Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed ----5/11/2026 has been entered.
Status of Claims
Claims 1-4 and 7-17 are currently pending and have been examined.
Claims 1, 7-9, and 16-17 have been amended.
Claims 5-6 have been canceled with claim 5 being newly canceled in the claims filed 5/11/2026.
Claims 1-4 and 7-17 have been rejected.
Information Disclosure Statement
The information disclosure statement filed 09/06/2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein that has been crossed through has not been considered.
The information disclosure statement (IDS) submitted on 2/9/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-4 and 7-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea without significantly more. Claims 1-4 and 7-17 are directed to a system, method, or product which are one of the statutory categories of invention. (Step 1: YES).
Independent Claim 1 discloses a method for analyzing electrocardiogram (ECG) data performed by a computing device, the method comprising: determining, by using a first model having ECG data acquired by ECG measurement as an input, whether an event related to at least one of atrial fibrillation or atrial flutter is found in the acquired ECG data; generating a first diagnosis result for the acquired ECG data when the event is found in the acquired ECG data; and generating a second diagnosis result for estimating a risk for at least one of the atrial fibrillation or the atrial flutter by using a second model having the acquired ECG data as an input when the event is not found in the acquired ECG data; wherein the second model is pre-trained based on: distinguishing normal ECG data in which the even does not occur and abnormal ECG data in which the event occurs in a dataset with a plurality of ECG data acquired for a first subject, and temporally aligning the distinguished normal ECG data and abnormal ECG data, and generating, based on time information corresponding to each of the distinguished normal ECG data and abnormal ECG data, training data including first training data in which first normal ECG data is labeled with a first class representing a high risk group, and second training data in which second normal ECG data is labeled with a second class representing a low risk group, and wherein the normal ECG data in which the abnormal ECG data including at least one of the atrial fibrillation or the atrial flutter within a predetermined time period from an acquisition time point of the normal ECG data exists is determined as the first normal ECG data.
Independent Claim 16 discloses a computing device for analyzing electrocardiogram (ECG) data, comprising: at least one processor: and a memory, wherein the at least one processor: [implements the method of claim 1].
Independent Claim 17 discloses a computer program stored in a computer readable storage medium, wherein when the computer program is executed by at least one processor, the computer program allows the at least one processor to perform [the method of claim 1].
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations are merely directed to analyzing patient ECG data and outputting a result for a doctor. The series of steps recited above describe managing personal behavior or relationships or interactions between people and thus are grouped as certain methods of organizing human activity which is an abstract idea. (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Independent Claim 1 discloses the following additional elements:
A computing device
Independent Claim 16 discloses the following additional elements:
A computing device
A processor
A memory
Independent Claim 17 discloses the following additional elements:
A computer program stored in a computer readable storage medium
A processor
In particular, the computing device (of claim 1 and 16), processor (of claim 16 and 17), memory (of claim 16), and computer program stored in a computer readable storage medium (of claim 17) are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception.
Applicant’s specification states in para 51 discloses, “The computing device 100 according to an embodiment of the present disclosure may include a processor 110 and a memory 130.” Para 53 discloses, “The computing device 100 may mean any type of user terminal or any type of server.” Para 58 discloses, “Further, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.” Para 59 discloses “the processor 110 processes data, information, signals, and the like input or output through the components included in the computing device 100 or drives the application program stored in a storage unit to provide or process information or a function appropriate for the user.” Thus, the components are performing as expected: the processor of the computing device is processing data, information, signals, and the like. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, claim(s) 1 and 16-17 are directed to an abstract idea(s) without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the computing device (of claim 1 and 16), processor (of claim 16 and 17), memory (of claim 16), and computer program stored in a computer readable storage medium (of claim 17) to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more"). Accordingly, even in combination, this additional element does not provide significantly more. As such the independent claims 1 and 16-17 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent claim(s) 2-4 and 7-15 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Dependent claims 3-4 do further disclose the additional element(s) of an artificial intelligence-based model (claims 3-4), a rule-based model (claim 3), deep learned based first sub-model (claim 12).
In particular, the artificial intelligence based model (of claims 3-4), rule based model (of claim 3), and deep learned based first sub-model (of claim 12) are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the artificial intelligence based model (of claims 3-4), rule based model (of claim 3), and deep learned based first sub-model (of claim 12) amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-4 and 7-17 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 8, 10-12, and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Attia (WO 2020/086865 A1) in view of Chakravarthy (KR 2022/0006080 A), further in view of Fornwalt (US PG Pub 2021/0076960 A1).
Regarding Claim 1, Attia discloses:
A method for analyzing electrocardiogram (ECG) data performed by a computing device, the method comprising:
generating a second diagnosis result for estimating a risk for at least one of the atrial fibrillation or the atrial flutter by using a second model having the acquired ECG data as an input when the event is not found in the acquired ECG data. (Para 40 discloses the atrial fibrillation prediction 120 can indicate a likelihood that the patient 102… is susceptible to developing atrial fibrillation. The prediction 120 can be expressed as a probability or confidence score representing a probability or confidence that the patient 102… is susceptible to developing atrial fibrillation. Para 53 discloses this example describes the results of a study in which an artificial intelligence (Al)-model including a convolutional neural network was developed and tested to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm. The model was developed to process an ECG signature for a patient using a standard 10-second, 12- lead ECG recording. The example implementation was trained based on ECGs acquired from a set of patients aged 18 years or older having at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the MAYO CLINIC ECG laboratory between December 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. See further: Para 5.)
wherein the second model is pre-trained based on: distinguishing normal ECG data in which the event does not occur and abnormal ECG data in which the event occurs in a dataset with a plurality of ECG data acquired for a first subject, and temporally aligning the distinguished normal ECG data and abnormal ECG data, and generating, based on time information corresponding to each of the distinguished normal ECG data and abnormal ECG data, training data(Para 40 discloses the prediction 120 is expressed as a selection of a particular classification from multiple possible classifications that represents a most likely condition of the patient 102. For example, a binary classification can be made indicating whether there is at least a threshold probability or confidence level that the patient 102…is susceptible to developing atrial fibrillation [wherein over the threshold is high risk of being susceptible to an AF, low risk is below the threshold]. Para 49 discloses a training engine 210 analyzes the predictions 208 and compares the predictions 208 to labels in the training examples 204 that indicate target predictions for each training example 204. The training engine 210 then generates updated model parameter values 214 by using an appropriate updating technique, e.g., stochastic gradient descent with backpropagation... the first component can represent an ECG of a patient under normal sinus rhythm, and the label can indicate whether that particular patient is known to have actually experienced atrial fibrillation at another time. In this way, the neural network 108 can be trained using sinus rhythm ECGs obtained in patients known and validated atrial fibrillation versus patients with no known atrial fibrillation... After training is complete, the training system 200 can provide a final set of parameter values 218 to the system 100 for use in making atrial fibrillation predictions 120. Para 57 discloses ECG Selection For Patients With Multiple ECGs. Many study patients had multiple ECs recorded over the inclusion period. The study defined a window of interest for each patient for the purpose of analysis (Figure 6). For patients who had had at least one atrial fibrillation rhythm recorded, the first recorded atrial fibrillation ECG was defined as the index ECG and the first day of the window of interest was defined as 31 days before the date of the index ECG. This window of interest was chosen with the assumption that the structural changes associated with atrial fibrillation would be present before the first recorded atrial fibrillation episode; a relatively short time interval was chosen as a conservative measure to avoid using ECGs before any structural changes developed. For patients with no ECGs with atrial fibrillation recorded, the index ECG was defined as the date of the first ECG available for that patient in the MAYO CLINIC Digital Data Vault. During training, all the ECGs in the window of interest were used to allow the network to have more samples [therefore, reading on “a dataset with a plurality of ECG data acquired for a first subject” as the claim does not exclude training based on ECG data from a plurality of subjects, it only requires that the dataset includes a plurality of ECG data acquired for a subject].)
including first training data in which first normal ECG data is labeled with a first class representing a high risk group, and second training data in which second normal ECG data is labeled with a second class representing a low risk group, and (Para 49 discloses a training engine 210 analyzes the predictions 208 and compares the predictions 208 to labels in the training examples 204 that indicate target predictions for each training example 204. The training engine 210 then generates updated model parameter values 214 by using an appropriate updating technique, e.g., stochastic gradient descent with backpropagation... the first component can represent an ECG of a patient under normal sinus rhythm, and the label can indicate whether that particular patient is known to have actually experienced atrial fibrillation at another time [thus reading on “labeled with a first class representing a high risk group” for patients who actually experienced atrial fibrillation at another time and “labeled with a second class representing a low risk group” for patients who did not actually experience atrial fibrillation at another time.] In this way, the neural network 108 can be trained using sinus rhythm ECGs obtained in patients known and validated atrial fibrillation versus patients with no known atrial fibrillation... After training is complete, the training system 200 can provide a final set of parameter values 218 to the system 100 for use in making atrial fibrillation predictions 120.)
While Attia discloses the above limitations, it does not fully disclose the following limitations that Chakravarthy discloses:
determining, by using a first model having ECG data acquired by ECG measurement as an input, whether an event related to at least one of atrial fibrillation or atrial flutter is found in the acquired ECG data; (Para 5 discloses the computing device receives the patient's electrocardiogram data sensed by the implantable medical device. The computing device obtains a first classification of the patient's arrhythmia through the feature-based description of the electrocardiogram data. The computing device applies a machine learning model to the received electrocardiogram data to obtain a second classification of the patient's arrhythmia. As an example, the computing device uses the first and second classifications to determine whether the patient has an arrhythmic episode. See further: Para 49)
generating a first diagnosis result for the acquired ECG data when the event is found in the acquired ECG data; and (Para 6 discloses in response to determining that an arrhythmic symptom has occurred in the patient, the computing device outputs a report indicating that an arrhythmic symptom has occurred and one or more cardiac features consistent with the arrhythmic symptom.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia with the arrhythmia detection by feature description and machine learning as taught by Chakravarthy in order to determine whether the patient has an arrhythmic episode (Para 5) [and not just a risk, likelihood, or prediction] and apply feature delineation to classify the detected cardiac arrhythmia symptoms into … a particular type.. such as atrial fibrillation (Para 43) to improve patient welfare based on the determination such as by making “adjustments to the implantable medical device” upon determining an arrhythmic symptom has occurred.
While Attia para 40 discloses, ”a binary classification can be made indicating whether there is at least a threshold probability or confidence level that the patient 102… is susceptible to developing atrial fibrillation,” and para 49 discloses, ”the first component can represent an ECG of a patient under normal sinus rhythm, and the label can indicate whether that particular patient is known to have actually experienced atrial fibrillation at another time,” and the second model with training data as presented above, the combination of Attia and Chakravarthy does not fully disclose the following limitation that Fornwalt discloses:
wherein the normal ECG data in which the abnormal ECG data including at least one of the atrial fibrillation or the atrial flutter within a predetermined time period from an acquisition time point of the normal ECG data exists is determined as the first normal ECG data. (Abstract discloses the method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from atrial fibrillation within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. Para 6 discloses For example, in the case of AF, if a physician is able to discern that a patient that does not currently suffer AF has an appreciable risk of AF in the future, that patient can be counseled on ways to change his or her lifestyle, or increase monitoring for example with a wearable device to detect AF, so as to prevent or reduce the possibility of future bad outcomes related to AF, such as stroke. Para 11 discloses even to the trained eye of a physician, there is no way to ascertain likelihood of future AF from analyzing an ECG trace that does not currently include features consistent with AF. Thus, where a physician determines that an ECG trace has no evidence of AF, the patient is simply instructed that he/she does not currently have AF without any sense of future AF likelihood or the likelihood of future AF related complications. Para 97 discloses the training data database 124 can include a number of ECGs and clinical data. In some embodiments, the clinical data can include outcome data, such as whether or not a patient developed AF in a time period following the day that the ECG was taken [whether or not the patient was within the first normal ECG data group or the second normal ECG data group]. Exemplary time periods may include 1 month [ranging to]… 10 years [predetermined time period].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia and the arrhythmia detection by feature description and machine learning as taught by Chakravarthy with the ECG based atrial fibrillation predictor systems and methods as taught by Fornwalt in order to differentiate between a high and low risk for developing incident AF to more accurately determine a patient’s risk for developing AF within a certain time period and thus improve patient outcomes by prescribing a treatment plan designed to help avoid the condition in the future (See Fornwalt Para 6).
Regarding Claim 2, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt further discloses the following limitation:
The method of claim 1, wherein the first model and the second model are trained based on different training data, and trained based on different training methods. (The combination of Attia and Chakravarthy discloses this limitation of claim 2. See Attia Para 53 as disclosed below (claim 4) where the model is trained on normal ECG data. See Chakravarthy Paras 14, 16, 17, and 60 as disclosed below (claim 3) where the model is trained on data including at least one of the atrial fibrillation or the atrial flutter.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia with the arrhythmia detection by feature description and machine learning as taught by Chakravarthy in order to determine whether the patient has an arrhythmic episode (Para 5) [and not just a risk, likelihood, or prediction] and apply feature delineation to classify the detected cardiac arrhythmia symptoms into … a particular type.. such as atrial fibrillation (Para 43) to improve patient welfare based on the determination such as by making “adjustments to the implantable medical device” upon determining an arrhythmic symptom has occurred.
Regarding Claim 3, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Chakravarthy further discloses:
The method of claim 1 wherein the first model includes at least one of: an artificial intelligence-based model trained based on ECG data including at least one of the atrial fibrillation or the atrial flutter, or a rule-based model generated based on feature information for at least one of the atrial fibrillation or the atrial flutter. (Para 14 discloses feature delineation of ECG data and machine learning models processing patient data, such as machine learning systems and/or artificial intelligence (AI) algorithms, to analyze single and multi-channel patient data to detect and classify cardiac arrhythmias in patients. Techniques are disclosed for combining multiple decision mechanisms, such as state-of-the-art signal processing algorithms that perform Such patient data may include, for example, cardiac electrogram data or electrocardiogram (ECG) data. Para 16 discloses machine learning techniques can be contrasted with feature description in that feature description relies on signal processing, and machine learning systems use the relationship between features and electrocardiograms that exhibit arrhythmia symptoms on behalf of the system designer without knowledge or understanding of the symptoms of the arrhythmia. It can "learn" the basic features present in the data. Para 17 discloses machine learning and AI methods for arrhythmia detection can be applied to various targets (e.g., atrial fibrillation (AF) detection, cardiac symptoms without arrhythmias without the need for feature engineering required by expert design or feature delineation algorithms). Para 60 discloses In some examples, the machine learning model implemented by the machine learning system 450 is trained with training data that includes electrocardiogram data for a plurality of patients marked with descriptive metadata. For example, during the training phase, the machine learning system 450 processes a plurality of ECG waveforms. Generally, a plurality of ECG waveforms originate from a plurality of different patients. Each ECG waveform is marked with one or more symptoms of one or more types of arrhythmias. For example, a training ECG waveform may include a plurality of segments, each segment specifying the absence of an arrhythmia or the presence of a particular class of arrhythmia (e.g., bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV block).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia and the ECG based atrial fibrillation predictor systems and methods as taught by Fornwalt with the arrhythmia detection by feature description and machine learning as taught by Chakravarthy in order to determine whether the patient has an arrhythmic episode (Para 5) [and not just a risk, likelihood, or prediction] and use both feature delineation and machine learning together can improve the accuracy of detecting arrhythmias in a patient (Para 8).
Regarding Claim 4, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Attia further discloses:
The method of claim 1, wherein the second model is an artificial intelligence- based model trained based on normal ECG data in which the event does not occur. (Para 53 discloses this example describes the results of a study in which an artificial intelligence (Al)-model including a convolutional neural network was developed and tested to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm. The model was developed to process an ECG signature for a patient using a standard 10-second, 12- lead ECG recording. The example implementation was trained based on ECGs acquired from a set of patients aged 18 years or older having at least one digital, normal sinus rhythm, standard 1O-second, l2-lead ECG [wherein the claim does not limit the training to only normal ECG data, just that the two models are trained differently] acquired in the supine position at the MAYO CLINIC ECG laboratory between December 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. See further: Para 5.)
Regarding Claim 8, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Fornwalt further discloses:
The method of claim 1, wherein the generating of the training data includes: determining a first time point corresponding to most recent ECG data among the plurality of ECG data when the abnormal ECG data does not exist in the plurality of ECG data acquired for the first subject, and generating the training data in which at least one ECG data acquired at time points prior to a first predetermined time period from the first time point is labeled with the second class representing the low-risk group. (Para 97 discloses the training data database 124 can include a number of ECGs and clinical data. In some embodiments, the clinical data can include outcome data, such as whether or not a patient developed AF in a time period following the day that the ECG was taken [wherein if the patient did not develop AF then there is no abnormal ECG data and the model outputs no risk if there is no abnormal ECG data thus reading on the non-risk group ECG of Fig. 4 of the instant application which is the low risk group]. Exemplary time periods may include 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months 12 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, or 10 years. The ECGs and clinical data can be used for training a model to generate AF risk scores. In some embodiments, the training data database 124 can include multi-lead ECGs taken over a period of time (such as ten seconds) and corresponding clinical data. In some embodiments, the trained models database 128 can include a number of trained models that can receive raw ECGs and output AF risk scores.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia and the arrhythmia detection by feature description and machine learning as taught by Chakravarthy with the ECG based atrial fibrillation predictor systems and methods as taught by Fornwalt in order to differentiate between a high and low risk for developing incident AF to more accurately determine a patient’s risk for developing AF within a certain time period and thus improve patient outcomes by prescribing a treatment plan designed to help avoid the condition in the future (See Fornwalt Para 6).
Regarding Claim 10, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Attia further discloses:
The method of claim 1, wherein the second model analyzes a structural change in an atria from normal ECG data in which the event does not occur to output the risk including a possibility of past or future occurrence of the atrial fibrillation or the atrial flutter. (Para 5 discloses due to structural irregularities of the heart, or the presence of other factors that can lead to atrial fibrillation, the patient’s ECG in normal sinus rhythm can include features, not detectable by the human eye, but which are nonetheless highly predictive of a patient that has experienced atrial fibrillation or is susceptible to atrial fibrillation. A neural network can be trained to learn these features and predict patients that have or will experience atrial fibrillation based on ECG recordings… if the prediction indicates a sufficiently high likelihood of past or expected atrial fibrillation and/or other SVTs, appropriate action may be taken such as administration of medication (e.g., anticoagulants), longer term monitoring (e.g., with an implantable loop recorder) to validate the prediction, or both. Para 8 discloses the ECG recording represented by the first neural network input can describe a normal sinus rhythm for the mammal, such that the neural network generates the atrial fibrillation prediction and/or other SVT prediction for the mammal based on features of the mammal’s normal sinus rhythm as indicated by the ECG recording.)
Regarding Claim 11, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Attia further discloses:
The method of claim 1, the second diagnosis result includes a result representing that at least one of the atrial fibrillation or the atrial flutter does not exist with respect to the acquired ECG data, but there is a possibility of past or future occurrence of at least one of the atrial fibrillation or the atrial flutter with respect to the acquired ECG data. (Para 5 discloses due to structural irregularities of the heart, or the presence of other factors that can lead to atrial fibrillation, the patient’s ECG in normal sinus rhythm can include features, not detectable by the human eye, but which are nonetheless highly predictive of a patient that has experienced atrial fibrillation or is susceptible to atrial fibrillation. A neural network can be trained to learn these features and predict patients that have or will experience atrial fibrillation based on ECG recordings… if the prediction indicates a sufficiently high likelihood of past or expected atrial fibrillation [past or future atrial fibrillation] and/or other SVTs, appropriate action may be taken such as administration of medication (e.g., anticoagulants), longer term monitoring (e.g., with an implantable loop recorder) to validate the prediction, or both. Para 8 discloses the ECG recording represented by the first neural network input can describe a normal sinus rhythm for the mammal, such that the neural network generates the atrial fibrillation prediction and/or other SVT prediction for the mammal based on features of the mammal’s normal sinus rhythm as indicated by the ECG recording.)
While Chakravarthy discloses the above limitations, the combination of Attia, Chakravarthy, and Fornwalt further discloses the following limitation that Chakravarthy discloses:
wherein the first diagnosis result includes a result representing that there is a possibility that at least one of the atrial fibrillation or the atrial flutter will exist with respect to the acquired ECG data, and wherein (Para 60 discloses the machine learning model implemented by the machine learning system 450 is trained with training data that includes electrocardiogram data for a plurality of patients marked with descriptive metadata. For example, during the training phase, the machine learning system 450 processes a plurality of ECG waveforms. Generally, a plurality of ECG waveforms originate from a plurality of different patients. Each ECG waveform is marked with one or more symptoms of one or more types of arrhythmias. For example, a training ECG waveform may include a plurality of segments, each segment specifying the absence of an arrhythmia or the presence of a particular class of arrhythmia (e.g., bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV block). Para 61 discloses machine learning system 450 may output a preliminary determination [a possibility] that the symptom of cardiac arrhythmia represents a particular type of arrhythmia, as well as a determination indicative of confidence in the determination as well as an estimate of certainty about the determination.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia and the ECG based atrial fibrillation predictor systems and methods as taught by Fornwalt with the arrhythmia detection by feature description and machine learning as taught by Chakravarthy in order to determine whether the patient has an arrhythmic episode (Para 5) [and not just a risk, likelihood, or prediction] and apply feature delineation to classify the detected cardiac arrhythmia symptoms into … a particular type.. such as atrial fibrillation (Para 43) to improve patient welfare based on the determination such as by making “adjustments to the implantable medical device” upon determining an arrhythmic symptom has occurred.
Regarding Claim 12, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Attia further discloses:
The method of claim 1, wherein the second model includes a deep learned based first sub-model which is trained based on a plurality of normal ECG data aligned in order of acquisition time and outputs the risk for at least one of the atrial fibrillation or the atrial flutter in response to the acquired ECG data. (Para 5 discloses a machine-learning model such as a deep neural network can be trained to process a short-recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient has or will experience atrial fibrillation (e.g., paroxysmal atrial fibrillation) and/or other supraventricular tachycardia (e.g., atrial flutter, atrial tachycardia)... the neural networks described herein can detect a likelihood of atrial fibrillation and/or other supraventricular tachycardia (SVT) in a patient from an ECG recording that nominally represents a normal sinus rhythm. Due to structural irregularities of the heart, or the presence of other factors that can lead to atrial fibrillation, the patient’s ECG in normal sinus rhythm can include features, not detectable by the human eye, but which are nonetheless highly predictive of a patient that has experienced atrial fibrillation or is susceptible to atrial fibrillation. Para 23-25 discloses the ECG recording of the mammal can be recorded over a first time interval, and the method can further include: obtaining a second neural network input, the second neural network input representing a second ECG recording of the mammal that was recorded over a second time interval, the first time interval and the second time interval separated by a third time interval; and processing the first neural network input along with the second neural network input with the neural network to generate the atrial fibrillation prediction and/or other SVT prediction for the mammal… The neural network can further process, along with the first neural network input and the second neural network input, a third neural network input that indicates a length of the third time interval between the first and second time intervals when the ECG recording and the second ECG recordings were recorded, respectively. Para 75 discloses various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially be claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination [thus it would have been obvious to utilize sub models of the embodiments of Attia].)
Regarding Claim 14, this claim recites the limitations of Claim 12 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Attia further discloses:
The method of claim 12, wherein the second model further includes a third sub- model which changes [the first threshold] to a second threshold, where the second threshold is lower than the first threshold, and wherein the third sub-model outputs the risk for at least one of the atrial fibrillation or the atrial flutter from the acquired ECG data. (Para 66 discloses it is noted that the threshold for a positive result (i.e., a positive classification of atrial fibrillation) could be altered to suit the purposes of different clinical applications. The current binary cutoff was chosen to balance sensitivity and specificity, but a more sensitive cutoff point might be useful in excluding patients who do not need monitoring of atrial fibrillation after stroke or a more specific cutoff point could be used for screening of otherwise healthy people with a low pretest probability of atrial fibrillation, for instance.)
While Attia discloses the above limitation, the combination of Attia and Chakravarthy disclose the following limitation that Chakravarthy further discloses:
a first threshold used in the first model which determines whether at least one of the atrial fibrillation or the atrial flutter is found in the acquired ECG data (Para 61 discloses the machine learning system 450 may output a preliminary determination that the symptom of cardiac arrhythmia represents a particular type of arrhythmia, as well as a determination indicative of confidence in the determination as well as an estimate of certainty about the determination. In response to determining that the certainty of the determination estimate is greater than a predetermined threshold (e.g., 50%, 75%, 90%, 95%, 99%), the computing device 400 determines the specific type of cardiac arrhythmia symptom.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia and the ECG based atrial fibrillation predictor systems and methods as taught by Fornwalt with the arrhythmia detection by feature description and machine learning as taught by Chakravarthy in order to determine whether the patient has an arrhythmic episode (Para 5) [and not just a risk, likelihood, or prediction] and apply feature delineation to classify the detected cardiac arrhythmia symptoms into … a particular type.. such as atrial fibrillation (Para 43) to improve patient welfare based on the determination such as by making “adjustments to the implantable medical device” upon determining an arrhythmic symptom has occurred.
Regarding Claim 15, this claim recites the limitations of Claim 12 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Attia further discloses:
The method of claim 12, wherein the second model further includes a fourth sub-model generated based on analyzing ECG characteristic indicators including at least one of a PR-interval, an RR-interval, QRS duration, a QT interval, or a QT-corrected interval for high-risk group ECG data and low-risk group ECG data, and wherein the fourth sub-model outputs the risk for at least one of the atrial fibrillation or the atrial flutter from the acquired ECG data. (Para 5 discloses if the prediction indicates a sufficiently high likelihood of… expected atrial fibrillation [high risk group] and/or other SVTs, appropriate action may be taken such as administration of medication (e.g., anticoagulants), longer term monitoring (e.g., with an implantable loop recorder) to validate the prediction, or both. Para 40 discloses the prediction 120 is expressed as a selection of a particular classification from multiple possible classifications that represents a most likely condition of the patient 102. For example, a binary classification can be made indicating whether there is at least a threshold probability or confidence level that the patient 102…is susceptible to developing atrial fibrillation [wherein over the threshold is high risk of being susceptible to an AF, low risk is below the threshold]. Para 44 discloses the atrial fibrillation detection neural network 108 is configured to process additional (auxiliary) information in generating atrial fibrillation prediction 120. For example, the network 108 may process a second neural network input 124 in addition to the first neural network input 118 to generate the atrial fibrillation prediction 120. The second neural network input 124 represents morphological features of the patient’s ECG. Figure 5, for instance, depicts a waveform or tracing 500 for a single beat from a patient’s ECG. The waveform includes several segments including a P-wave, a QRS-complex, and a T-wave. The interface 106 can provide the ECG recording 116 to a morphological feature extractor 110 for analysis, and the extractor 110 can measure various morphological features of one or more beats (or a composite or averaged beat) from the ECG recording 116. The morphological features are parameters that describe attributes of the shape of the beat, including attributes of individual segments of the beat and attributes between segments. A number of morphological features that may be employed for atrial fibrillation prediction are labeled in Figure 5, such as a duration of the QRS-complex, am amplitude of the P-wave, R-wave, or T-wave, an area of the P-wave, QRS-complex, or T-wave, slopes of any of the waves, distances between the waves, and centers- of-gravity of the waves. The morphological feature extractor 110 provides values for the morphological features 122 to the interface 106, and the interface 106 formats them into an acceptable form for processing by atrial fibrillation detection neural network 108 as second neural network input 124. The atrial fibrillation detection neural network 108 processes the first and second inputs 118, 124 to generate the atrial fibrillation prediction 120. Para 75 discloses various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination [and thus a sub model would have been obvious].)
Regarding claim 16, the claim is directed to the computing device implementing the method of claim 1 and further recites a least one processor and a memory (e.g., see Attia Para 67-68 teaching a tangible non-transitory storage medium and all kinds of apparatus, devices, and machines for processing data including a programmable processor, computer, or multiple processors or computers) and are similarly rejected.
Regarding claim 17, the claim is directed to the computer program implementing the method of claim 1 and further recites a least one processor and a computer readable storage medium (e.g., see Attia Para 67-68 teaching one or more computer programs i.e., one or more modules of computer program instructions encoded on a tangible non-transitory storage medium and all kinds of apparatus, devices, and machines for processing data including a programmable processor, computer, or multiple processors or computers) and are similarly rejected.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Attia (WO 2020/086865 A1) in view of Chakravarthy (KR 2022/0006080 A), further in view of Fornwalt (US PG Pub 2021/0076960 A1) and Nadarajah (Nadarajah, R., Wu, J., Frangi, A. F., Hogg, D., Cowan, C., & Gale, C. (2021, November 1). Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: Protocol of find-AF for developing a precision medicine prediction model using Artificial Intelligence. BMJ Open. https://bmjopen.bmj.com/content/11/11/e052887).
Regarding Claim 7, this claim recites the limitations of Claim 6 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Attia, Chakravarthy, and Fornwalt discloses the following limitation that Attia further discloses:
The method of claim 6, the training data to which the label corresponding to the first class representing the high-risk group or the second class representing the low-risk group is allocated with respect to the normal ECG data. (Para 5 discloses the neural networks described herein can detect a likelihood of atrial fibrillation and/or other supraventricular tachycardia (SVT) in a patient from an ECG recording that nominally represents a normal sinus rhythm. Para 40 discloses the prediction 120 is expressed as a selection of a particular classification from multiple possible classifications that represents a most likely condition of the patient 102. For example, a binary classification can be made indicating whether there is at least a threshold probability or confidence level that the patient 102…is susceptible to developing atrial fibrillation [wherein over the threshold is high risk of being susceptible to an AF, low risk is below the threshold].)
While Attia discloses the above limitation, the combination of Attia, Chakravarthy, and Fornwalt does not fully disclose the following limitation that Nadarajah discloses:
wherein the generating of the training data includes: generating, additionally based on at least one of prescription record information or surgery history information allocated to the distinguished normal ECG data, (Predictor variables discloses a systematic review has highlighted 22 predictor variables included in varying combinations by 10 preceding prediction models developed to detect incident AF in the community… The potential predictors may include the following: sociodemographic variables including age, sex, ethnicity and indices of multiple deprivation…all disease conditions during follow-up, including hospitalised diseases and procedures, such as other cardiovascular diseases, diabetes mellitus, chronic lung disease, renal disease, inflammatory disease, cancer, hypothyroidism and surgical procedures… Clinical assessments… Medications prescribed including antihypertensives, statins, antidepressants, anxiolytics/hypnotics and antipsychotics… lifestyle factors, and biomarkers. Development and external validation of model discloses preprocessed patient-level data in CPRD-GOLD will be randomly split into an 80:20 ratio to create training and testing samples. The split ratio is not a significant factor, given the volume of the sample size. The model parameters and dropout rate will be chosen through a grid search and 10-fold cross-validation will be used (i.e., 10% of the training data will be randomly selected as the cross-validation set).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia, the arrhythmia detection by feature description and machine learning as taught by Chakravarthy, and the ECG based atrial fibrillation predictor systems and methods as taught by Fornwalt with predictor variables as taught by Nadarajah in order to capture the full potential of deep learning (Nadarajah predictor variables).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Attia (WO 2020/086865 A1) in view of Chakravarthy (KR 2022/0006080 A), further in view of Fornwalt (US PG Pub 2021/0076960 A1) and Chang (Chang, C.-H., Lin, C.-S., Luo, Y.-S., Lee, Y.-T., & Lin, C. (2022, February 8). Electrocardiogram-based heart age estimation by a deep learning model provides more information on the incidence of cardiovascular disorders. Frontiers in cardiovascular medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC8860826/.)
Regarding Claim 13, this claim recites the limitations of Claim 12 and as to those limitations is rejected for the same basis and reasons as disclosed above. Attia discloses in para 75, “various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination [and thus a sub model would have been obvious],” the combination of Attia, Chakravarthy, and Fornwalt does not fully disclose the following limitation that Chang discloses:
The method of claim 12, wherein the second model further includes a second sub-model which estimates the heart age from the acquired ECG data and compares the estimated heart age with an actual age of a subject to output the risk for at least one of the atrial fibrillation or the atrial flutter. (Introduction discloses we developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs). Results discloses the higher difference between ECG-age and chronological age was related to more comorbidities and abnormal ECG rhythm. The cases with the difference of more than 7 years had higher risk on the all-cause mortality… AF (HR: 2.38, 95% CI: 1.86–3.04… The external validation sets also validated that an ECG-age >7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72–5.78) and 1.59-fold risk (95% CI: 1.45–1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all the known risk factors. Fig. 7A discloses the high residual group had an increased risk for the all-cause mortality (HR 1.61, 95%CI 1.23–2.12), CV-caused mortality (HR 3.49, 95%CI 1.74–7.01), newly onset HF (HR 2.79, 95%CI 2.25–3.45), and newly onset AF (HR 2.38, 95%CI 1.86–3.04).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the prediction of a likelihood that a patient has or will experience atrial fibrillation of the neural networks for atrial fibrillation screening as taught by Attia, the arrhythmia detection by feature description and machine learning as taught by Chakravarthy, and the ECG based atrial fibrillation predictor systems and methods as taught by Fornwalt with the ECG age as taught by Chang to further identify the parameters that contribute to future cardiovascular diseases (CVDs)… such as atrial fibrillation (See Chang Introduction).
Subject Matter Free of Prior Art
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the 35 U.S.C § 101 rejection is overcome.
Relevant prior art and reasons why Claim 9 contains subject matter free of prior art:
WO 2020/086865 A1 Attia Para 40 discloses the prediction 120 is expressed as a selection of a particular classification from multiple possible classifications that represents a most likely condition of the patient 102. For example, a binary classification can be made indicating whether there is at least a threshold probability or confidence level that the patient 102…is susceptible to developing atrial fibrillation [wherein over the threshold is high risk of being susceptible to an AF, low risk is below the threshold]. Para 49 discloses a training engine 210 analyzes the predictions 208 and compares the predictions 208 to labels in the training examples 204 that indicate target predictions for each training example 204. The training engine 210 then generates updated model parameter values 214 by using an appropriate updating technique, e.g., stochastic gradient descent with backpropagation... the first component can represent an ECG of a patient under normal sinus rhythm, and the label can indicate whether that particular patient is known to have actually experienced atrial fibrillation at another time. In this way, the neural network 108 can be trained using sinus rhythm ECGs obtained in patients known and validated atrial fibrillation versus patients with no known atrial fibrillation... After training is complete, the training system 200 can provide a final set of parameter values 218 to the system 100 for use in making atrial fibrillation predictions 120. Para 57 discloses ECG Selection For Patients With Multiple ECGs. Many study patients had multiple ECs recorded over the inclusion period. The study defined a window of interest for each patient for the purpose of analysis (Figure 6). For patients who had had at least one atrial fibrillation rhythm recorded, the first recorded atrial fibrillation ECG was defined as the index ECG and the first day of the window of interest was defined as 31 days before the date of the index ECG. This window of interest was chosen with the assumption that the structural changes associated with atrial fibrillation would be present before the first recorded atrial fibrillation episode; a relatively short time interval was chosen as a conservative measure to avoid using ECGs before any structural changes developed. For patients with no ECGs with atrial fibrillation recorded, the index ECG was defined as the date of the first ECG available for that patient in the MAYO CLINIC Digital Data Vault. During training, all the ECGs in the window of interest were used to allow the network to have more samples
KR 2022/0006080 A Chakravarthy Para 5 discloses the computing device receives the patient's electrocardiogram data sensed by the implantable medical device. The computing device obtains a first classification of the patient's arrhythmia through the feature-based description of the electrocardiogram data. The computing device applies a machine learning model to the received electrocardiogram data to obtain a second classification of the patient's arrhythmia. As an example, the computing device uses the first and second classifications to determine whether the patient has an arrhythmic episode. Para 6 discloses in response to determining that an arrhythmic symptom has occurred in the patient, the computing device outputs a report indicating that an arrhythmic symptom has occurred and one or more cardiac features consistent with the arrhythmic symptom.
US PG Pub 2021/0076960 A1 Fornwalt Para 97 discloses the training data database 124 can include a number of ECGs and clinical data. In some embodiments, the clinical data can include outcome data, such as whether or not a patient developed AF in a time period following the day that the ECG was taken. Exemplary time periods may include 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months 12 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, or 10 years. The ECGs and clinical data can be used for training a model to generate AF risk scores… the trained models database 128 can include a number of trained models that can receive raw ECGs and output AF risk scores and thus discloses the first time point of claim 9. However, it does not disclose “determining a second time point corresponding to first abnormal ECG data among the plurality of ECG data when the normal ECG data and the abnormal ECG data exist in the plurality of ECG data acquired for the first subject, determining a third time point corresponding most recent ECG data in at least one ECG data acquired at time points prior to a second predetermined time period from the second time point, and generating the training data in which at least one ECG data acquired at time points prior to a third predetermined time period from the third time point is labeled with the second class representing the low-risk group.”
Therefore, there is no obvious combination of references that determine all three claimed time points and predetermined time periods and that specifically determines “a second time point corresponding to first abnormal ECG data among the plurality of ECG data when the normal ECG data and the abnormal ECG data exist in the plurality of ECG data acquired for the first subject, determining a third time point corresponding most recent ECG data in at least one ECG data acquired at time points prior to a second predetermined time period from the second time point, and generating the training data in which at least one ECG data acquired at time points prior to a third predetermined time period from the third time point is labeled with the second class representing the low-risk group.”
Response to Arguments
Applicant’s arguments filed 5/11/2026 with respect to 35 U.S.C. § 112(b) have been fully considered. In regards to claim 9, the Applicant amended claim 9 to now include a first time point and a first predetermined time period. Therefore, the previous 112(b) rejection regarding claim 9 only having a second and third time point and time period and not a first time point and time period has been withdrawn.
Applicant’s arguments filed 12/29/2025 with respect to 35 U.S.C. § 101 have been fully considered, but are not persuasive. Applicant argues that the claims are not directed to a mental process or a mathematical concept. The Examiner submits that the abstract idea was not characterized as being directed to a mental process or a mathematical concept. The claimed invention was characterized as falling under Certain Methods of Organizing Human activity. As such, this argument cannot be persuasive.
The Applicant argues that the claims are not directed to a mere mental activity or a method of organizing human activity and instead recite a series of technical steps that are dependent on a computer-based data processing architecture, including temporally aligning and reconstructing physical acquisition time points of a large-scale electrocardiogram dataset for training a neural network model. The Examiner notes that temporally aligning the distinguished normal ECG data and abnormal ECG data was found to be a part of the abstract idea, the certain methods of organizing human activity. The claims do not disclose reconstructing physical acquisition time points. The claim merely discloses generating training data “based on time information corresponding to each of the distinguished normal ECG data and abnormal ECG data” which was also found to be a part of the abstract idea. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to analyze patient ECG data and output a result for a doctor. Applicant has not pointed to anything in the claims that fall outside of this characterization other than what was addressed above. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to analyze patient ECG data and output a result for a doctor, the claimed invention is directed to an abstract idea.
Further, the Applicant argues that the amended independent claims implement a technical improvement, thereby providing a practical application. The Examiner respectfully disagrees. MPEP 2106.04(d)(1) states “the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” Here, there is no improvement to the computer (the computer is performing as expected) nor is there an improvement to another technology. The Applicant argues that the claims “constitute a specific technical procedure that fundamentally improves the accuracy and operational performance of the predictive model by restructuring the training data so that the neural network model can effectively extract subtle risk indicators hidden within apparently normal ECG signals.” The Examiner respectfully disagrees that the claims are restructuring the data. The claims disclose labeling ECG data with a class representing a high risk group or with a class representing a low risk group and do not disclose restructuring the data. Labeling training data does not provide an improvement to how the model is functioning nor to how the model is being trained. Training a model with labeled data does not present an inventive concept. The claimed invention is not inventing a new way of training a model. Merely stating that the training data improves the accuracy and operational performance of the predictive model is not enough to provide a practical application to the abstract idea. The specification does not provide any more detail on why the specific labeled training data would result in an improvement to the accuracy and operational performance of the predictive model. As such, the claimed invention is broadly claimed to apply a computer as a tool to run a model to implement an abstract idea and any improvement present is an improvement to the abstract idea of, to paraphrase, analyzing patient ECG data and outputting a result for a doctor. Because neither type of improvement is present in the claims, an improvement to technology is not present and there is no practical application.
Applicant’s arguments filed 5/11/2026 with respect to 35 U.S.C. § 103 have been fully considered, but are not persuasive in determining if the independent claims contain subject matter free of prior art.
The Applicant argues that Attia does not read on, “quantitatively compar[ing], within multiple time-series data of a single subject, how far apart, in time, an acquisition time point of each individual normal electrocardiogram is from an occurrence time point of an abnormal electrocardiogram (i.e., a temporal distance). The Examiner notes that Attia was not cited for claims 8 and 9 which are the claims that discuss the various time points and therefore, this argument is not persuasive in determining if the independent claims contain subject matter free of prior art.
The Applicant further argues that Chakravarthy does not disclose, “distinguishing, within a plurality of electrocardiogram data acquired for a single subject, “normal data” in which no event has occurred and “abnormal data” in which an event has occurred, temporally aligning the same, and generating training data by assigning a “high-risk group” or a “low-risk group” label to the normal data based on a temporal interval therebetween. First, the Examiner notes that this is not clearly claimed. Specifically, claim 1 discloses, “distinguishing normal ECG data in which the event does not occur and abnormal ECG data in which the event occurs in a dataset with a plurality of ECG data acquired for a first subject, and temporally aligning the distinguished normal ECG data and abnormal ECG data, and…” Therefore, the claim language does not exclude a plurality of ECG data from a plurality of patients, it merely requires that the dataset includes a plurality of ECG data for a first subject. Therefore, the dataset could contain a plurality of ECG data for a plurality of ECG data as long as the dataset includes at least a plurality of ECG data for a first subject. Therefore, the claim does not disclose, “distinguishing, within a plurality of electrocardiogram data acquired for a single subject…” Further, amended claim 1 discloses, “generating, based on time information corresponding to each of the distinguished normal ECG data and abnormal ECG data, training data including…” and therefore does not clearly disclose that the training data is generated based on a time interval but rather broadly claims “based on time information…” Secondly, the Examiner did not cite Chakravarthy for these limitations, therefore, this argument is not persuasive in determining if the independent claims contain subject matter free of prior art.
The argument in regards to the pre-training and Fornwalt is rendered moot because the grounds for rejection for this limitation does not currently rely on Fornwalt. The Applicant further argues that the combination of Fornwalt, Attia, and Chakravarthy does not read on “determining and labeling a normal electrocardiogram as a first normal electrocardiogram (high-risk) according to whether an abnormal electrocardiogram exists within a predetermined time period from the time point at which the normal electrocardiogram of a single subject was acquired,” and, “distinguishing, among a plurality of electrocardiogram data acquired from the same subject, ‘normal data’ in which no event occurred and ‘abnormal data’ in which an event occurred, temporally aligning the data, and assigning a ‘high-risk group’ or ‘low-risk group’ label to the normal data based on the temporal interval therebetween so as to generate training data.” As described above, the Examiner notes that this is not clearly claimed. Specifically, claim 1 discloses, “distinguishing normal ECG data in which the event does not occur and abnormal ECG data in which the event occurs in a dataset with a plurality of ECG data acquired for a first subject, and temporally aligning the distinguished normal ECG data and abnormal ECG data, and…” Therefore, the claim language does not exclude a plurality of ECG data from a plurality of patients, it merely requires that the dataset includes a plurality of ECG data for a first subject. Therefore, the dataset could contain a plurality of ECG data for a plurality of ECG data as long as the dataset includes at least a plurality of ECG data for a first subject. Therefore, the claim does not disclose, “distinguishing, within a plurality of electrocardiogram data acquired for a single subject…” Further, amended claim 1 discloses, “generating, based on time information corresponding to each of the distinguished normal ECG data and abnormal ECG data, training data including…” and therefore does not clearly disclose that the training data is generated based on a time interval but rather broadly claims “based on time information…” As such, this argument is not persuasive in determining if the independent claims contain subject matter free of prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARA J MORICE DE VARGAS whose telephone number is (703)756-4608. The examiner can normally be reached M-F 8:30-5:30 pm.
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/SARA JESSICA MORICE DE VARGAS/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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