DETAILED ACTION
Applicant’s arguments, filed on 02/26/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 02/26/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-20 are the current claims hereby under examination.
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 .
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.
Claims 12 and 20 are 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.
Regarding claim 12, the claim recites the limitation “sample cardiac sound signals” in lines 1-2. It is unclear if this limitation is meant to refer to the measured cardiac audio samples from claim 1, line 8, or a different sample cardiac sound signals. If it is meant to refer to the cardiac audio samples from claim 1, it needs to refer back to it. If it is meant to refer to different cardiac sound signals, it needs to be distinguished from the cardiac audio samples from claim 1. For purposes of examination, it is being interpreted as referring to the cardiac audio samples from claim 1.
Further regarding claim 12, the claim recites the limitation “sample ECG signals” from line 2. It is unclear if this limitation is meant to refer to the sample measured ECG signals from claim 1, line 9, or different sample ECG signals. If it is meant to refer to the sample measured ECG signals from claim 1, it needs to refer back to it. If it is meant to refer to a different sample ECG signals, it needs to be distinguished from the sample measured ECG signals from claim 1. For purposes of examination, it is being interpreted as referring to the sample ECG signals from claim 1.
Further regarding claim 12, the claim recites the limitation “sample users” in line 3. It is unclear if this limitation is meant to refer to the sample users from claim 1, line 9, or different sample users. If it is meant to refer to the sample users from claim 1, it needs to refer back to it. If it is meant to refer to different sample users, it needs to be distinguished from the sample users from claim 1. For purposes of examination, it is being interpreted as referring to the sample users from claim 1.
Regarding claim 20, the claim recites the limitation “sample cardiac sound signals” in lines 1-2. It is unclear if this limitation is meant to refer to the measured cardiac audio samples from claim 13, line 6, or a different sample cardiac sound signals. If it is meant to refer to the cardiac audio samples from claim 13, it needs to refer back to it. If it is meant to refer to different cardiac sound signals, it needs to be distinguished from the cardiac audio samples from claim 13. For purposes of examination, it is being interpreted as referring to the cardiac audio samples from claim 13.
Further regarding claim 20, the claim recites the limitation “sample ECG signals” from line 2. It is unclear if this limitation is meant to refer to the sample measured ECG signals from claim 13, lines 6-7, or different sample ECG signals. If it is meant to refer to the sample measured ECG signals from claim 13, it needs to refer back to it. If it is meant to refer to a different sample ECG signals, it needs to be distinguished from the sample measured ECG signals from claim 13. For purposes of examination, it is being interpreted as referring to the sample ECG signals from claim 13.
Further regarding claim 20, the claim recites the limitation “sample users” in line 3. It is unclear if this limitation is meant to refer to the sample users from claim 13, line 7, or different sample users. If it is meant to refer to the sample users from claim 13, it needs to refer back to it. If it is meant to refer to different sample users, it needs to be distinguished from the sample users from claim 13. For purposes of examination, it is being interpreted as referring to the sample users from claim 13.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5, 7, 9-10, 12-13, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ko (US 20220087614) in view of Zhang (CN 115177260). Citations to CN 115177260 will refer to the English Machine Translation that accompanies this Office Action.
Regarding independent claim 1, Ko teaches a system for contactless monitoring of cardiac activities ([0002]: “The present disclosure relates to an electrocardiogram monitoring device and method and, more particularly, to a non-invasive electrocardiogram monitoring device and method using a small vibration sensor”), the system comprising:
a vibration sensor operable to collect audio input signals of a user ([0037]: “the vibration sensor unit 110 is implemented as a small vibration sensor to detect vibrations generated by a subject under observation”); and
a processor ([0110]: “The method according to the present disclosure may be implemented as a computer program stored in a medium for execution by a computer”. The computer contains a processor that executes the steps of the method.) operable to:
extract, using an audio model, cardiac sound data from the audio input signals (Abstract: “extract a seismocardiography (SCG) signal caused by a heart vibration of the subject under observation “; [0071]: “four core heart sounds S1 to S4 are also detected in the SCG signal. Specifically, the first heart sound S1 is the closing sound of the mitral valve, which is a valve between the left atrium and the left ventricle, and the second heart sound S2 is the closing sound of the aortic valve, which is a half-moon-shaped valve connecting the ventricles. In addition, the third heart sound S3 is an exogenous sound due to rapid filling of the aorta, and the fourth heart sound S4 is a gallop rhythm just before artery contraction, and all of the sounds are generated by the electrical activity of the heart.”; [0110]: “The method according to the present disclosure may be implemented as a computer program stored in a medium for execution by a computer”. The computer program includes the audio model, and the SCG signal is the cardiac sound data.),
transfer, using a trained neural network, the cardiac sound data into a simulated electrocardiogram (ECG) data based on a weak alignment between cardiac sounds and ECG ([0073]: “the ECG waveform acquisition unit 140 according to an embodiment enables ECG data to be extracted from the digitally converted SCG data using a pre-trained artificial neural network “; [0070]: “it is well known that a correlation between the waveform of the SCG signal and the waveform of the ECG signal is high”. The correlation between the SCG signal and the ECG signal is the weak alignment, as the SCG signal is the cardiac sounds.).
Ko teaches training the neural network based on measured cardiac audio samples and sample measured ECG signals simultaneously recorded ([0087]: “the ECG pattern estimation unit 141 may be trained using multiple pieces of learning data acquired by mapping the ECG data and the SCG data measured using separate measurement equipment, etc. in advance. The ECG pattern estimation unit 141 receives the SCG data of the learning data that is measured and acquired in advance as an input and outputs ECG data corresponding to the received SCG data. When the corresponding ECG data is output, the ECG pattern estimation unit 141, which is implemented as an artificial neural network, may be trained by calculating an error between the corresponding ECG data and the ECG data mapped to the SCG data input as the training data, propagating the calculated error back to the ECG pattern estimation unit 141, and repeating this process until the error is less than or equal to a predetermined reference error.”), however Ko does not teach training the neural network based on samples from a plurality of sample users.
Zhang discloses an ECG heart sound diagnosis method based on an artificial neural network. Specifically, Zhang teaches wherein the neural network is trained based on samples from a plurality of sample users (Claim 6: “Obtain a sample set and divide the sample set into a training set and a test set. The sample set includes the electrocardiogram and heart sound feature vectors of multiple patients and their corresponding labels”). Ko and Zhang are analogous art as they are both related to neural networks used to analyze heart sounds.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the plurality of users from Zhang into the system from Ko as it allows the neural network to be trained by more than one user, which can provide more information including different alignments from multiple users, which can allow for a broader range of data and a more accurate neural network.
Regarding claim 2, the Ko/Zhang combination teaches the system of claim 1, wherein the vibration sensor is selected from an audio sensor, an accelerometer sensor, or a combination thereof (Ko, [0037]: “the vibration sensor unit 110 may be implemented as a geophone”. A geophone is an audio sensor, therefore the vibration sensor is an audio sensor.).
Regarding claim 5, the Ko/Zhang combination teaches the system of claim 1, wherein the audio model extracts the cardiac sound data by filtering, normalizing, or segmenting the audio input signals (Ko, [0011]: “The filter unit may include a first filter unit implemented as a low pass filter and configured to receive the vibration signal and filter the vibration signal to remove a frequency band of more than a first predetermined frequency, and a second filter unit implemented as a high pass filter and configured to receive the filtered signal from the first filter unit and filter the received signal to remove a frequency band of less than a second predetermined frequency”; [0065]: “the sampling unit 130 receives the acquired SCG signal, samples the received SCG signal to perform digital conversion, and acquires SCG data. The sampling unit 130 may acquire the SCG data by sampling the SCG signal at a sampling rate of a predetermined frequency (e.g., 250 Hz) higher than the filtering frequency of the filter unit 120. In this case, the sampling unit 130 may normalize the sampled SCG signal to a predetermined range (here, e.g., [−1:1])”; [0098]: “the ECG waveform analysis unit 150 may extract various clinical indicators (e.g., time stamps such as P, Q, R, S, T, RR interval, and QRS segment length)”).
Regarding claim 7, the Ko/Zhang combination teaches the system of claim 5, wherein the normalization comprises adjusting amplitudes of the audio input signals by peak amplitude normalization, root mean square normalization, or loudness normalization (Ko, [0065]: “the sampling unit 130 receives the acquired SCG signal, samples the received SCG signal to perform digital conversion, and acquires SCG data. The sampling unit 130 may acquire the SCG data by sampling the SCG signal at a sampling rate of a predetermined frequency (e.g., 250 Hz) higher than the filtering frequency of the filter unit 120. In this case, the sampling unit 130 may normalize the sampled SCG signal to a predetermined range (here, e.g., [−1:1])”).
Regarding claim 9, the Ko/Zhang combination teaches the system of claim 1, wherein the weak alignment between the cardiac sounds and the ECG is established based on correlated pair features between the cardiac sounds and the ECG (Ko, [0070]: “it is well known that a correlation between the waveform of the SCG signal and the waveform of the ECG signal is high. Both of an SCG signal and an ECG signal are time-series signals collected from a human heart, and the SCG signal represents a vibration due to the periodic motion and blood flow of the heart, and the ECG signal represents a corresponding electrical signal. This is because, from a clinical point of view, the electrical activity of the heart causes periodic depolarization and repolarization, which induces periodic cardiac muscle contraction, cardiac muscle relaxation, and blood flow.”; [0071]: “peaks P, Q, R, S, and T of the ECG signal are generated by the electrical activity of the heart, and four core heart sounds S1 to S4 are also detected in the SCG signal. Specifically, the first heart sound S1 is the closing sound of the mitral valve, which is a valve between the left atrium and the left ventricle, and the second heart sound S2 is the closing sound of the aortic valve, which is a half-moon-shaped valve connecting the ventricles. In addition, the third heart sound S3 is an exogenous sound due to rapid filling of the aorta, and the fourth heart sound S4 is a gallop rhythm just before artery contraction, and all of the sounds are generated by the electrical activity of the heart.”).
Regarding claim 10, the Ko/Zhang combination teaches the system of claim 9, wherein the correlated pair features comprise R peaks or T peaks in the ECG and Si features or S2 features in the cardiac sounds (Ko, [0071]: “peaks P, Q, R, S, and T of the ECG signal are generated by the electrical activity of the heart, and four core heart sounds S1 to S4 are also detected in the SCG signal. Specifically, the first heart sound S1 is the closing sound of the mitral valve, which is a valve between the left atrium and the left ventricle, and the second heart sound S2 is the closing sound of the aortic valve, which is a half-moon-shaped valve connecting the ventricles. In addition, the third heart sound S3 is an exogenous sound due to rapid filling of the aorta, and the fourth heart sound S4 is a gallop rhythm just before artery contraction, and all of the sounds are generated by the electrical activity of the heart.”; Fig. 5).
Regarding claim 12, the Ko/Zhang combination teaches the system of claim 1, wherein the neural network is trained using sample cardiac sound signals and sample ECG signals, wherein the sample cardiac sound signals and the sample ECG signals are simultaneously recorded from same sample users (Ko, [0087]: “the ECG pattern estimation unit 141 may be trained using multiple pieces of learning data acquired by mapping the ECG data and the SCG data measured using separate measurement equipment, etc. in advance. The ECG pattern estimation unit 141 receives the SCG data of the learning data that is measured and acquired in advance as an input and outputs ECG data corresponding to the received SCG data. When the corresponding ECG data is output, the ECG pattern estimation unit 141, which is implemented as an artificial neural network, may be trained by calculating an error between the corresponding ECG data and the ECG data mapped to the SCG data input as the training data, propagating the calculated error back to the ECG pattern estimation unit 141, and repeating this process until the error is less than or equal to a predetermined reference error.”; Zhang. Claim 6: “Obtain a sample set and divide the sample set into a training set and a test set. The sample set includes the electrocardiogram and heart sound feature vectors of multiple patients and their corresponding labels”).
Regarding independent claim 13, Ko teaches a method for contactless monitoring of cardiac activities comprising ([0002]: “The present disclosure relates to an electrocardiogram monitoring device and method and, more particularly, to a non-invasive electrocardiogram monitoring device and method using a small vibration sensor”):
extracting, using an audio model, cardiac sound data from audio input signals of a user collected using a vibration sensor ([0037]: “the vibration sensor unit 110 is implemented as a small vibration sensor to detect vibrations generated by a subject under observation”; Abstract: “extract a seismocardiography (SCG) signal caused by a heart vibration of the subject under observation “; [0071]: “four core heart sounds S1 to S4 are also detected in the SCG signal. Specifically, the first heart sound S1 is the closing sound of the mitral valve, which is a valve between the left atrium and the left ventricle, and the second heart sound S2 is the closing sound of the aortic valve, which is a half-moon-shaped valve connecting the ventricles. In addition, the third heart sound S3 is an exogenous sound due to rapid filling of the aorta, and the fourth heart sound S4 is a gallop rhythm just before artery contraction, and all of the sounds are generated by the electrical activity of the heart.”; [0110]: “The method according to the present disclosure may be implemented as a computer program stored in a medium for execution by a computer”. The computer program includes the audio model, and the SCG signal is the cardiac sound data.); and
transferring, using a trained neural network, the cardiac sound data into a simulated electrocardiogram (ECG) data based on a weak alignment between cardiac sounds and ECG ([0073]: “the ECG waveform acquisition unit 140 according to an embodiment enables ECG data to be extracted from the digitally converted SCG data using a pre-trained artificial neural network “; [0070]: “it is well known that a correlation between the waveform of the SCG signal and the waveform of the ECG signal is high”. The correlation between the SCG signal and the ECG signal is the weak alignment, as the SCG signal is the cardiac sounds.).
Ko teaches training the neural network based on measured cardiac audio samples and sample measured ECG signals simultaneously recorded ([0087]: “the ECG pattern estimation unit 141 may be trained using multiple pieces of learning data acquired by mapping the ECG data and the SCG data measured using separate measurement equipment, etc. in advance. The ECG pattern estimation unit 141 receives the SCG data of the learning data that is measured and acquired in advance as an input and outputs ECG data corresponding to the received SCG data. When the corresponding ECG data is output, the ECG pattern estimation unit 141, which is implemented as an artificial neural network, may be trained by calculating an error between the corresponding ECG data and the ECG data mapped to the SCG data input as the training data, propagating the calculated error back to the ECG pattern estimation unit 141, and repeating this process until the error is less than or equal to a predetermined reference error.”), however Ko does not teach training the neural network based on samples from a plurality of sample users.
Zhang discloses an ECG heart sound diagnosis method based on an artificial neural network. Specifically, Zhang teaches wherein the neural network is trained based on samples from a plurality of sample users (Claim 6: “Obtain a sample set and divide the sample set into a training set and a test set. The sample set includes the electrocardiogram and heart sound feature vectors of multiple patients and their corresponding labels”). Ko and Zhang are analogous art as they are both related to neural networks used to analyze heart sounds.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the plurality of users from Zhang into the method from Ko as it allows the neural network to be trained by more than one user, which can provide more information including different alignments from multiple users, which can allow for a broader range of data and a more accurate neural network.
Regarding claim 18, the Ko/Zhang combination teaches the method of claim 13, wherein the weak alignment between the cardiac sounds and the ECG is established based on correlated pair features between the cardiac sounds and the ECG (Ko, [0070]: “it is well known that a correlation between the waveform of the SCG signal and the waveform of the ECG signal is high. Both of an SCG signal and an ECG signal are time-series signals collected from a human heart, and the SCG signal represents a vibration due to the periodic motion and blood flow of the heart, and the ECG signal represents a corresponding electrical signal. This is because, from a clinical point of view, the electrical activity of the heart causes periodic depolarization and repolarization, which induces periodic cardiac muscle contraction, cardiac muscle relaxation, and blood flow.”; [0071]: “peaks P, Q, R, S, and T of the ECG signal are generated by the electrical activity of the heart, and four core heart sounds S1 to S4 are also detected in the SCG signal. Specifically, the first heart sound S1 is the closing sound of the mitral valve, which is a valve between the left atrium and the left ventricle, and the second heart sound S2 is the closing sound of the aortic valve, which is a half-moon-shaped valve connecting the ventricles. In addition, the third heart sound S3 is an exogenous sound due to rapid filling of the aorta, and the fourth heart sound S4 is a gallop rhythm just before artery contraction, and all of the sounds are generated by the electrical activity of the heart.”), and the correlated pair features comprise R peaks or T peaks in the ECG and Si features or S2 features in the cardiac sounds (Ko, [0071]: “peaks P, Q, R, S, and T of the ECG signal are generated by the electrical activity of the heart, and four core heart sounds S1 to S4 are also detected in the SCG signal. Specifically, the first heart sound S1 is the closing sound of the mitral valve, which is a valve between the left atrium and the left ventricle, and the second heart sound S2 is the closing sound of the aortic valve, which is a half-moon-shaped valve connecting the ventricles. In addition, the third heart sound S3 is an exogenous sound due to rapid filling of the aorta, and the fourth heart sound S4 is a gallop rhythm just before artery contraction, and all of the sounds are generated by the electrical activity of the heart.”; Fig. 5).
Regarding claim 20, the Ko/Zhang combination teaches the method of claim 13, wherein the neural network is trained using sample cardiac sound signals and sample ECG signals, wherein the sample cardiac sound signals and the sample ECG signals are simultaneously recorded from same sample users (Ko, [0087]: “the ECG pattern estimation unit 141 may be trained using multiple pieces of learning data acquired by mapping the ECG data and the SCG data measured using separate measurement equipment, etc. in advance. The ECG pattern estimation unit 141 receives the SCG data of the learning data that is measured and acquired in advance as an input and outputs ECG data corresponding to the received SCG data. When the corresponding ECG data is output, the ECG pattern estimation unit 141, which is implemented as an artificial neural network, may be trained by calculating an error between the corresponding ECG data and the ECG data mapped to the SCG data input as the training data, propagating the calculated error back to the ECG pattern estimation unit 141, and repeating this process until the error is less than or equal to a predetermined reference error.”; Zhang, Claim 6: “Obtain a sample set and divide the sample set into a training set and a test set. The sample set includes the electrocardiogram and heart sound feature vectors of multiple patients and their corresponding labels”).
Claims 3-4, 6, 11, 14-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the Ko/Zhang combination as applied to claims 1, 2, 5, and 13 above, and further in view of Landgraf (US 20210345934).
Regarding claim 3, the Ko/Zhang combination teaches the system of claim 2.
However, the Ko/Zhang combination does not teach wherein the audio sensor is an air-coupled audio sensor, a condenser microphone, an electret microphone, or a piezoelectric microphone.
Landgraf discloses methods and systems for determining a condition of a subject. Specifically, Landgraf teaches wherein the audio sensor is an air-coupled audio sensor, a condenser microphone, an electret microphone, or a piezoelectric microphone ([0121]: “The audio sensor may comprise a piezoelectric sensor”). Ko, Zhang, and Landgraf are analogous art as they are all related to systems that use audio sensors to detect conditions of a patient.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the piezoelectric sensor from Landgraf into the system from the Ko/Zhang combination as the piezoelectric sensor from Landgraf is also an audio sensor that has the capability of sensing the necessary measurements for the analysis conducted in the system. This would be a simple substitution to include the piezoelectric sensor as the sensor, as piezoelectric sensors are known the art and would produce an audio measurement that can be used to determine the cardiac sounds, therefore it would have been obvious to use this sensor in this system.
Regarding claim 4, the Ko/Zhang combination teaches the system of claim 2, wherein the sensor is embedded in a fixture (Ko, [0040]: “the vibration sensor unit 110 is attached to various pieces of furniture, such as a bed, a chair, etc., where a subject under observation is located and is configured to detect vibrations only by indirect contact with the subject under observation”).
However, the Ko/Zhang combination does not teach the sensor being an accelerometer.
Landgraf discloses the sensor being an accelerometer ([0109]: “Examples of sensor modalities include electrical sensors (e.g., … accelerometers”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the accelerometer from Landgraf into the system from the Ko/Zhang combination as it also a sensor that has the capability of sensing the necessary measurements for the analysis conducted in the system. This would be a simple substitution to include the accelerometer as the sensor, as accelerometers are known the art and would produce an audio measurement that can be used to determine the cardiac sounds.
Regarding claim 6, the Ko/Zhang combination teaches the system of claim 5, wherein the system further comprises a signal processing filter to isolate frequencies of the audio input signals between a specific range (Ko, [0060]: “a frequency band of less than 5 Hz and a frequency band of more than 30 Hz are noise bands of the SM-24 geophone sensor. When the vibration sensor unit 110 is implemented as a vibration sensor other than the SM-24 geophone sensor, the noise bands may be changed”).
However, the Ko/Zhang combination does not teach the range being between 25 Hz and 50 Hz.
Ko discloses that the range can be changed relative to the sensor being used, therefore it would have been obvious for the range to be between 25 Hz and 50 Hz. Additionally, Landgraf discloses a variety of possible filters to be used to filter audio data for analysis ([0136]: “The ECG data may be filtered. In an example, filters may include low-pass filters to attenuate high-frequency components above the set frequency. The frequency of the low-pass filter may comprise at least about 20 Hz, 50 Hz, 100 Hz, 500 Hz, 1 kHz, 2 kHz, 3 kHz, 4 kHz, 5 kHz, 6 kHz, 7 kHz, 8 kHz, 9 kHz, 10 kHz, 15 kHz, 20 kHz, or more. In an example, filters may include high-pass filters to attenuate low-frequency components below the set frequency. The frequency of the high-pass filter may comprise at least about 20 Hz, 50 Hz, 100 Hz, 500 Hz, 1 kHz, 2 kHz, 3 kHz, 4 kHz, 5 kHz, 6 kHz, 7 kHz, 8 kHz, 9 kHz, 10 kHz, 15 kHz, 20 kHz, or more. In other examples, the filters may comprise band pass filters, Fourier filters, or other filters.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention it would have been obvious to adjust the range of the filters depending on the desired results and the specific sensor being used.
Regarding claim 11, the Ko/Zhang combination teaches the system of claim 1, wherein the neural network outputs the simulated ECG data based on estimated R peak locations and RR intervals (Ko, [0073]: “the ECG waveform acquisition unit 140 according to an embodiment enables ECG data to be extracted from the digitally converted SCG data using a pre-trained artificial neural network”; [0070]: “it is well known that a correlation between the waveform of the SCG signal and the waveform of the ECG signal is high. Both of an SCG signal and an ECG signal are time-series signals collected from a human heart, and the SCG signal represents a vibration due to the periodic motion and blood flow of the heart, and the ECG signal represents a corresponding electrical signal”).
However, the Ko/Zhang combination does not teach using heart rate in the analysis.
Landgraf teaches determining heart rate for analysis ([0236]: “a Heart Rate algorithm comprising a signal processing algorithm that processes ECG data or heart audio data to calculate the heart rate of a subject”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the heart rate determination from Landgraf into the system from the Ko/Zhang combination as it allows the system to have more information for analysis. Determining heart rate can assist the system in knowing how fast the heart was beating when the measurements were taken, which can allow the system to better identify the different peaks in the cardiac cycle, and allow for a more accurate and comprehensive analysis.
Regarding claim 14, the Ko/Zhang combination teaches the method of claim 13.
However, the Ko/Zhang combination does not teach wherein the audio sensor is an air-coupled audio sensor, a condenser microphone, an electret microphone, or a piezoelectric microphone.
Landgraf teaches wherein the audio sensor is an air-coupled audio sensor, a condenser microphone, an electret microphone, or a piezoelectric microphone ([0121]: “The audio sensor may comprise a piezoelectric sensor”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the piezoelectric sensor from Landgraf into the method from the Ko/Zhang combination as the piezoelectric sensor from Landgraf is also an audio sensor that has the capability of sensing the necessary measurements for the analysis conducted in the system. This would be a simple substitution to include the piezoelectric sensor as the sensor, as piezoelectric sensors are known the art and would produce an audio measurement that can be used to determine the cardiac sounds, therefore it would have been obvious to use this sensor in this system.
Regarding claim 15, the Ko/Zhang/Landgraf combination teaches the method of claim 14, wherein the sensor is embedded in a fixture (Ko, [0040]: “the vibration sensor unit 110 is attached to various pieces of furniture, such as a bed, a chair, etc., where a subject under observation is located and is configured to detect vibrations only by indirect contact with the subject under observation”).
However, the Ko/Zhang/Landgraf combination does not teach the sensor being an accelerometer.
Landgraf discloses the sensor being an accelerometer ([0109]: “Examples of sensor modalities include electrical sensors (e.g., … accelerometers”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the accelerometer from Landgraf into the method from the Ko/Zhang/Landgraf combination as it also a sensor that has the capability of sensing the necessary measurements for the analysis conducted in the system. This would be a simple substitution to include the accelerometer as the sensor, as accelerometers are known the art and would produce an audio measurement that can be used to determine the cardiac sounds.
Regarding claim 16, the Ko/Zhang combination teaches the method of claim 13, wherein: the audio model extracts the cardiac sound data by filtering, normalizing, or segmenting the audio input signals (Ko, [0011]: “The filter unit may include a first filter unit implemented as a low pass filter and configured to receive the vibration signal and filter the vibration signal to remove a frequency band of more than a first predetermined frequency, and a second filter unit implemented as a high pass filter and configured to receive the filtered signal from the first filter unit and filter the received signal to remove a frequency band of less than a second predetermined frequency”; [0065]: “the sampling unit 130 receives the acquired SCG signal, samples the received SCG signal to perform digital conversion, and acquires SCG data. The sampling unit 130 may acquire the SCG data by sampling the SCG signal at a sampling rate of a predetermined frequency (e.g., 250 Hz) higher than the filtering frequency of the filter unit 120. In this case, the sampling unit 130 may normalize the sampled SCG signal to a predetermined range (here, e.g., [−1:1])”; [0098]: “the ECG waveform analysis unit 150 may extract various clinical indicators (e.g., time stamps such as P, Q, R, S, T, RR interval, and QRS segment length)”); the filtering comprises isolating frequencies of the audio input signals between a specific range ([0060]: “a frequency band of less than 5 Hz and a frequency band of more than 30 Hz are noise bands of the SM-24 geophone sensor. When the vibration sensor unit 110 is implemented as a vibration sensor other than the SM-24 geophone sensor, the noise bands may be changed”); and the normalization comprises adjusting amplitudes of the audio input signals by peak amplitude normalization, root mean square normalization, or loudness normalization ([0065]: “the sampling unit 130 receives the acquired SCG signal, samples the received SCG signal to perform digital conversion, and acquires SCG data. The sampling unit 130 may acquire the SCG data by sampling the SCG signal at a sampling rate of a predetermined frequency (e.g., 250 Hz) higher than the filtering frequency of the filter unit 120. In this case, the sampling unit 130 may normalize the sampled SCG signal to a predetermined range (here, e.g., [−1:1])”).
However, the Ko/Zhang combination does not teach the range being between 25 Hz and 50 Hz.
Ko discloses that the range can be changed relative to the sensor being used, therefore it would have been obvious for the range to be between 25 Hz and 50 Hz. Additionally, Landgraf discloses a variety of possible filters to be used to filter audio data for analysis ([0136]: “The ECG data may be filtered. In an example, filters may include low-pass filters to attenuate high-frequency components above the set frequency. The frequency of the low-pass filter may comprise at least about 20 Hz, 50 Hz, 100 Hz, 500 Hz, 1 kHz, 2 kHz, 3 kHz, 4 kHz, 5 kHz, 6 kHz, 7 kHz, 8 kHz, 9 kHz, 10 kHz, 15 kHz, 20 kHz, or more. In an example, filters may include high-pass filters to attenuate low-frequency components below the set frequency. The frequency of the high-pass filter may comprise at least about 20 Hz, 50 Hz, 100 Hz, 500 Hz, 1 kHz, 2 kHz, 3 kHz, 4 kHz, 5 kHz, 6 kHz, 7 kHz, 8 kHz, 9 kHz, 10 kHz, 15 kHz, 20 kHz, or more. In other examples, the filters may comprise band pass filters, Fourier filters, or other filters.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention it would have been obvious to adjust the range of the filters depending on the desired results and the specific sensor being used.
Regarding claim 19, the Ko/Zhang combination teaches the method of claim 13, wherein the neural network outputs the simulated ECG data based on estimated R peak locations and RR intervals (Ko, [0073]: “the ECG waveform acquisition unit 140 according to an embodiment enables ECG data to be extracted from the digitally converted SCG data using a pre-trained artificial neural network”; [0070]: “it is well known that a correlation between the waveform of the SCG signal and the waveform of the ECG signal is high. Both of an SCG signal and an ECG signal are time-series signals collected from a human heart, and the SCG signal represents a vibration due to the periodic motion and blood flow of the heart, and the ECG signal represents a corresponding electrical signal”).
However, the Ko/Zhang combination does not teach using heart rate in the analysis.
Landgraf teaches determining heart rate for analysis ([0236]: “a Heart Rate algorithm comprising a signal processing algorithm that processes ECG data or heart audio data to calculate the heart rate of a subject”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the heart rate determination from Landgraf into the method from the Ko/Zhang combination as it allows the method to have more information for analysis. Determining heart rate can assist the method in knowing how fast the heart was beating when the measurements were taken, which can allow the system to better identify the different peaks in the cardiac cycle, and allow for a more accurate and comprehensive analysis.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the Ko/Zhang combination as applied to claims 1 and 13 above, and further in view of Kupryjanow (US 20220084535).
Regarding claim 8, the Ko/Zhang combination teaches the system of claim 1, wherein the neural network is operable to: feed the cardiac sound data to generate a representation of the cardiac sounds of the user (Ko, [0016]: “The ECG waveform acquisition unit may include a sampling unit configured to sample the SCG signal at a predetermined sampling rate to convert the SCG signal into SCG data, and an ECG pattern estimation unit implemented as a pre-trained Bidirectional Long/Short-Term Memory (Bi-LSTM) neural network and configured to estimate a pattern change over time of the SCG data”), estimate indicative parts of the cardiac sound data corresponding to the simulated ECG data, apply the estimated parts to the cardiac sound data to isolate the cardiac sound data representing denoised cardiac sounds of the user (Ko, [0060]: “a frequency band of less than 5 Hz and a frequency band of more than 30 Hz are noise bands of the SM-24 geophone sensor. When the vibration sensor unit 110 is implemented as a vibration sensor other than the SM-24 geophone sensor, the noise bands may be changed”), reconstruct the isolated cardiac sound data to generate the denoised cardiac sounds, and generate the simulated ECG data based on the denoised cardiac sounds and the weak alignment between the cardiac sounds and the ECG (Ko, [0073]: “the ECG waveform acquisition unit 140 according to an embodiment enables ECG data to be extracted from the digitally converted SCG data using a pre-trained artificial neural network “; [0070]: “it is well known that a correlation between the waveform of the SCG signal and the waveform of the ECG signal is high”).
However, the Ko/Zhang combination is silent on the specific structures and techniques of the neural network used to process the data in this analysis.
Kupryjanow discloses a noise suppression system. Specifically, Kupryjanow teaches wherein the neural network comprises an encoder, a separator, and a decoder, and uses masks to separate the cardiac data from noise (Claim 1: “dynamic noise suppression system, the system comprising: an encoder circuit to generate a magnitude spectrum and a phase spectrum of an input audio signal, the input audio signal comprising speech and dynamic noise; a separator circuit comprising a temporal convolution network (TCN) to generate a separation mask based on the magnitude spectrum … and a decoder circuit to reconstruct the input audio signal with reduced dynamic noise”). Ko and Kupryjanow are analogous arts as they are both related to systems that filter data to separate the desired sounds from excess noise.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the structures and techniques from Kupryjanow into the system from the Ko/Zhang combination as the system is silent on the structures and techniques, and Kupryjanow discloses suitable structures and techniques in an analogous device.
Regarding claim 17, the Ko/Zhang combination teaches the method of claim 13, wherein the neural network is operable to: feed the cardiac sound data to generate a representation of the cardiac sounds of the user (Ko, [0016]: “The ECG waveform acquisition unit may include a sampling unit configured to sample the SCG signal at a predetermined sampling rate to convert the SCG signal into SCG data, and an ECG pattern estimation unit implemented as a pre-trained Bidirectional Long/Short-Term Memory (Bi-LSTM) neural network and configured to estimate a pattern change over time of the SCG data”), estimate indicative parts of the cardiac sound data corresponding to the simulated ECG data, apply the estimated parts to the cardiac sound data to isolate the cardiac sound data representing denoised cardiac sounds of the user (Ko, [0060]: “a frequency band of less than 5 Hz and a frequency band of more than 30 Hz are noise bands of the SM-24 geophone sensor. When the vibration sensor unit 110 is implemented as a vibration sensor other than the SM-24 geophone sensor, the noise bands may be changed”), reconstruct the isolated cardiac sound data to generate the denoised cardiac sounds, and generate the simulated ECG data based on the denoised cardiac sounds and the weak alignment between the cardiac sounds and the ECG (Ko, [0073]: “the ECG waveform acquisition unit 140 according to an embodiment enables ECG data to be extracted from the digitally converted SCG data using a pre-trained artificial neural network “; [0070]: “it is well known that a correlation between the waveform of the SCG signal and the waveform of the ECG signal is high”).
However, the Ko/Zhang combination is silent on the specific structures and techniques of the neural network used to process the data in this analysis.
Kupryjanow discloses a noise suppression system. Specifically, Kupryjanow teaches wherein the neural network comprises an encoder, a separator, and a decoder, and uses masks to separate the cardiac data from noise (Claim 1: “dynamic noise suppression system, the system comprising: an encoder circuit to generate a magnitude spectrum and a phase spectrum of an input audio signal, the input audio signal comprising speech and dynamic noise; a separator circuit comprising a temporal convolution network (TCN) to generate a separation mask based on the magnitude spectrum … and a decoder circuit to reconstruct the input audio signal with reduced dynamic noise”). Ko and Kupryjanow are analogous arts as they are both related to systems that filter data to separate the desired sounds from excess noise.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the structures and techniques from Kupryjanow into the method from the Ko/Zhang combination as the method is silent on the structures and techniques, and Kupryjanow discloses suitable structures and techniques in an analogous device.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant’s arguments with respect to claims 1-20 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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