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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2 and 4-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claim 2 recites a process.
Step 2A, Prong 1
Claim 2 recites the limitations of “determining… a plurality of values” and “determining… an estimated value”. These steps, given their broadest reasonable interpretation, can be practically performed in the human mind. Namely, a physician could look at SCG or PCG data and determine/estimate values from that data.
Step 2A, Prong 2
Claim 2 does not include any additional elements that integrate the abstract idea into a practical application.
Claim 2 includes the elements of a processor, obtaining data from a multi-sensor device, and outputting data. The processor amounts to generic computer implementation of the abstract idea. Obtaining data from a multi-sensor device amounts to the insignificant, extra-solution activity of data gathering. Outputting data amounts to the insignificant, extra-solution activity of data output. Therefore, these elements do not amount to integrating the abstract idea into a practical application.
Step 2B
Claim 2 does not include any additional elements that amount to significantly more than the abstract idea.
Claim 2 includes the elements of a processor, obtaining data from a multi-sensor device, and outputting data. The processor amounts to generic computer implementation of the abstract idea. Obtaining data from a multi-sensor device amounts to the insignificant, extra-solution activity of data gathering. Outputting data amounts to the insignificant, extra-solution activity of data output. Therefore, these elements do not amount to significantly more than the abstract idea itself.
Claims 4-18 only further describe the abstract idea and describe generic computer implementation of the abstract idea.
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.
Claim(s) 2, 5, 7, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman (U.S. Patent Application Publication No. 2021/0259560), cited previously, in view of Shankar (US 20200337578).
Regarding claim 2, Venkatraman discloses a method to non-invasively estimate a presence, non-presence, and/or severity of hypertrophic cardiomyopathy in a mammalian subject, the method comprising:
obtaining, by one or more processors, one or more phonocardiographic signals (PCG signals) from a multi-sensor device placed or worn on a patient (Fig. 3: monitoring device 300, Par. 0131);
determining, by the one or more processors utilizing at least a portion of the one or more seismocardiographic signals and/or phonocardiographic signals, a plurality of values associated with a plurality of features or machine-learned-based analyses (Par. 192: “The analysis software 1308 comprises signal quality algorithms to assess the quality of the incoming ECG and PCG data.”; Par. 110: “The trained algorithm may be configured to accept a plurality of input variables and to produce one or more output values based on the plurality of input variables. The plurality of input variables may comprise ECG data and/or audio data.”); and
determining, by the one or more processors, an estimated value for the presence, non- presence and/or severity of hypertrophic cardiomyopathy using the plurality of values associated with the plurality of features or machine-learned-based analyses, outputting, by the one or more processors, the estimated value for the presence, non- presence, and/or severity of hypertrophic cardiomyopathy, wherein the estimated value for the presence, non-presence, and/or severity of hypertrophic cardiomyopathy is outputted for use in a diagnosis of hypertrophic cardiomyopathy and/or to direct treatment of the hypertrophic cardiomyopathy (Par. 110: “The one or more output values may comprise a state or condition of a subject (e.g., a state or condition of a heart, lung, bowel, or other organ or organ system of the subject). Further, in some examples, the trained algorithm may give more weight to certain characteristics of a state or condition.”; Par. 138: “The output indicative of the state or condition of the subject may comprises determining a presence or absence of hypertrophic cardiomyopathy.”).
However, Venkatraman does not specfically disclose obtaining, by one or more processors, one or more seismocardiographic signals (SCG signals) and phonocardiographic signals (PCG signals) from a multi-sensor device placed or worn on a patient. Shankar discloses obtaining, by one or more processors, one or more seismocardiographic signals (SCG signals) and phonocardiographic signals (PCG signals) from a multi-sensor device 101 placed or worn on a patient 100 (Fig. 7, Par. 0047, 0051). This allows for proper heart wall and blood flow signals to be measured and converted into electrical signals for proper diagnosis of the cardiac parameters of a user. Therefore it would have been obvious to one of ordinary skill in the art, at the time of the invention, to modify the device of Venkatraman by adding obtaining, by one or more processors, one or more seismocardiographic signals (SCG signals) and phonocardiographic signals (PCG signals) from a multi-sensor device placed or worn on a patient as taught by Shankar in order to facilitate proper heart wall and blood flow signals to be measured and converted into electrical signals for proper diagnosis of the cardiac parameters of a user.
Regarding claim 5, Venkatraman in view of Shankar, specfically Venkatraman discloses the method of claim 2, wherein the plurality of features or machine-learned-based analyses are configured to quantify beat-to-beat variations in cardiac signals (Par. 110: “For calculating heart rate and heart rate variability and the detection of atrial fibrillation, the trained algorithm may be able to analyze ambulatory ECG data and single-lead ECG signals.” – Heart rate variability is a measure of beat-to-beat variations).
Regarding claim 7, Venkatraman in view of Shankar, specfically Venkatraman discloses the method of claim 2, wherein the plurality of features or machine-learned-based analyses are configured to quantify dynamical characteristics of cardiac, PPG, SCG, and/or PCG signals (Par. 196: “If ECG data are absent or of poor quality, the heart rate is computed from the PCG signal if it has good signal quality using an auto-correlation based analysis.” – According to specification, quantifying dynamical characteristics of signals can be accomplished through “Lyapunov exponent, correlation dimension, entropy, mutual information, correlation, and/or nonlinear filtering correlation” (Par. 13)).
Regarding claim 13, Venkatraman in view of Shankar, specfically Venkatraman discloses the method of claim 2, wherein the plurality of features or machine-learned-based analyses are configured to quantify physiological aspects of the SCG and/or PCG signals (Par. 34: “The state or condition may comprise a biological or physiological state or condition. The state or condition may comprise a particular diagnosis or determination. The state or condition may comprise an unknown state. Determining the state or condition of the subject may comprise determining the state or condition of an organ of the subject, such as, for example, a heart, lung, bowel, or other organ of the subject”; Par. 131: “Such condition may be detected through measuring and/or analyzing/processing the heart audio data, (phonocardiogram/PCG), ECG data, and/or both.”; Par. 108: “the neural network may be used to screen for a certain state or condition of a subject. The neural network may calculate a combined score to provide a quantitative metric for a state or condition of a subject comprising the combination of several metrics such as recorded ECG data, recorded audio data, data from other sensors such as a weight scale or an implantable sensor, user-input data, or data from other sources.”).
Claims 20, 23-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jumbe (U.S. Patent Application Publication No. 2021/0345939), cited previously, in view of Shankar (US 20200337578).
Regarding claim 20, Jumbe discloses an apparatus comprising:
a sensor body configured to be externally worn or placed on a chest region of a subject to acquire biophysical signals from the subject's chest region, including signals of the subject's heart (Fig. 1A: sensing device 110; Par. 356: “In some variations, the sensing device may include a wearable housing. The wearable housing may, for example, be coupled to an adhesive patch configured for attachment to a surface (e.g., skin) of a subject in a suitable body location (e.g., chest, stomach, etc.).”); and
two or more MEMS-based sensors, including a first MEMS-based sensor and a second MEMS-based sensor, wherein the two or more MEMS-based sensors are located within the sensor body (Fig. 11A: MEMS pressure sensor 1132; Fig. 11B: MEMS microphone 1134) and connected to an electrode configured to be placed on a subject (Fig. 4B: electrodes 460), wherein the first MEMS-based sensor and the second MEMS-based sensor during operation generate a first seismographic signal and/or a first acoustic signal and a second seismographic signal and/or a second acoustic signal (Par. 439: “The method may, for example, utilize a vibroacoustic sensor module with a plurality of MEMS or other suitable sensors (e.g., accelerometer, pressure sensor, microphone, voice coil transducer, piezoelectric transducer, etc.)”) to be provided to an analysis system configured to evaluate a plurality of features or machine-learned-based analyses to generate an estimated value for a presence, non-presence, and/or severity of hypertrophic cardiomyopathy (Par. 7: “The sensor platform may include a sensing device such as a vibroacoustic sensor module including one or more sensors configured to detect a vibroacoustic signal, a signal processing system configured to extract, from the detected vibroacoustic signal, a vibroacoustic signal component originating from a subject, and at least one processor configured to characterize a bodily condition of the subject based at least in part on the extracted vibroacoustic signal component using, for example, a machine learning model. In some variations, the bodily condition of a subject may include a health condition of a subject”).
However, Jumbe does not specfically disclose wherein the biophysical signals comprise seismocardiographic signals and phonocardiographic signals. Shankar discloses wherein the biophysical signals comprise seismocardiographic signals and phonocardiographic signals (Fig. 7, Par. 0047, 0051). This allows for proper heart wall and blood flow signals to be measured and converted into electrical signals for proper diagnosis of the cardiac parameters of a user. Therefore, it would have been obvious to one of ordinary skill in the art, at the time of the invention, to modify the device of Jumbe by adding wherein the biophysical signals comprise seismocardiographic signals and phonocardiographic signals as taught by Shankar in order to facilitate proper heart wall and blood flow signals to be measured and converted into electrical signals for proper diagnosis of the cardiac parameters of a user.
Regarding claim 23, Jumbe in view of Shankar, specfically Jumbe discloses the apparatus of claim 20. Jumbe further discloses wherein the first MEMS-based sensor as an accelerometer or an acoustic sensor is configured to be placed non-invasively on the chest of the subject proximal to an apex region of the subject's heart (Par. 356: “In some variations, the sensing device may include a wearable housing. The wearable housing may, for example, be coupled to an adhesive patch configured for attachment to a surface (e.g., skin) of a subject in a suitable body location (e.g., chest, stomach, etc.).” – A “suitable body location” could reasonably be the apex region of the subject’s heart. )
Regarding claim 24, Jumbe in view of Shankar, specfically Jumbe discloses the apparatus of claim 20. Jumbe further discloses wherein the second MEMS-based sensor as an accelerometer or an acoustic sensor is configured to be placed non-invasively on the chest of the subject proximal to a base region of the subject's heart (Par. 356: “In some variations, the sensing device may include a wearable housing. The wearable housing may, for example, be coupled to an adhesive patch configured for attachment to a surface (e.g., skin) of a subject in a suitable body location (e.g., chest, stomach, etc.).” – A “suitable body location” could reasonably be the base region of the subject’s heart. ).
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman (U.S. Patent Application Publication No. 2021/0259560), cited previously, in view of Shankar (US 20200337578) and further in view of Ferek-Petric (U.S. Patent Application Publication No. 2003/0083587), cited previously.
Regarding claim 4, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman does not disclose wherein the plurality of features or machine-learned-based analyses are configured to quantify deviations of a VD wave trajectory from a trajectory of a three-dimensional-modeled VD wave. However, Ferek-Petric, in the same field of endeavor of cardiac monitoring, discloses “As described in the above-referenced '116 and '690 patents and illustrated in FIG. 2, the tip of the QRS vector which represents the cardiac wave front typically traces an oval or cardioid trajectory or loop during the course of each ventricular depolarization-repolarization of the cardiac cycle” (Par. 27) and “FIG. 2 is a representation of the QRS spatial VCG and its projection onto the frontal X,Y plane, the transverse X,Z plane, and the sagittal Y,Z plane;” (Par. 60) and “In order to simplify the VCG analysis, the average (or mean) axis vector, that is the average or all magnitudes and angular deviations of the instantaneous vector over the duration of the QRS wave or T-wave, could be determined for every single beat.” (Par. 138). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include a 3D model of electrical activation of the heart, as taught and suggested by Ferek-Petric, for the purpose of providing “valuable diagnostic information for the initial diagnosis and follow-up of the progression of or improvement with treatment of various cardiac disease states or congenital heart defect” (Par. 39).
Regarding claim 17, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned-based analyses are configured to quantify propagative characteristics of a ventricular depolarization (VD) wave and/or ventricular repolarization (VR) wave in three-dimensional space. However, Ferek-Petric, in the same field of endeavor of characterizing cardiac monitoring, discloses “as described in the above-referenced '116 and '690 patents and illustrated in FIG. 2, the tip of the QRS vector which represents the cardiac wave front typically traces an oval or cardioid trajectory or loop during the course of each ventricular depolarization-repolarization of the cardiac cycle. Clinical studies, using data from three-lead VCG systems, have indicated the diagnostic value of the maximal QRS vector and T-wave vector which are the vectors drawn from the starting point of the loop to the farthest points of the QRS and T loops… in each instance, the departures from the angles normally observed in a given patient are diagnostically significant.” (Par. 27). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to determine characteristics of ventricular depolarization, as taught and suggested by Ferek-Petric, for the purpose of determining “diagnostically significant” values (Par. 27).
Claim(s) 6 and 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman (U.S. Patent Application Publication No. 2021/0259560), cited previously, in view of Shankar (US 20200337578) and in view of Cox (U.S. Patent Application Publication No. 2011/0319724), cited previously.
Regarding claim 6, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned-based analyses are configured to quantify variability in registered landmarks in cardiac, PPG, SCG, and/or PCG signals via Poincare analysis and histogram analysis. However, Cox, in the same field of endeavor of cardiac monitoring, discloses “FIG. 1 is a diagram of an embodiment of a non-invasive early stage hemorrhage detection device. The input information regarding the patient is gathered from a group of vital sign sensors 100. The sensors may include… a photoplethysmogram (PPG) waveform… Such sensors are well-known in the art; any appropriate sensor may be used. The information from these sensors 100 is processed by pre-processing/filtering module 101, which performs Fourier and wavelet filtering. The results of the processing are sent to feature extraction module 102, which extracts such features as statistical models, data at different frequencies, long- and short-term trends, magnitude transfer functions, and non-linear characteristics (such as… Poincare plot indices).” (Par. 54). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Poincare and histogram analysis, as taught and suggested by Cox, for the purpose of “extracting… non-linear features from the filtered measurements” (Par. 19).
Regarding claim 8, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned-based analyses are configured to quantify (i) properties of cardiac, PPG, and/or SCG signals. However, Cox in the same field of endeavor of cardiac monitoring, discloses “FIG. 12: is a flowchart of an embodiment of PPG waveform morphology calculations.” (Par. 35). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include quantifying properties of a PPG waveform, as taught and suggested by Cox, for the purpose of determining PPG trend parameters by “calculating the slope of each parameter over, for example, a five minute window for short-term trends and 30 minute window for long-term trends” (Par. 95).
Regarding claim 9, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned-based analyses are configured to quantify main frequency components of a cardiac, PPG, SCG, and/or PCG signals using wavelet analysis. However, Cox, in the same field of endeavor of cardiac monitoring discloses “An embodiment of a pre-processing and extraction method for the ECG signal is shown in FIG. 3. The first step in processing the ECG data source 106 is to remove noise using a wavelet filter in block 107.” (Par. 55). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include wavelet analysis, as taught and suggested by Cox, for the purpose of pre-processing the signals (Par. 59: “The wavelet transform will hierarchically decompose the input signal into a series of successively lower resolution approximation signals and their associated detail signals. At each level, the approximation and detailed signals contain the information needed for reconstruction back to the next higher resolution level”).
Regarding claim 10, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned- based analyses are configured to quantify power spectrum and frequency contents of the SCG and/or PCG signals using power spectrum and coherence analysis. However, Cox, in the same field of endeavor of cardiac monitoring discloses “frequency analyses of the RR intervals are calculated in FIG. 3, block 112 using standard frequency analysis techniques for each of the physiological signals. The most common is power spectral analysis” (Par. 77) and “transfer function of two signals defines their gain and phase relations at any given frequency and provides a statistical measure of reliability (coherence) of the relation between two signals” (Par. 88). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include power spectrum and coherence analysis, as taught and suggested by Cox, for the purpose of providing “an effective technique for investigating the relationship between the different physiological measurements” (Par. 88).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman (U.S. Patent Application Publication No. 2021/0259560), cited previously, in view of Shankar (US 20200337578), in view of Giovangrandi (U.S. Patent Application Publication 2011/0021928), cited previously.
Regarding claim 12, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned-based analyses are configured to approximate a respiration waveform using either (i) PPG and cardiac signals or (ii) SCG and/or PCG signals to assess one of a (1) heart rate variability, (2) respiration rate, (3) discrepancy features representing a distance between respiration and modulation signals and (4) square coherence representing a correlation between modulation and respiration rate signals, wherein the approximated respiration waveform is employed for HCM assessment by being used to generate delineated inspiration and expiration portions of the SCG signals and/or PCG signals to be employed for the analysis. However, Giovangrandi, in the same field of endeavor of cardiorespiratory monitoring, discloses “FIGS. 7-1 through 7-4 illustrate respiration waveforms derived from the S1-S2 intervals,” (Par. 20) and “Chest-worn accelerometers have been shown to detect seismocardiogram (SCG) signals that contain indicators of the primary heart sounds S1 and S2” (Par. 84). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include approximating a respiration waveform, as taught and suggested by Giovangrandi, in order to “ to determine respiratory function and respiration rate and to indicate the presence, development or absence of a respiratory, cardiac or neurological (syncope) disorder.” (Par. 9).
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman (U.S. Patent Application Publication No. 2021/0259560), cited previously, in view of Shankar (US 20200337578) in view of Watrous (U.S. Patent Application Publication No. 2003/0055321), cited previously.
Regarding claim 14, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned-based analyses are configured to quantify characteristic variations in SCG and/or PCG signals associated with inspiration versus expiration versus a Valsalva maneuver to identify patients with HCM. However, Watrous, in the same field of endeavor of cardiac diagnostic devices, discloses “a wavelet decomposition circuit is employed to analyze the filtered signal using a wavelet decomposition to extract time-frequency information. Neural network feature extractors are trained from labeled examples to identify basic heart sounds, clicks and murmurs. In a preferred embodiment, the neural networks are of the time-delay variety, where the input span, number of layers, unit function, connectivity and initial weight selection are appropriately chosen according to well-known methods” (Par. 35) and “the data acquisition may be configured for acquiring a cardiac acoustic signal from various sites on surface of the patient's chest, with the patient in various postures (e.g., sitting, standing, reclining, etc.) and under various conditions (e.g., inspiration vs. expiration, static or dynamic, hand-grip, Valsalva maneuver, etc.).” (Par. 51) and “the PCP will then determine whether there are any abnormal heart sounds present, such as murmurs and/or clicks, by assessing the relative loudness, duration, intensity pattern, spectral quality and time sequence of the heart sounds. The heart sounds are interpreted in terms of the physiological model of the action of the heart muscle, valves and chambers. A hypothesis can be developed about any possible disease states based on the acoustic evidence and knowledge of the patient's medical history. Possible diagnoses are differentiated by varying the placement of the microphone, the patient's posture, or by having the patient execute different maneuvers that accentuate or diminish certain heart sounds. The accumulated evidence is evaluated for the presence of heart disease” (Par. 4). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include the use of machine learning with signals associated with inspiration, expiration, and a Valsalva maneuver to develop a diagnosis, as taught and suggested by Watrous, for the purpose of “develop[ing] a system for automated auscultation that would provide a standard for auscultation analysis and diagnosis” (Par. 12).
Regarding claim 15, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned- based analyses are configured to quantify characteristic variations in SCG and/or PCG signals associated with inspiration versus expiration versus a Valsalva maneuver to identify a subset of patients with HCM that have obstructive HCM (OHCM). However, Watrous, in the same field of endeavor of cardiac diagnostic devices, discloses “a wavelet decomposition circuit is employed to analyze the filtered signal using a wavelet decomposition to extract time-frequency information. Neural network feature extractors are trained from labeled examples to identify basic heart sounds, clicks and murmurs. In a preferred embodiment, the neural networks are of the time-delay variety, where the input span, number of layers, unit function, connectivity and initial weight selection are appropriately chosen according to well-known methods” (Par. 35) and “the data acquisition may be configured for acquiring a cardiac acoustic signal from various sites on surface of the patient's chest, with the patient in various postures (e.g., sitting, standing, reclining, etc.) and under various conditions (e.g., inspiration vs. expiration, static or dynamic, hand-grip, Valsalva maneuver, etc.).” (Par. 51) and “the PCP will then determine whether there are any abnormal heart sounds present, such as murmurs and/or clicks, by assessing the relative loudness, duration, intensity pattern, spectral quality and time sequence of the heart sounds. The heart sounds are interpreted in terms of the physiological model of the action of the heart muscle, valves and chambers. A hypothesis can be developed about any possible disease states based on the acoustic evidence and knowledge of the patient's medical history. Possible diagnoses are differentiated by varying the placement of the microphone, the patient's posture, or by having the patient execute different maneuvers that accentuate or diminish certain heart sounds. The accumulated evidence is evaluated for the presence of heart disease” (Par. 4). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include the use of machine learning with signals associated with inspiration, expiration, and a Valsalva maneuver to develop a diagnosis, as taught and suggested by Watrous, for the purpose of “develop[ing] a system for automated auscultation that would provide a standard for auscultation analysis and diagnosis” (Par. 12).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman (U.S. Patent Application Publication No. 2021/0259560), cited previously, in view of Shankar (US 20200337578), in view of Kaiser (U.S. Patent Application Publication No. 2019/0059748), cited previously.
Regarding claim 16, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned-based analyses are configured to approximate left ventricular ejection time using the one or more SCG and/or PCG signals. However, Kaiser, in the same field of endeavor of characterizing cardiac acoustic signals, discloses “In another embodiment, signal processing is accompanied with advanced machine learning methods to provide accurate computation of EF.” (Par. 8), “FIG. 11A and FIG. 11B show temporal feature extraction of the PCG signal cardiac cycle” (Par. 99), and “a large number of TA features have been extracted and determined to be of value in computing EF. The extracted temporal features include, but are not limited to: … 6) left ventricular ejection time (LVET)” (Par. 100). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include machine learning to estimate LVET, as taught and suggested by Kaiser, for the purpose of determining heart conditions based on changes in ejection fraction (Par. 117: “There are a number of medical conditions that may cause ICR computed EF to deviate significantly from values measured via echocardiography. For example, in the case of hypertrophic cardiomyopathy, heart walls become enlarged, resulting in reduced end-diastolic volume”).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman (U.S. Patent Application Publication No. 2021/0259560), cited previously, in view of Shankar (US 20200337578), in view of Houlton (WIPO Application Publication 2012/149652), cited previously.
Regarding claim 18, Venkatraman in view of Shankar discloses the method of claim 2. Venkatraman in view of Shankar does not disclose wherein the plurality of features or machine-learned-based analyses are evaluated (i) at an inspiration region of the one or more seismocardiographic signals and/or phonocardiographic signals and/or (ii) an expiration region of the one or more seismocardiographic signals and/or phonocardiographic signals. However, Houlton, in the same field of endeavor of cardiac signal monitoring, discloses “processing raw seismocardiogram (SCG) data optionally with ECG data to extract a respiration signal; following identification and extraction of the respiration signal, the signal is passed through band-pass filters having cut-off frequencies of about 0.5Hz and 20Hz to obtain the low frequency component; and in parallel from a high pass filter having cut-off frequency of 20 Hz to obtain the high frequency component; annotating cardiac events on the processed SCG data using deterministic rule set approach or the probabilistic machine learning approach or both; extracting features from magnitudes, slope, timing, power and frequency data; and estimating a cardiac contractility index based on said extracted feature using either a patient specific approach or a general regression based approach; wherein optionally in this estimation, different phases of respiration,” (Par. 19). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include evaluating respiration signals using SCG, as taught and suggested by Houlton, in order to “enable a differential analysis of seismocardiogram based on respiration phases” (Par. 52).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jumbe (U.S. Patent Application Publication 2021/0345939), cited previously, Shankar (US 20200337578), in view of Cox (U.S. Patent Application Publication 2011/0319724), cited previously.
Regarding claim 21, Jumbe in view of Shankar, specfically Jumbe discloses the apparatus of claim 19 further comprising:
a plurality of surface electrodes configured to be placed on surfaces of a chest region of a subject to provide a plurality of cardiac signals of the subject's heart (Par. 113: “The sensing device 400 may include the EPIC electrodes 460 and DRL electrodes 470 for collecting ECG data”), wherein the plurality of cardiac signals are provided to the analysis system to evaluate for the plurality of features or machine-learned-based analyses to generate the estimated value for the presence, non-presence, and/or severity of hypertrophic cardiomyopathy (Par. 105: “In some variations, electronic components (within the vibroacoustic sensor module and/or in the electronics system 340, for example) may perform various signal processing functions to extract a biological vibroacoustic signal component from sensor data, and/or perform analysis via artificial intelligence (e.g., utilizing one or more machine learning models) to characterize a bodily condition based on the biological vibroacoustic signal component.”); and
Jumbe in view of Shankar does not disclose a plurality of photoplethysmographic sensors configured to be placed on the subject to provide one or more photoplethysmographic signals, wherein the one or more photoplethysmographic signals are provided to the analysis system to evaluate for the plurality of features or machine-learned-based analyses to generate the estimated value for the presence, non-presence, and/or severity of hypertrophic cardiomyopathy. However, Cox, in the same field of endeavor of cardiac monitoring, discloses “The input information regarding the patient is gathered from a group of vital sign sensors 100. The sensors may include… a photoplethysmogram (PPG) waveform…” (Par. 54). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include photoplethysmographic sensors, as taught and suggested by Cox, for the purpose of “impart[ing] characteristics to the transmitted signal that can be analyzed to yield information regarding the physiological parameter of interest. Such monitoring of patients is desirable because it is noninvasive, typically yields substantially instantaneous and accurate results, and utilizes minimal medical resources, thereby proving to be cost effective” (Par. 91).
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jumbe (U.S. Patent Application Publication 2021/0345939), cited previously, in view of Shankar (US 20200337578), in view of Venkatraman (U.S. Patent Application Publication 2021/0259560), cited previously.
Regarding claim 26, Jumbe in view of Shankar, specfically Jumbe discloses a non-transitory computer-readable medium comprising instructions stored thereon (Par. 285: “The processor 2310 (e.g., CPU) and/or memory device 2320 (which can include one or more computer-readable storage mediums) may cooperate to provide a controller for operating the system”), wherein execution of the instructions by one or more processors causes the one or more processors to:
obtain one or more seismocardiographic signals (SCG signals) and/or phonocardiographic signals (PCG signals) from a multi-sensor device placed or worn on a patient (Par. 285: “the processor 2310 may receive sensor data”);
determine utilizing at least a portion of the one or more seismocardiographic signals and/or phonocardiographic signals, a plurality of values associated with a plurality of features or machine-learned-based analyses (Par. 8: “a method for characterizing a bodily condition may include detecting a periodic or aperiodic vibroacoustic signal with a vibroacoustic sensor module, the vibroacoustic sensor module comprising a plurality of sensors, extracting, from the detected vibroacoustic signal, a vibroacoustic signal component originating from a subject, and characterizing a bodily condition of the subject based at least in part on the extracted vibroacoustic signal component using a machine learning model”);
Jumbe in view of Shankar does not disclose:
determine an estimated value for a presence, non-presence and/or severity of hypertrophic cardiomyopathy using the plurality of values associated with the plurality of features or machine-learned-based analyses; and
output the estimated value for the presence, non-presence, and/or severity of hypertrophic cardiomyopathy, wherein the estimated value for the presence, non-presence, and/or severity of hypertrophic cardiomyopathy is outputted for use in a diagnosis of hypertrophic cardiomyopathy and/or to direct treatment of the hypertrophic cardiomyopathy.
However, Venkatraman, in the same field of endeavor of cardiac monitoring, discloses “The network may output a probability of state or condition of a heart for each segment. These probabilities may then be averaged across all or a fraction of the segments. The average may then be threshold to make a determination of whether a state or condition of an organ, such as a heart murmur is present” (Par. 127) and “The output indicative of the state or condition of the subject may comprises determining a presence or absence of hypertrophic cardiomyopathy” (Par. 138). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include instructions to determine an estimated value for a presence or non-presence of hypertrophic cardiomyopathy using machine learning, as taught and suggested by Venkatraman, for the purpose of “advantageously permit[ting] the subject to be monitored for a health or disease condition over a longer period of time” (Par. 5).
Response to Arguments
Applicant’s arguments with respect to claim(s) 2, 4-18, 20-21, 23-24, 26 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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/JON ERIC C MORALES/Primary Examiner, Art Unit 3796
/J.C.M/Primary Examiner, Art Unit 3796
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