies and the one or more first amplitudes, with the first health status; and associating the one or more second frequencies, the one or more second amplitudes, or the one or more second frequencies and the one or more second amplitudes, with the second health status; so as to obtain the computer-implemented machine learning model comprising a trained computer-implemented machine learning model; and analyzing the signals comprising non-training signals different from the training signals using the trained computer-implemented machine learning model, so as to determine the health status of the engine or the engine subsystem transmitting the non-training signals.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Jaime and Moeller to incorporate the technique of training the machine learning model using training data associated with frequency domain transformations of a training data audio signal as taught by Karnofski with reasonable expectation of success because acoustic signal analysis often requires expert interpretation and trained machine learning model can automate this process and therefore reducing the need for manual analysis and enables real-time decision in critical applications such as fault detection.
Regarding claim 6, the combination of Jaime in view of Moeller and Karnofski teaches The vehicle of claim 4,
the combination of Jaime in view of Moeller does not explicitly teach wherein the trained machine learning model is tested using testing data associated with the frequency domain transformations of a testing data audio signal.
Karnofski is directed to health monitoring system for health monitoring of turbomachinery on the aircraft, Karnofski teaches wherein the trained machine learning model is tested using testing data associated with the frequency domain transformations of a testing data audio signal. (see at least Claims 1-21 Fig. 2A- 7 [0067-0193]: training the computer-implemented machine learning model using the signals comprising training signals, including: obtaining the training signals during different operating conditions of the engine or the engine subsystem; obtaining at least one property of the training signals as a function of the different operating conditions, the at least one property selected from one or more frequencies of the acoustic waves and one or more amplitudes of the acoustic waves: the one or more frequencies including one or more first frequencies and one or more second frequencies, and the one or more amplitudes including one or more first amplitudes and one or more second amplitudes; determining a plurality of health statuses of the engine or the engine subsystem as a function of the different operating conditions, the health statuses including: a first health status associated with the engine comprising a healthy engine or the engine subsystem comprising a healthy engine subsystem, and a second health status associated with the engine comprising an unhealthy engine or the engine subsystem comprising an unhealthy engine subsystem, associating the one or more first frequencies, the one or more first amplitudes, or the one or more first frequencies and the one or more first amplitudes, with the first health status; and associating the one or more second frequencies, the one or more second amplitudes, or the one or more second frequencies and the one or more second amplitudes, with the second health status; so as to obtain the computer-implemented machine learning model comprising a trained computer-implemented machine learning model; and analyzing the signals comprising non-training signals different from the training signals using the trained computer-implemented machine learning model, so as to determine the health status of the engine or the engine subsystem transmitting the non-training signals. Wherein the analyzing comprises comparing the training signals and the non-training signals, wherein the health status is healthy if the one or more frequencies and/or the one or more amplitudes of the training signals are in a range encompassing the first frequencies and/or the first amplitudes. )
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Jaime and Moeller to incorporate the technique of testing a machine learning model using testing data associated with the frequency domain transformations of a testing data audio signal as taught by Karnofski with reasonable expectation of success because acoustic signal analysis often requires expert interpretation and trained machine learning model can automate this process and therefore reducing the need for manual analysis and enables real-time decision in critical applications such as fault detection.
Regarding claim 7, the combination of Jaime in view of Moeller and Karnofski teaches The vehicle of claim 4,
the combination of Jaime in view of Moeller does not explicitly teach wherein the trained machine learning model is trained using training data with values of the first speed determined by a sensor of the vehicle.
Karnofski is directed to health monitoring system for health monitoring of turbomachinery on the aircraft, Karnofski teaches wherein the trained machine learning model is trained using training data with values of the first speed determined by a sensor of the vehicle. (see at least Claims 1-21 Fig. 2A- 7 [0067-0193]: training the computer-implemented machine learning model using the signals comprising training signals, including: obtaining the training signals during different operating conditions of the engine or the engine subsystem; obtaining at least one property of the training signals as a function of the different operating conditions, the at least one property selected from one or more frequencies of the acoustic waves and one or more amplitudes of the acoustic waves: the one or more frequencies including one or more first frequencies and one or more second frequencies, and the one or more amplitudes including one or more first amplitudes and one or more second amplitudes; determining a plurality of health statuses of the engine or the engine subsystem as a function of the different operating conditions, the health statuses including: a first health status associated with the engine comprising a healthy engine or the engine subsystem comprising a healthy engine subsystem, and a second health status associated with the engine comprising an unhealthy engine or the engine subsystem comprising an unhealthy engine subsystem, associating the one or more first frequencies, the one or more first amplitudes, or the one or more first frequencies and the one or more first amplitudes, with the first health status; and associating the one or more second frequencies, the one or more second amplitudes, or the one or more second frequencies and the one or more second amplitudes, with the second health status; so as to obtain the computer-implemented machine learning model comprising a trained computer-implemented machine learning model; and analyzing the signals comprising non-training signals different from the training signals using the trained computer-implemented machine learning model, so as to determine the health status of the engine or the engine subsystem transmitting the non-training signals. Wherein the analyzing comprises comparing the training signals and the non-training signals, wherein the health status is healthy if the one or more frequencies and/or the one or more amplitudes of the training signals are in a range encompassing the first frequencies and/or the first amplitudes. )
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Jaime and Moeller to incorporate the technique of training the machine learning model using training data with values of the first speed determined by a sensor of the vehicle as taught by Karnofski with reasonable expectation of success because acoustic signal analysis often requires expert interpretation and trained machine learning model can automate this process and therefore reducing the need for manual analysis and enables real-time decision in critical applications such as fault detection.
Claim(s) 12, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jaime in view of Moeller and Sly et al. (US 2019/0346279 A1 hereinafter Sly).
Regarding claim 12 (similarly claim 19), the combination of Jaime in view of Moeller teaches The vehicle of claim 11 (similarly claim 18), wherein:
Jaime does not explicitly teach the first surface is located at a first side of the vehicle;
the second surface is located on a second side of the vehicle opposite to the first side; and
the one or more processors are further configured to determine a side slip measurement of the vehicle based on the first speed and the second speed.
Moeller is directed to system and method for determining a velocity relevant to a body using sound waves, Moeller teaches the first surface is located at a first side of the vehicle; (see at least Fig. 1-5)
the second surface is located on a second side of the vehicle opposite to the first side; (see at least Fig. 1-5) and
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Jaime’s aircraft to incorporate the technique of mounting the microphones on the interior to the cabin on opposite surface as taught by Moeller with reasonable expectation of success because mounting the microphone interior to the cabin would be preferable for the purposes of protecting the microphones (Moeller [0028]).
It may be alleged that the combination of Jaime in view of Moeller does not explicitly teach the one or more processors are further configured to determine a side slip measurement of the vehicle based on the first speed and the second speed.
Sly is directed to system and method for generating aircraft air data parameters based on various sensors position on the aircraft, Sly teaches the one or more processors are further configured to determine a side slip measurement of the vehicle based on the first speed and the second speed. (see at least [0047-0056]: The acoustic processing unit is configured to calculate the angle of sideslip, angle of attack, an airspeed and an air temperature of the aircraft based on the first and second sensed data.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Jaime and Moeller to incorporate the technique of determining a side slip measurement of the vehicle based on the first speed and the second speed as taught by Sly with reasonable expectation of success to provide redundancy, independence and dissimilarity of air data outputs to reduce the risk of common mode errors occurring amongst the separate sources of air data (Sly [0003]).
Regarding claim 14, the combination of Jaime in view of Moeller teaches The vehicle of claim 11, wherein:
The combination of Jaime and Moeller does not explicitly teach the first surface is located at a first side of the vehicle;
the second surface is located on a second side of the vehicle opposite to the first side; and
the one or more processors are further configured to determine a side slip measurement of the vehicle based on the first speed and the second speed.
Sly is directed to system and method for generating aircraft air data parameters based on various sensors position on the aircraft, Sly teaches the first surface is located on a bottom of the vehicle; (see at least Fig. 1-3 [0010-0056]: acoustic sensors may be positioned on the top and/or bottom of the aircraft)
the second surface is located on a top of the vehicle opposite the bottom; (see at least Fig. 1-3 [0010-0056]: acoustic sensors may be positioned on the top and/or bottom of the aircraft) and
the one or more processors are further configured to determine an angle of attack measurement based on the first speed and the second speed. (see at least [0047-0056]: The acoustic processing unit is configured to calculate the angle of sideslip, angle of attack, an airspeed and an air temperature of the aircraft based on the first and second sensed data.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Jaime and Moeller to incorporate the technique of determining an angle of attack measurement of the vehicle based on the first speed and the second speed as taught by Sly with reasonable expectation of success to provide redundancy, independence and dissimilarity of air data outputs to reduce the risk of common mode errors occurring amongst the separate sources of air data (Sly [0003]).
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Jaime in view of Moeller, Sly and Middleton (US 4814764 A).
Regarding claim 13, the combination of Jaime in view of Moeller and Sly teaches The vehicle of claim 12,
The combination of Jaime in view of Moeller and Sly does not explicitly teach wherein the vehicle managements comprise generating a side slip alert signa when the side slip measurement exceeds a threshold.
Middleton is directed system and method for warning of a high yaw condition in an aircraft, Middleton teaches wherein the vehicle managements comprise generating a side slip alert signal when the side slip measurement exceeds a threshold. (see at least Fig. 1-5 Col. 2 Line 30- Col. 6 Line 20: warning a pilot of an aircraft of an excessive yaw conditions based on measuring the presence of airflow differential at first and second locations of the aircraft.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Jaime, Moeller and Sly to incorporate the technique of warning a pilot when side slip angle exceeds a threshold as taught by Middleton with reasonable expectation of success so that the pilot is aware when the aircraft is approaching a yaw condition which may require maximum counteracting aileron during landing.
Conclusion
THIS ACTION IS MADE FINAL. 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 DANA F ARTIMEZ whose telephone number is (571)272-3410. The examiner can normally be reached M-F: 9:00 am-3:30 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris S. Almatrahi can be reached at (313) 446-4821. 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.
/DANA F ARTIMEZ/ Examiner, Art Unit 3667
/FARIS S ALMATRAHI/ Supervisory Patent Examiner, Art Unit 3667