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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/08/2025 has been entered.
Response to Arguments
Applicant’s arguments, see “Applicant Arguments/Remarks”, filed 09/08/2025, with respect to the rejections under U.S.C. 102 have been fully considered and are persuasive. Specifically, Sullivan does not teach wherein the cardiac input data is converted, using an optical character reader, the digital files into machine-encoded text. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Sullivan and Smith.
Applicant's arguments filed 09/08/2025 regarding the rejections under U.S.C. 101 have been fully considered but they are not persuasive.
Applicant’s first argument, that the usage of OCR on the cardiac input data renders the claims patent-eligible as the usage of OCR cannot be considered an abstract concept, is unpersuasive. The usage of OCR to broadly pre-process the cardiac input data, particularly as the machine-encoded data is not referenced at any other point in the claim, is merely using a computer as a tool to perform the mental process of data collection, see MPEP 2106.04(a)(2)(III)(C).
Applicant’s next argument, that the claims do not recite mathematical concepts in light of the August 4th Memorandum is likewise unpersuasive. Applicant’s claims may not recite equations or explicit mathematical symbols, but the limitations “generating a probability distribution for the case…” and “generate one or more cardiac indices… wherein at least one cardiac index of the one or more cardiac indices comprises a probably of the patient satisfying at least one grading threshold” describe in words that there are mathematical relationships and formulas present in the claim language.
Applicant’s next argument, that the amended claim language provides an improvement similar to the improvement set forth in Example 47, is likewise unpersuasive. Applicant states in their arguments that the improvement is by automating signal validation and diagnostic feedback, but automating known processes is merely routine and conventional, and cannot alone amount to significantly more than the abstract idea (see MPEP 2106.05(a), “Examples that the courts have indicated may not be sufificient to show an improvement in computer-functionality… iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);”). Applicant also refers to their invention as being performed in a real-time, proactive manner, which is not commensurate with the scope of the claims. The claims provide no basis for timing or active monitoring, nor for any alerting that would indicate that the cardiac data being input is current/in real-time. This logic also applies to Applicant’s arguments regarding similarity to Example 48, and the arguments under Section 2B.
For these reasons, the rejections under U.S.C. 101 are maintained, and have been updated to account for the new subject matter.
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 1-26 are rejected under 35 U.S.C. 101 because the claims are directed toward an abstract idea without significantly more.
Step 1: Independent Claims 1 and 14 are recite an apparatus and a method. Thus, they are directed to statutory categories of invention.
Step 2A, Prong 1: Independent Claims 1 and 14 recite the following limitations:
receive cardiac input data from a patient comprising a plurality of cardiac signals wherein receiving the cardiac input data comprises; comparing the cardiac input data against at least a quality assurance parameter
validating one or more digitized signals by classifying the one or more digitized signals to a plurality of cardiac parameters
generating a quality diagnostic of the cardiac input data as a function of the validation of the one or more digitized signals
input the cardiac input data into a cardiac panel, the cardiac panel comprising a plurality of cardiac models
the at least one cardiac model comprises at least one trained cardiac machine learning model configured to receive cardiac input data as inputs and output cardiac indices
and the at least one cardiac model is configured to calculate a cardiac index associated with diastolic dysfunction
classify a case of diastolic dysfunction to at least one category of a plurality of categories, based on a severity of a case of diastolic dysfunction, wherein classifying includes assigning the case to a category and generating a probability distribution for the case across the plurality of categories reflecting different levels of diastolic function including at least normal or abnormal
and generate one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the at least one cardiac machine learning model
wherein at least one cardiac index of the one or more cardiac indices comprises a probability of the patient satisfying at least one grading threshold
These limitations, under their broadest reasonable interpretation, cover concepts that can be practically performed in the human mind, i.e., using pen and paper. With a plurality of measurements for each physiological variable of a patient, a human could reasonably extract inputs and put them through a model to calculate an index associated with diastolic dysfunction, and estimate the probability that the index will occur.
Further, the cardiac model recites forming a mathematical relationship, and thus also falls within the ‘mathematical concepts’ grouping of abstract ideas.
Step 2A, Prong 2:
Claims 1 and 14 recite the further limitations:
a processor
Wherein the cardiac data comprises digital files
A memory in communication with the processor, wherein the memory contains instructions to configurate the processor
Converting, using an optical character reader, the digital files into machine-encoded text
The processor and memory are merely reciting both the processors and computer storage media at a high-level of generality to carry out the steps of the method. In other words, the computer components are being used as a tool to carry out the method (See MPEP 2106.05(f)). Thus, the abstract idea is not integrated into a practical application.
The usage of OCR to pre-process cardiac input data is merely the pre-solution activity of data-gathering, and is recited at a high level of generality. Thus, the limitation does not amount to significantly more than the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept.
Dependent Claims:
Dependent Claims 1-10, 13, 15-23, and 26 further define the type of information used to obtain the data and train the cardiac model, but as a whole in combination do not provide significantly more than the abstract idea.
Dependent Claims 11-12 and 24-25 discloses a display to visual the detected data, but "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, does not overcome the abstract idea. Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-5, 7-12, 13-18, 20-26 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 20160135706 awarded to Sullivan et al, hereinafter Sullivan as applied to the claims above, and further in view of U.S. Patent Publication 20240366158 awarded to Smith et al, hereinafter Smith.
Regarding Claims 1 and 14, Sullivan teaches an apparatus and method for tracking cardiac indices (abstract, Para. 0011), the apparatus comprising: a processor and a memory communicatively connected to the processor, wherein the memory contains instructions configurating the processor (Para.0007) to: receive cardiac input data from a patient comprising a plurality of cardiac signals (Para. 0298, “The control unit 120 of the wearable medical device 100 can monitor ECG signals provided by the plurality of ECG electrodes 112 and, where provided, some or all of the signals provided by other sensors. The control unit 120 can calculate event estimation of risk scores associated with the potential of an adverse cardiac event for a subject 104 during associated time periods based on the signals provided from the ECG electrodes 112 and, where provided, some or all of the signals provided by other sensors”); wherein the cardiac input data comprises digital files (Para. 0302), wherein receiving the cardiac input data comprises; comparing the cardiac input data against at least a quality assurance parameter (Para. 0433, “The measurement update equations are responsible for the feedback, i.e., for incorporating a new measurement into the a priori estimate to obtain an improved a posteriori estimate. The time update equations may also be thought of as predictor equations, while the measurement update equations may be thought of as corrector equations. Indeed, the final estimation algorithm resembles that of a predictor-corrector algorithm for solving numerical problems”); validating one or more digitized signals by classifying the one or more digitized signals to a plurality of cardiac parameters (Para. 0334, “In some examples, clustering techniques can be implemented during validation to select the most predictive set of classifier models. For example, the most predictive, validated set of classifiers can be identified based on sensitivity values occurring in a predetermined range, (e.g., exceeding 0.25 or any other predefined threshold). In some examples, a single model, such as example model 80, as shown above, may be selected and used for determining an event estimation of risk score for a given patient”); generating a quality diagnostic of the cardiac input data as a function of the validation of the one or more digitized signals (Para. 0433, “The measurement update equations are responsible for the feedback, i.e., for incorporating a new measurement into the a priori estimate to obtain an improved a posteriori estimate. The time update equations may also be thought of as predictor equations, while the measurement update equations may be thought of as corrector equations. Indeed, the final estimation algorithm resembles that of a predictor-corrector algorithm for solving numerical problems”); input the cardiac input data into a cardiac panel, the cardiac panel comprising a plurality of cardiac models (Para. 0379, “FIG. 8F is a flow chart of a process for determining event estimation of risk scores and corresponding classifications according to some embodiments. Input data from ECG and Non-ECG data sources are gathered and cleaned at stage 802 to provide a set of weighted metrics for each subject. Metrics can include but are not limited to measurement of heart rate, count of premature ventricular contractions, gender and age. At stage 804, models such as the predictive machine learning algorithms or classifiers are applied and event estimation of risk scores are generated. At stage 806, thresholds (e.g., predetermined thresholds) based upon performance criteria, such as sensitivity and specificity as described herein, are used to interpret a subject's event estimation of risk score by gradation of risk to classify the risk score”), wherein: at least one cardiac model of the cardiac panel is configured to calculate a cardiac index associated with a heart condition (Para. 0380, “In an example, a patient's electrocardiographic data (ECG) 8005 is received by the risk prediction system 8000, e.g., by an external medical device. Input of data across the system boundary, e.g., by the device monitoring the physiological parameters of the subject, results in development of ECG based metrics 8010, such as but not limited to heart rate, heart rate variability, S-T segment elevation, premature ventricular contractions, heart rhythm morphology, etc.”); the at least one cardiac model comprises at least one trained cardiac machine learning model configured to receive cardiac input data as inputs and output cardiac indices (Para. 0380, “The complete dataset of metrics are delivered to the classifier 8030, which calculates a risk score and returns a risk classification 8040 for that patient. In the event that the risk classification of the patient is elevated above that of the typical patient, e.g., above a threshold set for the classifier model (e.g., at thresholds set for elevated risk or elevated and immediate risk as described above), the system can prompt certain actions. For example, actions 8050 may be directed at the external medical device (e.g., the wearable defibrillator), such as but not limited to triggering changes in the controller so that a time from detection of sudden cardiac arrest to treatment is decreased”); and the at least one cardiac model is configured to calculate a cardiac index associated with diastolic dysfunction (Para. 0248, “S4 metrics can indicate an increase in heart filling pressure, e.g., an increased resistance to filling of the left or right ventricle because of a reduction in ventricular wall compliance, and it can be accompanied by a disproportionate rise in ventricular end-diastolic pressure. For example, S4 in individuals of about age 50 or more may be predictive of patients with or worsening systemic hypertension, aortic stenosis, hypertrophic cardiomyopathy and coronary heart disease”); classify a case of diastolic dysfunction to at least one category of a plurality of categories, based on a severity of a case of diastolic dysfunction (Para. 0248 states that a disproportionate rise in end-diastolic pressure is predictive of a plurality of categories of heart conditions), wherein classifying includes assigning the case to a category and generating a probability distribution for the case across the plurality of categories reflecting different levels of diastolic function including at least normal or abnormal (Para. 0215, “For example, for a plurality of time periods, an event estimation of risk score can be calculated (e.g., such that each time period has its own respective event estimation of risk score). In this way, multiple different event estimation of risk scores can be calculated for multiple different time periods, (e.g., less than about ten minutes, less than about one hour, less than about three hours, less than about one day, less than about one week, and/or less than about one month, etc.). The event estimation of risk scores provide a measure of the likelihood or probability of one or more medical event(s) occurring within the associated time period”), and generate one or more cardiac indices from the cardiac panel as a function of the cardiac input data (Para. 0242, “In some examples, such acoustic and/or combined acoustic and ECG metrics can include electromechanical activation time (EMAT) metrics. For example, EMAT metrics as used herein describe an interval from some fiducial timepoint in the electrocardiograph (ECG) to some fiducial timepoint in a subsequent mechanical activity of the heart”) and the at least one cardiac machine learning model (Para. 0246, “As described above, S3 and S4 heart sounds metrics may be used in certain predictive models described herein, e.g., for estimating risks scores associated with certain outcomes. For example, S3 and S4 heart sound metrics may be predictive of worsening heart failure. Accordingly, S3 and S4 metrics may be included along with other metrics input into the predictive models described herein. In accordance with the principles described herein, S3 and/or S4 may be predictive of a need for increasing medicine or preparing for hospitalization relating to myocardial infarction and/or heart failure complications”), wherein at least one cardiac index of the one or more cardiac indices comprises a probability of the patient satisfying at least one grading threshold (Para. 0245, “For example, one EMAT metric includes percent EMAT (% EMAT), which may be computed as the EMAT time measure divided by a measure of a dominant RR interval. The % EMAT metric relates to an efficiency of the cardiac pump function. For example, in some cases, % EMAT >15% may be predictive of a patient's likelihood for developing heart failure related complications (e.g., re-hospitalization may be needed), as described in Chao T et al. EMAT in the Prediction of Discharge Outcomes in Patients Hospitalized with AHFS. Internal Med. 2010, 49: 2031-2037”). Sullivan does not teach wherein the cardiac input data is converted from digital files into machine-encoded text using an optical character reader.
However, in the art of cardiac biosignal processing (Para. 0033), Smith teaches the usage of OCR to process cardiac signals (Para. 0025) to ensure all forms of cardiac data are captured during signal processing to improve signal processing (Para. 0024).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sullivan by Smith, i.e. by using the OCR system of Smith in the system of Sullivan, to improve Sullivan in the same manner as Smith, as taught by Smith in Para. 0024 above.
Regarding Claims 2 and 15, Sullivan modified by Smith makes obvious the apparatus and method of claims 1 and 14. Sullivan further teaches wherein the cardiac input data includes at least one electrocardiogram (ECG) (Para. 0298, “The control unit 120 of the wearable medical device 100 can monitor ECG signals provided by the plurality of ECG electrodes 112 and, where provided, some or all of the signals provided by other sensors. The control unit 120 can calculate event estimation of risk scores associated with the potential of an adverse cardiac event for a subject 104 during associated time periods based on the signals provided from the ECG electrodes 112 and, where provided, some or all of the signals provided by other sensors”).
Regarding Claims 3 and 16, Sullivan modified by Smith makes obvious the apparatus and method of claims 1 and 14. Sullivan further teaches wherein training the at least one cardiac machine learning model comprises: receiving a plurality of cardiac training data associated with a plurality of patients (Para. 0204, FIG. 8L shows state change results including sensitivity and specificity percentages for predicting medical premonitory events from an example testing set of two hundred patients”); pretraining the at least one cardiac machine learning model as a function of the plurality of cardiac training data by adjusting one or more parameters within the at least one cardiac machine learning model (Para. 0389, “In some implementations, a patient being identified as belonging to an increased risk class (e.g., a patient who is classified as being at “immediate risk” or “elevated risk”) may cause one or more actions to occur. For example, the medical device may adjust one or more operational parameters in response to the patient being identified as belonging to an increased risk class. In some implementations, the medical device can adjust one or more threshold values for determining whether a medical event has occurred or is likely to occur. For example, if the patient is prone to a particular cardiac condition, the medical device may relax one or more threshold requirements for physiological parameters used to diagnose the particular cardiac condition (e.g., to minimize false negatives)”); and retraining the at least one cardiac machine learning model as a function of the adjusted one or more parameters and a labeled subset of the cardiac training data (Para. 0389, “For example, if the patient is prone to a particular cardiac condition, the medical device may relax one or more threshold requirements for physiological parameters used to diagnose the particular cardiac condition (e.g., to minimize false negatives)).
Regarding Claims 4 and 17, Sullivan modified by Smith makes obvious the apparatus and method of claims 3 and 16. Sullivan further teaches wherein the at least one cardiac model includes a classification model that classifies a case of diastolic dysfunction under one category of a plurality of categories (Para. 0248, “S4 metrics can indicate an increase in heart filling pressure, e.g., an increased resistance to filling of the left or right ventricle because of a reduction in ventricular wall compliance, and it can be accompanied by a disproportionate rise in ventricular end-diastolic pressure. For example, S4 in individuals of about age 50 or more may be predictive of patients with or worsening systemic hypertension, aortic stenosis, hypertrophic cardiomyopathy and coronary heart disease”).
Regarding Claims 5 and 18, Sullivan modified by Smith makes obvious the apparatus and method of claims 1 and 14. Sullivan further teaches wherein: at least one cardiac index of the one or more cardiac indices includes an elevated left ventricular filling pressure; and the at least one grading threshold includes a grading threshold in elevated left ventricular filling pressure (Para. 0248, “S4 metrics can indicate an increase in heart filling pressure, e.g., an increased resistance to filling of the left or right ventricle because of a reduction in ventricular wall compliance”).
Regarding Claims 7 and 20, Sullivan modified by Smith makes obvious the apparatus and method of claims 1 and 14. Sullivan further teaches wherein: inputting the cardiac input data into the cardiac panel comprises inputting a first plurality of time series data containing the cardiac input data (Para. 0023); and generating the one or more cardiac indices comprises generating a second plurality of time series data containing the one or more cardiac indices (Para. 0024, “wherein the one or more event estimation of risk thresholds for a first time period are different than the one or more event estimation of risk thresholds for a second time period”).
Regarding Claims 8 and 21, Sullivan modified by Smith makes obvious the apparatus and method of claims 7 and 14. Sullivan further teaches wherein at least one of the one or more cardiac indices includes a cardiac index deviation (Para. 0392, “The control unit 120 can calculate a variant, a mean, a median, a standard deviation, or other statistical value of the multivariate parameter signal for each time interval and determine whether a distance of the statistical value for the individual time interval from the baseline satisfies one or more thresholds”).
Regarding Claims 9 and 22, Sullivan modified by Smith makes obvious the apparatus and method of claims 1 and 14. Sullivan further teaches wherein generating the one or more cardiac indices comprises: receiving at least one user update (Para. 0515, “Examples of the request sent to the subject and the response from the subject may include requesting that the user indicate whether he or she is experiencing a particular symptom, such as tingling in the arm. The subject enters data on the computing device and the data is sent to the wearable medical device”); and updating at least one cardiac index of the one or more cardiac indices as a function of the at least one user update (Para. 0518, “At stage 1716, the control unit 120 calculates an enhanced event estimation of risk score for the multiple time-until-event periods based on the measured ECG data (and, e.g., the additional information received from the mobile device 550)”).
Regarding Claims 10 and 23, Sullivan modified by Smith makes obvious the apparatus and method of claims 1 and 14. Sullivan further teaches wherein generating the at least one cardiac index of the one or more cardiac indices comprises: comparing the one or more cardiac indices to one or more cardiac baselines (Para. 0392, “The control unit 120 may determine a baseline of the multivariate parameter signal on an interval from the start of the signal to the first or any subsequent change point or on an interval from the start of the signal to a desired timepoint. The control unit 120 may apply a distance analysis at stage 754 at various time intervals to the extracted parameters of the multivariate parameter signal with respect to the baseline or any other period. The detected change points or other arbitrary points in time may be used to determine the time intervals. For example, the time intervals may be situated about corresponding change points in the multivariate parameter signal. As shown in FIG. 8J, example extracted parameter signals, e.g., QRS height, maxNN, sdNN, and detrended heart rate, which form a multivariate parameter signal, may be broken into time intervals. For example, time interval R2, e.g., eventTime, may be associated with a change point of the multivariate parameter signal”); calculating one or more distance metrics as a function of the comparison (Para. 0392, “The control unit 120 can calculate a variant, a mean, a median, a standard deviation, or other statistical value of the multivariate parameter signal for each time interval and determine whether a distance of the statistical value for the individual time interval from the baseline satisfies one or more thresholds”); and generating the one or more cardiac indices as a function of at least one distance metric of the one or more distance metrics (Para. 0392, “For example, a mean calculated for a given time interval which is associated with a change point in the multivariate parameter signal, can be compared to a baseline R.sub.B, which is determined based on a time interval from the start the multivariate parameter signal to a first change point detected therein, to determine a distance between the two scores, i.e., D.sub.i=Rd?R.sub.B, where R.sub.B is the baseline score, R.sub.i is the score for an interval i, and D.sub.i is the distance between the two scores for the interval I”).
Regarding Claims 11 and 24, Sullivan modified by Smith makes obvious the method of Claims 10 and 23. Sullivan further teaches wherein the processor is further configured to display at least one cardiac index of the one or more cardiac indices through a user interface (Para. 0406).
Regarding Claims 12 and 25, Sullivan modified by Smith makes obvious the apparatus and method of claims 11 and 24. Sullivan further teaches wherein displaying the at least one cardiac index of the one or more cardiac indices further comprises generating visualization as a function of the at least one cardiac index of the one or more cardiac indices and the at least one distance metric of the one or more distance metrics (Para. 0301, “The visual display 531 may display information about the predicted risk for the subject 104. The information may be displayed graphically to allow the subject 104 and/or a caregiver to assess both the current risk and future risk of an adverse cardiac event. For example, briefly referring to FIG. 6A, a bar graph can provide a visual representation of example cardiac event estimation of risk scores for multiple time periods. The visual representations of the event estimation of risk scores for the various time periods may include information related to a reliability of the event estimate of risk score, e.g., a confidence value, associated with the corresponding event estimation of risk score. For example, a visual representation 602a-h of the confidence value may be superimposed on each bar of the bar graph. The visual representations 602a-h of the confidence values can indicate a likelihood that an event may occur for the associated time period, such that the visual representations that span a relatively greater distance (e.g., the visual representations 602g and 602h) indicate a relatively lower confidence in the event estimation of risk score than the visual representations that span a relatively lesser distance (e.g., the visual representations 602a and 602b)”). Sullivan does not teach wherein the display is color-coded.
However, in a different display embodiment Sullivan teaches the usage of color coding to indicate statistical differences in a display (Para. 0253).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sullivan, i.e. by using color coding on the distance display of Sullivan as in the separate display embodiment of Sullivan, as Sullivan sets forth the motivation to use color to indicate differences in statistics in the display.
Regarding Claims 13 and 26, Sullivan modified by Smith makes obvious the apparatus and method of claims 1 and 14. Sullivan further teaches wherein the processor is further configured to predict a projected cardiac index as a function of the cardiac input data and the at least one cardiac machine learning model (Para. 0447, “After each time and measurement update pair, the process is repeated with the previous a posteriori estimates used to project or predict the new a priori estimates”).
Claim 6 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 20160135706 awarded to Sullivan et al, hereinafter Sullivan as applied to the claims 1 and 14 above, further in view of U.S. Patent Publication 20240366158 awarded to Smith et al, hereinafter Smith, and further in view of U.S. Patent Publication 20200397324 awarded to Paak et al, hereinafter Paak.
Regarding Claims 6 and 19, Sullivan modified by Smith makes obvious the apparatus and method of claims 1 and 14. Sullivan further teaches wherein the cardiac index is predicting cardiopulmonary events (Para. 0225) and systemic hypertension (Para. 0248). Sullivan does not teach wherein: at least one cardiac index of the one or more cardiac indices is associated with pulmonary hypertension; and the at least one grading threshold includes a grading threshold associated with pulmonary hypertension.
However, in the art of cardiac machine learning, Paak teaches the detection and grading of pulmonary hypertension (Para. 0169, “Indeed, synchronicity between acquired photoplethysmographic signals (e.g., where acquired raw signals are merely processed to remove baseline wander and high frequency noise) and a cardiac signal based on triggers defined in the photoplethysmographic signal may be used to assess for the presence, non-presence, severity, and/or localization (where applicable) of coronary artery disease (CAD), pulmonary hypertension, heart failure in various forms, among other diseases and conditions”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sullivan by Paak, i.e. by using the system of Sullivan to train and detect for pulmonary hypertension, as Sullivan already teaches the need for detecting for cardiopulmonary/hypertension issues.
Conclusion
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/JLM/
Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792