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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/14/2025 has been entered.
Response to Arguments
Applicant's arguments filed on 11/14/2025 have been fully considered but they are not persuasive.
35 USC 101
Applicant argues that claim 1 integrates the abstract idea into a practical application because the claimed signal acquisition methodology and corresponding advanced processing operations in combination provides for the estimation of the presence or non-presence, of a cardiac disease state or condition or a determination of a recommended treatment. Examiner respectfully disagrees and maintains that the claims fail to recite significantly more as the signal acquisition is directed to mere data gathering while the processing operations amount to mere instructions to perform the abstract idea using generally recited computer components.
Applicant also argues that instant claim 1 is analogous to the patent eligible claims in Diamond v. Diehr, 450 U.S. 175 (1981) and Thales Visionix, Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017). Regarding Applicants arguments with respect to Diehr, Examiner respectfully disagrees that claim 1 should be found patent eligible in view of Diehr. Referring to MPEP 2106 (I), "Diehr explained that while an abstract idea, law of nature, or mathematical formula could not be patented, ‘an application of a law of nature or mathematical formula to a known structure or process may well be deserving of patent protection.’"; Diehr, 450 U.S. at 187, 192 n.14, 209 USPQ at 10 n.14 (explaining that the process in Parker v. Flook was ineligible not because it contained a mathematical formula, but because it did not provide an application of the formula). Examiner maintains that instant claim 1 fails to integrate the abstract idea into a practical application and is therefore patent ineligible.
Regarding Applicant’s arguments with respect to Thales, Examiner respectfully disagrees that claim 1 should be found patent eligible in view of Thales. The Claims at issue in Thales were focused on specific systems and methods that use inertial sensors in a non-conventional manner to reduce errors in measuring the relative position and orientation of a moving object on a moving reference frame. According to the court, the Thales claims specify a particular configuration of inertial sensors and a particular method of using the raw data from the sensors in order to more accurately calculate the position and orientation of an object on a moving platform. The mathematical equations are a consequence of the arrangement of the sensors and the unconventional choice of reference frame in order to calculate position and orientation. Far from claiming the equations themselves, the claims seek to protect only the application of physics to the unconventional configuration of sensors as disclosed.
However, none of Claims 1-20 are directed to an unconventional configuration of sensors or a specific process for reducing errors in measuring the relative position and orientation of a moving object on a moving reference frame as discussed in Thales. Further, unlike the claims at issue in Thales, Claims 1-20 merely apply an abstract idea to a computer and do not either improve the performance of the computer itself or computer technology in any way.
Applicant further argues that the claimed invention is integrated into a practical application because the features of claim 1 “applies or uses a judicial exception to effect a particular treatment of prophylaxis for a disease or medical condition”. Examiner respectfully disagrees as claim 1 recites, “…direct treatment of the cardiac disease state…” which fails to recite a particular treatment and does not amount to significantly more (MPEP 2106.04(d)(2)).
Prior Art Rejections
Applicant argues that the prior art of record fails to disclose:
determining, by the one or more processors and based, in part, on the orthogonal projection of the phase space model, values of one or more visual-predictor associated properties of at least one waveform region of interest in the biophysical signal data set,
wherein the visual- predictor associated properties characterize properties of repetitive cycles between the at least one cardiac signal and the PPG signal, and
wherein the visual-predictor associated properties include characteristics of projections of cardiac vectors onto the plurality of orthogonal planes and within a quadrant region of a respective orthogonal plane.
Examiner respectfully disagrees as Shadforth discloses:
determining, by the one or more processors (pre-processed biophysical signal data set 118 (shown as 118a)) and based, in part, on the orthogonal projection of the phase space model (Figs. 2A-2G: large orthogonal loops), values of one or more visual-predictor associated properties of at least one waveform region of interest in the biophysical signal data set ([0087] these geometric and tomographic features relating to observable loops in the residue point-cloud model/data set are representation of a more restrictive set of possible states as isolated in the residue point-cloud model/data set 125),
wherein the visual-predictor associated properties characterize properties of repetitive cycles between the at least one cardiac signal and the PPG signal ([0150] analysis of certain volumetric object generated from the residue point cloud model/data set may be represented visually with a repetitive set of distinct paths, or loops, in phase space), and
wherein the visual-predictor associated properties include characteristics of projections of cardiac vectors onto the plurality of orthogonal planes and within a quadrant region of a respective orthogonal plane ([0087] corresponding markers 202a-202h; Examiner notes that Fig. 2A shows the data set 125 in a three dimensional space, so any projections from any of the orthogonal loops would necessarily be projections of cardiac vectors (since this is cardiac data) onto the plurality of orthogonal planes and within a quadrant region of a respective orthogonal plane).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of analyzing gathered data to estimate the presence of a cardiac disease state) without significantly more.
Step 1
The claimed invention in claims 1-20 are directed to statutory subject matter as the claims recite a method/system for analyzing gathered data to estimate the presence of a cardiac disease state.
Step 2A, Prong One
Regarding claims 1-20, the recited steps are directed to mental processes of performing concepts in a human mind or by a human using a pen and paper (See MPEP 2106.05(a)(2) subsection (III)).
Regarding claims 1, 19, and 20, the limitations of “generating, by the one or more processors, an orthogonal projection…”, “determining, by the one or more processors and based, in part, on the orthogonal projection of the phase space model…”, and “determining, by the one or more processors, an estimated value for presence of a metric…” are a process, as drafted, that can be performed by a human mind (including an observation, evaluation, and judgment) under the broadest reasonable interpretation but for the recitation of generic computing components.
Step 2A, Prong Two
For claims 1-20, the judicial exception is not integrated into a practical application. For claims 1, 19, and 20, the additional limitations of “one or more processors” and “a memory”, “ are recited at a high level of generality and amount to nothing more than parts of a generic computer. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into a practical application. Further, the limitations of “obtaining, by one or more processors, a biophysical signal data set…” amounts to nothing more than the pre-solution activity of data gathering (MPEP 2106.05(g)).
Moreover, the additional limitation of “…direct treatment of the cardiac disease state or condition…” fails to integrate the abstract idea into a practical application. In order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. The treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration.
Step 2B
The recitation of the above-identified additional limitations of “a memory” and “a processor” in claims 1-20 amount to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Dependent claims 2-3, 11, and 15-16 are directed towards insignificant extra-solution activities and do not introduce any additional elements which amount to significantly more under the Step 2A prong 2 and Step 2B.
The use of an optical sensor to acquire photoplethysmography data is considered to be well-known, routine, and conventional in the art. For examples see Shadforth (US 2020/0211713) [0011] and Khosousi (US 2020/0205745) [0009].
Examiner also notes that “a display” and “a computing device” are generally recited and amount to nothing more than generic computing components.
The use of an acquisition circuit to measure voltage gradient signals over one or more channels is also considered to be well-known, routine, and conventional in the art. For examples see Shadforth (US 2020/0211713) [0011] and Khosousi (US 2020/0205745) [0009].
Dependent claims 4-10, 12-14, and 17-18 are further directed towards the abstract idea and do not introduce any additional elements which amount to significantly more under the Step 2A prong 2 and Step 2B.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 11-12, 14-16, and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shadforth (US 2020/0211713).
Regarding claims 1 and 20, Shadforth discloses a method to non-invasively assess a metric associated with a cardiac disease state or abnormal condition of a subject, the method comprising:
obtaining, by one or more processors, a biophysical signal data set of the subject ([0024] obtaining by one or more processors, a plurality of biophysical signal data sets of a subject) including at least one cardiac signal and a photoplethysmogram (PPG) signal ([0071] acquisition of cardiac and PPG signals);
generating, by the one or more processors, a phase space model of the biophysical signal data set ([0024] generating, by the one or more processors, a residue model comprising a point-cloud residue data set generated from an analysis of the plurality of biophysical signal data sets);
generating, by the one or more processors, an orthogonal projection of the phase space model of the biophysical signal data set ([0024] generating, by the one or more processors, a three-dimensional volumetric object from the point-cloud residue data stet; [0087] connected loops that are large, distinct and off-axis (or orthogonal) to one another),
wherein the orthogonal projection defines a plurality of orthogonal planes (Figs. 2A-2G show orthogonal loops in a three dimensional space; Examiner notes this would necessarily define a plurality of orthogonal planes);
determining, by the one or more processors (pre-processed biophysical signal data set 118 (shown as 118a)) and based, in part, on the orthogonal projection of the phase space model (Figs. 2A-2G: large orthogonal loops), values of one or more visual-predictor associated properties of at least one waveform region of interest in the biophysical signal data set ([0087] these geometric and tomographic features relating to observable loops in the residue point-cloud model/data set are representation of a more restrictive set of possible states as isolated in the residue point-cloud model/data set 125),
wherein the visual-predictor associated properties characterize properties of repetitive cycles between the at least one cardiac signal and the PPG signal ([0150] analysis of certain volumetric object generated from the residue point cloud model/data set may be represented visually with a repetitive set of distinct paths, or loops, in phase space), and
wherein the visual-predictor associated properties include characteristics of projections of cardiac vectors onto the plurality of orthogonal planes and within a quadrant region of a respective orthogonal plane ([0087] corresponding markers 202a-202h; Examiner notes that Fig. 2A shows the data set 125 in a three dimensional space, so any projections from any of the orthogonal loops would necessarily be projections of cardiac vectors (since this is cardiac data) onto the plurality of orthogonal planes and within a quadrant region of a respective orthogonal plane); and
determining, by the one or more processors, an estimated value for presence of a metric associated with the cardiac disease state or abnormal condition based, in part, on an application of the determined values of the one or more visual-predictor associated properties to an estimation model ([0080] the three-dimensional volumetric object generated from a residue analysis, and parameters derived therefrom, may be interpreted manually or used as part of a machine learned classifier or predictor module that may be configured to assist in the determination of the presence or absence of disease or condition),
wherein the estimated value for the presence of the metric is used in the estimation model to non-invasively estimate presence or non-presence of the cardiac disease state or to direct treatment of the cardiac disease state or condition based on the estimated value ([0080] generate indicators 134 of presence or absence of disease or conditions).
Shadforth also discloses a non-transitory computer readable medium [0175] configured to non-invasively assess a metric associated with a cardiac disease state or abnormal condition of a subject [0006].
Regarding claim 2, Shadforth discloses wherein the biophysical signal data set comprises biopotential signals acquired for three channels of measurements ([0069] The biophysical signal data set 108 includes a plurality of acquired signals (e.g., acquired from three distinct channels), which can be combined together to generate a multi-dimensional data set, e.g., a three-dimensional phase space representation, of the biophysical-signal data set 108).
Regarding claim 3, Shadforth discloses wherein the biophysical signal data set comprises photoplethysmographic signals acquired from optical sensors ([0011] A “biophysical signal” is not limited to a cardiac signal, a neurological signal, or a photoplethysmographic signal but encompasses any physiological signal from which information may be obtained. Not intending to be limited by example, one may classify biophysical signals into types or categories that can include, for example, optical; Claim 18: “the noninvasive equipment comprises a phase space recorder and/or an optical photoplethysmograph system”).
Regarding claim 11, Shadforth discloses 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 ([0031] causing, by the one or more processors, generation of a visualization of generated volumetric object as a three-dimensional object, wherein the three-dimensional object is rendered and displayed at a display of a computing device and/or presented in a report).
Regarding claim 12, Shadforth discloses wherein the values of one or more visual-predictor associated properties are used in the estimation model selected from the group consisting of a linear model, a decision tree model, a random forest model, a support vector machine model, a neural network model ([0134] the parametric features are derived from the volumetric object generated from the residue point cloud model/data set and are assessed by a trained neural network classifier configured to assess for presence or non-presence of a disease state or other condition (e.g., significant coronary artery disease)).
Regarding claim 14, Shadforth discloses wherein the cardiac disease state or abnormal condition is selected from the group consisting of coronary artery disease, pulmonary hypertension, pulmonary arterial hypertension, pulmonary hypertension due to left heart disease, rare disorders that lead to pulmonary hypertension, 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 ([0013] In the context of the present disclosure, techniques for acquiring and analyzing biophysical signals are described in particular for use in diagnosing the presence, non-presence, localization (where applicable), and/or severity of certain disease states or conditions in, associated with, or affecting, the cardiovascular (or cardiac) system, including for example pulmonary hypertension (PH), coronary artery disease (CAD), and heart failure (e.g., left-side or right-side heart failure)).
Regarding claim 15, Shadforth discloses acquiring, by one or more acquisition circuits of a measurement system, voltage gradient signals over one or more channels, wherein the voltage gradient signals are acquired at a frequency greater than about 1 kHz; and generating, by the one or more acquisition circuits, the obtained biophysical data set from the acquired voltage gradient signals ([0032] each of the acquired biophysical signal data sets comprises a wide-band phase gradient biopotential signal data set that is simultaneously acquired at a sampling rate selected from the group consisting of about 1 kHz, about 2 kHz, about 3 kHz, about 4 kHz, about 5 kHz, about 6 kHz, about 7 kHz, about 8 kHz, about 9 kHz, about 10 kHz, and greater than 10 kHz).
Regarding claim 16, Shadforth discloses acquiring, by one or more acquisition circuits of a measurement system, one or more photoplethysmographic signals ([0011] A “biophysical signal” is not limited to a cardiac signal, a neurological signal, or a photoplethysmographic signal; [0029] the acquired plurality of biophysical signal data sets are derived from measurements acquired via the noninvasive equipment); and generating, by the one or more acquisition circuits, the obtained biophysical data set from the acquired voltage gradient signals ([0032] each of the acquired biophysical signal data sets comprises a wide-band phase gradient biopotential signal data set that is simultaneously acquired at a sampling rate selected from the group consisting of about 1 kHz, about 2 kHz, about 3 kHz, about 4 kHz, about 5 kHz, about 6 kHz, about 7 kHz, about 8 kHz, about 9 kHz, about 10 kHz, and greater than 10 kHz).
Regarding claim 18, Shadforth discloses wherein the one or more processors are located in a local computing device ([0080] the three-dimensional volumetric object generated from a residue analysis, and parameters derived therefrom, may be interpreted manually or used as part of a machine learned classifier or predictor module that may be configured to assist in the determination of the presence or absence of disease or condition. Such a module may be local or remote to the assessment system 110).
Regarding claim 19, Shadforth discloses a system comprising:
a processor ([0069] assessment system 110); and
a memory having instructions stored thereon [0037], wherein execution of the instructions by the processor cause the processor to:
obtain a biophysical signal data set of a subject ([0024] obtaining, by one or more processors, a plurality of biophysical signal data sets of a subject) including at least one cardiac signal and photoplethysmogram (PPG) signal ([0071] acquisition of cardiac and PPG signals);
generate a phase space model of the biophysical signal data set ([0024] generating, by the one or more processors, a residue model comprising a point-cloud residue data set generated from an analysis of the plurality of biophysical signal data sets);
generate an orthogonal projection of the phase space model of the biophysical signal data set ([0024] generating, by the one or more processors, a three-dimensional volumetric object from the point-cloud residue data stet; [0087] connected loops that are large, distinct and off-axis (or orthogonal) to one another),
wherein the orthogonal projection defines a plurality of orthogonal planes (Figs. 2A-2G show orthogonal loops in a three dimensional space; Examiner notes this would necessarily define a plurality of orthogonal planes);
determine based at least in part on the orthogonal projection of the phase space model (Figs. 2A-2G: large orthogonal loops), values of one or more visual-predictor associated properties of at least one waveform region of interest in the biophysical signal data set ([0087] these geometric and tomographic features relating to observable loops in the residue point-cloud model/data set are representation of a more restrictive set of possible states as isolated in the residue point-cloud model/data set 125),
wherein the visual-predictor associated properties characterize properties of repetitive cycles between the at least one cardiac signal and PPG signal ([0150] analysis of certain volumetric object generated from the residue point cloud model/data set may be represented visually with a repetitive set of distinct paths, or loops, in phase space); and
determine an estimated value for presence of a metric associated with the a cardiac disease state or abnormal condition based, in part, on an application of the determined values of the one or more visual-predictor associated properties to an estimation model ([0080] the three-dimensional volumetric object generated from a residue analysis, and parameters derived therefrom, may be interpreted manually or used as part of a machine learned classifier or predictor module that may be configured to assist in the determination of the presence or absence of disease or condition),
wherein the estimated value for the presence of the metric is used in the estimation model to non-invasively estimate presence or non-presence of the cardiac disease state or abnormal condition or to direct treatment of the cardiac disease state or condition based on the estimated value ([0080] generate indicators 134 of presence or absence of disease or conditions).
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.
Claim(s) 4-10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Shadforth (US 2020/0211713).
Regarding claim 4, Shadforth discloses the method of claim 1 as discussed above, but fails to explicitly disclose determining, by the one or more processors, one or more values of one or more features extracted from the phase space model, wherein the one or more features are selected from the group consisting of: a feature associated with a three-dimensional perimeter of the phase space model; a feature associated with an area enclosed in a max fit plane determined in the phase space model; a feature associated with a three-dimensional mean curvature or three- dimensional maximum curvature determined in the phase space model; a feature associated with a macroscopic measure of a rotation of points that defines a loop in the phase space model; a feature associated with an average of a magnitude of curl vectors determined in the phase space model; and a feature associated with a Euclidean distance between consecutive points in a loop defined in the phase space model.
However, Shadforth further discloses determining, by the one or more processors, machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object [0024]. It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Shadforth with determining, by the one or more processors, one or more values of one or more features extracted from the phase space model, wherein the one or more features are selected from the group consisting of: a feature associated with a three-dimensional perimeter of the phase space model; a feature associated with an area enclosed in a max fit plane determined in the phase space model; a feature associated with a three-dimensional mean curvature or three- dimensional maximum curvature determined in the phase space model; a feature associated with a macroscopic measure of a rotation of points that defines a loop in the phase space model; a feature associated with an average of a magnitude of curl vectors determined in the phase space model; and a feature associated with a Euclidean distance between consecutive points in a loop defined in the phase space model since such a modification would provide the predictable results of using the determined features as an indicator of a disease state [0024].
Regarding claim 5, Shadforth discloses the method of claim 1 as discussed above, but fails to explicitly disclose determining, by the one or more processors, one or more values of one or more features extracted from the orthogonal projection, wherein the one or more features are selected from the group consisting of: a feature associated with a two-dimensional perimeter of a loop defined in the orthogonal projection; a feature defining a quadrant having a maximum two-dimensional perimeter of the loop defined in the orthogonal projection; a feature associated with a two-dimensional area of the loop defined in the orthogonal projection; a feature defining a quadrant having a maximum two-dimensional area of the loop defined in the orthogonal projection; a feature associated with an eccentricity of the loop defined in the orthogonal projection; a feature associated with a two-dimensional mean curvature or two-dimensional maximum curvature determined in the orthogonal projection; and a feature associated with a macroscopic measure of a rotation of points that defines the loop in the orthogonal projection.
However, Shadforth further discloses determining, by the one or more processors, machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object [0024], and a number of connected loops that are large, distinct and off-axis (or orthogonal) to one another as well as corresponding markers 202a-202h that are shown in FIG. 2A (residue point-cloud model/data set) and each of FIGS. 2B-2G [0087]. It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Shadforth with determining, by the one or more processors, one or more values of one or more features extracted from the orthogonal projection, wherein the one or more features are selected from the group consisting of: a feature associated with a two-dimensional perimeter of a loop defined in the orthogonal projection; a feature defining a quadrant having a maximum two-dimensional perimeter of the loop defined in the orthogonal projection; a feature associated with a two-dimensional area of the loop defined in the orthogonal projection; a feature defining a quadrant having a maximum two-dimensional area of the loop defined in the orthogonal projection; a feature associated with an eccentricity of the loop defined in the orthogonal projection; a feature associated with a two-dimensional mean curvature or two-dimensional maximum curvature determined in the orthogonal projection; and a feature associated with a macroscopic measure of a rotation of points that defines the loop in the orthogonal projection since such a modification would provide the predictable results of using geometric and tomographic features relating to the observable loops as representation of a more restrictive set of possible states [0087].
Regarding claim 6, Shadforth discloses determining, by the one or more processors, one or more values of one or more features extracted from the biophysical signal data set comprising a photoplethysmographic signal or a derivative thereof ([0011] A “biophysical signal” is not limited to a cardiac signal, a neurological signal, or a photoplethysmographic signal but encompasses any physiological signal from which information may be obtained), but fails to explicitly disclose wherein the one or more features are selected from the group consisting of: a feature defined by a vector joining a pre-defined origin landmark in the photoplethysmographic signal to a peak location determined in the photoplethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in a velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) a peak location determined in the velocity-plethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in the velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) a minimum location determined in the velocity-plethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in the velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) a base location determined in the velocity-plethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in an acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) a peak location determined in the acceleration-plethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in the acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) a minimum location determined in the acceleration-plethysmographic signal; and a feature defined by a vector joining i) the pre-defined origin landmark in the acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) a base location determined in the acceleration-plethysmographic signal.
However, Shadforth further discloses determining, by the one or more processors, machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object [0024], and a number of connected loops that are large, distinct and off-axis (or orthogonal) to one another as well as corresponding markers 202a-202h that are shown in FIG. 2A (residue point-cloud model/data set) and each of FIGS. 2B-2G [0087]. It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Shadforth with the one or more features are selected from the group consisting of: a feature defined by a vector joining a pre-defined origin landmark in the photoplethysmographic signal to a peak location determined in the photoplethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in a velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) a peak location determined in the velocity-plethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in the velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) a minimum location determined in the velocity-plethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in the velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) a base location determined in the velocity-plethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in an acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) a peak location determined in the acceleration-plethysmographic signal; a feature defined by a vector joining i) the pre-defined origin landmark in the acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) a minimum location determined in the acceleration-plethysmographic signal; and a feature defined by a vector joining i) the pre-defined origin landmark in the acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) a base location determined in the acceleration-plethysmographic signal since such a modification would provide the predictable results of using geometric and tomographic features relating to the observable loops as representation of a more restrictive set of possible states [0087].
Regarding claim 7, Shadforth discloses the method of claim 1 as discussed above, but fails to explicitly disclose determining, by the one or more processors, one or more values of features extracted from the biophysical signal data set, wherein the one or more features are selected from the group consisting of: a feature defined by a three-dimensional vector joining a pre-defined origin landmark in a cardiac signal peak landmark in a cardiac signal; a feature defined by a three-dimensional vector joining a pre-defined origin landmark in the photoplethysmographic signal to a peak, a minimum, or a base location determined in the photoplethysmographic signal; a feature defined by an elevation angle defining the three-dimensional vector joining the pre-defined origin landmark to the peak landmark in the cardiac signal; a feature defined by a three-dimensional vector joining i) the pre-defined origin landmark in a velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) a peak, a minimum, or a base location determined in the velocity-plethysmographic signal; a feature defined by a three-dimensional vector joining i) the pre-defined origin landmark in an acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) a peak, a minimum, or a base location determined in the acceleration- plethysmographic signal; a feature defined by an elevation angle defining the three-dimensional vector joining the pre-defined origin landmark in the photoplethysmographic signal to the peak, the minimum, or the base location determined in the photoplethysmographic signal; a feature defined by an elevation angle defining the three-dimensional vector joining i) the pre-defined origin landmark in the velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) the peak, the minimum, or the base location determined in the velocity-plethysmographic signal; and a feature defined by an elevation angle defining the three-dimensional vector joining i) the pre-defined origin landmark in the acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) the peak, the minimum, or the base location determined in the acceleration-plethysmographic signal.
However, Shadforth further discloses determining, by the one or more processors, machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object [0024], and a number of connected loops that are large, distinct and off-axis (or orthogonal) to one another as well as corresponding markers 202a-202h that are shown in FIG. 2A (residue point-cloud model/data set) and each of FIGS. 2B-2G [0087]. It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Shadforth with determining, by the one or more processors, one or more values of features extracted from the biophysical signal data set, wherein the one or more features are selected from the group consisting of: a feature defined by a three-dimensional vector joining a pre-defined origin landmark in a cardiac signal peak landmark in a cardiac signal; a feature defined by a three-dimensional vector joining a pre-defined origin landmark in the photoplethysmographic signal to a peak, a minimum, or a base location determined in the photoplethysmographic signal; a feature defined by an elevation angle defining the three-dimensional vector joining the pre-defined origin landmark to the peak landmark in the cardiac signal; a feature defined by a three-dimensional vector joining i) the pre-defined origin landmark in a velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) a peak, a minimum, or a base location determined in the velocity-plethysmographic signal; a feature defined by a three-dimensional vector joining i) the pre-defined origin landmark in an acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) a peak, a minimum, or a base location determined in the acceleration- plethysmographic signal; a feature defined by an elevation angle defining the three-dimensional vector joining the pre-defined origin landmark in the photoplethysmographic signal to the peak, the minimum, or the base location determined in the photoplethysmographic signal; a feature defined by an elevation angle defining the three-dimensional vector joining i) the pre-defined origin landmark in the velocity-plethysmographic signal derived from the photoplethysmographic signal to ii) the peak, the minimum, or the base location determined in the velocity-plethysmographic signal; and a feature defined by an elevation angle defining the three-dimensional vector joining i) the pre-defined origin landmark in the acceleration-plethysmographic signal derived from the photoplethysmographic signal to ii) the peak, the minimum, or the base location determined in the acceleration-plethysmographic signal since such a modification would provide the predictable results of using geometric and tomographic features relating to the observable loops as representation of a more restrictive set of possible states [0087].
Regarding claim 8, Shadforth discloses the method of claim 4 as discussed above, but fails to explicitly disclose determining, by the one or more processors, one or more values of features extracted from the biophysical signal data set comprising a photoplethysmographic signal or a derivative thereof, wherein the one or more features are selected from the group consisting of: a feature defined by a two-dimensional magnitude of a vector defined between i) an origin location in a projection of one or more of orthogonal planes defined in the phase space model and ii) a peak, a minimum, or a base location in the orthogonal plane; and a feature defined by an angle of the vector defined between i) the origin location in the projection of the one or more of the orthogonal planes defined in the phase space model and ii) the peak, the minimum, or the base location in the orthogonal plane.
However, Shadforth further discloses determining, by the one or more processors, machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object [0024], and a number of connected loops that are large, distinct and off-axis (or orthogonal) to one another as well as corresponding markers 202a-202h that are shown in FIG. 2A (residue point-cloud model/data set) and each of FIGS. 2B-2G [0087]. It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Shadforth with determining, by the one or more processors, one or more values of features extracted from the biophysical signal data set comprising a photoplethysmographic signal or a derivative thereof, wherein the one or more features are selected from the group consisting of: a feature defined by a two-dimensional magnitude of a vector defined between i) an origin location in a projection of one or more of orthogonal planes defined in the phase space model and ii) a peak, a minimum, or a base location in the orthogonal plane; and a feature defined by an angle of the vector defined between i) the origin location in the projection of the one or more of the orthogonal planes defined in the phase space model and ii) the peak, the minimum, or the base location in the orthogonal plane since such a modification would provide the predictable results of using geometric and tomographic features relating to the observable loops as representation of a more restrictive set of possible states [0087].
Regarding claim 9, Shadforth discloses the method of claim 4 as discussed above, but fails to explicitly disclose determining, by the one or more processors, one or more values of features extracted from the biophysical signal data set comprising a photoplethysmographic signal or a derivative thereof, wherein the one or more features includes a feature defined by a surface area or a volume parameter of a defined geometric shape in the phase space model ([0024] and determining, by the one or more processors, machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object; the machine extractable features are selected from the group consisting of a 3D object volume value, a void volume value, a surface area value).
Regarding claim 10, Shadforth discloses wherein the defined geometric shape comprises an alpha hull shape or a convex hull shape ([0025] the step of generating the three-dimensional volumetric object comprises performing a triangulation operation on the point-cloud residue of the plurality of biophysical signal data sets, wherein the triangulation operation is selected from the group consisting of Delaunay triangulation, Mesh generation, Alpha Hull triangulation, and Convex Hull triangulation).
Regarding claim 13, Shadforth discloses the method of claim 12 as discussed above, but fails to explicitly disclose wherein the model further includes features selected from the group consisting of: one or more depolarization or repolarization wave propagation associated features; 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 or PPG signal power spectral density associated features; one or more cardiac or PPG signal visual associated features; and one or more predictability features.
However, Shadforth further discloses determining, by the one or more processors, machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object [0024], and a number of connected loops that are large, distinct and off-axis (or orthogonal) to one another, and that geometric and tomographic features relating to observable loops in the residue point-cloud model/data set are representation of a more restrictive set of possible states as isolated in the residue point-cloud model/data set 125 [0087]. ]. It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Shadforth with the model further includes features selected from the group consisting of: one or more depolarization or repolarization wave propagation associated features; 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 or PPG signal power spectral density associated features; one or more cardiac or PPG signal visual associated features; and one or more predictability features since such a modification would provide the predictable results of using geometric and tomographic features relating to the observable loops as representation of a more restrictive set of possible states [0087].
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Shadforth (US 2020/0211713) in view of Shadforth et al (US Patent No. 10806349) hereinafter Shadforth(2020).
Regarding claim 17, Shadforth discloses the method of claim 1 as discussed above, but fails to disclose wherein the one or more processors are located in a cloud platform. However, Shadforth (2020) discloses one or more processors located in a cloud platform (Claim 17: wherein execution of the instructions by one or more processors of one or more computing devices cause the one or more processors to… execute an analysis service in the one or more cloud platforms). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Shadforth with or more processors located in a cloud platform as taught by Shadforth(2020) since such a modification would provide the predictable results of remote analysis.
Conclusion
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/WILLOW GRACE WELCH/Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792