DETAILED ACTION
Claims 1, 3, 5-6, 8-11, 13-14, 16, 18-19, 22-23 and 25-26 are pending. Claims 2, 4, 7, 12, 15, 17, 20-21 and 24 are cancelled.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/12/2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 2/17/26, have been fully considered but are not persuasive.
Applicant’s arguments regarding the rejection under 35 U.S.C. § 103 (pages 8-11) are moot in view of the newly cited reference, Lanman.
For at least these reasons, the rejection of the claims is maintained.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 5-6, 9-11, 13-14, 16, 18-19, 22 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mizobuchi et al. U.S. Patent Publication No. 20210255613 (hereinafter Mizobuchi) in view of Thoren et al. ‘Using Python for Analysis and Verification of Mixed-mode Signal Chains’ PROC. OF THE 20th PYTHON IN SCIENCE CONF. (SCIPY 2021), pages 6-13, published 2021 (hereinafter Thoren) and further in view of Kummari et al. U.S. Patent Publication No. 20190152011 (hereinafter Kummari) and further in view of Lanman et al. U.S. Patent Publication No. 20170150268 (hereinafter Lanman).
Regarding claim 1, Mizobuchi teaches a computer-implemented method [0007 — an abnormality predicting method including: inputting processing target data including at least one of audio data and vibration data acquired from a target device; storing information related to an abnormality prediction of the processing target data; calculating an abnormality degree of the processing target data; executing a processing related to the abnormality prediction, the processing related to the abnormality prediction including a failure occurrence prediction using a latest abnormality degree transition and a past abnormality degree transition of the processing target data; generating a display screen for displaying a processing result including an abnormality degree transition and a result of the failure occurrence prediction; and displaying the display screen on a display unit; 0039-0043, Figs. 1-2 — The abnormality predicting system includes an information processing device (computer) having a processor and a memory, and includes a data processing device 10 that executes various processing of the audio data or the vibration data acquired as the processing target data] comprising:
obtaining audio data associated with at least one device [0039 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22).; 0070-0071, Figs. 2 and 9 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22).];
modifying at least a portion of the obtained audio data using one or more data processing techniques [0044 — data input unit 11 includes an audio interface to which the microphone 61 is connected, and includes an acoustic analysis unit 111 and an abnormality degree calculation unit 112, and performs a processing by inputting the audio data as the processing target data. The acoustic analysis unit 111 executes a preprocessing of the acquired audio data such as an analog-to-digital conversion processing, a frequency conversion processing by Fast Fourier Transform (FFT), and a feature amount extraction processing by Mel-Frequency Cepstrum Coefficient (MFCC), or envelope component extraction of an amplitude value.; 0070-0071, Figs. 2 and 9 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22).], wherein modifying at least a portion of the obtained audio data using one or more data processing techniques comprises converting at least a portion of the obtained audio data to one or more digital signals [0044 — acoustic analysis unit 111 executes a preprocessing of the acquired audio data such as an analog-to-digital conversion processing];
predicting at least one failure associated with the at least one device by classifying, into at least one of multiple device failure-related categories, at least a portion of the modified audio data using one or more artificial intelligence techniques [0047 —the machine learning processing may be performed using one or more statistical classification techniques. Examples of the statistical classification technique include linear classifiers, support vector machines, quadratic classifiers, kernel estimation, decision trees, artificial neural networks, Bayesian techniques and/or networks, hidden Markov models, binary classifiers, multi-class classifiers, a clustering technique, a random forest technique, a logistic regression technique, a linear regression technique, and a gradient boosting technique; 0063 — current abnormality degree transition 611, the matching rate 612 of the abnormality degree transitions, and the future prediction abnormality degree transition 613 are displayed, and an abnormality degree ratio 616 for each abnormality cause and a message 617 indicating that there is a possibility of failure are displayed. In the fourth example, the data processing device 10 calculates an abnormality degree by classifying one piece of acquired audio data according to each abnormality cause from information included in the audio data, and executes the abnormality determination and the failure occurrence prediction for each abnormality cause over the past, the current time, and the future in the evaluation unit 13. The abnormality degree for each abnormality cause can be calculated based on transition of characteristics of the audio data. For example, when the target device is a cooling fan, examples of the abnormality cause include a plurality of abnormality causes, such as blade chipping, clogging, and belt loosening of the fan]; and
performing one or more automated actions based at least in part on the classifying of the at least a portion of the modified audio data [0079 — the possibility of failure in the near future is low, a monitor display screen for displaying determination of normality is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S33)… when there is a possibility of failure in the near future, a monitor display screen for displaying an abnormality degree transition having a possibility in the future is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S34).], wherein performing one or more automated actions for the at least one device comprises performing one or more actions in response to the at least one predicted failure [0079 — the possibility of failure in the near future is low, a monitor display screen for displaying determination of normality is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S33)… when there is a possibility of failure in the near future, a monitor display screen for displaying an abnormality degree transition having a possibility in the future is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S34)];
wherein the method is performed by at least one processing device comprising a processor coupled to a memory [0039-0043, Figs. 1-2 — The abnormality predicting system includes an information processing device (computer) having a processor and a memory, and includes a data processing device 10 that executes various processing of the audio data or the vibration data acquired as the processing target data].
But Mizobuchi fails to clearly specify determining at least one sample rate based at least in part on one or more audio analysis libraries, and converting at least a portion of the obtained audio data to one or more digital signals in accordance with the at least one sample rate, adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter; and automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure.
However, Thoren teaches determining at least one sample rate based at least in part on one or more audio analysis libraries, and converting at least a portion of the obtained audio data to one or more digital signals in accordance with the at least one sample rate [pages 6-7, Fig. 1 — analog-to-digital converter (ADC) is shown… pyadi-iio library… my_ad7124.sample_rate = 128 # Set sample rate].
Mizobuchi and Thoren are analogous art. They relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Mizobuchi, by incorporating the above limitations, as taught by Thoren.
One of ordinary skill in the art would have been motivated to do this modification to take advantage of already available software libraries, hence requiring less software be written, and to ensure an appropriate sampling rate, e.g. to provide efficient data sampling consistent with the Nyquist criterion and compatible with the software/hardware.
But the combination of Mizobuchi and Thoren fails to clearly specify adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter; and automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure.
However, Kummari teaches automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure [0020-0021 — the system can predict when tool failure will occur and how far into the future the tool failure will occur… acquired sensor information may identify cutting information such as cutting force, acoustic emissions (sound); 0045 — The system can also perform real-time tool health monitoring, failure prediction and sending real time alerts. The system enables monitoring tool life by estimating the remaining life of a tool based on survival analysis and sending out real time recommendation to replace the tool whenever significant anomalies are detected (initiating reparative action)].
Mizobuchi, Thoren and Kummari are analogous art. Mizobuchi and Thoren relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals. And Mizobuchi and Kummari relate to relate to abnormality prediction based on audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Mizobuchi and Thoren, by incorporating the above limitations, as taught by Kummari.
One of ordinary skill in the art would have been motivated to do this modification to facilitate replacing a component at an optimum time, as suggested by Kummari [0041], thus improving operation of a device.
But the combination of Mizobuchi, Thoren and Kummari fails to clearly specify adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter.
However, Lanman teaches adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter [0041-0044 — audio processor 410 receives and processes a digital ambient audio stream, such as the ambient audio stream 222, to provide a personal audio stream, such as the personal audio stream 232. The audio processor 410 may perform processes including filtering, equalization, compression, limiting, and/or other processes. Filtering may include high-pass, low-pass, band-pass, and band-reject filtering... the active processing parameter set 425 may define filtering by a low pass filter with a particular cut-off frequency (the frequency at which the filter start to attenuate) and slope (the rate of change of attenuation with frequency); 0068 — the parameter set learning application 526 may use known machine learning techniques such as neural nets, fuzzy logic, adaptive neuro-fuzzy inference systems, or combinations of these and other machine learning methodologies to learn revised and/or new processing parameter sets].
Mizobuchi, Thoren, Kummari and Lanman are analogous art. Mizobuchi, Thoren and Lanman relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals. And Mizobuchi and Kummari relate to relate to abnormality prediction based on audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Mizobuchi, Thoren, Kummari by incorporating the above limitations, as taught by Lanman.
One of ordinary skill in the art would have been motivated to do this modification to remove unwanted noise by automatically adapting audio filter parameters, as suggested by Lanman [0007-0008, 0041-0044].
Regarding claim 3, the combination of Mizobuchi, Thoren, Kummari and Lanman teaches all the limitations of the base claims as outlined above.
Further, Mizobuchi teaches that modifying at least a portion of the obtained audio data using one or more data processing techniques comprises extracting one or more features from the obtained audio data in connection with using one or more Mel-frequency cepstral coefficients [0044 — data input unit 11 includes an audio interface to which the microphone 61 is connected, and includes an acoustic analysis unit 111 and an abnormality degree calculation unit 112, and performs a processing by inputting the audio data as the processing target data. The acoustic analysis unit 111 executes a preprocessing of the acquired audio data such as an analog-to-digital conversion processing, a frequency conversion processing by Fast Fourier Transform (FFT), and a feature amount extraction processing by Mel-Frequency Cepstrum Coefficient (MFCC), or envelope component extraction of an amplitude value].
Regarding claim 5, the combination of Mizobuchi, Thoren, Kummari and Lanman teaches all the limitations of the base claims as outlined above.
Further, Mizobuchi teaches that classifying at least a portion of the modified audio data comprises processing the at least a portion of the modified audio data using at least one neural network trained to classify one or more audio data features into at least one of the multiple device failure-related categories [0047 —the machine learning processing may be performed using one or more statistical classification techniques. Examples of the statistical classification technique include linear classifiers, support vector machines, quadratic classifiers, kernel estimation, decision trees, artificial neural networks, Bayesian techniques and/or networks, hidden Markov models, binary classifiers, multi-class classifiers, a clustering technique, a random forest technique, a logistic regression technique, a linear regression technique, and a gradient boosting technique; 0063 — current abnormality degree transition 611, the matching rate 612 of the abnormality degree transitions, and the future prediction abnormality degree transition 613 are displayed, and an abnormality degree ratio 616 for each abnormality cause and a message 617 indicating that there is a possibility of failure are displayed. In the fourth example, the data processing device 10 calculates an abnormality degree by classifying one piece of acquired audio data according to each abnormality cause from information included in the audio data, and executes the abnormality determination and the failure occurrence prediction for each abnormality cause over the past, the current time, and the future in the evaluation unit 13. The abnormality degree for each abnormality cause can be calculated based on transition of characteristics of the audio data. For example, when the target device is a cooling fan, examples of the abnormality cause include a plurality of abnormality causes, such as blade chipping, clogging, and belt loosening of the fan; 0044 — data input unit 11 includes an audio interface to which the microphone 61 is connected, and includes an acoustic analysis unit 111 and an abnormality degree calculation unit 112, and performs a processing by inputting the audio data as the processing target data. The acoustic analysis unit 111 executes a preprocessing of the acquired audio data such as an analog-to-digital conversion processing, a frequency conversion processing by Fast Fourier Transform (FFT), and a feature amount extraction processing by Mel-Frequency Cepstrum Coefficient (MFCC), or envelope component extraction of an amplitude value.; 0070-0071, Figs. 2 and 9 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22)].
Regarding claim 6, the combination of Mizobuchi, Thoren, Kummari and Lanman teaches all the limitations of the base claims as outlined above.
Further, Mizobuchi teaches predicting at least one failure associated with the at least one device comprises associating the multiple device failure-related categories with one or more device-specific failures using the one or more artificial intelligence techniques [0047 —the machine learning processing may be performed using one or more statistical classification techniques. Examples of the statistical classification technique include linear classifiers, support vector machines, quadratic classifiers, kernel estimation, decision trees, artificial neural networks, Bayesian techniques and/or networks, hidden Markov models, binary classifiers, multi-class classifiers, a clustering technique, a random forest technique, a logistic regression technique, a linear regression technique, and a gradient boosting technique; 0063 — current abnormality degree transition 611, the matching rate 612 of the abnormality degree transitions, and the future prediction abnormality degree transition 613 are displayed, and an abnormality degree ratio 616 for each abnormality cause and a message 617 indicating that there is a possibility of failure are displayed. In the fourth example, the data processing device 10 calculates an abnormality degree by classifying one piece of acquired audio data according to each abnormality cause from information included in the audio data, and executes the abnormality determination and the failure occurrence prediction for each abnormality cause over the past, the current time, and the future in the evaluation unit 13. The abnormality degree for each abnormality cause can be calculated based on transition of characteristics of the audio data. For example, when the target device is a cooling fan, examples of the abnormality cause include a plurality of abnormality causes, such as blade chipping, clogging, and belt loosening of the fan].
Regarding claim 9, the combination of Mizobuchi, Thoren, Kummari and Lanman teaches all the limitations of the base claims as outlined above.
Further, Mizobuchi teaches that obtaining audio data associated with at least one device comprises implementing one or more auditory sensors in connection with the at least one device [0039-0040, Figs. 1-2 — microphone 61 (auditory sensor) as an example of a data input device (sound collection unit)].
Regarding claim 10, the combination of Mizobuchi, Thoren, Kummari and Lanman teaches all the limitations of the base claims as outlined above.
Further, Mizobuchi teaches that obtaining audio data associated with at least one device comprises obtaining audio data generated by the at least one device [0039-0040, Figs. 1-2 — microphone 61 (auditory sensor) as an example of a data input device (sound collection unit)… data processing device 10 acquires audio data or vibration data generated by a cooling fan 50 of the computer as the target device].
Regarding claim 11, Mizobuchi teaches a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device [0039-0043, claim 1, Figs. 1-2 — The abnormality predicting system includes an information processing device (computer) having a processor and a memory, and includes a data processing device 10 that executes various processing of the audio data or the vibration data acquired as the processing target data… a memory having instructions that, when executed by the at least one processor, cause the at least one processor to execute operations]:
to obtain audio data associated with at least one device [0039 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22).; 0070-0071, Figs. 2 and 9 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22).];
to modify at least a portion of the obtained audio data using one or more data processing techniques [0044 — data input unit 11 includes an audio interface to which the microphone 61 is connected, and includes an acoustic analysis unit 111 and an abnormality degree calculation unit 112, and performs a processing by inputting the audio data as the processing target data. The acoustic analysis unit 111 executes a preprocessing of the acquired audio data such as an analog-to-digital conversion processing, a frequency conversion processing by Fast Fourier Transform (FFT), and a feature amount extraction processing by Mel-Frequency Cepstrum Coefficient (MFCC), or envelope component extraction of an amplitude value.; 0070-0071, Figs. 2 and 9 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22).], wherein modifying at least a portion of the obtained audio data comprises converting at least a portion of the obtained audio data to one or more digital signals [0044 — acoustic analysis unit 111 executes a preprocessing of the acquired audio data such as an analog-to-digital conversion processing];
to predict at least one failure associated with the at least one device by classifying, into at least one of multiple device failure-related categories, at least a portion of the modified audio data using one or more artificial intelligence techniques [0047 —the machine learning processing may be performed using one or more statistical classification techniques. Examples of the statistical classification technique include linear classifiers, support vector machines, quadratic classifiers, kernel estimation, decision trees, artificial neural networks, Bayesian techniques and/or networks, hidden Markov models, binary classifiers, multi-class classifiers, a clustering technique, a random forest technique, a logistic regression technique, a linear regression technique, and a gradient boosting technique; 0063 — e current abnormality degree transition 611, the matching rate 612 of the abnormality degree transitions, and the future prediction abnormality degree transition 613 are displayed, and an abnormality degree ratio 616 for each abnormality cause and a message 617 indicating that there is a possibility of failure are displayed. In the fourth example, the data processing device 10 calculates an abnormality degree by classifying one piece of acquired audio data according to each abnormality cause from information included in the audio data, and executes the abnormality determination and the failure occurrence prediction for each abnormality cause over the past, the current time, and the future in the evaluation unit 13. The abnormality degree for each abnormality cause can be calculated based on transition of characteristics of the audio data. For example, when the target device is a cooling fan, examples of the abnormality cause include a plurality of abnormality causes, such as blade chipping, clogging, and belt loosening of the fan]; and
to perform one or more automated actions based at least in part on the classifying of the at least a portion of the modified audio data [0079 — the possibility of failure in the near future is low, a monitor display screen for displaying determination of normality is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S33)… when there is a possibility of failure in the near future, a monitor display screen for displaying an abnormality degree transition having a possibility in the future is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S34)], wherein performing one or more automated actions for the at least one device comprises performing one or more actions in response to the at least one predicted failure [0079 — the possibility of failure in the near future is low, a monitor display screen for displaying determination of normality is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S33)… when there is a possibility of failure in the near future, a monitor display screen for displaying an abnormality degree transition having a possibility in the future is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S34)].
But Mizobuchi fails to clearly specify determining at least one sample rate based at least in part on one or more audio analysis libraries, and converting at least a portion of the obtained audio data to one or more digital signals in accordance with the at least one sample rate, adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure.
However, Thoren teaches determining at least one sample rate based at least in part on one or more audio analysis libraries, and converting at least a portion of the obtained audio data to one or more digital signals in accordance with the at least one sample rate [pages 6-7, Fig. 1 — analog-to-digital converter (ADC) is shown… pyadi-iio library… my_ad7124.sample_rate = 128 # Set sample rate].
Mizobuchi and Thoren are analogous art. They relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above non-transitory processor-readable storage medium, as taught by Mizobuchi, by incorporating the above limitations, as taught by Thoren.
One of ordinary skill in the art would have been motivated to do this modification to take advantage of already available software libraries, hence requiring less software be written, and to ensure an appropriate sampling rate, e.g. to provide efficient data sampling consistent with the Nyquist criterion and compatible with the software/hardware.
But the combination of Mizobuchi and Thoren fails to clearly specify adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure.
However, Kummari teaches automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure [0020-0021 — the system can predict when tool failure will occur and how far into the future the tool failure will occur… acquired sensor information may identify cutting information such as cutting force, acoustic emissions (sound); 0045 — The system can also perform real-time tool health monitoring, failure prediction and sending real time alerts. The system enables monitoring tool life by estimating the remaining life of a tool based on survival analysis and sending out real time recommendation to replace the tool whenever significant anomalies are detected (initiating reparative action)].
Mizobuchi, Thoren and Kummari are analogous art. Mizobuchi and Thoren relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals. And Mizobuchi and Kummari relate to relate to abnormality prediction based on audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above non-transitory processor-readable storage medium, as taught by the combination of Mizobuchi and Thoren, by incorporating the above limitations, as taught by Kummari.
One of ordinary skill in the art would have been motivated to do this modification to facilitate replacing a component at an optimum time, as suggested by Kummari [0041], thus improving operation of a device.
But the combination of Mizobuchi, Thoren and Kummari fails to clearly specify adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter.
However, Lanman teaches adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter [0041-0044 — audio processor 410 receives and processes a digital ambient audio stream, such as the ambient audio stream 222, to provide a personal audio stream, such as the personal audio stream 232. The audio processor 410 may perform processes including filtering, equalization, compression, limiting, and/or other processes. Filtering may include high-pass, low-pass, band-pass, and band-reject filtering... the active processing parameter set 425 may define filtering by a low pass filter with a particular cut-off frequency (the frequency at which the filter start to attenuate) and slope (the rate of change of attenuation with frequency); 0068 — the parameter set learning application 526 may use known machine learning techniques such as neural nets, fuzzy logic, adaptive neuro-fuzzy inference systems, or combinations of these and other machine learning methodologies to learn revised and/or new processing parameter sets].
Mizobuchi, Thoren, Kummari and Lanman are analogous art. Mizobuchi, Thoren and Lanman relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals. And Mizobuchi and Kummari relate to relate to abnormality prediction based on audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Mizobuchi, Thoren, Kummari by incorporating the above limitations, as taught by Lanman.
One of ordinary skill in the art would have been motivated to do this modification to remove unwanted noise by automatically adapting audio filter parameters, as suggested by Lanman [0007-0008, 0041-0044].
Regarding claim 13, the combination of Mizobuchi, Thoren and Kummari teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 3.
Regarding claim 14, the combination of Mizobuchi, Thoren and Kummari teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 5.
Regarding claim 16, Mizobuchi teaches an apparatus [0039-0043, Figs. 1-2 — The abnormality predicting system includes an information processing device (computer) having a processor and a memory, and includes a data processing device 10 that executes various processing of the audio data or the vibration data acquired as the processing target data] comprising: at least one processing device comprising a processor coupled to a memory [0039-0043, claim 1, Figs. 1-2 — The abnormality predicting system includes an information processing device (computer) having a processor and a memory, and includes a data processing device 10 that executes various processing of the audio data or the vibration data acquired as the processing target data]; the at least one processing device being configured:
to obtain audio data associated with at least one device [0039 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22).; 0070-0071, Figs. 2 and 9 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22)];
to modify at least a portion of the obtained audio data using one or more data processing techniques [0044 — data input unit 11 includes an audio interface to which the microphone 61 is connected, and includes an acoustic analysis unit 111 and an abnormality degree calculation unit 112, and performs a processing by inputting the audio data as the processing target data. The acoustic analysis unit 111 executes a preprocessing of the acquired audio data such as an analog-to-digital conversion processing, a frequency conversion processing by Fast Fourier Transform (FFT), and a feature amount extraction processing by Mel-Frequency Cepstrum Coefficient (MFCC), or envelope component extraction of an amplitude value.; 0070-0071, Figs. 2 and 9 — data processing device 10 acquires audio data (or vibration data) of the cooling fan 50 collected by the microphone 61 (S21), and performs a preprocessing such as a frequency conversion processing to analyze a signal of the audio data in the acoustic analysis unit 111 of the data input unit 11 (S22)], wherein modifying at least a portion of the obtained audio data comprises converting at least a portion of the obtained audio data to one or more digital signals [0044 — acoustic analysis unit 111 executes a preprocessing of the acquired audio data such as an analog-to-digital conversion processing];
to predict at least one failure associated with the at least one device by classifying, into at least one of multiple device failure-related categories, at least a portion of the modified audio data using one or more artificial intelligence techniques [0047 —the machine learning processing may be performed using one or more statistical classification techniques. Examples of the statistical classification technique include linear classifiers, support vector machines, quadratic classifiers, kernel estimation, decision trees, artificial neural networks, Bayesian techniques and/or networks, hidden Markov models, binary classifiers, multi-class classifiers, a clustering technique, a random forest technique, a logistic regression technique, a linear regression technique, and a gradient boosting technique; 0063 — e current abnormality degree transition 611, the matching rate 612 of the abnormality degree transitions, and the future prediction abnormality degree transition 613 are displayed, and an abnormality degree ratio 616 for each abnormality cause and a message 617 indicating that there is a possibility of failure are displayed. In the fourth example, the data processing device 10 calculates an abnormality degree by classifying one piece of acquired audio data according to each abnormality cause from information included in the audio data, and executes the abnormality determination and the failure occurrence prediction for each abnormality cause over the past, the current time, and the future in the evaluation unit 13. The abnormality degree for each abnormality cause can be calculated based on transition of characteristics of the audio data. For example, when the target device is a cooling fan, examples of the abnormality cause include a plurality of abnormality causes, such as blade chipping, clogging, and belt loosening of the fan]; and
to perform one or more automated actions based at least in part on the classifying of the at least a portion of the modified audio data [0079 — the possibility of failure in the near future is low, a monitor display screen for displaying determination of normality is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S33)… when there is a possibility of failure in the near future, a monitor display screen for displaying an abnormality degree transition having a possibility in the future is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S34)] , wherein performing one or more automated actions for the at least one device comprises performing one or more actions in response to the at least one predicted failure [0079 — the possibility of failure in the near future is low, a monitor display screen for displaying determination of normality is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S33)… when there is a possibility of failure in the near future, a monitor display screen for displaying an abnormality degree transition having a possibility in the future is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S34)].
But Mizobuchi fails to clearly specify determining at least one sample rate based at least in part on one or more audio analysis libraries, and converting at least a portion of the obtained audio data to one or more digital signals in accordance with the at least one sample rate, adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter; and automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure.
However, Thoren teaches determining at least one sample rate based at least in part on one or more audio analysis libraries, and converting at least a portion of the obtained audio data to one or more digital signals in accordance with the at least one sample rate [pages 6-7, Fig. 1 — analog-to-digital converter (ADC) is shown… pyadi-iio library… my_ad7124.sample_rate = 128 # Set sample rate].
Mizobuchi and Thoren are analogous art. They relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above apparatus, as taught by Mizobuchi, by incorporating the above limitations, as taught by Thoren.
One of ordinary skill in the art would have been motivated to do this modification to take advantage of already available software libraries, hence requiring less software be written, and to ensure an appropriate sampling rate, e.g. to provide efficient data sampling consistent with the Nyquist criterion and compatible with the software/hardware.
But the combination of Mizobuchi and Thoren fails to clearly specify adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter; and automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure.
However, Kummari teaches automatically initiating one or more reparative actions for the at least one device in response to the at least one predicted failure [0020-0021 — the system can predict when tool failure will occur and how far into the future the tool failure will occur… acquired sensor information may identify cutting information such as cutting force, acoustic emissions (sound); 0045 — The system can also perform real-time tool health monitoring, failure prediction and sending real time alerts. The system enables monitoring tool life by estimating the remaining life of a tool based on survival analysis and sending out real time recommendation to replace the tool whenever significant anomalies are detected (initiating reparative action)].
Mizobuchi, Thoren and Kummari are analogous art. Mizobuchi and Thoren relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals. And Mizobuchi and Kummari relate to relate to abnormality prediction based on audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above apparatus, as taught by the combination of Mizobuchi and Thoren, by incorporating the above limitations, as taught by Kummari.
One of ordinary skill in the art would have been motivated to do this modification to facilitate replacing a component at an optimum time, as suggested by Kummari [0041], thus improving operation of a device.
But the combination of Mizobuchi, Thoren and Kummari fails to clearly specify adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter.
However, Lanman teaches adjusting (i) one or more cutoff frequency parameters of at least one bandpass filter, and (ii) one or more slope parameters of the at least one band-pass filter; and filtering the one or more digital signals using the at least one band-pass filter [0041-0044 — audio processor 410 receives and processes a digital ambient audio stream, such as the ambient audio stream 222, to provide a personal audio stream, such as the personal audio stream 232. The audio processor 410 may perform processes including filtering, equalization, compression, limiting, and/or other processes. Filtering may include high-pass, low-pass, band-pass, and band-reject filtering... the active processing parameter set 425 may define filtering by a low pass filter with a particular cut-off frequency (the frequency at which the filter start to attenuate) and slope (the rate of change of attenuation with frequency); 0068 — the parameter set learning application 526 may use known machine learning techniques such as neural nets, fuzzy logic, adaptive neuro-fuzzy inference systems, or combinations of these and other machine learning methodologies to learn revised and/or new processing parameter sets].
Mizobuchi, Thoren, Kummari and Lanman are analogous art. Mizobuchi, Thoren and Lanman relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals. And Mizobuchi and Kummari relate to relate to abnormality prediction based on audio signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Mizobuchi, Thoren, Kummari by incorporating the above limitations, as taught by Lanman.
One of ordinary skill in the art would have been motivated to do this modification to remove unwanted noise by automatically adapting audio filter parameters, as suggested by Lanman [0007-0008, 0041-0044].
Regarding claim 18, the combination of Mizobuchi, Thoren and Kummari teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 3.
Regarding claim 19, the combination of Mizobuchi, Thoren and Kummari teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 5.
Regarding claim 22, the combination of Mizobuchi, Thoren and Kummari teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 6.
Regarding claim 25, the combination of Mizobuchi, Thoren and Kummari teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 6.
Claim(s) 8, 23 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Mizobuchi, Thoren, Kummari and Lanman in view of Allard et al. U.S. Patent Publication No. 20200380391 (hereinafter Allard).
Regarding claim 8, the combination of Mizobuchi, Thoren, Kummari and Lanman teaches all the limitations of the base claims as outlined above.
Further, Mizobuchi teaches performing one or more automated actions [0079 — the possibility of failure in the near future is low, a monitor display screen for displaying determination of normality is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S33)… when there is a possibility of failure in the near future, a monitor display screen for displaying an abnormality degree transition having a possibility in the future is generated in the abnormality degree transition display unit 141 and the abnormality prediction result display unit 142 of the display processing unit 14, and is displayed on the monitor 62 (S34)].
But the combination of Mizobuchi, Thoren, Kummari and Lanman fails to clearly specify automatically training at least a portion of the one or more artificial intelligence techniques based at least in part on feedback related to the at least one predicted failure.
However, Allard teaches automatically training at least a portion of the one or more artificial intelligence techniques based at least in part on feedback related to the at least one predicted failure [0028-0034 — The sensor data may be sent, via a network 16, to an artificial intelligence (AI) system 18. The AI system 18 may process the sensor data to determine, at least in part, a predictive model (e.g., an analytic model) for implementing predictive diagnostics… sensors 14 may record (e.g., determine) acoustic data, such as sounds or acoustic emissions generated by the device 12. The acoustic data may be associated in particular with a component or aspect of the device 12 that is vulnerable to failure and/or is a subject of the predictive diagnostics; 0089-0092, Fig. 8 — At step 830, one or more machine learning processes may be used to update the predictive model based on the received sensor data, such as determining one or more data anomalies in the plurality of sensor data time series. For example, in the temporal analysis (ML) module 652 of FIG. 6, one or more anomalous data points in each of one or more sensor data time series of the plurality of sensor data time series may be determined. Additionally or alternatively, in the population analysis (ML) module 654 of FIG. 6, one or more anomalous sensor data time series from the plurality of sensor data time series may be determined. Further, updating the predictive model may comprise comparing two or more of the determined anomalous sensor time series to determine a predictive trend)].
Mizobuchi, Thoren, Kummari, Lanman and Allard are analogous art. Mizobuchi, Thoren and Lanman relate to audio data acquisition and signal processing systems and Mizobuchi specifically relates to abnormality prediction based on signal processing of audio signals; Mizobuchi and Kummari relate to relate to abnormality prediction based on audio signals; and Mizobuchi and Allard relate to abnormality prediction based on acoustic signals.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Mizobuchi, Thoren, Kummari and Lanman, by incorporating the above limitations, as taught by Allard.
One of ordinary skill in the art would have been motivated to do this modification to facilitate continuously improving the predictive algorithm including determining a predictive trend, as suggested by Allard [0091].
Regarding claim 23, the combination of Mizobuchi, Thoren, Kummari and Lanman teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 8.
Regarding claim 26, the combination of Mizobuchi, Thoren, Kummari and Lanman teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 8.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Klimanis et al. U.S. Patent Publication No. 20200389718 discloses noise suppression systems and methods for audio processing.
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119