DETAILED ACTION
Status
This Final Office Action is in response to the communication filed on 29 July 2025. Claims 2, 7, and 12 have been cancelled, claims 1, 3, 6, 8, 11, and 13 have been amended, and no new claims have been added. Therefore, claims 1, 3-6, 8-11, and 13-15 are pending and presented for examination.
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 .
Response to Amendment
A summary of the Examiner’s Response to Applicant’s amendment:
Applicant’s amendment overcomes the claim objection(s); therefore, the Examiner withdraws the objection(s).
Applicant’s amendment overcomes the rejection(s) under 35 USC § 112; therefore, the Examiner withdraws the rejection(s).
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment overcomes the rejection(s) under 35 USC §§ 102 and/or 103; therefore, the Examiner places new grounds of rejection.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
Examiner’s Note
The Examiner notes that the terms “left R-peak” and “right R-peak” are merely referring to, in the light of the specification, the R-peak (i.e., heartbeat) on the left and right peaks of a 2-beat segment of the ECG. In reference to Figs. 4A-4C, the heartbeats of Fig. 4A are “segmented” into peaks or beats 1 and 2 at Fig. 4B, and peaks or beats 3 and 4 are “segmented” at Fig. 4C. Therefore, the generating element of claim 1 necessarily is merely showing (i.e., “generating”) the normally displayed peaks of a traditionally or usual ECG.
The Examiner further notes that the terms “ECG” and “EKG” are understood to be synonymous.
The Examiner notes that claims 5, 10, and 15 recite “the 2D representation establishes a temporal dependency between different R- peaks”; however, as best understood by light of the specification, any “temporal dependency” is a fact of, or is established by, the time information of the ECG signal – the 2D representation apparently merely illustrates that there may be some variation or variance between different segments, i.e., heart beats.
The Examiner notes that the term “CVDs” is used at claims 1, 6, and 11 without an indication of what a “CVD” is; however, since dependent claims 3, 8, and 13 indicates this as meaning “cardiovascular diseases (CVDs) – without apparently trying to further limit the term “CVDs” – the Examiner is interpreting “CVDs” at claims 1, 6, and 11 as being cardiovascular diseases.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4-6, 9-11, and 14-15 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.
Independent claims 1, 6, and 11 each recite the limitations "the extracted structural information" and “the domain knowledge the structural information has been transformed to” in the “training a data model …” element that is added by amendment. There is insufficient antecedent basis for this limitation in the claim.
The Examiner notes that dependent claims 3, 8, and 13 indicate the extraction of structural information as well as the transforming of that information into domain knowledge. However, dependent claims 3, 8, and 13 are limiting the independent claims, so although they appear to be definite on their own, they do not cure the deficiency at the independent claims or impact the resulting indefiniteness at other dependent claims. Therefore, the structural information and transformation of structural information at the independent claims lacks antecedent basis and may be refer to any possible structural information
For purposes of examination, the indications at dependent claims 3, 8, and 13 are being used as the interpretation(s) at independent claims 1, 6, and 11.
Claims 4-5, 9-10, and 14-15 depend from claims 1, 6, and 11, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 4-5, 9-10, and 14-15 are also indefinite.
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, 3-6, 8-11, and 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a method (claims 1 and 3-5), system (claims 6 and 8-10), and non-transitory machine-readable medium (claims 11 and 13-15), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a processor implemented method, comprising: obtaining, via sensors attached to a user, a raw Electrocardiogram (ECG) signal as input; removing, via a bandpass filter implemented by the one or more hardware processors, noise data from the raw ECG signal to obtain a clean signal; segmenting, via the one or more hardware processors, the clean signal to obtain a plurality of segments, wherein each of the plurality of segments comprises a left R-peak and a right R-peak; arranging, via the one or more hardware processors, the plurality of segments by aligning the left R-peak of the plurality of segments; determining, via the one or more hardware processors, variability between the plurality of segments, in terms of position of the left R-peak and the right R-peak of consecutive segments; and generating (212), via the one or more hardware processors, 2- Dimensional (2D) representation of the segments, wherein in the 2D representation comprises information on relative position of the left R-peak and the right R-peak of each of the segments are captured; training a data model using a) the ECG signal, b) the extracted structural information, and c) information on the domain knowledge the structural information has been transformed to, as a training data; processing, using the trained data model, a plurality of real-time ECG data inputs from one more users; determining, using the trained data model, whether the one or more users are suffering from the plurality of CVDs; and generating output in a verbal format upon determining that the one or more users are suffering from the plurality of CVDs, wherein the verbal format includes a sequence of words describing the plurality of CVDs.
Independent claims 6 and 11 are analyzed in the same manner since claim 6 is directed to a system, comprising: one or more hardware processors; a communication interface; and a memory comprising a plurality of instructions, wherein the plurality of instructions cause the one or more hardware processors to perform the same or similar activities as at claim 1 above, and claim 11 is directed to one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause the same or similar activities as at claim 1 above.
The dependent claims (claims 2-5, 7-10, and 12-15) appear to be encompassed by the abstract idea of the independent claims since they merely indicate extracting important regions of the ECG signal using an attention mechanism (a form of deep learning in artificial intelligence or machine learning) and training a data model (claims 3, 8, and 13), segmenting based on R-R intervals (claims 4, 9, and 14), and/or what the 2D representation illustrates – a temporal or timing difference between R-peaks of different segments (i.e., heart beats) (claims 5, 10, and 15).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of representing ECG/EKG timing differences or variations (such as an R-R interval) from one segment (i.e., heart beat) to another; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) based on the determining variability of segments, using an attention mechanism (a form of artificial intelligence or machine learning) and training a data model (at some dependent claims);
Certain methods of organizing human activity (e.g. … managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) based on using a bandpass filter to remove noise, segmenting ECG/EKG signal data, arranging segments by aligning the initial R-peaks, and generating a 2D representation of segments; and
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) based on the evaluation or judgments to segment ECG/EKG signal data, determine variability between segments, and observe the relative positions of R-peaks.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are that the method is a processor implemented method, implemented by … one or more hardware processors (at claim 1) a system, comprising: one or more hardware processors; and a memory comprising a plurality of instructions, wherein the plurality of instructions cause the one or more hardware processors to perform activities (at claim 6), and one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause the activities (at claim 11). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. Applicant ¶ 020 as submitted, 0021 as published, indicates that the processor(s) may be any of a general list indicating a general-purpose computer.
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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 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.
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 of this title, 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.
Claims 1-15 rejected under 35 U.S.C. 103 as being unpatentable over Geva et al. (U.S. Patent Application Publication No. 2004/0230105, hereinafter Geva) in view of Vernalis et al. (U.S. Patent Application Publication No. 2022/0061797, hereinafter Vernalis) and in further view of Fukushima et al (U.S. Patent Application Publication No. 2022/0175325, hereinafter Fukushima) .
Claim 1: Geva discloses a processor implemented method, comprising:
obtaining, via one or more hardware processors, a raw Electrocardiogram (ECG) signal as input (see Geva at least at, e.g., ¶ 0203, “"Adaptive Feature Extraction" (AFE) process, which is described immediately herein below. Immediately after obtaining the time series, first features are extracted in step 12 (herein referred to as the "initial features"), which are directly related to the sampled signals. For example, such features may be the morphology of heartbeats and/or shape of portions of heartbeats, and/or heartbeat rate, all of which are derived, in this example, from ECG signals”; citation hereafter by number only) ;
removing, via a … filter implemented by the one or more hardware processors, noise data from the raw ECG signal to obtain a clean signal (0258, “An integral and essential part of the features extraction stage is d[e]riving, from features obtained from raw biomedical signals (being herein referred to as the "initial" features), various new features signals of medical importance (being herein referred to as the "secondary features signals")…. Accordingly, after filtering out noises and environmental artifacts, in step 24 (FIG. 2), essentially noiseless and artifact-free features are obtained, which could be utilized for generation of new features (i.e., secondary features) there from (reference numeral 25)”);
segmenting, via the one or more hardware processors, the clean signal to obtain a plurality of segments, wherein each of the plurality of segments comprises a left R-peak and a right R-peak (0214, “an ECG signal is relatively easily segmented, in a first segmentation process, into heartbeats, which are not necessarily quasi-stationary segments, and each heartbeat is further segmented, in a second segmentation process, into physiological/pathologically significant segments, such as PR, QRS, QT, ST, etc. The first segmentation process (i.e., into heartbeats) is based on detection of "R-waves" of the heartbeats and is carried out by employing the known "Wavelet Transform Algorithm" on the ECG signal”);
determining, via the one or more hardware processors, variability between the plurality of segments, in terms of position of the left R-peak and the right R-peak of consecutive segments (0032, “Referring to an ECG signal, an exemplary initial feature could be the Heart Rate (HR) or the shape of a heartbeat, or of portions thereof, while an exemplary secondary feature could be the Heart Rate complexity index, variance, duration, etc.”, 0077 and 0081, “Temporal Analysis Features… Mean, variance and skewness amplitude”, 0164, “FIG. 11 depicts extraction of Mean, Variance and Duration from the exemplary heart rate (HR) signal shown in FIG. 10”); and
generating (212), via the one or more hardware processors, 2-Dimensional (2D) representation of the segments, wherein in the 2D representation comprises information on relative position of the left R-peak and the right R-peak of each of the segments are captured (Figs. 12a and 12b, 0331, 0334);
training a data model using a) the ECG signal, b) the extracted structural information, and c) information on the domain knowledge the structural information has been transformed to, as a training data (0127, “Different HMM models may be trained to characterize different global physiological/pathological behavior, which may be associated with, e.g., specific group of population, sleep stage or any health condition”, 0239, “the system keeps on updating (i.e., training) itself by referring to additional clinically significant changes that would be used for predicting future events”, 0270, “Two main algorithms are utilized for the training stage: the Baum-Welch algorithm, which is an Expectation Maximization (EM) based algorithm, which is utilized for Maximum Likelhood Estimation (MLE)”);
processing, using the trained data model, a plurality of real-time ECG data inputs from one more users (0127, “Different HMM models may be trained to characterize different global physiological/pathological behavior, which may be associated with, e.g., specific group of population, sleep stage or any health condition”, 0239, “the system keeps on updating (i.e., training) itself by referring to additional clinically significant changes that would be used for predicting future events”, 0270, “Two main algorithms are utilized for the training stage: the Baum-Welch algorithm, which is an Expectation Maximization (EM) based algorithm, which is utilized for Maximum Likelhood Estimation (MLE)”);
determining, using the trained data model, whether the one or more users are suffering from the plurality of CVDs (Geva at 0004, “life-threatening cardiac arrhythmias (LTCA)”, 0008, “Recent research demonstrated that changes in RR-interval (RRI) series might be a more accurate predictor of imminent LTCA”, 0030, “The present invention is directed to a method for predicting changes of physiological/pathological states in a patient, based on sampling, processing and analyzing a plurality of aggregated noisy biomedical signals”, 0031, “the present invention is characterized by allowing identifying physiological/pathological information that precedes physiological and pathological states, such as heart attacks and epilepsy”, 0099-0102, “employing local maxima detection method, for identifying the R-peaks, P-peaks and T-peaks in the filtered resulting summation, the R, P and T peaks being utilized for characterizing the corresponding heartBeats Under Test (BUTs), the P and T peaks being utilized also for further segmentation of heartbeats. Preferably, obtaining features from ECG signals comprises the steps: a) detecting `R-R` time-intervals between each two consecutive R-peaks; and b) identifying characterizing points `P`, `Q`, `S` and `T` of the corresponding BUTs, by utilizing the `R-R` time-intervals, at least some of the points being utilized for obtaining features related thereto”, 0258, “An integral and essential part of the features extraction stage is d[e]riving, from features obtained from raw biomedical signals (being herein referred to as the ‘initial’ features), various new features signals of medical importance (being herein referred to as the ‘secondary features signals’)”).
Geva, however, does not appear to explicitly disclose that the filter is a bandpass filter, and arranging, via the one or more hardware processors, the plurality of segments by aligning the left R-peak of the plurality of segments; and generating output in a verbal format upon determining that the one or more users are suffering from the plurality of CVDs, wherein the verbal format includes a sequence of words describing the plurality of CVDs.
. Where Geva does align R-peaks of signal data (Geva at 0413 and Figs. 23a, 23b, and 23c), this is apparently not a stacking of an individual segment or heartbeat. Vernalis, however, indicates displaying a stacked plot of individual segment heart beat signals (Vernalis at 0256 and Fig. 49) and “the data acquisition process 800 commences with step (802) by pre-filtering the electronic signal from the associated auscultatory sound sensor 12, 121′, 122′, 123′, 121″, 122″, 123″ with an analog anti-aliasing filter, for example, an analog second-order band-pass filter having a pass band in the range of 3 Hz to 2.5 KHz, for which the upper-cutoff frequency is sufficiently below the sampling frequency (i.e. no more than half) so as to prevent high frequency components in the signal being sampled from appearing as low frequency components of the sampled signal, i.e. so as to prevent aliasing” (Vernalis at 0123). Therefore, the Examiner understands and finds that to use a band-pass filter and to display a stacked set of left R-peak aligned signals is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to display the variance of the R-R interval.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the ECG analysis and display of Geva with the filtering and display of Vernalis in order to use a band-pass filter and to display a stacked set of left R-peak aligned signals so as to display the variance of the R-R interval.
The rationale for combining in this manner is that to use a band-pass filter and to display a stacked set of left R-peak aligned signals is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to display the variance of the R-R interval as explained above.
Geva in view of Vernalis, however, does not appear to explicitly disclose generating output in a verbal format upon determining that the one or more users are suffering from the plurality of CVDs, wherein the verbal format includes a sequence of words describing the plurality of CVDs. Fukushima, however, teaches a learned model related to the risk and/or probability of developing a disease such as cardiovascular disease (Fukushima at 0063 and 0114), where the model may be a machine learning model, “technology regarding natural language processing (such as Sequence to Sequence) may be applied” and “a dialogue engine (a dialogue model, a learned model for dialogue) that responds to the examiner with an output of characters or speech may be applied” (Fukushima at 0228). Therefore, the Examiner understands and finds that to provide verbal format output is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to allow even blind persons to readily receive the disease-related information.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the ECG interpretation of Geva in view of Vernalis with the verbal/speech output of Fukushima in order to provide verbal format output so as to allow even blind persons to readily receive the disease-related information.
The rationale for combining in this manner is that to provide verbal format output is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to allow even blind persons to readily receive the disease-related information as explained above.
Claim 3: Geva in view of Vernalis in further view of Fukushima discloses the processor implemented method of claim 1 further comprising:
extracting a structural information from the 2D representation, by processing the 2D representation using an attention mechanism, wherein the structural information comprises information on a plurality of important regions of the ECG signal that contribute to classification of the ECG signal as being associated with one or more of a plurality of cardiovascular diseases (CVDs) (Geva at 0004, “life-threatening cardiac arrhythmias (LTCA)”, 0008, “Recent research demonstrated that changes in RR-interval (RRI) series might be a more accurate predictor of imminent LTCA”, 0030, “The present invention is directed to a method for predicting changes of physiological/pathological states in a patient, based on sampling, processing and analyzing a plurality of aggregated noisy biomedical signals”, 0031, “the present invention is characterized by allowing identifying physiological/pathological information that precedes physiological and pathological states, such as heart attacks and epilepsy”, 0099-0102, “employing local maxima detection method, for identifying the R-peaks, P-peaks and T-peaks in the filtered resulting summation, the R, P and T peaks being utilized for characterizing the corresponding heartBeats Under Test (BUTs), the P and T peaks being utilized also for further segmentation of heartbeats. Preferably, obtaining features from ECG signals comprises the steps: a) detecting `R-R` time-intervals between each two consecutive R-peaks; and b) identifying characterizing points `P`, `Q`, `S` and `T` of the corresponding BUTs, by utilizing the `R-R` time-intervals, at least some of the points being utilized for obtaining features related thereto”, 0258, “An integral and essential part of the features extraction stage is d[e]riving, from features obtained from raw biomedical signals (being herein referred to as the ‘initial’ features), various new features signals of medical importance (being herein referred to as the ‘secondary features signals’)”);
transforming the structural information into an ECG domain knowledge, wherein the ECG domain knowledge represents a determined classification of the ECG signal as being associated with one or more of the plurality of CVDs (Geva at 0030-0031, 0099-0102, 0258, as above); and
Claim 4: Geva in view of Vernalis in further view of Fukushima discloses the processor implemented method of claim 1, wherein the clean signal is segmented based on R-R intervals (Geva at 0413 and Figs. 23a, 23b, and 23c; Vernalis at 0256, Fig. 49, and 0123, as combined above and using the rationale as at the combination above).
Claim 5: Geva in view of Vernalis in further view of Fukushima discloses the processor implemented method of claim 1, wherein the 2D representation establishes a temporal dependency between different R-peaks comprising the left R-peaks and right R-peaks of the plurality of segments, and represents relative positions between the R-peaks in different segments (Geva at 0099-0102, “employing local maxima detection method, for identifying the R-peaks, P-peaks and T-peaks in the filtered resulting summation, the R, P and T peaks being utilized for characterizing the corresponding heartBeats Under Test (BUTs), the P and T peaks being utilized also for further segmentation of heartbeats. Preferably, obtaining features from ECG signals comprises the steps: a) detecting `R-R` time-intervals between each two consecutive R-peaks; and b) identifying characterizing points `P`, `Q`, `S` and `T` of the corresponding BUTs, by utilizing the `R-R` time-intervals, at least some of the points being utilized for obtaining features related thereto”).
Claims 6, 8-11, and 13-15 are rejected on the same basis as claims 1 and 3-5 above since Geva in view of Vernalis in further view of Fukushima discloses a system, comprising: one or more hardware processors; a communication interface; and a memory comprising a plurality of instructions, wherein the plurality of instructions cause the one or more hardware processors to perform the activities indicated at claims 1 and 3-5 above (for claims 6 and 8-10), and one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause the activities indicated at claims 1nad 3-5 above (for claims 11 and 13-15) (see Geva at 0143-0152, the acquisition and processing means indicating a computer as performing the operations described; Vernalis at 0109, 0111, the system in communication indicating computer activities).
Response to Arguments
Applicant's arguments filed 29 July 2025 have been fully considered but they are not persuasive.
Applicant first argues the claim objections and 112 rejections (Remarks at 7-8); however, these objections and rejections are overcome and withdrawn. Therefore, the arguments are considered moot and not persuasive.
Applicant then argues the 101 rejection (Id. at 8-12), first alleging “amended claims 1, 6 and 11 integrate a judicial exception into a practical application with an improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a) i.e., extraction of data from the signals thereby allowing end to end automation of ECG analysis” (Id. at 8, emphasis at original). However, first, there is no indication of what analogy or reason Applicant is making so as to possibly indicate any improvement indicated. Second, the claims do nothing to improve the functioning of a computer, other technology, or a technical field. The claims literally recite generating a model based on one, single ECG signal. A model cannot reasonably be built from a single data point; however, even if the ECG signal were required or assumed to have several heartbeats, the claims are still just building a model of one, single person, i.e., “a user”. There is no available or reasonable inference or indication that the one user from whom the ECG signal was obtained either has, or conversely, does not have any form or type of cardiovascular disease such that a model could be generated that could determine “whether the one or more users are suffering from the plurality of CVDs” (as at the independent claims). The “structural information” may be (per dependent claims 3, 8, and 11) extracted using a model or deep learning (i.e., “an attention mechanism”); however, that specifically indicates that this is already known and established – the claims are merely inputting information into a known model to extract the structural information. Further, as far as the Examiner can tell from the light of the specification, the “transforming” of the structural information into domain knowledge is merely calculating or measuring the temporal/time variation(s) (if any) between R-peaks and calling that “domain knowledge” (see, e.g., Applicant ¶¶ 029-031 as submitted, 0030-0032 as published, and Figs. 4A-E). This is all part of the abstract idea by reading an ECG so as to be representing ECG/EKG timing differences or variations (such as an R-R interval) from one segment (i.e., heart beat) to another.
When the claims recite “processing … a plurality of real-time ECG data inputs from one or more users”, if or when the “one or more users” is anyone other than the “a user” that has been modeled, it does not appear at all apparent that the model could reasonably provide any indication of whether that other user may have either one or a plurality of CVDs. At best, it would appear, the model could really only apparently show whether there was some difference in heart rate (i.e., time between heart beats – the R-peaks) and/or whether the temporal variation for R-peaks is somehow different for one individual from one ECG signal to the next; however, that may or may not provide some possibility of indicating a CVD. Otherwise, if the model is attempting to compare ECG data of two separate individuals, then any person with a 60-beat-per-minute average heart rate would appear quite different from a person with an average heart rate of 80-beats-per-minute. But neither or both individuals may have a CVD – the model does not appear to be at all likely in predicting whether a CVD is potentially present.
As such, the only apparent analogy based on the MPEP sections cited by Applicant would be that the claims are “Mere automation of manual processes, such as using a generic computer” as at MPEP § 2106.05(a)(I), under “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality” (emphasis added).
Applicant then “asserts that amended claims 1, 6 and 11 integrate a judicial exception into a practical application with an improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a) i.e., segmenting ECG signal, aligning left side R-peaks of each two segments that indicate the potential health condition of the user” (Remarks at 9, emphasis at original). However, first, there is no indication of what analogy or reason Applicant is making so as to possibly indicate any improvement indicated. Second, this aspect apparently merely indicates showing two heart-beat segments of an ECG, one above the other, which is part of the abstract idea as one of certain methods of organizing human activity and/or mental processes in reading and using observation, judgment, etc. to discern differences (if any) between ECG signal heart-beats.
Applicant then “submits that independent claim 1 integrates a judicial exception into a practical application with the effect of a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2); i.e. determine area of the ECG signal where an abnormal spike is visible in an important region and classify ECG signal being associated with a particular CVD.” (Id., emphasis at original). However, MPEP § 2106.04(d)(2) indicates that “Examiners should keep in mind that 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” (at the 4th paragraph of the indicated MPEP section). There is no indication of any treatment nor prophylaxis at the claims (or the description, apparently) – the claims may (possibly, or allegedly) “determin[e] … whether the one or more users are suffering from the plurality of CVDs”, but that would at best (if or when it is assumed that someone is determined to have a CVD) a diagnosis, and not a treatment or prophylaxis for any disease.
Applicant then “submits that independent claim 1 integrates a judicial exception into a practical application with effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); i.e. transform structural information into an ECG domain knowledge to represent a determined classification of the ECG signal associated with cardiovascular diseases (CVDs)” (Remarks at 10, emphasis at original). However, first, the independent claims do not recite any transformation – they merely indicate the use of data that “has been transformed”; therefore, the argument is not correlated to the scope of the independent claims being argued. Second, if or when claims 3, 8, and 11 are considered, even a cursory or rudimentary reading of MPEP § 2106.05(c) indicates that
An "article" includes a physical object or substance. The physical object or substance must be particular, meaning it can be specifically identified. "Transformation" of an article means that the "article" has changed to a different state or thing. Changing to a different state or thing usually means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which thoughts or human based actions are "changed" are not considered an eligible transformation. For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).
(MPEP § 2106.05(c), at the 5th paragraph).
The claims do not include or represent a physical object or substance. The claims do not transform any physical object or substance. What is “transformed” (by whatever that term means at the claims) is literally “information” and the transforming of that information is apparently just recognizing the information and/or calculating or measuring the temporal/time variations so as to call that information “domain knowledge”. Therefore, there is no relevant transformation at the claims.
Applicant then “asserts that amended claims 1, 6 and 11 integrate a judicial exception into a practical application with an improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a) i.e., dynamically update the ECG domain knowledge information as required” (Remarks at 10, emphasis at original). However, none of the claims recite, indicate, or imply any dynamic updating – the claims literally train one data model based on one ECG signal. As such, the argument is not commensurate or correlated to the scope of the claims.
Applicant then argues Step 2B, alleging that “Applicant believes that the subject matter of amended claim 1 achieves significantly more in terms of determine users are suffering from the plurality of CVDs” (Remarks at 10, emphasis at original). However, this is very literally just what the abstract idea would include, for example a doctor telling a patient “based on your ECG, you appear to have a cardiovascular disease”. The abstract idea itself does not comprise “significantly more” than the abstract idea.
Applicant then alleges that “Further, generates [sic] 2D representation of ECG signals, to allow easy interpretation and extraction of data from the signals that include the ECG signal is segmented according to the R- R intervals. Further, the variability of the R-R intervals is well captured in the second band of the R-peaks and the positional information relative to previous R-peak is restored. Further, segmenting ECG signal, aligning left side R-peaks of each two segments” (Id. at 11, emphasis at original), and alleging analogy to Core Wireless (Id.). However, Core Wireless was deemed eligible based on the data used not being available to a person; therefore, a person could not perform the analysis (mentally or otherwise) – it was rooted in technology. Applicant’s claims oppose that finding since people can and have read and interpreted ECG’s so as to determine variations and abnormalities and indicate whether the ECG data leads to a diagnosis of actual or possible cardiovascular disease. Therefore, analogy to Core Wireless is inappropriate.
Applicant then argues the prior art rejections (Remarks at 12-15), indicating that “Regarding the claim limitation ‘generating (212), via the one or more hardware processors, 2-Dimensional (2D) representation of the segments, wherein in the 2D representation comprises information on relative position of the left R-peak and the right R-peak of each of the segments are captured.’ the Examiner contends that ." [sic] the Examiner contends that Geva discloses this limitation …” (Id. at 12) and then indicates what Applicant believes the rejection indicates. However, this does not appear to indicate any argument that the rejection is somehow wrong or incorrect (i.e., there is no argument the Examiner has been able to identify).
Applicant then indicates that “the claimed subject matter generates 2D representation of ECG signals, to allow easy interpretation and extraction of data from the signals that include the ECG signal is segmented according to the R-R intervals. Further, each R-peak of the ECG waveform is placed in the next row at the same location and the total image length includes 2 R-peaks of immediate occurring. This way, all the R-peaks are aligned in the same column along with P-wave and QRS complexes. Further, the variability of the R-R intervals is well captured in the second band of the R-peaks and the positional information relative to previous R-peak is restored (paragraphs [0016, 0029,0031] [Emphasis Added]).” However, first, the Examiner has searched for this in Applicant’s specification – including ¶¶ 0016, 0029, and 0031, both as submitted and as published – and finds no indication of columns, QRS, complex(s), second bands, and/or restoring of positional information. Therefore, based on this alone, the Examiner is not persuaded – Applicant appears to be arguing limitations that are not required by the claims. Second, it appears that the entirety of the argument is based on results that may or may not be expected, and not based on the actual claim activities.
Applicant then argues, apparently, that “Applicant's published application at paragraphs [0049]” is not disclosed by Geva (perhaps also including the results indicated, rather than the actual generating of a display showing R-peak segments). However, MPEP 2111.01(I) indicates “IT IS IMPROPER TO IMPORT CLAIM LIMITATIONS FROM THE SPECIFICATION” (at the section title) and "Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim”). The claim merely requires “generating … [sic] 2-Dimensional representation of the segments, wherein … relative position of the left R-peak and the right R-peak of each of the segments are captured”. Since “left” and “right” merely indicate a relative position – i.e., the “left R-peak” is shown, displayed, positioned to the left of the right R-peak, and the “right R-peak” is shown, displayed, positioned to the right of the left R-peak – this would apparently be true for ANY and EVERY ECG display having more than one R-peak.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Electrocardiogram (ECG/EKG), Cleveland Clinic, downloaded 25 April 2025 from https://my.clevelandclinic.org/health/diagnostics/16953-electrocardiogram-ekg indicates that “You may hear the terms EKG and ECG. Both terms mean the same thing: an electrocardiogram. EKG comes from the German word, which uses ‘k’ instead of ‘c.’” (at p. 3)
Ganesh, Prakhar, Attention Mechanism in Deep Learning : Simplified, dated 29 February 2020, downloaded from https://medium.com/@prakhargannu/attention-mechanism-in-deep-learning-simplified-d6a5830a079d, explaining what is meant by “attention mechanism”.
Zhaoyang Niu, Guoqiang Zhong, Hui Yu, “A review on the attention mechanism of deep learning”, Neurocomputing, Volume 452, 2021, Pages 48-62, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2021.03.091. Downloaded from https://www.sciencedirect.com/science/article/pii/S092523122100477X on 20 October 2025, indicating “Attention has arguably become one of the most important concepts in the deep learning field. It is inspired by the biological systems of humans that tend to focus on the distinctive parts when processing large amounts of information. With the development of deep neural networks, attention mechanism has been widely used in diverse application domains. This paper aims to give an overview of the state-of-the-art attention models proposed in recent years. Toward a better general understanding of attention mechanisms, we define a unified model that is suitable for most attention structures. Each step of the attention mechanism implemented in the model is described in detail. Furthermore, we classify existing attention models according to four criteria: the softness of attention, forms of input feature, input representation, and output representation. Besides, we summarize network architectures used in conjunction with the attention mechanism and describe some typical applications of attention mechanism. Finally, we discuss the interpretability that attention brings to deep learning and present its potential future trends.” (at Abstract).
Hametner et al., Aortic Pulse Wave Velocity Predicts Cardiovascular Events and Mortality in Patients Undergoing Coronary Angiography, Hypertension, Vol. 77, No. 2, pp. 571-581, February 2021, downloaded 21 October 2025 from https://www.ahajournals.org/doi/epub/10.1161/HYPERTENSIONAHA.120.15336, indicating “Aortic pulse wave velocity (PWV) is directly related to arterial stiffness. Different methods for the determination of PWV coexist. The aim of this prospective study was to evaluate the prognostic value of PWV in high-risk patients with suspected coronary artery disease undergoing invasive angiography and to compare 3 different methods for assessing PWV.” (at Abstract).
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685
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