Detailed Action
This Office Action is in response to the Appeal Brief filed on 06/16/2026. Claims 1-20 are remaining pending.
In view of the Appeal Brief filed on 16 June 2026, PROSECUTION IS HEREBY REOPENED. A non-final office action is set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/ABDULLAH AL KAWSAR/ Supervisory Patent Examiner, Art Unit 2127
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 9th, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "utilizes sound information and accelerometer data" in line 2. It is unclear whether the ‘sound information’ and ‘accelerometer data’ are newly collected data different from the ‘sound information’ and ‘accelerometer data’ recited in claim 1.
For purpose of examination, the examiner interprets the limitation to mean: Sound information and accelerometer data are the sound information and the accelerometer data recited in claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2 and 4-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1: Claim 1 recites a computer implemented method. Therefore, it is directed to the statutory category of Processes. Claims 2 and 4-9 depend from claim 1. Therefore, they are directed to the statutory category of Processes.
2A Prong 1:
generating a training data set utilizing at least the vibrational information and a sound perception score associated with the vibrational information in a lab environment,
based on in an end of line (EOL) environment. (a mental process of evaluation – scoring the characteristics based on given information)
2A Prong 2:
A computer-implemented method, comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
receiving sound information from one or more microphones and vibrational data from one or more accelerometers associated with an electrical drive of a device, wherein the sound information is associated with the device; (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
wherein the training data set is sent to an un-trained machine learning model, (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics) wherein the sound perception score is automatically programmed and the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic machine learning training process to apply an exception)
receiving real-time vibrational information from the electrical drive associated with the device; and (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
based on the trained machine learning model, outputting a score (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity, combination of generic computer functions that are restricted to field of use are implemented to perform the disclosed abstract idea above.
2B:
A computer-implemented method, comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
receiving sound information from one or more microphones and vibrational data from one or more accelerometers associated with an electrical drive of a device, wherein the sound information is associated with the device; (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Thus, the limitation is re-evaluated as well understood, routine and conventional MPEP 2106.05(d)(II)(i) of transmitting and receiving data over a network Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
wherein the training data set is sent to an un-trained machine learning model, (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Thus, the limitation is re-evaluated as well understood, routine and conventional MPEP 2106.05(d)(II)(i) of transmitting data over a network Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) wherein the sound perception score is automatically programmed and the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic machine learning training process to apply an exception)
receiving real-time vibrational information from the electrical drive associated with the device; and (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Thus, the limitation is re-evaluated as well understood, routine and conventional MPEP 2106.05(d)(II)(i) of transmitting and receiving data over a network Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
based on the trained machine learning model, outputting a score (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions and usage of elements that are restricted to field of use that are implemented to perform the disclosed abstract idea above.
Regarding claim 2,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the trained machine learning model is trained utilizing only the vibrational information and minimizing a score prediction error output by the un-trained machine learning model. (mere instructions to apply an exception using a generic computer MPEP 2106.05(f) – generic training process of a machine learning model)
2B: wherein the trained machine learning model is trained utilizing only the vibrational information and minimizing a score prediction error output by the un-trained machine learning model. (mere instructions to apply an exception using a generic computer MPEP 2106.05(f) – generic training process of a machine learning model)
Regarding claim 4,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the training data utilizes sound information and accelerometer data obtained from a noise-free environment. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the training data utilizes sound information and accelerometer data obtained from a noise-free environment. (a field of use and technological environment MPEP 2106.05(h))
Regarding claim 5,
2A Prong 1: The computer-implemented method of claim 1, wherein the sound perception scored is generated manually in response to the sound information. (a mental process of evaluation – manually scoring the sound information can be performed in the human mind)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 6,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the vibrational information is accelerometer data. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the vibrational information is accelerometer data. (a field of use and technological environment MPEP 2106.05(h))
Regarding claim 7,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the machine learning model is a U-Net or Transformer network. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the machine learning model is a U-Net or Transformer network. (a field of use and technological environment MPEP 2106.05(h))
Regarding claim 8,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the real-time sound perception score is generated utilizing only the real-time vibrational information. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the real-time sound perception score is generated utilizing only the real-time vibrational information. (a field of use and technological environment MPEP 2106.05(h))
Regarding claim 9,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the machine learning model is a deep learning network. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the machine learning model is a deep learning network. (a field of use and technological environment MPEP 2106.05(h))
Claim 10 is a method claim which recites the same feature as claim 1 and rejected for at least the same reasons. Additional limitations not addressed in claim 1 are addressed below.
Step 1: Claim 10 recites a computer-implemented method. Therefore, it is directed to the statutory category of Processes.
2A Prong 1: Similar to claim 1
2A Prong 2: A computer-implemented method, comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
receiving a first set of sound information from one or more microphones and a first set of vibrational information from one or more accelerometers associated with an electrical drive of a device in a first environment; (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
receiving real-time vibrational information from the electrical device associated with the device in a second environment; and (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
2B: A computer-implemented method, comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
receiving a first set of sound information from one or more microphones and a first set of vibrational information from one or more accelerometers associated with an electrical drive of a device in a first environment; (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Thus, the limitation is re-evaluated as well understood, routine and conventional MPEP 2106.05(d)(II)(i) of transmitting and receiving data over a network Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
receiving real-time vibrational information from the electrical device associated with the device in a second environment; and (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Thus, the limitation is re-evaluated as well understood, routine and conventional MPEP 2106.05(d)(II)(i) of transmitting and receiving data over a network Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Regarding claim 11,
2A Prong 1: Incorporates the rejection of claim 10.
2A Prong 2: wherein the vibrational data includes accelerometer data. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the vibrational data includes accelerometer data. (a field of use and technological environment MPEP 2106.05(h))
Claim 12 is a method claim which recites the same feature as claim 7 and rejected for at least the same reasons.
Claim 13 is a method claim which recites the same feature as claim 8 and rejected for at least the same reasons.
Regarding claim 14 is a method claim which recites the same feature as claim 9 and rejected for at least the same reasons.
Regarding claim 15,
2A Prong 1: Incorporates the rejection of claim 10.
2A Prong 2: wherein the first environment is a laboratory environment and the second environment is an end-of-line factory environment. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the first environment is a laboratory environment and the second environment is an end-of-line factory environment. (a field of use and technological environment MPEP 2106.05(h))
Claim 16 is a system claim which recites similar features as claim 10 and rejected for at least the same reasons. Additional limitations not addressed in claim 10 are addressed below.
Step 1: Claim 16 recites a system comprising: a processor. Therefore, it is directed to the statutory category of a machine. Claims 17-20 depend from claim 16. Therefore, they are directed to the statutory category of a machine.
2A Prong 1: Similar to claim 10.
2A Prong 2: A system, comprising: a processor, wherein the processor is programmed to: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: A system, comprising: a processor, wherein the processor is programmed to: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 17,
2A Prong 1: Incorporates the rejection of claim 16.
2A Prong 2: wherein the vibrational information includes three- dimensional information. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the vibrational information includes three- dimensional information. (a field of use and technological environment MPEP 2106.05(h))
Regarding claim 18,
2A Prong 1:
2A Prong 2: wherein the processor is further programmed to generate (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein the processor is further programmed to generate (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 19,
2A Prong 1: Incorporates the rejection of claim 16.
2A Prong 2: wherein machine learning model includes two or more neural networks utilized to output a real-time sound perception score. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – using a model to generate sound perception score)
2B: wherein machine learning model includes two or more neural networks utilized to output a real-time sound perception score. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – using a model to generate sound perception score)
Regarding claim 20,
2A Prong 1: Incorporates the rejection of claim 16.
2A Prong 2: wherein the first environment and the second environment are not a same environment. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the first environment and the second environment are not a same environment. (a field of use and technological environment MPEP 2106.05(h))
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, 4-6, 8-11, 13-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“Novel method for identifying and diagnosing electric vehicle shock absorber squeak noise based on a DNN”, 2019, hereinafter ‘Huang’) in view of Mikhailov & Vladimirovish (US 20200211544 A1, hereinafter ‘Mikhailov’) and further in view of Krishnaveni et al. (Krishnaveni et al. “Analysis and control of the motor vibration using arduino and machine learning model”, 2021, hereinafter ‘Krishnaveni’).
Regarding claim 1, Huang teaches:
A computer-implemented method, comprising: ([Huang, page 442, lines 2-7] and [page 444, 3.2.1. Vibration signal collection, last 2 lines] shows that the method is implemented using a computer)
receiving sound information from one or more microphones and vibrational data from one or more accelerometers associated with an electrical drive of a device, wherein the sound information is associated with the device; ([Huang, page 442, lines 4-8] shows that the artificial head is the microphone that collects interior sounds of the EV. [page 444, 3.2.1. Vibration signal collection] discloses collecting vibration signal (i.e., vibrational data) using an accelerometer placed on the piston rod to measure its vertical acceleration. [3.2.2. Vibration signal preprocessing] discloses using the collected vibrational signal of the piston rod for identifying the squeak noise of the shock absorber through the DNN model)
generating a training data set utilizing at least the vibrational information and a sound perception score associated with the vibrational information in a lab environment, wherein the training data set is sent to an un-trained machine learning model, wherein the sound perception score is automatically programmed and the training data set includes accelerometer data ([Huang, Fig. 7], [page 449, (2) Data acquisition] The vibration time signal of the shock absorber piston rod are collected as the model input. The raw input samples are preprocessed to reduce redundancy for the classification process (automatically programmed). One hundred and fifty samples in the dataset are randomly selected as the training set (generating the training set). Each input dataset is normalized to the range [0, 1]. [page 442, 3.1.2. Subjective evaluation of the interior squeak noise, line 1 – page 443, line 10] and [page 444, 3.2.2. Vibration signal preprocessing, line 1 – page 445, line 10] show that the vibration time signals of the piston rod includes SA grades (subjective annoyance grade) which are collected in the laboratory conditions included a temperature of 24C and low background noise of 35dB)
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model; ([Huang, page 450, (5) DNN identification] The DNN is trained and employed (output) to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. [page 448, equation (7) and the paragraph below the equation] and [page 452, lines 6-10] collectively show the training error calculated based on the equation and minimizing the local minima of the loss and converging training errors of the DNN)
receiving electrical drive associated with the device; and ([Huang, page 444, 3.2. Bench test of the shock absorber, lines 1-6] Sixty shock absorber excitation samples measured during the electric vehicle road test are used for the experiment to collect vibration signals)
based on the trained machine learning model and the in an end of line (EOL) environment. ([Huang, page 450, (5) DNN identification] and [page 454, 4.3. DNN diagnosis and validation] The DNN is trained and employed to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. Test data can be used to diagnose the performance of the model. [page 451, lines 20-24] discloses that the method can help automobile companies and component companies specifically diagnose this noise and balance the cost and noise quality of shock absorbers in mass production environment)
However, Huang does not specifically disclose:
… the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
receiving real-time vibrational information from the … device;
based on the trained machine learning model and the real-time vibrational information, outputting a real-time
Mikhailov teaches:
receiving real-time vibrational information from the … device; ([0029] The VA 130 may record user audio for on-the-fly or subsequent analysis of user audio. The term ‘on-the-fly’ means ‘done in real time when the user is communicating with VA’. [0073] and [Claim 5] The speaker may collect vibration data and may be sent to the VA for segmentation)
based on the trained machine learning model and the real-time vibrational information, outputting a real-time (output) [Mikhailov, 0050] The audio software package 311 is trained to recognize audio characteristics. [0054] The software package performs speaker identification, speech segmentation, command identification to perform on-the-fly analysis of audio signal 120 (generating real-time output). [0073] and [Claim 5] The speaker may collect vibration data and may be sent to the VA for segmentation)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to apply the method of receiving and generating training vibrational data by Mikhailov to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the prediction model by leveraging more diverse data related to the received sounds and by optimizing the performance of the model by adjusting parameters [Mikhailov, 0057].
However, Huang in view of Mikhailov do not specifically disclose:
… the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
Krishnaveni teaches:
… the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information; ([Krishnaveni, page 2552, left col, 2. Proposed model, line 1-17] discloses that in training of machine learning model, motor signal is collected and stored in cloud, which is connected to the Arduino device, collected data (generated data in a lab environment) is used to train and test the model. [Krishnaveni, page 2553, left col, 2.4. MEMS accelerometer, line 1-14] discloses collecting X, Y, and Z axis data using the 3-axis accelerometer)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to apply the method of receiving vibrational data from accelerometer that includes x, y and z axis of Krishnaveni to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the prediction model by enabling the system to figure out more accurate and precise relationship between x axis vibration, y axis vibration, and z axis vibration and sound perception scores.
Regarding claim 2, Huang teaches:
The computer-implemented method of claim 1, wherein the trained machine learning model is trained utilizing only the vibrational information ([Huang, page 449, (2) Data acquisition, lines 1-3] shows that the vibration time signals of the shock absorber piston rod are collected as the model input, and the subjective evaluation of the shock absorber squeak noise is obtains as the output label, not the input)
Huang does not specifically disclose:
trained utilizing … minimizing a score prediction error output by the un-trained machine learning model.
Mikhailov teaches:
trained utilizing … minimizing a score prediction error output by the un-trained machine learning model. ([Mikhailov, 0079] If the error is below the threshold value (step 1040, NO), process 1000 may proceed to step 1010 of acquiring a next training audio signal (outputting machine learning model). If the error is above the threshold value (step 1040, YES), process 1000 may proceed to a step 1050 of modifying model parameters and subsequently returning to step 1020. In various embodiments, the machine learning model may be rated based on the average error generated by the model)
Regarding claim 4, Huang teaches:
wherein the training data utilizes sound information and accelerometer data obtained from a noise-free environment. ([Huang, page 443, lines 2-10] shows that the jury test (subjective sound evaluation test) is performed in the laboratory conditions included a temperature of 24C and low background noise of 35dB)
Regarding claim 5, Huang teaches:
The computer-implemented method of claim 1, wherein the sound perception scored is generated manually in response to the sound information. ([Huang, Fig. 7], [page 449, (2) Data acquisition] The vibration time signal of the shock absorber piston rod are collected as the model input, and the subjective evaluation of the shock absorber squeak noise is obtained as the output label. One hundred and fifty samples in the dataset are randomly selected as the training set (generating the training set). Each input dataset is normalized to the range [0, 1]. [page 443, lines 2-10] shows that the jury test (subjective sound evaluation test) is performed in the laboratory conditions included a temperature of 24C and low background noise of 35dB)
Regarding claim 6, Huang teaches:
The computer-implemented method of claim 1, wherein the vibrational information is accelerometer data. ([Huang, page 444, 3.2.1. Vibration signal collection] discloses collecting vibration signal (i.e., vibrational data) using an accelerometer placed on the piston rod to measure its vertical acceleration)
Regarding claim 8, Huang teaches:
The computer-implemented method of claim 1, wherein the real-time sound perception score is generated utilizing only the real-time vibrational information. ([Huang, page 449, (2) Data acquisition, lines 1-3] shows that the vibration time signals of the shock absorber piston rod are collected as the model input, and the subjective evaluation of the shock absorber squeak noise is obtains as the output label, not the input)
Regarding claim 9, Huang teaches:
The computer-implemented method of claim 1, wherein the machine learning model is a deep learning network. ([Huang, page 450, (5) DNN identification] The DNN is trained and employed to diagnose the annoyance grade of the EV suspension shock absorber squeak noise)
Regarding claim 10, Huang teaches:
A computer-implemented method, comprising: ([Huang, page 442, lines 2-7] and [page 444, 3.2.1. Vibration signal collection, last 2 lines] shows that the method is implemented using a computer)
receiving a first set of sound information from one or more microphones and a first set of vibrational information from one or more accelerometers associated with an electrical drive of a device in a first environment; ([Huang, page 442, lines 4-8] shows that the artificial head is the microphone that collects interior sounds of the EV. [page 444, 3.2.1. Vibration signal collection] discloses collecting vibration signal (i.e., vibrational data) using an accelerometer placed on the piston rod to measure its vertical acceleration. [3.2.2. Vibration signal preprocessing] discloses using the collected vibrational signal of the piston rod for identifying the squeak noise of the shock absorber through the DNN model)
generating a training data set utilizing at least the first set of vibrational information and an associated sound perception score, wherein the training data set is sent to an un-trained machine learning model, wherein the sound perception score is automatically programmed and the training data set includes accelerometer data ; ([Huang, Fig. 7], [page 449, (2) Data acquisition] The vibration time signal of the shock absorber piston rod are collected as the model input. The raw input samples are preprocessed to reduce redundancy for the classification process (automatically programmed). One hundred and fifty samples in the dataset are randomly selected as the training set (generating the training set). Each input dataset is normalized to the range [0, 1]. [page 442, 3.1.2. Subjective evaluation of the interior squeak noise, line 1 – page 443, line 10] and [page 444, 3.2.2. Vibration signal preprocessing, line 1 – page 445, line 10] show that the vibration time signals of the piston rod includes SA grades (subjective annoyance grade) which are collected in the laboratory conditions included a temperature of 24C and low background noise of 35dB)
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model; ([Huang, page 450, (5) DNN identification] The DNN is trained and employed (output) to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. [page 448, equation (7) and the paragraph below the equation] and [page 452, lines 6-10] collectively show the training error calculated based on the equation and minimizing the local minima of the loss and converging training errors of the DNN)
receiving electrical device associated with the device in a second environment; and ([Huang, page 444, 3.2. Bench test of the shock absorber, lines 1-6] Sixty shock absorber excitation samples measured during the electric vehicle road test are used for the experiment to collect vibration signals)
based on the trained machine learning model and the in an end of line (EOL) environment. ([Huang, page 450, (5) DNN identification] and [page 454, 4.3. DNN diagnosis and validation] The DNN is trained and employed to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. Test data can be used to diagnose the performance of the model. [page 451, lines 20-24] discloses that the method can help automobile companies and component companies specifically diagnose this noise and balance the cost and noise quality of shock absorbers in mass production environment)
However, Huang does not specifically disclose:
the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
receiving real-time vibrational information from the electrical device associated with the device in a second environment;
based on the trained machine learning model and the real-time vibrational information, outputting a real-time
Mikhailov teaches:
receiving real-time vibrational information from the electrical device associated with the device in a second environment; (The limitation merely discloses deploying and using the trained model in a real environment. [0029] The VA 130 may record user audio for on-the-fly or subsequent analysis of user audio. The term ‘on-the-fly’ means ‘done in real time when the user is communicating with VA’. [0073] and [Claim 5] The speaker may collect vibration data and may be sent to the VA for segmentation)
based on the trained machine learning model and the real-time vibrational information, outputting a real-time [Mikhailov, 0050] The audio software package 311 is trained to recognize audio characteristics. [0054] The software package performs speaker identification, speech segmentation, command identification to perform on-the-fly analysis of audio signal 120 (generating real-time output). [0073] and [Claim 5] The speaker may collect vibration data and may be sent to the VA for segmentation)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to apply the method of receiving and generating training vibrational data by Mikhailov to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the prediction model by leveraging more diverse data related to the received sounds and by optimizing the performance of the model by adjusting parameters [Mikhailov, 0057].
However, Huang in view of Mikhailov does not specifically disclose:
the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
Krishnaveni teaches:
the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information; ([Krishnaveni, page 2552, left col, 2. Proposed model, line 1-17] discloses that in training of machine learning model, motor signal is collected and stored in cloud, which is connected to the Arduino device, collected data (generated data in a lab environment) is used to train and test the model. [Krishnaveni, page 2553, right col, 2.4. MEMS accelerometer, line 1-14] discloses collecting X, Y, and Z axis data using the 3-axis accelerometer)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to apply the method of receiving vibrational data from accelerometer that includes x, y and z axis of Krishnaveni to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the prediction model by enabling the system to figure out more accurate and precise relationship between x axis vibration, y axis vibration, and z axis vibration and sound perception scores.
Regarding claim 11, Krishnaveni teaches:
The computer-implemented method of claim 10, wherein the vibrational data includes accelerometer data. ([Huang, page 444, 3.2.1. Vibration signal collection] discloses collecting vibration signal (i.e., vibrational data) using an accelerometer placed on the piston rod to measure its vertical acceleration)
Claim 13 is a method claim which recites the same feature as claim 8 and rejected for at least the same reasons.
Regarding claim 14 is a method claim which recites the same feature as claim 9 and rejected for at least the same reasons.
Regarding claim 15, Huang teaches:
The computer-implemented method of claim 10, wherein the first environment is a laboratory environment and the second environment is an end-of-line factory environment. ([Huang, page 443, line 8-10] discloses collecting training sound data form the laboratory condition. [page 450, (5) DNN identification] and [page 454, 4.3. DNN diagnosis and validation] The DNN is trained and employed to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. Test data can be used to diagnose the performance of the model. [page 451, lines 20-24] discloses that the method can help automobile companies and component companies specifically diagnose this noise and balance the cost and noise quality of shock absorbers in mass production environment)
Regarding claim 16, Huang teaches:
A system, comprising: a processor, wherein the processor is programmed to: ([Huang, page 442, lines 2-7] and [page 444, 3.2.1. Vibration signal collection, last 2 lines] shows that the method is implemented using a computer)
receive sound information from one or more microphones and vibrational information from one or more accelerometers associated with a device in a first environment; ([Huang, page 442, lines 4-8] shows that the artificial head is the microphone that collects interior sounds of the EV. [page 444, 3.2.1. Vibration signal collection] discloses collecting vibration signal (i.e., vibrational data) using an accelerometer placed on the piston rod to measure its vertical acceleration. [3.2.2. Vibration signal preprocessing] discloses using the collected vibrational signal of the piston rod for identifying the squeak noise of the shock absorber through the DNN model)
generate a training data set utilizing at least the vibrational information , wherein the sound perception score is automatically programmed ; ([Huang, Fig. 7], [page 449, (2) Data acquisition] The vibration time signal of the shock absorber piston rod are collected as the model input. The raw input samples are preprocessed to reduce redundancy for the classification process (automatically programmed). One hundred and fifty samples in the dataset are randomly selected as the training set (generating the training set). Each input dataset is normalized to the range [0, 1]. [page 442, 3.1.2. Subjective evaluation of the interior squeak noise, line 1 – page 443, line 10] and [page 444, 3.2.2. Vibration signal preprocessing, line 1 – page 445, line 10] show that the vibration time signals of the piston rod includes SA grades (subjective annoyance grade) which are collected in the laboratory conditions included a temperature of 24C and low background noise of 35dB)
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model; ([Huang, page 450, (5) DNN identification] The DNN is trained and employed (output) to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. [page 448, equation (7) and the paragraph below the equation] and [page 452, lines 6-10] collectively show the training error calculated based on the equation and minimizing the local minima of the loss and converging training errors of the DNN)
receive [Huang, page 444, 3.2. Bench test of the shock absorber, lines 1-6] Sixty shock absorber excitation samples measured during the electric vehicle road test are used for the experiment to collect vibration signals)
based on the in an end of line (EOL) environment. ([Huang, page 450, (5) DNN identification] and [page 454, 4.3. DNN diagnosis and validation] The DNN is trained and employed to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. Test data can be used to diagnose the performance of the model. [page 451, lines 20-24] discloses that the method can help automobile companies and component companies specifically diagnose this noise and balance the cost and noise quality of shock absorbers in mass production environment)
However, Huang does not specifically disclose:
the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
receive real-time vibrational information from the device in a second environment;
based on the real-time vibrational information as an input to the trained machine learning model, output a real-time
Mikhailov teaches:
receive real-time vibrational information from the device in a second environment; ([0029] The VA 130 may record user audio for on-the-fly or subsequent analysis of user audio. The term ‘on-the-fly’ means ‘done in real time when the user is communicating with VA’. [0073] and [Claim 5] The speaker may collect vibration data and may be sent to the VA for segmentation)
based on the real-time vibrational information as an input to the trained machine learning model, output a real-time sound emitted from the device. ([Mikhailov, 0050] The audio software package 311 is trained to recognize audio characteristics. [0054] The software package performs speaker identification, speech segmentation, command identification to perform on-the-fly analysis of audio signal 120 (generating real-time output). [0073] and [Claim 5] The speaker may collect vibration data and may be sent to the VA for segmentation)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to apply the method of receiving and generating training vibrational data by Mikhailov to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the prediction model by leveraging more diverse data related to the received sounds and by optimizing the performance of the model by adjusting parameters [Mikhailov, 0057].
However, Huang in view of Mikhailov do not specifically disclose:
the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
Krishnaveni teaches:
the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information; ([Krishnaveni, page 2552, left col, 2. Proposed model, line 1-17] discloses that in training of machine learning model, motor signal is collected and stored in cloud, which is connected to the Arduino device, collected data (generated data in a lab environment) is used to train and test the model. [Krishnaveni, page 2553, right col, 2.4. MEMS accelerometer, line 1-14] discloses collecting X, Y, and Z axis data using the 3-axis accelerometer)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to apply the method of receiving vibrational data from accelerometer that includes x, y and z axis of Krishnaveni to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the prediction model by enabling the system to figure out more accurate and precise relationship between x axis vibration, y axis vibration, and z axis vibration and sound perception scores.
Regarding claim 17, Krishnaveni teaches:
The system of claim 16, wherein the vibrational information includes three-dimensional information. ([Krishnaveni, page 2552, left col, 2. Proposed model, line 1-17] discloses that in training of machine learning model, motor signal is collected and stored in cloud, which is connected to the Arduino device, collected data (generated data in a lab environment) is used to train and test the model. [Krishnaveni, page 2553, right col, 2.4. MEMS accelerometer, line 1-14] discloses collecting X, Y, and Z axis data using the 3-axis accelerometer)
Regarding claim 18, Huang teaches:
the system of claim 16, wherein the processor is further programmed to generate the training data set utilizing both the vibrational information and the sound information. ([Huang, Fig. 7], [page 449, (2) Data acquisition] The vibration time signal of the shock absorber piston rod are collected as the model input, and the subjective evaluation of the shock absorber squeak noise is obtained as the output label. One hundred and fifty samples in the dataset are randomly selected as the training set (generating the training set). Each input dataset is normalized to the range [0, 1]. [page 443, lines 2-10] shows that the jury test (subjective sound evaluation test) is performed in the laboratory conditions included a temperature of 24C and low background noise of 35dB)
Regarding claim 20, Huang in view of Mikhailov teaches:
the system of claim 16, wherein the first environment and the second environment are not a same environment. ([Huang, page 443, line 8-10] discloses collecting training sound data form the laboratory condition. [page 450, (5) DNN identification] and [page 454, 4.3. DNN diagnosis and validation] The DNN is trained and employed to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. Test data can be used to diagnose the performance of the model. [page 451, lines 20-24] discloses that the method can help automobile companies and component companies specifically diagnose this noise and balance the cost and noise quality of shock absorbers in mass production environment)
Claims 3, 7, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Mikhailov in view of Krishnaveni in view of Effinger et al (US 10841666 B1, hereinafter ‘Effinger’) and further in view of Mansour & Seifert (US 20190278990 A1, hereinafter ‘Mansour’).
Regarding claim 3, Huang in view of Mikhailov teaches:
The computer-implemented method of claim 1.
However, Huang in view of Mikhailov and further in view of Krishnaveni do not specifically disclose:
wherein the trained machine learning model is trained via an in-direct method, wherein a first neural network of the machine learning model is trained utilizing the sound information and a second neural network is trained to predict measured sound utilizing the vibrational information and obtain a predicted sound;
feeding the predicted sound into a score prediction network to generate a human-perception score; and
freezing weights associated with the score prediction network and training the weights of the sound prediction network to minimize a weighted sum of sound and score prediction errors.
Effinger teaches:
wherein the trained machine learning model is trained via an in-direct method, wherein a first neural network of the machine learning model is trained utilizing the sound information and a second neural network is trained to predict measured sound utilizing the vibrational information and obtain a predicted sound; ([Effinger, col 5, line 59 – col 6, line 3] discloses processing features from the feature generator unit 122 using the scoring subsystem 120. [Effinger, col 6, line 61-67; Fig. 1] discloses applying the pre-trained aural transformer model included in the feature generator unit 122 to generate aural features (second neural network). [Effinger, col 7, line 41-62; col 8, line 2-16; Fig. 1] discloses the scoring subsystem 120 including a grading unit 124 that can determine scores 125 for respective candidate content using aural features associated with the content insertion points. The grading unit 124 can apply a machine-learning model 125 (first neural network) to the received aural features such as a CNN. [Effinger, col 8, line 23-26] discloses training the scoring model on audio data)
feeding the predicted sound into a score prediction network to generate a human-perception score; and ([Effinger, col 5, line 59 – col 6, line 3] discloses processing features from the feature generator unit 122 using the scoring subsystem 120. [Effinger, col 6, line 61-65; Fig. 1] discloses applying the aural transformer model included in the feature generator unit 122 to generate aural features (second neural network) )
freezing weights associated with the score prediction network [Effinger, col 7, line 41-62; col 8, line 2-16; Fig. 1] discloses the scoring subsystem 120 including a grading unit 124 that can determine scores 125 for respective candidate content using aural features associated with the content insertion points. The grading unit 124 can apply a machine-learning model 125 (first neural network) to the received aural features such as a CNN. [Effinger, col 8, line 23-26] discloses training the scoring model on audio data. Since the scoring model will not be trained again, the model is frozen)
It would have been obvious before the effective filing date of the claimed invention to a person
having ordinary skill in the art to apply the method of utilizing both a sound prediction neural network and a score prediction neural network (indirect prediction) by Effinger to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the prediction model by generating a prediction based on different variables.
However, Huang in view of Mikhailov in view of Krishnaveni and further in view of Effinger does not specifically disclose:
freezing weights associated with the
Mansour teaches:
freezing weights associated with the [Mansour, 0033] discloses training the first neural network while freezing the weights of the second neural network. [Mansour, 0034] discloses that the training is performed based on a model loss total L.sub.Total is a weighted sum of the individual loss functions L.sub.1, L.sub.2, L.sub.n. The objective is to minimize the weighted sum of the individual loss functions)
It would have been obvious before the effective filing date of the claimed invention to a person
having ordinary skill in the art to apply the method of freezing weights of a first prediction network while training a second prediction network by Mansour to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the efficiency of the machine learning model by not re-training pre-trained neural networks with sufficient performance.
Regarding claim 7, Huang in view of Mikhailov in view of Krishnaveni and further in view of Effinger teaches:
the computer-implemented method of claim 1, wherein the machine learning model is a U-Net or Transformer network. ([Effinger, col 6, line 61-65; Fig. 1] discloses applying the aural transformer model included in the feature generator unit 122 to generate aural features (second neural network) )
Claim 12 is a method claim which recites the same feature as claim 7 and rejected for at least the same reasons.
Regarding claim 19, Huang in view of Mikhailov in view of Krishnaveni and further in view of Effinger teaches:
the system of claim 16, wherein machine learning model includes two or more neural networks utilized to output a real-time sound perception score. ([Effinger, col 5, line 59 – col 6, line 3] discloses processing features from the feature generator unit 122 using the scoring subsystem 120. [Effinger, col 6, line 61-65; Fig. 1] discloses applying the aural transformer model included in the feature generator unit 122 to generate aural features (second neural network). [Effinger, col 7, line 41-62; col 8, line 2-16; Fig. 1] discloses the scoring subsystem 120 including a grading unit 124 that can determine scores 125 for respective candidate content using aural features associated with the content insertion points. The grading unit 124 can apply a machine-learning model 125 (first neural network) to the received aural features such as a CNN)
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 9 and 16 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3 and 5 of copending Application No. 17/957,581 (reference application) in view of Huang and further in view of Krishnaveni. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitations in Co-pending application 17/957,581 are substantially similar as highlighted in the table below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding claim 1,
Claim #
Reference application 17/957,581 (10/29/2025)
Claim #
Instant application 17/957,588
1
A computer-implemented method, comprising:
receiving vibrational information from one or more accelerometers and sound information from one or more microphones associated with a test device in a first environment;
1
A computer-implemented method, comprising:
receiving sound information from one or more microphones and vibrational data from one or more accelerometers associated with an electrical drive of a device, wherein the sound information is associated with the device;
generating a training data set that includes paired vibrational information and corresponding sound information that are aligned to represent a common operating state of an associated device, wherein the training data set is sent to a machine learning model;
generating a training data set utilizing at least the vibrational information and a sound perception score associated with the vibrational information in a lab environment, wherein the training data set is sent to an un-trained machine learning model, wherein the sound perception score is automatically programmed and the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
in response to meeting a convergence threshold of the machine learning model utilizing the training data set, outputting a trained machine learning model;
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model;
receiving real-time vibrational information from one or more second accelerometers associated with a run-time device in a second environment; and
receiving real-time vibrational information from the electrical drive associated with the device; and
based on the trained machine learning model and the real-time vibrational information, outputting a reconstructed sound representation indicating purported sound of operation emitted from the run-time device.
based on the trained machine learning model and the real-time vibrational information, outputting a real-time sound perception score indicating characteristics associated with sound emitted from the device in an end of line (EOL) environment.
However, the reference application 17/957,581 does not specifically disclose:
generating a training data set utilizing a sound perception score associated with the vibrational information in a lab environment,
wherein the sound perception score is automatically programmed and the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
outputting a real-time sound perception score indicating characteristics associated with sound emitted from the device;
Huang discloses:
generating a training data set utilizing a sound perception score associated with the vibrational information in a lab environment, … wherein the sound perception score is automatically programmed ([Huang, Fig. 7], [page 449, (2) Data acquisition] The vibration time signal of the shock absorber piston rod are collected as the model input. The raw input samples are preprocessed to reduce redundancy for the classification process (automatically programmed). One hundred and fifty samples in the dataset are randomly selected as the training set (generating the training set). Each input dataset is normalized to the range [0, 1]. [page 442, 3.1.2. Subjective evaluation of the interior squeak noise, line 1 – page 443, line 10] and [page 444, 3.2.2. Vibration signal preprocessing, line 1 – page 445, line 10] show that the vibration time signals of the piston rod includes SA grades (subjective annoyance grade) which are collected in the laboratory conditions included a temperature of 24C and low background noise of 35dB)
outputting a ([Huang, page 450, (5) DNN identification] and [page 454, 4.3. DNN diagnosis and validation] The DNN is trained and employed to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. Test data can be used to diagnose the performance of the model. [page 451, lines 20-24] discloses that the method can help automobile companies and component companies specifically diagnose this noise and balance the cost and noise quality of shock absorbers in mass production environment)
It would have been obvious before the effective filing date of the claimed invention to a person
having ordinary skill in the art to apply the method of generating a training data set utilizing manually scored sound perception score and generating a real-time sound perception score using the trained model as taught by Huang to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the performance of the model by quantifying subjective data and applying the trained model to actual data.
However, the instant application in view of Huang do not specifically disclose:
the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
Krishnaveni teaches:
the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information; ([Krishnaveni, page 2552, left col, 2. Proposed model, line 1-17] discloses that in training of machine learning model, motor signal is collected and stored in cloud, which is connected to the Arduino device, collected data (generated data in a lab environment) is used to train and test the model. [Krishnaveni, page 2553, right col, 2.4. MEMS accelerometer, line 1-14] discloses collecting X, Y, and Z axis data using the 3-axis accelerometer)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to apply the method of receiving vibrational data from accelerometer that includes x, y and z axis of Krishnaveni to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the prediction model by enabling the system to figure out more accurate and precise relationship between x axis vibration, y axis vibration, and z axis vibration and sound perception scores.
Claim #
Reference application 17/957,581
Claim #
Instant application 17/957,588
3
The computer-implemented method of claim 1, wherein machine learning model is trained to predict sound utilizing the vibrational data as input and adjusting its trainable weights to minimize a sound prediction error.
2
The computer-implemented method of claim 1, wherein the trained machine learning model is trained utilizing only the vibrational information and minimizing a score prediction error output by the un-trained machine learning model.
5
The computer-implemented method of claim 1, wherein the trained machine learning model is a deep neural network.
9
The computer-implemented method of claim 1, wherein the machine learning model is a deep learning network.
Claim 16 is a system claim which recites a similar feature as claim 1. Therefore, it is rejected under the same rationale.
Claims 10-11 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 10 and 11 of copending Application No. 17/957,581 (reference application) in view of Huang.
Regarding claim 10,
Claim #
Reference application 17/957,581 (10/29/2025)
Claim #
Instant application 17/957,588
10
A computer-implemented method, comprising:
receiving vibrational information from one or more accelerometers and sound information associated with corresponding vibrational information from a test device in a first environment, wherein the sound information is obtained from one or more microphones;
10
A computer-implemented method, comprising:
receiving a first set of sound information from one or more microphones and a first set of vibrational information from one or more accelerometers associated with an electrical drive of a device in a first environment;
generating a training data set utilizing at least the vibrational data and the sound information associated with the vibrational data, wherein the training data set is sent to a machine learning model configured to output sound predictions;
Claim 11: The computer-implemented method of claim 10, wherein the vibrational information includes accelerometer data including x-axis, y-axis, and z-axis accelerometer information.
generating a training data set utilizing at least the first set of vibrational information and an associated sound perception score, wherein the training data set is sent to an un-trained machine learning model, wherein the sound perception score is automatically programmed and the training data set includes accelerometer data that includes x-axis, y-axis, and z-axis information;
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model;
receiving real-time vibrational information from a run-time device operating an actuator or electric dive in a second environment; and
receiving real-time vibrational information from the electrical device associated with the device in a second environment; and
based on the machine learning model and the real-time vibrational information, output a sound prediction indicating purported sound emitted from operating the run-time device.
based on the trained machine learning model and the real-time vibrational information, outputting a real-time sound perception score indicating characteristics associated with sound emitted from the device in an end of line (EOL) environment.
However, the reference application 17/957,581 does not specifically disclose:
generating a training data set utilizing at least the first set of vibrational information and an associated sound perception score;
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model;
outputting a real-time sound perception score indicating characteristics associated with sound;
Huang discloses:
generating a training data set utilizing at least the first set of vibrational information and an associated sound perception score; ([Huang, Fig. 7], [page 449, (2) Data acquisition] The vibration time signal of the shock absorber piston rod are collected as the model input. The raw input samples are preprocessed to reduce redundancy for the classification process (automatically programmed). One hundred and fifty samples in the dataset are randomly selected as the training set (generating the training set). Each input dataset is normalized to the range [0, 1]. [page 442, 3.1.2. Subjective evaluation of the interior squeak noise, line 1 – page 443, line 10] and [page 444, 3.2.2. Vibration signal preprocessing, line 1 – page 445, line 10] show that the vibration time signals of the piston rod includes SA grades (subjective annoyance grade) which are collected in the laboratory conditions included a temperature of 24C and low background noise of 35dB)
in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model; ([Huang, page 450, (5) DNN identification] The DNN is trained and employed (output) to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. [page 448, equation (7) and the paragraph below the equation] and [page 452, lines 6-10] collectively show the training error calculated based on the equation and minimizing the local minima of the loss and converging training errors of the DNN)
outputting a ([Huang, page 450, (5) DNN identification] and [page 454, 4.3. DNN diagnosis and validation] The DNN is trained and employed to diagnose the annoyance grade of the EV suspension shock absorber squeak noise. Test data can be used to diagnose the performance of the model. [page 451, lines 20-24] discloses that the method can help automobile companies and component companies specifically diagnose this noise and balance the cost and noise quality of shock absorbers in mass production environment)
It would have been obvious before the effective filing date of the claimed invention to a person
having ordinary skill in the art to apply the method of generating a training data set utilizing manually scored sound perception score and generating a real-time sound perception score using the trained model as taught by Huang to improve the performance of the machine learning method of the present invention. The suggestion and/or motivation for doing so is to improve the performance of the model by quantifying subjective data and applying the trained model to actual data.
Claim #
Reference application 17/957,581 (10/29/2025)
Claim #
Instant application 17/957,588
11
The computer-implemented method of claim 10, wherein the vibrational information includes accelerometer data including x-axis, y-axis, and z-axis accelerometer information.
11
The computer-implemented method of claim 10, wherein the vibrational data includes accelerometer data.
Response to Arguments
Response to Arguments under 35 U.S.C. 101
Arguments: Applicant asserts (a) that the claimed invention improves the functioning of evaluating a machine learning model utilizing a specific type of technical process (utilizing a shallow repressor model and residuals to generate a modified effect of an optimized model), a specific technological area, and (b) that USPTO Example 37 and 42 (Training a Neural Network for Facial Detection), and USPTO Director Squires’ statement support Applicant’s argument. [Remarks, page 6-7]
Examiner’s Response: Examiner respectfully disagrees. Regarding (a), MPEP 2106.05(a) indicates that, when considering whether additional elements integrate a judicial exception into a practical application, the examiner must evaluate if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. If the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Examiner performed this evaluation and concluded that the specification [0022]-[0023] set forth the improvement but in a conclusory manner. The specification merely discloses the conclusion that the sound prediction model and EOL testing setups with different accelerometer positioning improves the technology without sufficient details necessary to be apparent to a person of ordinary skill in the art.
Regarding (b), Examiner would first note that, the USPTO Example 37 and USPTO Director Squires’ statement cited by Applicant are nonprecedential and do not bind Examiner in the instant case.
Second, the USPTO Example 37 and 42 (Training a Neural Network for Facial Detection) is distinguishable from the instant application. The USPTO Example is eligible because the claim does not recite any mathematical relationships, formulas, or calculations. While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims. Further, the claim does not recite a mental process because the steps are not practically performed in the human mind. However, the instant application recites mental processes of evaluation. The limitations of “generating a training data set utilizing at least the vibrational information and a sound perception score associated with the vibrational information in a lab environment” is directed to a mental process of judgment as selecting and combining information and score can practically be performed in the human mind. The limitations of “based on the real-time vibrational information, outputting a real-time sound perception score indicating characteristics associated with sound emitted from the device in an end of line (EOL) environment” is directed to a mental process of evaluation as the limitation recites scoring process which can be performed with the aid of pen and paper. The limitations of ‘receiving sound information from one or more microphones and vibrational data from one or more accelerometers …’ are directed to an insignificant extra-solution activity MPEP 2106.05(g) in Step 2A Prong 2 analysis and well understood, routine and conventional activity MPEP 2106.05(d) in Step 2B, and ‘outputting a trained machine learning model’ is directed to mere instructions to perform the abstract idea of selecting and combining information and scoring the data records using a generic computer component MPEP 2106.05(f).
Lastly, Desjardins cited in Squires’ statement is distinguishable from the instant application. As an initial matter, it is worth noting that Desjardins does not stand for the proposition that all claims directed to methods of training neural networks are per se eligible. Furthermore, the claims at issue in Desjardins explicitly linked the steps recited to an improvement in training by reciting “optimizing performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task.” However, there is no such nexus in the instant claims.
Accordingly, the arguments regarding claim 1 are not persuasive. Similarly, the arguments regarding claims 10 and 16 are not persuasive. Therefore, the arguments to claims 2-9, 11-15, and 17-20 depend from independent claims 1, 10 and 16 are not persuasive.
Response to Arguments under 35 U.S.C. 103
Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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