DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 of U.S. Application No. 18/392,371 filed on 12/21/2023 were examined. Examiner filed a non-final office action on 06/18/2028.
Applicant filed remarks and amendments on 09/17/2025. Claims 1, 4-5, 8, 10, 15 and 17-18 were amended. Claim 9 was cancelled. Claims 1-2, 5-7, 11-16 and 19-20 are presented and pending examination.
Response to Arguments
Regarding the claim rejections under 35 USC 101: Applicant's arguments filed 09/17/2025 have been fully considered and they are persuasive. The previously given claim interpretations under 35 USC 101 are withdrawn.
Regarding the claim rejections under 35 USC 103: Applicant's arguments filed 09/17/2025 with respect to Yoo (US20210031725A1) in view of Schneider et al. (US20230206942A1) have been fully considered but they are not persuasive.
Regarding claims 1, 5, and 15, applicant argues that, “independent claims 1, 5, and 15 (as amended) are patentably distinguished from the combination of Yoo in view of Schneider, asserting that the amendments—particularly the addition of “controlling, based at least in part on the determining that the environment is outside of the approved ODD, movement of the vehicle”.
However, this argument is not persuasive, Yoo discloses (“The processor may transmit a rainfall step determination result to a wiper controller to control a movement of a wiper.” [0021]). Yoo teaches controlling different components of the vehicle based on outside environmental conditions.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-7, 11-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo (US20210031725A1) in view of Schneider et al. (US20230206942A1), hereinafter referred to as Yoo and Schneider respectively.
Regarding claims 1, 5, 14, 15 and 20, Yoo discloses A vehicle comprising:
a microphone (“the rain sensor includes a microphone” [0009]);
one or more processors (“and a processor that determines precipitation” [0011]);
and one or more non-transitory computer-readable media storing instructions (“The memory (not illustrated) may be implemented with at least one of storage media such a flash memory, a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Electrically Erasable and Programmable ROM (EEPROM), an Erasable and Programmable ROM (EPROM), a register, and the like.” [0044]) which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving an audio signal from the microphone (“The processor 130 receives sensing signals” [0044]);
estimating, based at least in part on the determined number of raindrops, a characteristic associated with an environment of the vehicle (“the processor 130 may estimate (measure) the amount and speed of raindrops in view of strength and frequency characteristics of the sound signal.” [0046]);
determining, based at least in part on the estimated characteristic, that the environment is outside of an approved operational design domain (ODD) for the vehicle (“determining, by the rain sensor, a rainfall step based on at least one of the sound signal or the detected capacitance change; and controlling, by a wiper controller, a movement of the wiper according to the rainfall step.” [0023]);
and controlling, based at least in part on the determining that the environment is outside of the approved ODD, movement of the vehicle (“The processor may transmit a rainfall step determination result to a wiper controller to control a movement of a wiper.” [0021])
Yoo does not explicitly teach applying a short-time Fourier transform (STFT) to the received audio signal to generate a spectrogram of the received audio signal
processing, using a convolutional neural network (CNN), at least part of the generated spectrogram to determine a number of raindrops striking the vehicle in a vicinity of the microphone within an interval
However, Schneider does teach applying a short-time Fourier transform (STFT) to the received audio signal to generate a spectrogram of the received audio signal (“In some embodiments, the 2D representation of the vibration waveform 1239 is generated by a log STFT operation 1235 and a vibrational waveform 1232.” [0510]);
processing, using a convolutional neural network (CNN), at least part of the generated spectrogram to determine a number of raindrops striking the vehicle in a vicinity of the microphone within an interval (“An unprocessed vibration waveform 1240 may be retained for processing by the vibration fusion convolutional neural network.” [0510]). Both Yoo and Schneider teach methods for determining vehicle environment characteristics. However, Schneider explicitly teaches applying a short-time Fourier transform (STFT) to the received audio signal to generate a spectrogram of the received audio signal, processing, using a convolutional neural network (CNN), at least part of the generated spectrogram to determine a number of raindrops striking the vehicle in a vicinity of the microphone within an interval.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the monitoring and classification method of Yoo to also include applying a short-time Fourier transform (STFT) to the received audio signal to generate a spectrogram of the received audio signal, processing, using a convolutional neural network (CNN), at least part of the generated spectrogram to determine a number of raindrops striking the vehicle in a vicinity of the microphone within an interval, as taught by Schneider, with a reasonable expectation of success. Doing so improves the detection of vehicle environment events (With regard to this reasoning, see at least [Schneider, 0004 - 0007]).
Regarding claims 2, 6 and 16, Yoo discloses The vehicle of claim 1,
and the estimating of the characteristic comprises estimating, using the data indicative of the speed of the vehicle and the determined number of raindrops striking the vehicle in within the interval, the absolute rain rate (“The processor may determine a rainfall step by estimating a size, an amount, and a speed of a raindrop by analyzing the capacitance change.” [0020])
Yoo does not explicitly teach wherein: the operations comprise obtaining data indicative of a speed of the vehicle within the interval;
However, Schneider does teach wherein: the operations comprise obtaining data indicative of a speed of the vehicle within the interval;(“ In some embodiments, vehicle data such as audio data and engine vibration data may be recorded by the user’s mobile device or a separate device, such as an MVDD. Then, engine data streams, such as rpms, voltage, and system temperature, may be obtained from the OBDII port by the user’s mobile device or the MVDD.” [0562]). Both Yoo and Schneider teach methods for determining vehicle environment characteristics. However, Schneider explicitly teaches the operations comprise obtaining data indicative of a speed of the vehicle within the interval.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the monitoring and classification method of Yoo to also include the operations comprise obtaining data indicative of a speed of the vehicle within the interval, as taught by Schneider, with a reasonable expectation of success. Doing so improves the detection of vehicle environment events (With regard to this reasoning, see at least [Schneider, 0004 - 0007]).
Regarding claims 7, 12, 13 and 19, Yoo discloses The method of claim 5,
Yoo does not explicitly teach comprising obtaining data indicative of an environmental condition, wherein: the processing comprises processing, using the neural network, the data indicative of the environmental condition together with the audio feature
and the environmental condition comprises any of wind velocity, humidity, air temperature, or air pressure
However, Schneider does teach comprising obtaining data indicative of an environmental condition, wherein: the processing comprises processing, using the neural network, the data indicative of the environmental condition together with the audio feature (“instructions that when executed by the at least one computer hardware processor perform a method for using a trained machine learning (ML) model to detect presence of environmental noise in audio acquired at least in part during operation of an engine of a vehicle, the method comprising: obtaining a first audio recording that was acquired, using at least one acoustic sensor, at least in part during operation of the engine; and processing the first audio recording, using the trained ML model, to detect the presence of environmental noise in the first audio recording, the processing comprising: generating an audio waveform from the first audio recording, and processing the audio waveform using the trained ML model to obtain output indicating whether environmental noise was present in the first audio recording.” See at least [0110]);
and the environmental condition comprises any of wind velocity, humidity, air temperature, or air pressure (“In some embodiments, the environmental noise includes one or more types of noise selected from the group consisting of: rain noise, water flow noise, wind noise, human speech, sound generated by a device not attached to vehicle, sound generated by one or more vehicles different from the vehicle.” [0115]). Both Yoo and Schneider teach methods for determining vehicle environment characteristics. However, Schneider explicitly teaches the operations comprise obtaining data indicative of a speed of the vehicle within the interval.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the monitoring and classification method of Yoo to also include the operations comprise obtaining data indicative of a speed of the vehicle within the interval, as taught by Schneider, with a reasonable expectation of success. Doing so improves the detection of vehicle environment events (With regard to this reasoning, see at least [Schneider, 0004 - 0007]).
Regarding claim 11, Yoo discloses The method of claim 5,
Yoo does not explicitly teach wherein the neural network is a fine-tuned neural network, the method comprising: obtaining a set of base layers of a pre-trained neural network, wherein the pre-trained neural network has been trained to detect audio events
adding a network head to the set of base layers to obtain an intermediate neural network
and training the intermediate neural network using data comprising training audio features with respective labels indicating respective characteristics associated with the training audio features, thereby to obtain the fine-tuned neural network
However, Schneider does teach wherein the neural network is a fine-tuned neural network, the method comprising: obtaining a set of base layers of a pre-trained neural network, wherein the pre-trained neural network has been trained to detect audio events (“Some embodiments provide for a method for using a trained machine learning (ML) model to detect presence of vehicle defects from audio acquired at least in part during operation” [0004]);
adding a network head to the set of base layers to obtain an intermediate neural network (“a first neural network portion comprising a plurality of one-dimensional (1D) convolutional layers configured to process the audio waveform” [0019]);
and training the intermediate neural network using data comprising training audio features with respective labels indicating respective characteristics associated with the training audio features, thereby to obtain the fine-tuned neural network (“he training data may include audio, vibration, and/or any other types of signals acquired by MVDD sensors from vehicles.” [0288]). Both Yoo and Schneider teach methods for determining vehicle environment characteristics. However, Schneider explicitly teaches the operations comprise obtaining data indicative of a speed of the vehicle within the interval.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the monitoring and classification method of Yoo to also include the operations comprise obtaining data indicative of a speed of the vehicle within the interval, as taught by Schneider, with a reasonable expectation of success. Doing so improves the detection of vehicle environment events (With regard to this reasoning, see at least [Schneider, 0004 - 0007]).
Claims 3, 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Schneider and in further view of Smith et al . (US20200066257A1), hereinafter referred to as Yoo, Schneider and Smith respectively.
Regarding claims 3, 8 and 17, Yoo in view of Schneider discloses The vehicle of claim 1, wherein: the microphone is a first microphone disposed at a first position on the vehicle (“The first sensor may include a Micro Electro-Mechanical Systems (MEMS) microphone.” [0012]);
the audio signal is a first audio signal (a first sensor that is disposed on a first surface of the substrate and that senses a sound signal” [0011]);
Yoo in view of Schneider does not explicitly teach the operations comprise receiving a second audio signal from a second microphone disposed at a second position on the vehicle
and the estimating of the characteristic is further based on the second audio signal
However, Smith does teach the operations comprise receiving a second audio signal from a second microphone disposed at a second position on the vehicle (“the second microphone configured to output a second audio signal, whereby a difference in the audio signal output by the microphone disposed in the housing and the second audio signal is used to improve the accuracy of event classification.” [0032]);
and the estimating of the characteristic is further based on the second audio signal (“the second audio signal is used to improve the accuracy of event classification.” [0032]). Both Yoo in view of Schneider and Smith teach methods for determining vehicle environment characteristics. However, Smith explicitly teaches the operations comprise receiving a second audio signal from a second microphone disposed at a second position on the vehicle.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the monitoring and classification method of Yoo in view of Schneider to also include the operations comprise receiving a second audio signal from a second microphone disposed at a second position on the vehicle, as taught by Smith, with a reasonable expectation of success. Doing so improves the detection of vehicle environment events (With regard to this reasoning, see at least [Smith, 0004 - 0007]).
Claims 4, 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Schneider and in further view of Kawamoto (US11835954B2), hereinafter referred to as Yoo, Schneider and Kawamoto respectively.
Regarding claims 4, 10 and 18, Yoo in view of Schneider discloses The vehicle of claim 1,
Yoo in view of Schneider does not explicitly teach wherein the operations comprise suspending full autonomous driving operations based at least in part on determining that the environment is outside of the approved ODD.
However, Kawamoto does teach wherein the operations comprise suspending full autonomous driving operations based at least in part on determining that the environment is outside of the approved ODD (“In step S105, the autonomous driving control section 53 terminates autonomous driving mode to switch to manual driving mode.”[Col.22 ln 14-16]). Both Yoo in view of Schneider and Kawamoto teach methods for determining vehicle environment characteristics. However, Kawamoto explicitly teaches wherein the operations comprise suspending full autonomous driving operations based at least in part on determining that the environment is outside of the approved ODD.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the monitoring and classification method of Yoo in view of Schneider to also include wherein the operations comprise suspending full autonomous driving operations based at least in part on determining that the environment is outside of the approved ODD, as taught by Kawamoto, with a reasonable expectation of success. Doing so improves the detection of vehicle environment events (With regard to this reasoning, see at least [Kawamoto, Col.22]).
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 extension fee 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 AHMED ALKIRSH whose telephone number is (703) 756-4503. The examiner can normally be reached M-F 9:00 am-5:00 pm EST.
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AHMED ALKIRSHExaminer, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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