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 .
DETAILED ACTION
Applicant is advised that the new art unit number is 2692. Please use the new art unit number for all future communications.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/25/2024, 10/30/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 15, 18, 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Farahanisamani et al. (US 2023/0197048).
Regarding claim 15, Farahanisamani discloses a system comprising:
a vehicle audio system including at least one transducer (speaker 16) for providing an audio output to a user in a vehicle (¶ 0047 and see Fig. 1);
a vehicle sensor system (10a-10c, 24a-24b, and 40) for obtaining sensor inputs in the vehicle (¶ 0058, 00472, 0044 and see Fig. 1); and
a machine learning (ML) based road noise cancelation (RNC) system (30, which includes neural network circuit 70, ¶ 0061) connected with the vehicle audio system and the vehicle sensor system (see Figs. 1 and 2), the ML based RNC system configured to:
receive inputs from the vehicle audio system and the vehicle sensor system (¶ 0061 and see Fig. 2);
apply a set of parameters (filter coefficients) defining noise cancelation signals based on the inputs (¶ 0054 and see Fig. 2); and
generate noise cancelation signals for output by the at least one transducer based on the applied set of parameters (¶ 0055 and see Fig. 2).
Regarding claim 18, Farahanisamani discloses the system of claim 15, wherein the at least one transducer is a near-field (NF) transducer proximate a passenger of the vehicle (Fig. 1: see location of 16).
Regarding claim 19, Farahanisamani discloses the system of claim 15, wherein the ML based RNC system is configured to be updated based on the generated road noise cancelation signals (¶ 0054, 0064: 48 is adaptive, and thus updates).
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.
Claim(s) 8, 10-12, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Farahanisamani in view of Bastyr (US 2023/0085506).
Regarding claim 8, Farahanisamani discloses a method of running a machine learning (ML) based road noise cancelation (RNC) system (30, which includes neural network circuit 70, ¶ 0061) in a vehicle (Abstract), the method comprising:
providing inputs to the ML based RNC system, the inputs obtained from: at least one transducer (40), an accelerometer (¶ 0049), a set of cabin microphones in the vehicle (any of 10a-10c and 24a-24b),
applying a set of parameters (filter coefficients) defining noise cancelation signals in the ML based RNC system based on the inputs (¶ 0054 and see Fig. 2); and
generating noise cancelation signals for output by the at least one transducer based on the applied set of parameters (¶ 0055 and see Fig. 2).
Farahanisamani is not relied upon to disclose the inputs obtained from a controller area network (CAN) bus.
In a similar field of endeavor, Bastyr discloses inputting a speed (via 342) into a noise cancellation system (¶ 0037) via a CAN bus (¶ 0051).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to: inputting a speed into the noise cancellation system of Farahanisamani via a CAN bus,
which would result in: the inputs obtained from a controller area network (CAN) bus,
the motivation being to reduce sound amplitude of engine orders (Bastyr - ¶ 0038-0039).
Regarding claim 10, Farahanisamani-Bastyr discloses the method of claim 8, and Farahanisamani discloses wherein the at least one transducer is a near-field (NF) transducer proximate a passenger of the vehicle (Fig. 1: see location of 16).
Regarding claim 11, Farahanisamani-Bastyr discloses the method of claim 8, and Bastyr discloses wherein the inputs from the CAN bus include at least one vehicle input including: revolutions per minute (RPM) of the drive system, speed, torque, throttle, braking, positioning, steering angle, temperature, pressure, seat position, user position, or seat occupancy (¶ 0037: speed).
The teachings of Bastyr relied upon above are combinable with Farahanisamani-Bastyr for the same reasons set forth above in the claim 8 rejection.
Regarding claim 12, Farahanisamani-Bastyr discloses the method of claim 8, and Farahanisamani discloses further comprising updating the machine learning (ML) based road noise cancelation (RNC) system based on the generated road noise cancelation signals (¶ 0054, 0064: 48 is adaptive, and thus updates).
Regarding claim 16, Farahanisamani discloses the system of claim 15, wherein the inputs are received from the at least one transducer (40) and the sensor system, the inputs from the sensor system including inputs from: an accelerometer (¶ 0049), a set of cabin microphones in the vehicle (any of 10a-10c and 24a-24b),
Farahanisamani is not relied upon to disclose the inputs from the sensor system including a controller area network (CAN) bus,
wherein the inputs from the CAN bus include at least one vehicle input including:
revolutions per minute (RPM) of the drive system, speed, torque, throttle, braking, positioning, steering angle, temperature, pressure, seat position, user position, or seat occupancy.
In a similar field of endeavor, Bastyr discloses inputting a speed (via 342) into a noise cancellation system (¶ 0037), the input including a CAN bus (¶ 0051).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to: inputting a speed into the noise cancellation system of Farahanisamani, the input including a CAN bus,
which would result in:
the inputs from the sensor system including a controller area network (CAN) bus,
wherein the inputs from the CAN bus include at least one vehicle input including:
revolutions per minute (RPM) of the drive system, speed, torque, throttle, braking, positioning, steering angle, temperature, pressure, seat position, user position, or seat occupancy,
the motivation being to reduce sound amplitude of engine orders (Bastyr - ¶ 0038-0039).
Claim(s) 1-6, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Farahanisamani in view of Bastyr in view of Aval et al. (US 2022/0208168).
Regarding claim 1, Farahanisamani discloses a method of training a machine learning (ML) based road noise cancelation (RNC) system (30, which includes neural network circuit 70, ¶ 0061) for a vehicle (Abstract), the method comprising:
providing inputs to the ML based RNC system, the inputs obtained from:
adapting a set of parameters (filter coefficients) defining noise cancelation signals in the ML based RNC system based on the inputs (¶ 0054 and see Fig. 2); and
generating noise cancelation signals for output by the at least one transducer based on the adapted set of parameters (¶ 0055 and see Fig. 2).
Farahanisamani is not relied upon to disclose the inputs obtained from a controller area network (CAN) bus.
In a similar field of endeavor, Bastyr discloses inputting a speed (via 342) into a noise cancellation system (¶ 0037) via a CAN bus (¶ 0051).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to: inputting a speed into the noise cancellation system of Farahanisamani via a CAN bus,
which would result in: the inputs obtained from a controller area network (CAN) bus,
the motivation being to reduce sound amplitude of engine orders (Bastyr - ¶ 0038-0039).
Farahanisamani-Bastyr is not relied upon to disclose the inputs obtained from a set of ear-mounted microphones on a user of the vehicle, wherein the inputs from the set of ear-mounted microphones on the user approximate detected road noise by the user.
In a similar field of endeavor, Aval discloses wherein an RNC system is trained using inputs from user-worn input microphones (140) that approximate road noise detected by a user's ears (¶ 0034, 0027: harmonics from rotating equipment) to create transfer functions to be used with cabin microphones (240) to estimate what a signal at an ear microphone would be if present (¶ 0040-0041 and see Fig. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to: the inputs obtained from a set of ear-mounted microphones on a user of the vehicle, wherein the inputs from the set of ear-mounted microphones on the user approximate detected road noise by the user, the motivation being to create transfer functions to be used with the cabin microphones (of Farahanisamani) to estimate what a signal at an ear microphone would be if present (Aval - ¶ 0040-0041).
Regarding claim 2, Farahanisamani-Bastyr-Aval discloses the method of claim 1, and Aval discloses wherein the ear-mounted microphones only provide inputs during the training (¶ 0040).
The teachings of Aval relied upon above are combinable with Farahanisamani-Bastyr-Aval for the same reasons set forth above in the claim 1 rejection.
Regarding claim 3, Farahanisamani-Bastyr-Aval discloses the method of claim 1, and Aval discloses wherein the ear-mounted microphones are located proximate an ear canal entrance of the user (¶ 0040),
wherein the inputs from the set of ear-mounted microphones on the user represent road noise as detected by the user at each ear (¶ 0040, 0034, 0027: harmonics from rotating equipment).
The teachings of Aval relied upon above are combinable with Farahanisamani-Bastyr-Aval for the same reasons set forth above in the claim 1 rejection.
Regarding claim 4, Farahanisamani-Bastyr-Aval discloses the method of claim 1, and Farahanisamani discloses wherein the at least one transducer is a near-field (NF) transducer proximate a passenger of the vehicle (Fig. 1: see location of 16).
Regarding claim 5, Farahanisamani-Bastyr-Aval discloses the method of claim 1, and Bastyr discloses wherein the inputs from the CAN bus include at least one vehicle input including: revolutions per minute (RPM) of the drive system, speed, torque, throttle, braking, positioning, steering angle, temperature, pressure, seat position, user position, or seat occupancy (¶ 0037: speed).
The teachings of Bastyr relied upon above are combinable with Farahanisamani-Bastyr for the same reasons set forth above in the claim 1 rejection.
Regarding claim 6, Farahanisamani-Bastyr-Aval discloses the method of claim 1, and Farahanisamani discloses further comprising updating the machine learning (ML) based road noise cancelation (RNC) system based on the generated road noise cancelation signals (¶ 0054, 0064: 48 is adaptive, and thus updates).
Regarding claim 9, Farahanisamani-Bastyr discloses the method of claim 8.
Farahanisamani-Bastyr is not relied upon to disclose wherein the ML based RNC system is trained using inputs from user-worn input microphones that approximate road noise detected by a user's ears.
In a similar field of endeavor, Aval discloses wherein an RNC system is trained using inputs from user-worn input microphones (140) that approximate road noise detected by a user's ears (¶ 0034, 0027: harmonics from rotating equipment) to create transfer functions to be used with cabin microphones (240) to estimate what a signal at an ear microphone would be if present (¶ 0040-0041 and see Fig. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to: wherein the ML based RNC system is trained using inputs from user-worn input microphones that approximate road noise detected by a user's ears, the motivation being to create transfer functions to be used with the cabin microphones (of Farahanisamani) to estimate what a signal at an ear microphone would be if present (Aval - ¶ 0040-0041).
Allowable Subject Matter
Claims 7, 13, 14, 17, 20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
In regard to claim 7, the prior art of record alone or in combination fails to teach or suggest the following limitations of the claim in combination with the rest of the limitations of the claim:
“wherein the ML-based RNC system includes a model with a set of non-linear pathways defined as sequences of steps between distinct sets of parameters, and wherein steps between the distinct sets of parameters are alterable during the training,
wherein common input signals result in distinct noise cancelation signals for output based on changes in parameters during the training,
wherein during the training, each parameter is updated at every step based on the inputs, wherein updating of each parameter is based on a derivative of an error detected for each parameter, and
wherein after the training, the steps between the distinct sets of parameters are fixed.”
In regard to claim 13, the prior art of record alone or in combination fails to teach or suggest the following limitations of the claim in combination with the rest of the limitations of the claim:
“wherein the ML-based RNC system includes a model with a set of non-linear pathways defined as sequences of steps between distinct sets of parameters, wherein steps between the distinct sets of parameters are fixed during operation, wherein noise cancelation signals are deterministic of input signals based on the fixed sets of parameters.”
In regard to claim 14, the prior art of record alone or in combination fails to teach or suggest the following limitations of the claim in combination with the rest of the limitations of the claim:
“wherein the ML-based RNC system is configured for training before and after operation, wherein the ML-based RNC system has at least one distinction in a set of parameters in the training mode as compared with the operation mode.”
In regard to claim 17, the prior art of record alone or in combination fails to teach or suggest the following limitations of the claim in combination with the rest of the limitations of the claim:
“wherein the ML based RNC system is configured to run in a plurality of modes,
wherein the plurality of modes includes a training mode and an operational mode,
wherein in the training mode the ML based RNC system is trained using inputs from user-worn input microphones that approximate road noise detected by a user's ears,
wherein the training mode is configured to be run before at and after the operation mode, and
wherein the ML-based RNC system has at least one distinction in a set of parameters in the training mode as compared with the set of parameters in the operation mode.”
In regard to claim 20, the prior art of record alone or in combination fails to teach or suggest the following limitations of the claim in combination with the rest of the limitations of the claim:
“wherein the ML-based RNC system includes a model with a set of non-linear pathways defined as sequences of steps between distinct sets of parameters, and wherein steps between the distinct sets of parameters are fixed during an operation mode,
wherein noise cancelation signals are deterministic of input signals result based on the fixed sets of parameters.”
Conclusion
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/MARK FISCHER/Primary Examiner, Art Unit 2692
/CAROLYN R EDWARDS/Supervisory Patent Examiner, Art Unit 2692