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 .
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code as in [0003]. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Drawings
New corrected drawing in compliance with 37 CFR 1.121(d) is required in this application because Fig. 2B to be clearly legible. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
Claim Objections
Claim 15 objected to because of the following informalities: typo in line 1; ". Appropriate correction is required.
Claim 25 objected to because of the following informalities: typo in line 1; missing "medium" after ” as recited in lines 2-3. Appropriate correction is required.
Claim Interpretation
Under broadest reasonable interpretation and in light of the written description, the term “quality score”, as recited in but not limited to claims 1, 14, and 25, is interpreted to at least include any metric or output derived from a transformation of a predefined length of ECG signal data wherein a comparison leads to a reasonable determination about the data.
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 2, 15 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. Claims 2, 15 recite “centered around.” It is unclear if a cardiac beat is at the midline of the epoch and if said cardiac beat must be of the same type such as an RR interval. In epochs with an even number of cardiac beats, it is unclear if the centered cardiac beat is at the midline of the epoch and if this centering can result in a change to the number of cardiac beats inside of the epoch. With a two cardiac beat epoch, it is unclear when the epoch is centered on one of the cardiac beats and the second cardiac beat lies outside of the epoch, if the epoch includes only one cardiac beat thereby precluding the limitation of plural cardiac beats from claim 2. Therefore, one of ordinary skill in the art would not be reasonably apprised to determine the metes and bounds of the claimed invention. For the purpose of continued examination, under broadest reasonable interpretation and in view of the written description, “centered around one of the cardiac beats” is interpreted to require only one of the beats to be entirely within the epoch.
Claim 16 is 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. Claim 15 recites “wherein each epoch includes plural cardiac beats.” Claim 16 is dependent on claim 15 and recites “wherein each epoch includes at least 1 cardiac beat.” Claim 15 precludes claim 16 from including only 1 cardiac beat in each epoch. Therefore, one of ordinary skill in the art would not be reasonably apprised to determine the metes and bounds of the claimed invention. For the purpose of continued examination, claim 16 is interpreted to depend from claim 14.
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 1-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas under the mental processes and mathematical concepts groupings, without significantly more.
The framework for establishing a prima facie case of lack of subject matter eligibility requires that the Examiner determine: (1) Does the claim fall within the four categories of patent eligible subject matter; (2a) prong 1: Does the claim recite an abstract idea, law of
nature, or natural phenomenon and (2a) prong 2: Does the claim recite additional elements
that integrate the judicial exception into a practical application; and (2b) Does the claim recite additional elements that amount of significantly more than the judicial exception.
Under Step 1:
Independent claims 1, 14, and 25 are directed to a method and a system, and thus, the claims all fall under one of the four patent eligible categories.
Under Step 2A, Prong 1:
Claims 1, 14, 25 recite steps for decomposing a continuous ECG signal into plural epochs; generating a quality score for each epoch generated from the decomposed ECG signal; applying a first rolling window of a first specified width to the decomposed ECG signal to capture plural quality scores from a sequence of epochs in the plural epochs; computing, by the processor, a local metric from the plural quality scores captured by the first rolling window; generating a quality alert signal including streaming ECG data obtained from at least one portion of the received ECG signal and a notification based on the local metric associated with the at least one portion of the received ECG signal; generating a quality visualization signal for displaying a color map for one or more segments of the received ECG signal, the color maps corresponding to a local metric associated with the one or more segments of the received ECG signal; and sending, by the processor, an output of the one or more executed application modules to a user interface. Under broadest reasonable interpretation, these limitations appear to be directed to mental processes because they concern observing and evaluating signal data using mathematical formulas and interpreting this data to identify metrics, alerts, and visualizations which can be performed in the mind or with pen and paper. A trained clinician can evaluate a set of ECG data gathered from a patient; identify epochs of a specified length; determine quality scores from a sequence of said epochs; calculate metrics from quality scores; identify quality alert signals and notifications from ECG data; and create a color map using said metrics. Accordingly, claims 1, 14, 25 are directed to a judicial exception including one or more abstract ideas under mental processes.
Dependent claims 2-4, 15-17 recite additional limitations of each epoch includes plural cardiac beats centered around one of the plural cardiac beats; includes at least 1 cardiac beat and/or has a duration of at least 1.2 seconds; and quality score has a value range from 0 to 1 but appears to be directed to mental processes because they further limit an abstract process and concern observations, calculations, and judgements which can be performed in the mind or with pen and paper.
Dependent claims 5-8, 18-21 recite additional limitations of computing the local metric; determining a minimum epoch score within the first rolling window; determining a maximum epoch score within the first rolling window; determining an average epoch score within the first rolling window; and determining a standard deviation of epoch scores within the first rolling window but appears to be directed to mental processes because they further limit an abstract process and concern observations, calculations, and judgements which can be performed in the mind or with pen and paper.
Dependent claims 9, 23, 26 recite additional limitations of applying a second rolling window of a specified width to the decomposed ECG signal to capture plural local metrics associated with a sequence of epochs in the plural epoch; and computing an occurrence metric from the plural local metrics captured by the second rolling window but appear to be directed to mental processes because they further limit an abstract process and concern observations, calculations, and judgements which can be performed in the mind or with pen and paper.
Dependent claims 10, 22 recite an additional limitation of the second window has a duration of at least a width of the first rolling window but appears to be directed to mental processes because it further limits an abstract process and concerns observations, calculations, and judgements which can be performed in the mind or with pen and paper.
Dependent claim 11 recites an additional limitation of the occurrence metric has a value range from 0 to 1 but appears to be directed to mental processes because it further limits an abstract process and concerns observations, calculations, and judgements which can be performed in the mind or with pen and paper.
Dependent claims 12, 13, 24, 27 recite additional limitations of extracting a signal strip of fixed duration from the received ECG signal, the strip having a highest mean occurrence metric among plural occurrence metrics of the decomposed ECG signal; and generating an abnormal pattern signal comparing at least one of a current local metric and current occurrence metric with a previous local metric and a previous occurrence metric, respectively but appears to be directed to mental processes because it further limits an abstract process and concerns observations, calculations, and judgements which can be performed in the mind or with pen and paper.
Under Step 2A, Prong 2:
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
Claims 1, 14, 25 fail to include any additional elements that integrate the abstract idea into a practical application. Claim 14 includes a processor, a trained epoch model, user interface, and the steps of decomposing a continuous ECG signal, generating a quality score, and send an output of the one or more executed application modules to a user interface wherein the processor is a generic computer structure for implementing the abstract idea on a computer; generating and send are insignificant post-solution activity; a metric generation module and the steps of applying a first rolling window of a first specified width to the decomposed ECG signal and compute a local metric from the plural quality scores wherein the metric generation module is a generic computer structure for implementing the abstract idea on a computer; one or more application modules and the steps of generating a quality alert signal including streaming ECG data and a notification based on the local metric wherein application modules are a generic computer structure for implementing the abstract idea on a computer and generating, streaming, and a notification, are insignificant post-solution activity.
Claims 5-8, 18-21 include steps for computing the local metric comprising determining a minimum, maximum, average, and standard deviation of epoch scores within the first rolling window wherein determining is insignificant pre-solution activity (data gathering).
Claims 24, 27 include application modules and the step to generate an abnormal pattern signal wherein the application module is a generic computer structure for implementing the abstract idea on a computer and generate is insignificant post-solution activity.
Despite the fact that the abstract ideas claimed are performed on a generic computer, the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, “[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016)
(holding that computer-implemented method for “anonymous loan shopping” was an abstract idea because it could be “performed by humans without a computer”). See MPEP 2106.04(a)(2)(III).
Furthermore, generic computer components that perform abstract ideas are still
abstract mental processes unless the claim limitation cannot be practically performed in the
mind. As such, “processor”, “metric generation module”, and “application module” appear to amount to nothing more than a suggestion to “apply it” on a computer; Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Under Step 2b:
Claims 1, 14, 25 fail to include any additional elements that, alone or in combination, amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements of “processor”, “metric generation module”, “application module”, “trained epoch model”, and “user interface” are well-understood, routine, and conventional activities previously known in the field of electrostimulation as indicated in the following references:
US 2023/0181121 A1:
See [0016] electronics module for processor, metric generation module, application module.
See [0085] pre-trained neural network for trained epoch model.
See [0016] mobile device and ([0129], Fig. 4) clinician dashboard for user interface.
US 2019/0298206 A1:
See [0033] processor; detection module, advice module for metric generation module and application module.
See [0035] user interface.
US 2016/0183835 A1:
See [0051], [0056] remote computing system and smartphone for processor, metric generation module, application module, user interface;
See [0056] software application for metric generation module, application module
US2024/0055125 A1:
See [0164] trained neural network for trained epoch model
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.
Claim(s) 1-4, 9-17, 22-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Varadan et al (US Pre Grant Publication 2023/0181121 A1), and in view of Sinha et al (US Pre Grant Publication 2024/0055125 A1).
Regarding claim 1, Varadan teaches a method for assessing quality of an electrocardiogram (ECG) signal, the method comprising:
a) decomposing, by a processor (electronics module, [0043]), a continuous ECG signal into plural epochs, wherein each epoch is of a specified length ([0123-0124], Fig. 1A; unsupervised learning methods applied to input data or ECG signal data for cluster classification; data aggregated into epochs of equal duration);
b) generating, by the processor, a quality score for each epoch generated from the decomposed ECG signal, ([0113], [0121], [0125-0126], Fig. 5; step (120) transforms cluster membership to one-dimensional metric or quality score; process of applying data to a neural network is similar to applying transfer function to data such as Logit function; quality score calculated as mean of quantified change for each cluster);
executing, by the processor, one or more application modules configured for:
generating a quality alert signal including streaming ECG data obtained from at least one portion of the received ECG signal and a notification based on the local metric associated with the at least one portion of the received ECG signal ([0129], Fig. 4; data transmitted to clinician dashboard) ([0153]; “New patient data could be matched against these templates to perform anomaly detection and raise alerts or alarms to emphasize patients who require additional clinician attention.”);
generating a quality visualization signal for displaying a color map for one or more segments of the received ECG signal, the color maps corresponding to a local metric associated with the one or more segments of the received ECG signal ([0168], aggregated data for different epochs can be transformed into a scatter plot using cluster data or feature extraction transformations; scatter points can be presented as a contour or density map with colors representing aggregate points over fixed durations of epochs or specific time intervals); and
sending, by the processor, an output of the one or more executed application modules ([0131], Fig. 4; ECG data and risk or quality score streamed to dashboard, [0153]; alerts or alarms necessitate output to a user interface, [0168]; density maps with colors necessitate output to a user interface) to a user interface ([0129], Fig. 4; clinician dashboard).
Varadan does not disclose,
c) applying, by the processor, a first rolling window of a first specified width to the decomposed ECG signal to capture plural quality scores from a sequence of epochs in the plural epochs;
d) computing, by the processor, a local metric from the plural quality scores captured by the first rolling window;
repeating steps c) and d) across an entirety of the decomposed ECG signal,
as claimed.
However, Sinha teaches a system and a method to determine data quality from cardiovascular parameters obtained from but not limited to a plethysmography device. Sinha is analogous to the claimed invention as it is reasonably pertinent to the problem of generating prediction measures from processed data signals.
Sinha further teaches a method,
c) applying, by the processor, a first rolling window of a first specified width to the decomposed ECG signal ([0089], Fig. 42; time window can be specified by a length of time, running (overlapping), and/or sliding) to capture plural quality scores from a sequence of epochs in the plural epochs ([0499]; filter processing is done over windows of 3 consecutive beats);
d) computing, by the processor, a local metric from the plural quality scores captured by the first rolling window ([0501], [0506]; each passed 3-beat window is averaged to calculate final reading; deriving a final average reading necessarily requires calculating each 3-beat window average or local metric);
repeating steps c) and d) across an entirety of the decomposed ECG signal ([0500]; if at least one 3-beat window is not filtered, then processing continues across entirety of signal until all 3-beat windows are processed).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method of Varadan with the method and system for applying, by the processor, a first rolling window of a first specified width to the decomposed ECG signal to capture plural quality scores from a sequence of epochs in the plural epochs, computing, by the processor, a local metric from the plural quality scores captured by the first rolling window, and repeating steps c) and d) across an entirety of the decomposed ECG signal as taught by Sinha. One of ordinary skill in the art would have been motivated to make these modifications to improve data quality by associating a target feature to a specific window for analysis (Sinha, [0089]).
Regarding claims 2 & 15, Varadan in view of Sinha teaches the method of claim 1 as well as the system of claim 14, and Sinha further teaches wherein each epoch includes plural cardiac beats and is centered around one of the plural cardiac beats ([0499]; each epoch comprises a 3-beat window and necessarily centered around one of the beats).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method and system, as taught by Varadan and Sinha, with each epoch that includes plural cardiac beats and is centered around one of the plural cardiac beats as taught by Sinha. One of ordinary skill in the art would have been motivated to make these modifications to improve data quality by associating a target feature to a specific window for analysis (Sinha, [0089]).
Regarding claims 3 and 16, Varadan teaches the method of claim 2 as well as the system of claim 14, and further teaches that each epoch includes at least 1 cardiac beat (implicit in that the segment is 5min) and/or has a duration of at least 1.2 seconds ([0105], Table 1 & 2 (pg. 25-32); feature extraction includes transformations; Table 1 (67) SDNNI calculated as mean of standard deviation of all NN intervals for each 5 min segment where each segment is one epoch, which overlaps with “at least 1.2 seconds” as claimed and necessarily includes a plurality of heartbeats).
Varadan fails to explicitly recite that the epoch spans at least one beat, or specifically teach the duration being at least 1.2 seconds. However, should applicant not immediately see the interpretation of the prior art as presented, examiner submits that it has been held that a prima facie case of obviousness exists when the claimed ranges overlap with ranges disclosed by the prior art (See MPEP § 2144.05 (I). In re Wertheim, 541 F.2d 257, 191 USPQ 90 (CCPA 1976); In re Woodruff, 919 F.2d 1575, 16 USPQ2d 1934 (Fed. Cir. 1990)).
Furthermore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method and system, as taught by Varadan, with an epoch that includes at least 1 cardiac beat and/or a duration of at least 1.2 seconds. One of ordinary skill in the art would have been motivated to make these modifications to select features that occur at different granularities in time by adjusting input aggregation windows (Varadan, [0161]).
Regarding claims 4 & 17, Varadan in view of Sinha teaches the method of claim 1 as well as the system of claim 15, and Varadan further teaches that the quality score for each epoch has a value range from 0 to 1 ([0125], Fig. 1B; quantified change calculated as mean or ratio and necessitates a resulting value between 0 and 1; determined from interval consisting of present and previous epoch).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method and system, as taught by Varadan and Sinha, with the quality score for each epoch that has a value range from 0 to 1 as taught by Sinha. One of ordinary skill in the art would have been motivated to make these modifications to improve accuracy of the quality scores by determining confidence levels (Varadan, [0132]).
Regarding claims 9 & 26, Varadan in view of Sinha teaches the method of claim 1 as well as the system of claim 25, but do not disclose further comprising:
e) applying, by the processor, a second rolling window of a second specified width to the decomposed ECG signal to capture plural local metrics associated with a sequence of epochs in the plural epochs; and
f) computing, by the processor, an occurrence metric from the plural local metrics captured by the second rolling window; and
repeating steps e) and f) across an entirety of the decomposed ECG signal,
as claimed.
Sinha teaches a method,
e) applying, by the processor, a second rolling window of a second specified width ([0089]; running time window overlaps another time window; data segment corresponding to a time window within a larger time range, wherein multiple data segments can be aggregated, suggests a second running time window that aggregates multiple data segments wherein a first running time window aggregates data within a data segment) to the decomposed ECG signal to capture plural local metrics associated with a sequence of epochs in the plural epochs ([0537-0538], Fig. 42; 50% sampling window whereby each second rolling window is a 2-second instance);
repeating steps e) and f) across an entirety of the decomposed ECG signal ([0537], Fig. 42; sampling windows are determined across entire data segment),
but does not disclose,
f) computing, by the processor, an occurrence metric from the plural local metrics captured by the second rolling window,
as claimed.
However, Varadan further teaches,
f) computing, by the processor, an occurrence metric from the plural local metrics captured by the second rolling window ([0122]; burden is the number of deviations detected over the total number of assessments or over a specified period of time);
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method and system of Varadan and Sinha with the applying, by the processor, a second rolling window of a second specified width to the decomposed ECG signal to capture plural local metrics associated with a sequence of epochs in the plural epochs, as taught by Sinha, and computing, by the processor, an occurrence metric from the plural local metrics captured by the second rolling window as further taught by Varadan. One of ordinary skill in the art would have been motivated to make these modifications to improve prediction accuracy by analyzing and comparing trends between historical and current values of input data (Varadan, [0138]).
Regarding claims 10 & 22, Varadan in view of Sinha teaches the method of claim 9 as well as the system of claim 15, but does not disclose wherein the second rolling window has a duration of at least a width of the first rolling window.
However, Sinha teaches wherein the second rolling window has a duration of at least a width of the first rolling window ([0089], Fig. 42; running time window overlaps another time window; data segment corresponding to a time window within a larger time range, wherein multiple data segments can be aggregated, suggests a second running time window that aggregates multiple data segments wherein a first running time window aggregates data within a data segment; a second running time window which aggregates multiple data segments necessitates a width of at least one data segment).
Sinha fails to explicitly recite that the second rolling window has a duration of at least a width of the first rolling window. However, should applicant not immediately see the interpretation of the prior art as presented, examiner submits that it has been held that a prima facie case of obviousness exists when the claimed ranges overlap with ranges disclosed by the prior art (See MPEP § 2144.05 (I). In re Wertheim, 541 F.2d 257, 191 USPQ 90 (CCPA 1976); In re Woodruff, 919 F.2d 1575, 16 USPQ2d 1934 (Fed. Cir. 1990)).
Furthermore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method and system, as taught by Varadan and Sinha, with the second rolling window has a duration of at least a width of the rolling window. One of ordinary skill in the art would have been motivated to make these modifications to select features that occur at different granularities in time by adjusting input aggregation windows (Varadan, [0161]).
Regarding claim 11, Varadan and Sinha teach the method of claim 9, and Varadan further teaches a method wherein the occurrence metric has a value range from 0 to 1 ([0122]; burden or occurrence is the number of deviations detected over the total number of assessments or over a specified period of time).
Regarding claim 12, Varadan in view of Sinha teaches the method of claim 9, and teaches a method further comprising:
executing, by the processor, one or more application modules configured for:
extracting a signal strip of fixed duration from the received ECG signal (Table 1 (66); standard deviation of average normal-to-normal intervals, over 5 min segments during a 24-hour recording), the signal strip having a highest mean occurrence metric among plural occurrence metrics of the decomposed ECG signal ([0138], Table 1; burden metric counts trends in the rate of detection of anomalies; standard deviation calculation requires mean determination).
Regarding claim 13, Varadan in view of Sinha teaches the method of claim 9, and teaches a method further comprising:
executing, by the processor, one or more application modules configured for:
generating an abnormal pattern signal comparing at least one of a current local metric and current occurrence metric with a previous local metric, respectively ([0155]; patient input data compared to historical input data from patient; observations or inputs subjected to anomaly detection techniques) and a previous occurrence metric ([0138], Fig. 5; anomaly detection determines burden per epoch using past and current values of input data).
Regarding claims 14, and 25, Varadan teaches a system for assessing quality of an electrocardiogram (ECG) signal (wearable remote electrophysiological monitoring system, [0044]), the system comprising:
a processor (electronics module, [0043]) encoded with program code, which when executed causes the processor to be configured to perform the operations of ([0043-0044]; electronics module acquires signals and communicates wirelessly to mobile device or remote monitoring system; system makes treatment determinations and assesses data):
a trained epoch model ([0085]; pre-trained neural network, trained for classification or regression task with data not related to patient data, possesses mechanism to infer attributes given any input data; selected input data or ECG signal data retrains model predictions in context to selected ECG data) configured to decompose a continuous ECG signal into plural epochs, and generate a quality score for each epoch ([0123-0124], Fig. 1A; unsupervised learning methods applied to input data or ECG signal data for cluster classification; data aggregated into epochs of equal duration), ([0113], [0121], [0125-0126], Fig. 5; step (120) transforms cluster membership to one-dimensional metric or quality score; process of applying data to a neural network is similar to applying transfer function to data such as Logit function; quality score calculated as mean of quantified change for each cluster);
one or more application modules configured to:
generate a quality alert signal including streaming ECG data obtained from at least one portion of the received ECG signal and a notification based on the local metric associated with the at least one portion of the received ECG signal ([0129], Fig. 4; data transmitted to clinician dashboard) ([0153]; “New patient data could be matched against these templates to perform anomaly detection and raise alerts or alarms to emphasize patients who require additional clinician attention.”);
generate a quality visualization signal for displaying a color map for one or more segments of the received ECG signal, the color maps corresponding to a local metric associated with the one or more segments of the received ECG signal ([0168], aggregated data for different epochs can be transformed into a scatter plot using cluster data or feature extraction transformations; scatter points can be presented as a contour or density map with colors representing aggregate points over fixed durations of epochs or specific time intervals); and
the processor further configured to send an output of the one or more executed application modules ([0131], Fig. 4; ECG data and risk or quality score streamed to dashboard, [0153]; alerts or alarms necessitate output to a user interface, [0168]; density maps with colors necessitate output to a user interface) to a user interface ([0129], Fig. 4; clinician dashboard).
Varadan does not disclose,
iteratively apply a first rolling window of a first specified width to the decomposed ECG signal to capture plural quality scores from a sequence of epochs in the plural epochs and compute a local metric from the plural quality scores captured by the first rolling window,
as claimed.
However, Sinha teaches a method,
iteratively apply a first rolling window ([0500]; if at least one 3-beat window is not filtered, then processing continues across entirety of signal until all 3-beat windows are processed) of a first specified width to the decomposed ECG signal to capture plural quality scores from a sequence of epochs in the plural epochs ([0089], Fig. 42; time window can be specified by a length of time, running (overlapping), and/or sliding) to capture plural quality scores from a sequence of epochs in the plural epochs ([0499]; filter processing is done over windows of 3 consecutive beats) and compute a local metric from the plural quality scores captured by the first rolling window ([0501], [0506]; each passed 3-beat window is averaged to calculate final reading; deriving a final average reading necessarily requires calculating each 3-beat window average or local metric).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method of Varadan with the method and system to iteratively apply a first rolling window of a first specified width to the decomposed ECG signal to capture plural quality scores from a sequence of epochs in the plural epochs and compute a local metric from the plural quality scores captured by the first rolling window as taught by Sinha. One of ordinary skill in the art would have been motivated to make these modifications to improve data quality by associating a target feature to a specific window for analysis (Sinha, [0089]).
Regarding claims 23, Varadan in view of Sinha teaches the system of claim 14, but does not disclose wherein the one or more applications modules is further configured to:
iteratively apply a second rolling window of a second specified width to the decomposed ECG signal to capture plural local metrics associated with a sequence of epochs in the plural epochs and compute an occurrence metric from the plural local metrics captured by the second rolling window,
as claimed.
Sinha teaches wherein the one or more applications modules is further configured to:
iteratively apply ([0537], Fig. 42; sampling windows are determined across entire data segment) a second rolling window of a second specified width ([0089]; running time window overlaps another time window; data segment corresponding to a time window within a larger time range, wherein multiple data segments can be aggregated, suggests a second running time window to aggregate multiple data segments wherein a first running time window aggregates data within a data segment) to the decomposed ECG signal to capture plural local metrics associated with a sequence of epochs in the plural epochs ([0537-0538], Fig. 42; 50% sampling window whereby each second rolling window is a 2-second instance), but does not disclose compute an occurrence metric from the plural local metrics captured by the second rolling window,
as claimed.
However, Varadan further teaches, compute an occurrence metric from the plural local metrics captured by the second rolling window ([0122]; burden is the number of deviations detected over the total number of assessments or over a specified period of time).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the system of Varadan and Sinha with the system to iteratively apply a second rolling window of a second specified width to the decomposed ECG signal to capture plural local metrics associated with a sequence of epochs in the plural epochs, as taught by Sinha, and compute an occurrence metric from the plural local metrics captured by the second rolling window, as further taught by Varadan. One of ordinary skill in the art would have been motivated to make these modifications to improve prediction accuracy by analyzing and comparing trends between historical and current values of input data (Varadan, [0138]).
Regarding claims 24 & 27, Varadan in view of Sinha teaches the system of claim 23 as well as claim 27, and further teaches a system further comprising one or more application modules configured to:
extract a signal strip of fixed duration from the received ECG signal (Table 1 (66); standard deviation of average normal-to-normal intervals, over 5 min segments during a 24-hour recording), the signal strip having a highest mean occurrence metric among plural occurrence metrics of the decomposed ECG signal ([0138], Table 1; burden metric counts trends in the rate of detection of anomalies; standard deviation calculation requires mean determination); and
generate an abnormal pattern signal comparing at least one of a current local metric and current occurrence metric with a previous local metric and a previous occurrence metric, respectively ([0153]; input data is a superset of all input data that can be obtained; distance measure used to compare metrics for correlation; “New patient data could be matched against these templates to perform anomaly detection and raise alerts or alarms to emphasize patients who require additional clinician attention.”).
Claim(s) 5-8, 18-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Varadan et al (US Pre Grant Publication 2023/0181121 A1), and in view of Sinha et al (US Pre Grant Publication 2024/0055125 A1), and in further view of Fontanarava et al (US Pre Grant Publication 2022/0039729 A1).
Regarding claims 5 & 18, Varadan in view of Sinha teaches the method of claim 1 as well as the system of claim 15, but does not disclose wherein computing the local metric comprises determining a minimum epoch score within the first rolling window.
However, Fontanavara teaches a system and a method to detect and predict cardiac events by processing ECGs. Fontanavara is analogous to the claimed invention as it is reasonably pertinent to the problem of generating prediction measures from processed data signals.
Fontanavara further teaches wherein computing the local metric comprises determining a minimum epoch score within the first rolling window ([0039]; “Alternatively, the combiner may determines a minimum value of the plurality of outputs.”), ([0125]; time windows assign heartbeats to bins).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the system and method, as taught by Varadan and Sinha, to determine a minimum epoch score within the first rolling window as taught by Fontanarava. One of ordinary skill in the art would have been motivated to make these modifications to gauge the accuracy of an event probability by determining a confidence score through statistical means (Fontanavara, [0039]).
Regarding claims 6 & 19, Varadan in view of Sinha teaches the method of claim 1 as well as the system of claim 15, but does not disclose wherein computing the local metric comprises determining a maximum epoch score within the first rolling window.
However, Fontanavara teaches wherein computing the local metric comprises determining a maximum epoch score within the first rolling window ([0039]; “In another example, the combiner may determines a maximum value of the plurality of outputs.”), ([0125]; time windows assign heartbeats to bins).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the system and method, as taught by Varadan and Sinha, to determine a maximum epoch score within the first rolling window as taught by Fontanarava. One of ordinary skill in the art would have been motivated to make these modifications to gauge the accuracy of an event probability by determining a confidence score through statistical means (Fontanavara, [0039]).
Regarding claims 7 & 20, Varadan in view of Sinha teaches the method of claim 1 as well as the system of claim 15, but does not disclose wherein computing the local metric comprises determining an average epoch score within the first rolling window.
However, Fontanavara teaches wherein computing the local metric comprises determining an average epoch score within the first rolling window ([0039]; “The combiner may determines an average value by averaging the plurality of outputs.”), ([0125]; time windows assign heartbeats to bins).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the system and method, as taught by Varadan and Sinha, to determine an average epoch score within the first rolling window as taught by Fontanarava. One of ordinary skill in the art would have been motivated to make these modifications to gauge the accuracy of an event probability by determining a confidence score through statistical means (Fontanavara, [0039]).
Regarding claims 8 & 21, Varadan teaches the method of claim 1 as well as the system of claim 15, but does not disclose wherein computing the local metric comprises determining a standard deviation of epoch scores within the first rolling window.
However, Fontanavara teaches wherein computing the local metric comprises determining a standard deviation of epoch scores within the first rolling window ([0039]; ”The computerized-system may further analyze the plurality of outputs using a combiner to determine a probability of atrial fibrillation and a confidence score indicative of an accuracy of the probability of atrial fibrillation.”; confidence score necessitates deriving standard deviation from epoch scores), ([0125]; time windows assign heartbeats to bins).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the system and method, as taught by Varadan and Sinha, to determine a standard deviation of epoch scores within the first rolling window as taught by Fontanarava. One of ordinary skill in the art would have been motivated to make these modifications to gauge the accuracy of an event probability by determining a confidence score through statistical means (Fontanavara, [0039]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Walker et al (2019/0298206 A1) discloses context scores to improve accuracy of ECG data; defibrillator-monitor system with ECG port [0030], Fig. 3; measurement circuit (320) converts physiological signals to data [0031]; advice module (334) based on outputs from detection module (332); context score (325, Fig. 3).
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/DWANE COLLARD/Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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