DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/09/2026 has been entered.
Response to Amendment
The amendment of 02/09/2026 has been entered and fully considered by the examiner. Claims 1-6 , 19 and 20 have been amended. Claims 1-20 are currently pending in the application with claims 1, 19 and 20 being independent claims.
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 1-20 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.
The term “high spectral power” in claims 1, 19, and 20 is a relative term which renders the claim indefinite. The term “high” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In particular, it is not clear to one of ordinary skill in the art reading the claim what spectral power would be considered “high”As a result, the term is considered indefinite and unclear as it is open to the subjective interpretation of the reader.
Claims 2-18 depend upon indefinite claim 1 and are considered to be indefinite at least due to their dependency upon an indefinite base claim.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 7, 9-11, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shadforth et al. (U.S. Publication No. 2020/0211713) hereinafter “Shadforth” in view of Hariton et al. (U.S. Publication No. 2021/0298687) hereinafter “Hariton”.
Regarding claim 1, Shadforth discloses a method for non-invasively [see [0024] estimating values of one or more metrics associated with a disease state or abnormal condition [see abstract of Shadforth], the method comprising:
acquiring, by one or more processors, [see [0070]-[0071]; pre-processing module 116] a biophysical-signal data set of a subject comprising one or more first biophysical signals; [see [0093] of Shadforth]
generating, by the one or more processors, a plurality of binarized [see [0080] disclosing that the shapes are binary indication of disease presence ] spectral images [see [0080] and [0129] and [0137] disclosing that a 3D shape is created; see FIG. 2H (i.e. spectral image)] from a wavelet transformation of the biophysical-signal data set, [see [0074] of Shadforth] wherein, each binarized spectral image [see [0137] of Shadforth] corresponds to one or more characteristics of a waveform of interest. [the binary representing whether or not the disease is present; see [0137] of Shadforth]
determining, by the one or more processors, values of wavelet-based features [see [0074] of Shadforth] or parameters that characterize properties or geometric shapes of the binarized spectral images; [see [0080] and [0129] disclosing that a 3D shape is created]
determining, by the one or more processors, an estimated value for a presence of a disease state or abnormal condition-based, in part, on the determined values of the one or more wavelet associated properties, wherein the estimated value for the of the disease state or abnormal condition is used in a model to non-invasively estimate the presence of the expected disease state or condition, and, [see [0069] disclosing: “, the clinical output includes an assessment of the presence or non - presence of a disease and / or an estimated physiological characteristic of the physiological system under study”; see also [0084]
using the estimated value in a diagnosis of the expected disease state or condition or to direct treatment of the expected disease state or condition [see [0069] disclosing: “, the clinical output includes an assessment of the presence or non - presence of a disease and / or an estimated physiological characteristic of the physiological system under study”; see also [0084]
using estimated value in a diagnosis of the expected disease state or condition or to direct treatment of the expected disease state or condition. [see FIGs. 15A-F and [0061]; examples of outputted classification result]
Shadforth does not disclose that the binarized data object corresponds to one or more high spectral power characteristics of a waveform of interest.
Hariton, directed towards diagnostic data processing [see abstract of Hariton] further discloses that the binarized data [see [0162] disclosing that the predicted probability may be binary] object corresponds to one or more high spectral power characteristics of a waveform of interest. [see FIG. 8-12 and [0071]-[0075] of Hariton; disclosing generating a graphical representation (i.e. object) that discloses the frequency of different diagnosis and the high spectral intensity which are above threshold are highlighted]
It would have been obvious to a person of ordinary skill level at the time of the filing of the invention to modify the design of Shadforth such that the binarized data object corresponds to one or more high spectral power characteristics of a waveform of interest according to the teachings of Hariton in order to aid in determining and confirming whether a patient is subject to a medical condition and/or differentiating between those conditions. [see [0003] of Hariton]
Regarding claim 2, Shadforth further discloses that determining the values of the wavelet-based features or parameters comprises: determining, by the one or more processors, a wavelet-based model of a plurality of identified periodic cycles of a signal of the biophysical signal data set; [see FIG. 6 and [0111]; step 606 includes a generating a residue point cloud model based on the data] , generating by the one or more processors, a plurality of spectral image or data of the wavelet-based model; [see [0042] and FIG. 2A; an example of a volumetric object generated from the model/dataset] and determining, by the one or more processors, one or more values of features extracted from two or three-dimensional objects identified within the plurality of spectral image or data. [see [0069] disclosing: “, the clinical output includes an assessment of the presence or non - presence of a disease and / or an estimated physiological characteristic of the physiological system under study”; see also [0084]
Regarding claim 7, Shadforth further discloses that the wavelet-based model is based on a photoplethysmographic signal. [see [0012] of Shadforth disclosing that the signal acquisition methods could include PPG]
Regarding claim 9, Shadforth further discloses that the wavelet-based model is based on a cardiac/biopotential signal. [see [0012] of Shadforth disclosing that the signal includes cardiac signals from ECG/EKG or wide-band biopotential signals]
Regarding claim 10, Shadforth further discloses that determining the values of the wavelet-based features or parameters comprises: determining, by the one or more processors, a wavelet-based model [see [0042] and FIG. 2A; an example of a volumetric object generated from the model/dataset] of a plurality of pre-defined portions within identified periodic cycles of a cardiac signal of the biophysical signal data set, [see FIGs. 2A-2F disclosing various portions of the heart] wherein each of the plurality of pre-defined portions comprises an isolated cardiac waveform associated with atrial depolarization, [see [0096] Shadforth] ventricular depolarization, [see [0096] Shadforth] or ventricular repolarization; and determining, by the one or more processors, one or more values of features extracted from high-energy components of the wavelet-based model. [see [0170] of Shadforth]
Regarding claim 11, Shadforth further discloses that the one or more features are selected from the group consisting of: a feature associated with a statistical assessment of a plurality of power spectral density values determined within the wavelet-based model [see [0170]-[0171] of Shadforth disclosing: “. A spectral density analysis was then performed to ascertain the modeling bias as a ratio of i ) the difference between the power spectral energy approximation algorithm and the power spectral density of the Fast Fourier Transform over the power spectral density of the Fast Fourier Transform per Equation 1”] comprising a plurality of isolated cardiac waveform associated with atrial depolarization; a feature associated with a statistical assessment of a plurality of power spectral density values determined within the wavelet-based model [see [0170]-[0171] of Shadforth disclosing: “. A spectral density analysis was then performed to ascertain the modeling bias as a ratio of i ) the difference between the power spectral energy approximation algorithm and the power spectral density of the Fast Fourier Transform over the power spectral density of the Fast Fourier Transform per Equation 1”] comprising a plurality of isolated cardiac waveform associated with ventricular depolarization; and a feature associated with a statistical assessment of a plurality of power spectral density values determined within the wavelet-based model [see [0170]-[0171] of Shadforth disclosing: “. A spectral density analysis was then performed to ascertain the modeling bias as a ratio of i ) the difference between the power spectral energy approximation algorithm and the power spectral density of the Fast Fourier Transform over the power spectral density of the Fast Fourier Transform per Equation 1”] comprising a plurality of isolated cardiac waveform associated with ventricular repolarization.
Regarding claim 15, Shadforth further discloses that causing, by the one or more processors, generation of a visualization of the estimated value for the presence of the disease state or abnormal condition, wherein the generated visualization is rendered and displayed at a display of a computing device and/or presented in a report. [see FIG. 1 and [0077]; the result of a coronary tree report is presented to the user]
Regarding claim 16, Shadforth further discloses that the values of one or more wavelet associated properties are used in the model selected from the group consisting of a linear model, a decision tree model, [see FIG. 1 and [0077]; the result of a coronary tree report is presented to the user] a random forest model, a support vector machine model, a neural network model.
Regarding claim 17, Shadforth further discloses that the model [see [0042] and FIG. 2A; an example of a volumetric object generated from the model/dataset] further includes features selected from the group consisting of: one or more depolarization or repolarization wave propagation associated features; [see [0096] Shadforth] one or more depolarization wave propagation deviation associated features; one or more cycle variability associated features; one or more dynamical system associated features; one or more cardiac waveform topologic and variations associated features; one or more PPG waveform topologic and variations associated features; one or more cardiac [see FIGs. 2A-2F disclosing various portions of the heart] or PPG signal power spectral density associated features; one or more cardiac or PPG signal visual associated features; and one or more predictability features.
Regarding claim 18, Shadforth further discloses that the disease state or abnormal condition is selected from the group consisting of coronary artery disease, [see FIG. 1 and [0077]; the result of a coronary tree report is presented to the user] pulmonary hypertension, pulmonary arterial hypertension, pulmonary hypertension due to left heart disease, rare disorders that lead to pulmonary hypertension, [see [0013] of Shadforth] left ventricular heart failure or left-sided heart failure, right ventricular heart failure or right-sided heart failure, systolic heart failure, diastolic heart failure, ischemic heart disease, and arrhythmia.
Regarding claim 19, Shadforth discloses a system [see abstract of Shadforth] comprising:
a processor; [see [0070]-[0071]; pre-processing module 116] and a memory having instructions stored thereon, wherein execution of the instructions by the processor causes the processor [see [0037] of Shadforth] to:
acquire a biophysical-signal data set of a subject comprising one or more first biophysical signals; [see [0093] of Shadforth]
generate a plurality of binarized spectral images [see [0080] and [0129] and [0137] disclosing that a 3D shape is created] from a wavelet transformation of the biophysical-signal data set, [see [0074] of Shadforth] wherein each binarized spectral image [see [0137] of Shadforth] corresponds to one or more characteristics of a waveform of interest. [the binary representing whether or not the disease is present; see [0137] of Shadforth]
determine values of wavelet-based features [see [0074] of Shadforth] or parameters that characterize properties or geometric shapes of a binarized data object [see [0080] and [0129] disclosing that a 3D shape is created]
determine an estimated value for a presence of the disease state or abnormal condition- based, in part, on the determined values of the one or more wavelet associated properties, [see [0069] disclosing: “, the clinical output includes an assessment of the presence or non - presence of a disease and / or an estimated physiological characteristic of the physiological system under study”; see also [0084]
wherein the estimated value for the of the disease state or abnormal condition is used in a model to non-invasively estimate the presence of an expected disease state or condition,
use the estimated value in a diagnosis of the expected disease state or condition or to direct treatment of the expected disease state or condition. [see FIGs. 15A-F and [0061]; examples of outputted classification result]
Shadforth does not disclose that the binarized data object corresponds to one or more high spectral power characteristics of a waveform of interest.
Hariton, directed towards diagnostic data processing [see abstract of Hariton] further discloses that the binarized data [see [0162] disclosing that the predicted probability may be binary] object corresponds to one or more high spectral power characteristics of a waveform of interest. [see FIG. 8-12 and [0071]-[0075] of Hariton; disclosing generating a graphical representation (i.e. object) that discloses the frequency of different diagnosis and the high spectral intensity which are above threshold are highlighted]
It would have been obvious to a person of ordinary skill level at the time of the filing of the invention to modify the design of Shadforth such that the binarized data object corresponds to one or more high spectral power characteristics of a waveform of interest according to the teachings of Hariton in order to aid in determining and confirming whether a patient is subject to a medical condition and/or differentiating between those conditions. [see [0003] of Hariton]
Regarding claim 20, Shadforth discloses a non-transitory computer-readable medium having instructions stored thereon, [see abstract and [0037] of Shadforth] wherein execution of the instructions by a processor causes the processor to:
acquire a biophysical-signal data set of a subject comprising one or more first biophysical signals; [see [0093] of Shadforth]
generate a plurality of binarized spectral images [see [0080] and [0129] and [0137] disclosing that a 3D shape is created] from a wavelet transformation of the biophysical-signal data set, [see [0074] of Shadforth] wherein each binarized spectral image [see [0137] of Shadforth] corresponds to one or more characteristics of a waveform of interest. [the binary representing whether or not the disease is present; see [0137] of Shadforth]
determine values of wavelet-based features [see [0074] of Shadforth] or parameters that characterize properties or geometric shapes of a binarized spectral iamge generated from a wavelet transform of the biophysical-signal data set; [see [0080] and [0129] disclosing that a 3D shape is created]
determine an estimated value for a presence of the disease state or abnormal condition- based, in part, on the determined values of the one or more wavelet associated properties, [see [0069] disclosing: “, the clinical output includes an assessment of the presence or non - presence of a disease and / or an estimated physiological characteristic of the physiological system under study”; see also [0084]
wherein the estimated value for the of the disease state or abnormal condition is used in a model to non-invasively estimate the presence of an expected disease state or condition,
use the estimated value in a diagnosis of the expected disease state or condition or to direct treatment of the expected disease state or condition. [see FIGs. 15A-F and [0061]; examples of outputted classification result]
Shadforth does not disclose that the binarized data object corresponds to one or more high spectral power characteristics of a waveform of interest.
Hariton, directed towards diagnostic data processing [see abstract of Hariton] further discloses that the binarized data [see [0162] disclosing that the predicted probability may be binary] object corresponds to one or more high spectral power characteristics of a waveform of interest. [see FIG. 8-12 and [0071]-[0075] of Hariton; disclosing generating a graphical representation (i.e. object) that discloses the frequency of different diagnosis and the high spectral intensity which are above threshold are highlighted]
It would have been obvious to a person of ordinary skill level at the time of the filing of the invention to modify the design of Shadforth such that the binarized data object corresponds to one or more high spectral power characteristics of a waveform of interest according to the teachings of Hariton in order to aid in determining and confirming whether a patient is subject to a medical condition and/or differentiating between those conditions. [see [0003] of Hariton]
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Shadforth et al. (U.S. Publication No. 2020/0211713) hereinafter “Shadforth” and Hariton et al. (U.S. Publication No. 2021/0298687) hereinafter “Hariton” as applied to claim 1 above, and further in view of Devani (U.S. Publication NO. 2022/0218198) hereinafter “Devani”.
Regarding claim 3, Shadforth as modified by Hariton discloses all the limitations of claim 2 [see rejection of claim 2 above]
Shadforth as modified by Hariton does not expressly disclose that the plurality fo spectral images are converted to the plurality of binarized spectral images or data by a threshold operator, and wherein the one or more values of the features are extracted from two or three-dimensional objects identified in one or more binarized regions of the plurality of binarized spectral images
Devani, directed towards analyzing ppg data to determine cardiac health [see abstract of Devani] further discloses that spectral image or data is converted to a binarized spectral images or data by a threshold operator, and wherein the one or more values of the features are extracted from two or three-dimensional objects identified in one or more binarized regions of the plurality of binarized spectral images. [see [00258] of Devani]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Shadforth as modified by Hariton such that the spectral image or data is converted to a binarized image or data by a threshold operator, and wherein the one or more values of the features are extracted from two or three-dimensional objects identified in one or more binarized regions of the binarized spectral images according to the teachings of Devani in order to simplify the data based on the binarization and arrive at the classification faster and in a more efficient way. [see [0258] of Devani]
Regarding claim 4, Shadforth as modified by Hariton discloses all the limitations of claim 2 [see rejection of claim 2 above]
Shadforth as modified by Hariton does not expressly disclose that the plurality of spectral image or data are converted to the plurality of binarized spectral images or data by a plurality of corresponding threshold operators, and wherein the one or more values of the features are extracted from two or three-dimensional objects identified in one or more binarized regions of the plurality of binarized spectral imags.
Devani further discloses that the plurality of spectral images or data are converted to a plurality of binarized image or data by a plurality of corresponding threshold operators, and wherein the one or more values of the features are extracted from two or three-dimensional objects identified in one or more binarized regions of the plurality of binarized spectral images. [see [0258] of Devani disclosing binarization of 2D image data based on a threshold operator]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Shadforth as modified by Hariton such that the spectral image or data is converted to a plurality of binarized image or data by a plurality of corresponding threshold operators, and wherein the one or more values of the features are extracted from two or three-dimensional objects identified in one or more binarized regions of the plurality of threshold spectral image or data according to the teachings of Devani in order to simplify the data based on the binarization and arrive at the classification faster and in a more efficient way. [see [0258] of Devani]
Regarding claim 5, Shadforth as modified by Hariton discloses all the limitations of claim 2 [see rejection of claim 2 above]
Shadforth as modified by Hariton does not expressly disclose that the spectral image or data is converted to a second binarized image or data by a second threshold operator, the method further comprising: determining, by the one or more processors, one or more values of features extracted from a distribution of a second power of the one or more second binarized regions, wherein the second threshold operator has a value lower than that of the threshold operator, and wherein the second power excludes power of the two or three-dimensional objects.
Devani further discloses that the plurality of spectral images or data are converted to a second plurality of binarized spectral images by a second threshold operator,[see [0131] of Devani] the method further comprising: determining, by the one or more processors, one or more values of features extracted from a distribution of a second power of the one or more second plurality of binarized spectral images, wherein the second threshold operator has a value lower than that of the threshold operator [see [0158] the thresholds can be set manually and can be greater or less than other thresholds], and wherein the second power excludes power of the two or three-dimensional objects. [see [0143]-[0145] of Devani disclosing that each decision function uses a threshold to improve the overall performance]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Shadforth as modified by Hariton such that the spectral image or data is converted to a second binarized image or data by a second threshold operator, the method further comprising: determining, by the one or more processors, one or more values of features extracted from a distribution of a second power of the one or more second binarized regions, wherein the second threshold operator has a value lower than that of the threshold operator, and wherein the second power excludes power of the two or three-dimensional objects in order to simplify the data based on the binarization and arrive at the classification faster and in a more efficient way. [see [0258] of Devani]
Regarding claim 6, Shadforth as modified by Hariton discloses all the limitations of claim 2 [see rejection of claim 2 above]
Devani further discloses that the one or more features are selected from the group consisting of: a feature associated with a time range of the one or more binarized regions identified in the spectral image or data; a feature associated with a frequency range of the one or more binarized regions identified in the spectral image or data; [see [0133] and [0166] of Devani] a feature associated with a time centroid of the one or more binarized regions identified in the spectral image or data; a feature associated with a surface area of the one or more binarized regions identified in the plurality of spectral images [see [0236] od Devani]; a feature associated with a measure of eccentricity of at least one of the one or more binarized regions identified in the spectral image or data; a feature associated with a measure of circularity of the at least one of the one or more binarized regions identified in the plurality of spectral images; [see [0280]-[0281] of Devani] a feature associated with a binarized regions extent identified in the spectral image; or data a feature associated with an orientation of an ellipse identified in the spectral image or data; and a feature associated with a power centroid identified in the plurality of spectral images.
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Shadforth as modified by Hariton such that the one or more features are selected from the group consisting of: a feature associated with a time range of the one or more binarized regions identified in the spectral image or data; a feature associated with a frequency range of the one or more binarized regions identified in the spectral image or data; a feature associated with a time centroid of the one or more binarized regions identified in the spectral image or data; a feature associated with a surface area of the one or more binarized regions identified in the spectral image or data; a feature associated with a measure of eccentricity of at least one of the one or more binarized regions identified in the spectral image or data; a feature associated with a measure of circularity of the at least one of the one or more binarized regions identified in the spectral image or data according to the teachings of Devani in order to simplify the data based on the binarization and arrive at the classification faster and in a more efficient way[see [0258] of Devani]
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shadforth et al. (U.S. Publication No. 2020/0211713) hereinafter “Shadforth” and Hariton et al. (U.S. Publication No. 2021/0298687) hereinafter “Hariton” as applied to claim 1 above, and further in view of Lee et al. (U.S. Publication No. 2016/0206212) hereinafter “Lee”.
Regarding claim 8, Shadforth as modified by Hariton discloses all the limitations of claim 2 [see rejection of claim 2 above]
Shadforth as modified by Hariton does not expressly disclose that the wavelet-based model is based on a velocity- plethysmographic signal derived from a photoplethysmographic signal.
Lee, directed towards detecting ppg data from a user and determining physiological parameters [see abstract of Lee] further discloses that the wavelet-based model is based on a velocity- plethysmographic signal derived from a photoplethysmographic signal. [see [0032] of Lee]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Shadforth as modified by Hariton such that the wavelet-based model is based on a velocity- plethysmographic signal derived from a photoplethysmographic signal according to the teachings of Lee in order to determine metrics of blood pressure and determine its relevant physiological parameters [see [0032] of Lee]
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Shadforth et al. (U.S. Publication No. 2020/0211713) hereinafter “Shadforth” and Hariton et al. (U.S. Publication No. 2021/0298687) hereinafter “Hariton” as applied to claim 1 above, and further in view of Joshi et al. (WO 2019/224113) hereinafter “Joshi”.
Regarding claim 12, Shadforth as modified by Hariton discloses all the limitations of claim 1 [see rejection of claim 1 above]
Shadforth as modified by Hariton does not expressly disclose that the one or more features include a feature associated with an assessment of deviations from linearity in a quantile-quantile probability assessed between (i) a power spectral density values determined within the wavelet-based models and (ii) a base power spectral density function.
Joshi, directed towards ppg data and its statistical analysis [see abstract of Joshi] further discloses that the one or more features include a feature associated with an assessment of deviations from linearity in a quantile-quantile probability assessed [see page 14, first full paragraph disclosing determination of the deviations between the signal and its statistical value] between (i) a power spectral density values determined within the wavelet-based models [see page 12, last paragraph of the page] and (ii) a base power spectral density function [see page 12, last paragraph to page 13, first paragraph of the page]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Shadforth as modified by Hariton such that the one or more features include a feature associated with an assessment of deviations from linearity in a quantile-quantile probability assessed between (i) a power spectral density values determined within the wavelet-based models and (ii) a base power spectral density function according to the teachings of Joshi in order to perform statistical analysis on the spectral data for more accurate diagnosis and classification.
Regarding claim 13, Shadforth as modified by Hariton discloses all the limitations of claim 1 [see rejection of claim 1 above]
Shadforth as modified by Hariton does not expressly disclose that the one or more features include a feature associated with an assessment in a quantile-quantile probability assessed between (i) a cumulative density distribution (CCD) values determined within the wavelet-based models and (ii) a cumulative density distribution function.
Joshi, directed towards ppg data and its statistical analysis [see abstract of Joshi] further discloses that the one or more features include a feature associated with an assessment in a quantile-quantile probability assessed between (i) a cumulative density distribution (CCD) values determined within the wavelet-based models and (ii) a cumulative density distribution function. [see page 12, last paragraph of the page continued in page 13, first paragraph]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Shadforth as modified by Hariton such that the one or more features include a feature associated with an assessment in a quantile-quantile probability assessed between (i) a cumulative density distribution (CCD) values determined within the wavelet-based models and (ii) a cumulative density distribution function according to the teachings of Joshi in order to perform statistical analysis on the spectral data for more accurate diagnosis and classification.
Regarding claim 14, Shadforth as modified by Hariton discloses all the limitations of claim 1 [see rejection of claim 1 above]
Shadforth as modified by Hariton does not expressly disclose that the one or more features include a feature associated with an assessment of a kernel density estimator (KDE) fitted to a probability density distribution (PDD) function of the power spectral density function (PSD).
Joshi further discloses that the one or more features include a feature associated with an assessment of a kernel density estimator (KDE) fitted to a probability density distribution (PDD) function of the power spectral density function (PSD). [see page 13, first paragraph]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Shadforth as modified by Hariton such that the one or more features include a feature associated with an assessment of a kernel density estimator (KDE) fitted to a probability density distribution (PDD) function of the power spectral density function (PSD) according to the teachings of Joshi in order to perform statistical analysis on the spectral data for more accurate diagnosis and classification.
Response to Arguments
Arguments regarding rejection of claims under U.S.C. 101
Applicant’s amendments, filed 02/09/2026, with respect to independent claims 1, 19 , and 20 have been fully considered and are persuasive. The U.S.C. 101 rejection of claims has been withdrawn.
Arguments regarding rejection of claims under U.S.C. 102
Applicant should submit an argument under the heading “Remarks” pointing out disagreements with the examiner’s contentions. Applicant must also discuss the references applied against the claims, explaining how the claims avoid the references or distinguish from them.
Conclusion
No claim is allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJAN - SABOKTAKIN whose telephone number is (303)297-4278. The examiner can normally be reached M-F 9 am-5pm CT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Carey can be reached at (571) 270-7235. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARJAN SABOKTAKIN/Examiner, Art Unit 3797
/MICHAEL J CAREY/Supervisory Patent Examiner, Art Unit 3795