CTFR 18/849,592 CTFR 97728 DETAILED ACTION The present office action represents a final action on the merits. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority This application claims the priority date of 371 of PCT/JP2022/015450 application of March 29, 2022. 12-151 AIA 26-51 12-51 Status of Claims Claims 1, 9, and 17 are amended, claims 19-20 are new, and claims 1-9 and 17-20 are pending. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claim 20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 20 is missing the claim number for which it depends on. Examiner is interpreting Claim 20 as depending on Claim 4. Appropriate action is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-9 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-8 and 18-20 are drawn to an electrocardiogram evaluation method, which is within the four statutory categories (i.e., process). Claim 9 is drawn to an electrocardiogram evaluation apparatus, which is within the four statutory categories (i.e., machine). Claim 17 is drawn to a non-transitory computer-readable storage medium storing a computer program, which is within the four statutory categories (i.e., machine). Claims 1-8 and 18-20 recite an electrocardiogram evaluation method executed by an electrocardiogram evaluation apparatus connected via a network to an electrocardiogram measurement apparatus and an electronic medical record database managed by a medical institution , the method comprising : acquiring, from the electrocardiogram measurement apparatus , 12-lead electrocardiogram data measured from a person ; causing the electronic medical record database to store the electrocardiogram data in association with identification information of the electrocardiogram measurement apparatus indicating a measurement location or a wearable device and an electronic medical record of the person ; evaluating the electrocardiogram data recorded in the electronic medical record database by using an evaluation model generated through machine learning of normal electrocardiograms and anomalous electrocardiograms, to obtain an evaluation result of a physical condition of the person and a likelihood indicating accuracy of the evaluation result ; in a case where the likelihood is lower than a threshold , determining that additional data to be used for evaluating the electrocardiogram data is required, and automatically acquiring the additional data from the electronic medical record identified based on the identification information, the additional data including at least one of vital data and medical condition data of the person including at least one of heart rate, blood pressure, state of consciousness, current or past disease and a course of events that the electrocardiogram has been taken; and evaluating the electrocardiogram data using the evaluation model in consideration of the additional data to update the evaluation result, and outputting, to a display apparatus , the updated evaluation result together with at least part of the additional data as information for a medical professional, wherein the evaluation model is trained to distinguish between anomalous electrocardiogram patterns and measurement noise by correlating electrocardiogram waveforms with corresponding vital and medical condition data from the electronic medical record database , thereby improving the specificity of electrocardiogram evaluation beyond conventional waveform analysis, and the automatic acquisition of additional data is triggered only when the likelihood falls below the threshold, thereby reducing unnecessary data retrieval and optimizing processing resources of the electrocardiogram evaluation apparatus . Claim 9 recites an electrocardiogram evaluation apparatus comprising : at least one memory storing processing instructions ; and at least one processor configured to execute the processing instructions to : acquire, from the electrocardiogram measurement apparatus , 12-lead electrocardiogram data measured from a person ; determine that additional data to be used for evaluating the electrocardiogram data is required, and automatically acquire the additional data from the electronic medical record identified based on the identification information, the additional data including at least one of vital data and medical condition data of the person including at least one of heart rate, blood pressure, state of consciousness, current or past disease and a course of events that the electrocardiogram has been taken; and evaluate the electrocardiogram data using the evaluation model in consideration of the additional data to update the evaluation result, and outputting, to a display apparatus , the updated evaluation result together with at least part of the additional data as information for a medical professional . Claim 17 recites a non-transitory computer-readable storage medium storing a computer program, the computer program comprising instructions for causing a computer to execute processes to : acquire, from the electrocardiogram measurement apparatus , 12-lead electrocardiogram data measured from a person ; determine that additional data to be used for evaluating the electrocardiogram data is required, and automatically acquire the additional data from the electronic medical record identified based on the identification information, the additional data including at least one of vital data and medical condition data of the person including at least one of heart rate, blood pressure, state of consciousness, current or past disease and a course of events that the electrocardiogram has been taken; and evaluate the electrocardiogram data using the evaluation model in consideration of the additional data to update the evaluation result, and outputting, to a display apparatus , the updated evaluation result together with at least part of the additional data as information for a medical professional . The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and mathematical concepts, but for the recitation of generic computer components (e.g., in this case an electrocardiogram evaluation apparatus, memory.). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity and mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below. Dependent claims 2-8 and 18-20 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 as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. These limitations only serve to further limit the abstract idea (or contain the same additional elements found in the independent claim), and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 9, and 17 . The dependent claims recite additional limitations but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 9, and 17 . The additional elements from claims 1 include: an electronic medical record database (apply it, MPEP 2106.05(f)). a network (apply it, MPEP 2106.05(f)). a medical institution (generally linking, MPEP 2106.05(h)). a wearable device (apply it, MPEP 2106.05(f)). trained (apply it, MPEP 2106.05(f)). The additional elements from claims 1, 9 include: an electrocardiogram evaluation apparatus (apply it, MPEP 2106.05(f)). The additional elements from claims 1, 9, and 17 include: an electrocardiogram measurement apparatus (apply it, MPEP 2106.05(f)). a display apparatus (apply it, MPEP 2106.05(f)). The additional elements from claim 9 include: at least one memory storing processing instructions (apply it, MPEP 2106.05(f)). at least one processor configured to execute the processing instructions to (apply it, MPEP 2106.05(f)). The additional elements from claim 17 include: a non-transitory computer-readable storage medium storing a computer program, the computer program comprising instructions for causing a computer to execute processes to (apply it, MPEP 2106.05(f)). The dependent claims recite the same abstract idea that is recited in claim 1. These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which: amount to mere instructions to apply an exception – for example, the recitation of an electrocardiogram evaluation apparatus, at least one memory, at least one processor, a non-transitory computer-readable storage medium, the computer program, See Specification paragraphs [0020] and [0056] (See MPEP 2106.05(f)). amount to generally linking - for example, the recitation of a medical institution (See MPEP 2106.05(h)). Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which: amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by: The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Specification, paragraphs [0020] and [0056] discloses that the additional elements (i.e., an electrocardiogram evaluation apparatus, at least one memory, at least one processor, a non-transitory computer-readable storage medium, the computer program) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e., receiving and transmitting data) that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare, electrocardiogram evaluation method.); Relevant court decisions: The following example of court decision demonstrating well understood, routine and conventional activities, e.g., see MPEP 2106.05(d)(II): e.g., see Intellectual Ventures v. Symantec – similarly, the current invention acquires data. Dependent claims 2-8 and 18-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than receiving or transmitting/indicate data over a system (e.g., acquire data claims 2- 4 and 7 .). Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves any other technology, and their collective functions merely provide conventional computer implementation. The application, is an attempt to organize human activity or mathematical concepts, using an electrocardiogram evaluation method. The inventive concept is the electrocardiogram evaluation method, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-9 and 17-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 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: 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 9 and 17 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Anastasia (U.S. Pub. No. 2021/0315506 A1) . Regarding claim 9, Anastasia discloses electrocardiogram evaluation apparatus comprising: at least one memory storing processing instructions (Paragraph [0085] discusses processor-executable program codes for directing the processor to carry out functions are stored in the program memory.); and at least one processor configured to execute the processing instructions to (Paragraph [0085] discusses at least one processor (e.g., the analyzer processor) is directed to take the action by way of programmable codes or processor-executable codes or instructions defining or forming part of the application.): acquire, from the electrocardiogram measurement apparatus, 12-lead electrocardiogram data measured from a person (Paragraphs [0063]-[0065] discuss ECG signals from various medical devices and the ECG data source may be configured to provide to the ECG analyzer, ECG data representing one or more sensed ECG traces for a patient, the ECG data source may include 12 leads coupled to the patient and configured to sense ECG data and so the ECG data may represent 12 sensed ECG traces for the patient.); determine that additional data to be used for evaluating the electrocardiogram data is required, and automatically acquire the additional data from the electronic medical record identified based on the identification information, the additional data including at least one of vital data and medical condition data of the person including at least one of heart rate, blood pressure, state of consciousness, current or past disease and a course of events that the electrocardiogram has been taken (Paragraph [0132] discusses direct the analyzer processor to apply a confidence threshold to the principal component scores to identify those ECG segment records which represent ECG segments that are too dissimilar to the overall ECG segment matrix and determine whether additional ECG traces need to be considered, determine whether there are any additional sensed ECG trace records stored, which have not yet been considered as a subject sensed ECG trace and direct the analyzer process to consider one of the one or more sensed ECG traces if the sensed ECG trace record has not yet been considered.); and evaluate the electrocardiogram data using the evaluation model in consideration of the additional data to update the evaluation result, and outputting, to a display apparatus, the updated evaluation result together with at least part of the additional data as information for a medical professional (Paragraphs [0060], [0063], [0072]-[0074], [0090], [0099], [0108], and [0132] discuss facilitate ECG analysis or classification and the analyzer process may then be executed with respect to the newly considered sensed ECG trace record, for example, diagnosis of disorders from patterns in conventional ECG trace data recognized by a deep-learning model trained with ECG data; at least one neural network classifier to be trained using the representative training ECG traces and the diagnoses, the at least one neural network classifier configured to output and display one or more diagnostically relevant scores related to at least one diagnosis.). Regarding claim 17, Anastasia discloses non-transitory computer-readable storage medium storing a computer program, the computer program comprising instructions for causing a computer to execute processes to (Paragraph [0029] discusses there is provided a non-transitory computer readable medium having stored thereon codes which when executed by at least one processor cause the at least one processor to perform.): acquire, from the electrocardiogram measurement apparatus, 12-lead electrocardiogram data measured from a person (Paragraphs [0063]-[0065] discuss ECG signals from various medical devices and the ECG data source may be configured to provide to the ECG analyzer, ECG data representing one or more sensed ECG traces for a patient, the ECG data source may include 12 leads coupled to the patient and configured to sense ECG data and so the ECG data may represent 12 sensed ECG traces for the patient.); determine that additional data to be used for evaluating the electrocardiogram data is required, and automatically acquire the additional data from the electronic medical record identified based on the identification information, the additional data including at least one of vital data and medical condition data of the person including at least one of heart rate, blood pressure, state of consciousness, current or past disease and a course of events that the electrocardiogram has been taken (Paragraph [0132] discusses direct the analyzer processor to apply a confidence threshold to the principal component scores to identify those ECG segment records which represent ECG segments that are too dissimilar to the overall ECG segment matrix and determine whether additional ECG traces need to be considered, determine whether there are any additional sensed ECG trace records stored, which have not yet been considered as a subject sensed ECG trace and direct the analyzer process to consider one of the one or more sensed ECG traces if the sensed ECG trace record has not yet been considered.); and evaluate the electrocardiogram data using the evaluation model in consideration of the additional data to update the evaluation result, and outputting, to a display apparatus, the updated evaluation result together with at least part of the additional data as information for a medical professional (Paragraphs [0060], [0063], [0072]-[0074], [0090], [0099], [0108], and [0132] discuss facilitate ECG analysis or classification and the analyzer process may then be executed with respect to the newly considered sensed ECG trace record, for example, diagnosis of disorders from patterns in conventional ECG trace data recognized by a deep-learning model trained with ECG data; at least one neural network classifier to be trained using the representative training ECG traces and the diagnoses, the at least one neural network classifier configured to output and display one or more diagnostically relevant scores related to at least one diagnosis.) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-8 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Anastasia in view of Fornwalt (U.S. Pub. No. 2021/0076960 A1), Kalidas (U.S. Pub. No. 2022/0015711 A1), and Hughes (U.S. Pub. No. 2018/0242876 A1) . Regarding claim 1 , Anastasia discloses an electrocardiogram evaluation method comprising (Paragraph [0016] discusses a method of facilitating electrocardiogram analysis.): acquiring, from the electrocardiogram measurement apparatus, 12-lead electrocardiogram data measured from a person (Paragraphs [0063]-[0065] discuss ECG signals from various medical devices and the ECG data source may be configured to provide to the ECG analyzer, ECG data representing one or more sensed ECG traces for a patient, the ECG data source may include 12 leads coupled to the patient and configured to sense ECG data and so the ECG data may represent 12 sensed ECG traces for the patient.); causing the device to store the electrocardiogram data in association with identification information of the electrocardiogram measurement apparatus indicating a measurement location or a wearable device and an electronic medical record of the person (Paragraphs [0063], [0065], [0086] discuss ECG signals from various medical devices (including implantable and wearable devices as well as stationary and portable clinical and home monitors) may suffice for the system to facilitate diagnosis and/or recognition of arrhythmia signals, the ECG analyzer may store the ECG data in memory.); evaluating the electrocardiogram data recorded in the electronic medical record database by using an evaluation model generated through machine learning of normal electrocardiograms and anomalous electrocardiograms, to obtain an evaluation result of a physical condition of the person and a likelihood indicating accuracy of the evaluation result (Paragraphs [0086], [0110], [0150]-[0156], [0184] discuss ECG data stored in a database and the ECG neural network trainer may be configured to use ECG data, including data taken from the ECG training data source to train one or more neural network classifiers and direct the trainer processor to produce associated quantitative performance data that characterizes the accuracy of the classifications, the ECG analysis causing at least one neural network classifier to be applied to the one or more determined representative ECG traces to determine one or more diagnostically relevant scores related to at least one diagnosis of the patient and facilitate faster and accurate analysis by the ECG analyzer.); in a case where the likelihood is lower than a threshold (Paragraphs [0115], [0142], , discuss direct the analyzer processor to apply a confidence threshold to the principal component scores to identify those ECG segment records that represent ECG segments that are too dissimilar to the overall ECG segment matrix; further, a diagnosis score greater or less than a threshold may be associated with various diagnosis.), determining that additional data to be used for evaluating the electrocardiogram data is required, and automatically acquiring the additional data from the electronic medical record identified based on the identification information, the additional data including at least one of vital data and medical condition data of the person including at least one of heart rate, blood pressure, state of consciousness, current or past disease and a course of events that the electrocardiogram has been taken (Paragraph [0132] discusses direct the analyzer processor to apply a confidence threshold to the principal component scores to identify those ECG segment records which represent ECG segments that are too dissimilar to the overall ECG segment matrix and determine whether additional ECG traces need to be considered, determine whether there are any additional sensed ECG trace records stored, which have not yet been considered as a subject sensed ECG trace and direct the analyzer process to consider one of the one or more sensed ECG traces if the sensed ECG trace record has not yet been considered.); and evaluating the electrocardiogram data using the evaluation model in consideration of the additional data to update the evaluation result and outputting, to a display apparatus, the updated evaluation result together with at least part of the additional data as information for a medical professional (Paragraphs [0060], [0063], [0072]-[0074], [0090], [0099], [0108], and [0132] discuss facilitate ECG analysis or classification and the analyzer process may then be executed with respect to the newly considered sensed ECG trace record, for example, diagnosis of disorders from patterns in conventional ECG trace data recognized by a deep-learning model trained with ECG data; at least one neural network classifier to be trained using the representative training ECG traces and the diagnoses, the at least one neural network classifier configured to output and display one or more diagnostically relevant scores related to at least one diagnosis.). Anastasia does not explicitly disclose: the method executed by an electrocardiogram evaluation apparatus connected via a network to an electrocardiogram measurement apparatus and an electronic medical record database managed by a medical institution, the method comprising; causing the electronic medical record database to store the electrocardiogram data; wherein the evaluation model is trained to distinguish between anomalous electrocardiogram patterns and measurement noise by correlating electrocardiogram waveforms with corresponding vital and medical condition data from the electronic medical record database, thereby improving the specificity of electrocardiogram evaluation beyond conventional waveform analysis; and the automatic acquisition of additional data is triggered only when the likelihood falls below the threshold, thereby reducing unnecessary data retrieval and optimizing processing resources of the electrocardiogram evaluation apparatus. Fornwalt teaches: the method executed by an electrocardiogram evaluation apparatus connected via a network to an electrocardiogram measurement apparatus and an electronic medical record database managed by a medical institution, the method (Paragraphs [0024], [0091], [0094]-[0096], [0123] discuss a method including receiving patient electrocardiogram data associated with a patient and an electrocardiogram configuration including a plurality of leads and a time interval from an electrocardiogram device and an ECG database; ECG extracted from institutional clinical database and a computing device can execute at least a portion of an ECG analysis application with a communication network.); causing the electronic medical record database to store the electrocardiogram data (Paragraphs [0165], [0175], [0178] discuss electronic health record system include patient data, ECG machines create a “portable document format” (i.e. PDF) from the voltage-time traces which may then be stored in the medical record.). Therefore, it would have been obvious to one of ordinary skill in the art to modify Anastasia to include, executed by an electrocardiogram evaluation apparatus connected via a network to an electrocardiogram measurement apparatus and an electronic medical record database managed by a medical institution, the method, as taught by Fornwalt , in order to ascertain likelihood of future AF from analyzing an ECG trace that does not currently include features consistent with AF. ( Fornwalt Paragraph [0011].). Kalidas teaches: wherein the evaluation model is trained to distinguish between anomalous electrocardiogram patterns and measurement noise by correlating electrocardiogram waveforms with corresponding vital and medical condition data from the electronic medical record database, thereby improving the specificity of electrocardiogram evaluation beyond conventional waveform analysis (Paragraphs [0066], [0101], [0138], and [0158] discuss training a model for noise detection and network learns to distinguish between noise artifact sand true complexes, distinguishing based on morphology on ECG signals, for example, use of Markov models offers the advantage that sequential pattern changes in heart rates can be effectively captured, thus aiding in better distinction between AF and other arrhythmias with prominent heart rate variations.). Therefore, it would have been obvious to one of ordinary skill in the art to modify Anastasia to include, wherein the evaluation model is trained to distinguish between anomalous electrocardiogram patterns and measurement noise by correlating electrocardiogram waveforms with corresponding vital and medical condition data from the electronic medical record database, thereby improving the specificity of electrocardiogram evaluation beyond conventional waveform analysis, as taught by Kalidas , in order to provide timely and accurate detection of arrhythmias for patient care and well-being in the long term. ( Kalidas Paragraph [0003].). Hughes teaches: the automatic acquisition of additional data is triggered only when the likelihood falls below the threshold, thereby reducing unnecessary data retrieval and optimizing processing resources of the electrocardiogram evaluation apparatus (Paragraph [0173] discusses ECG data transmission is enabled without need for patient intervention (for example not NFC or wired transmission), data transfer may initiate automatically and opportunistically without requiring further patient interaction beyond symptom trigger.) (Examiner is interpreting “opportunistically” to include “when the likelihood falls below the threshold, thereby reducing unnecessary data retrieval and optimizing processing resources”.) Therefore, it would have been obvious to one of ordinary skill in the art to modify Anastasia to include, the automatic acquisition of additional data is triggered only when the likelihood falls below the threshold, thereby reducing unnecessary data retrieval and optimizing processing resources of the electrocardiogram evaluation apparatus, as taught by Hughes , in order to enhance the patient experience and to make diagnosis of cardiac arrhythmias more accurate and timely. ( Hughes Paragraph [0005].). Regarding claim 2 , Anastasia discloses comprising acquiring the additional data from an electronic medical record of the person (Paragraph [0132] discusses direct the analyzer processor to consider a patient sensed ECG trace record from the location of the storage memory if the additional sensed ECG trace record has not yet been considered.). Regarding claim 3 , Anastasia discloses comprising acquiring past electrocardiogram data of the person from the electronic medical record of the person, as the additional data (Paragraph [0132] discusses determine whether there are any additional sensed ECG trace records stored, which have not yet been considered as a subject sensed ECG trace and direct the analyzer process to consider one of the one or more sensed ECG traces if the sensed ECG trace record has not yet been considered.). Regarding claim 4 , Anastasia discloses comprising acquiring the additional data based on data representing a sender of the electrocardiogram data (Paragraphs [0132]-[0134] and [0137]-[0139] discuss determine whether there are additional sensed ECG trace records and consider the additional sensed ECG trace record from the location of the storage memory and the patient data may have been previously provided by a medical professional.). Regarding claim 5 , Anastasia discloses comprising: outputting a result of the evaluation of the electrocardiogram data (Paragraph [0072] discusses the ECG analyzer using the ECG traces outputs a diagnosis score.); and accepting a request for the additional data input in accordance with the output of the result of the evaluation, and acquiring the additional data (Paragraphs [0090], [0099], [0108], and [0132]-[0136] discuss direct the analyzer processor to consider an additional sensed ECG trace record from the location of the storage memory if the sensed ECG trace record has not yet been considered and the analyzer process may then be executed with respect to the newly considered sensed ECG trace record, and at least one neural network classifier to be applied to determine one or more diagnostically relevant scores related to at least one diagnosis of the patient.). Regarding claim 6, Anastasia discloses comprising: outputting a result of the evaluation of the electrocardiogram data and also information based on the additional data (Paragraphs [0074]-[0075], [0090], [0099], [0108], and [0132]-[0136] discuss direct the analyzer processor to consider an additional sensed ECG trace record from the location of the storage memory if the sensed ECG trace record has not yet been considered and the analyzer process may then be executed with respect to the newly considered sensed ECG trace record, and at least one neural network classifier to be applied to determine one or more diagnostically relevant scores related to at least one diagnosis of the patient and the ECG analyzer may display diagnostically relevant score indicating likelihood a patient has a diagnosis.); and accepting a request for the additional data input in accordance with the output of the result of the evaluation, and acquiring the additional data (Paragraphs [0075], [0090], [0099], [0108], and [0132]-[0136] discuss direct the analyzer processor to consider an additional sensed ECG trace record from the location of the storage memory if the sensed ECG trace record has not yet been considered and the analyzer process may then be executed with respect to the newly considered sensed ECG trace record, and at least one neural network classifier to be applied to determine one or more diagnostically relevant scores related to at least one diagnosis of the patient and action may be taken based on the displayed representation of the diagnostically relevant score, which may help to prevent future life-threatening events, for example, a BrS diagnosis score indicating a high likelihood of the patient having BrS, may cause a physician to perform or modify further diagnostic and/or therapeutic procedures.). Regarding claim 7, Anastasia discloses comprising: acquiring 12-lead electrocardiogram data (Paragraph [0064] discusses the ECG data source may include 12 leads coupled to the patient and configured to sense ECG data and so the ECG data may represent 12 sensed ECG traces for the patient.); evaluating the 12-lead electrocardiogram data (Paragraphs [0064]-[0065] and [0071] discuss the ECG analyzer may receive 12 leads ECG data and these traces may act as excellent inputs for a neural network classifier or function in determining a diagnostically relevant score for the patient.); acquiring monitored electrocardiogram data in accordance with a result of the evaluation of the 12-lead electrocardiogram data (Paragraphs [0064] and [0066] discuss the ECG data source may include 12 leads coupled to the patient and configured to sense ECG data and so the ECG data may represent 12 sensed ECG traces for the patient, and the ECG analyzer may identify a plurality of corresponding sensed ECG trace segments, for example, the sensed ECG trace segments may be chosen such that each ECG trace segment represents sensed ECG data for a single heartbeat.); and evaluating using the 12-lead electrocardiogram data and the monitored electrocardiogram data (Paragraphs [0064] and [0072] discuss the ECG data source provides to the ECG analyzer, ECG data, for example, the ECG data source may include 12 leads coupled to the patient to sense ECG data and represent 12 sensed ECG traces for the patient and the ECG analyzer using the ECG traces analyzes the data and outputs a diagnosis score.). Regarding claim 8, Anastasia discloses comprising: evaluating the electrocardiogram data and also calculating a likelihood of a result of the evaluation (Paragraphs [0064]-[0065] and [0071] discuss the ECG analyzer may receive 12 leads ECG data and these traces may act as excellent inputs for a neural network classifier or function in determining a diagnostically relevant score for the patient.); and acquiring the additional data in accordance with a value of the likelihood (Paragraphs [0075] discuss action may be taken based on the displayed representation of the diagnostically relevant score, which may help to prevent future life-threatening events, for example, a BrS diagnosis score indicating a high likelihood of the patient having BrS, may cause a physician to perform or modify further diagnostic and/or therapeutic procedures.). Regarding claim 18, Anastasia discloses comprising evaluating the electrocardiogram data based on the additional data using an evaluation model generated through machine learning (Paragraphs [0063], [0132], and [0153] discuss the system may be configured to aid in the diagnosis of BrS from patterns in conventional ECG trace data recognized by a deep-learning model trained with ECG data and in a training phase, the ECG neural network trainer may collect ECG trace information from one or more patient ECG data sources, the data may direct the analyzer processor to consider a patient sensed ECG trace record from the location of the storage memory if the additional sensed ECG trace record has not yet been considered .). Regarding claim 19, Anastasia discloses comprising identifying an installation location of the electrocardiogram measurement apparatus based on data representing the sender of the electrocardiogram data (Paragraphs [0063], [0084], [0188] discuss ECG signals from various medical devices (including implantable and wearable devices as well as stationary and portable clinical and home monitors), consumer-wearable devices, such as smartwatches, smartphones, and/or other multipurpose electronic products, the device described as receiving or sending information, it may be understood that the device receives signals representing the information via an interface of the device or produces signals representing the information and transmits the signals to the other device via an interface of the device.); and acquiring the additional data from the electronic medical record identified from the installation location (Paragraphs [0189] and [0193] discuss a portable embodiment of the ECG data source may collect an ECG trace record during a longer time period of measurement, such as overnight or over a period of one or more days, for example, in order to capture rare or isolated heartbeat intervals of diagnostic utility, all of the data associated with a particular patient.). Regarding claim 20, Anastasia discloses comprising identifying the installation location from an identification information of the electrocardiogram measurement apparatus associated with the electrocardiogram data (Paragraphs [0063], [0084], [0188] discuss ECG signals from various medical devices (including implantable and wearable devices as well as stationary and portable clinical and home monitors), consumer-wearable devices, such as smartwatches, smartphones, and/or other multipurpose electronic products, the device described as receiving or sending information, it may be understood that the device receives signals representing the information via an interface of the device or produces signals representing the information and transmits the signals to the other device via an interface of the device.); identifying a record location of the electronic medical record from the installation location (Paragraphs [0065], [0086] discuss the ECG analyzer may receive the ECG data representing the one or more sensed ECG traces for the patient from the ECG data source, the ECG analyzer may store the ECG data in memory, the storage memory includes a plurality of storage locations including location for storing sensed ECG data, location for storing patient data, and location for storing diagnosis score data.); and acquiring the additional data from the electronic medical record identified from the record location (Paragraphs [0189] and [0193] discuss a portable embodiment of the ECG data source may collect an ECG trace record during a longer time period of measurement, such as overnight or over a period of one or more days, for example, in order to capture rare or isolated heartbeat intervals of diagnostic utility, all of the data associated with a particular patient.). Response to Arguments Applicant’s arguments filed 2/03/2026 have been fully considered. Rejections under 35 U.S.C. 101: With respect to claim 1 and the Prong 1 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 recites an abstract idea, a method of organizing human activity and/or mathematical concepts. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People. Applicant states the amended claims are, “directed to a concrete technological process for improving the accuracy of electrocardiogram diagnosis - a recognized problem in medical data processing where ECG waveforms can be indistinguishable between pathological conditions and measurement noise. The method is inextricably tied to a particular technological environment: a networked medical system comprising specific hardware (an ECG measurement apparatus, an evaluation apparatus, and a display) and a structured data source (an electronic medical record database). The steps involve acquiring specific 12-lead ECG data, storing the data with contextual metadata, applying a trained machine learning model to generate a likelihood metric, implementing a conditional, threshold-based trigger to retrieve multi-modal patient data, and updating and outputting a refined diagnostic result for clinical use. This ordered combination transforms specific types of medical data into a clinically actionable output through a structured, automated workflow, which is a concrete application of technology rather than an abstract idea.” (Remarks, page 8). Examiner respectfully disagrees. Here, Applicant’s claims are directed to evaluation of electrocardiogram data, which is not a technical problem rooted in the technology. Further, transforming specific types of medical data into a clinically actionable output is not a technical problem. The improvement here is to the abstract idea. Practical application is a way to overcome the Prong 2 35 U.S.C 101 rejection, however, here, the claims do not recite additional elements that integrate the exception into a practical application. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicate that a practical application may be present where the claimed invention provides a technical solution to a technical problem. Applicant states, “provide a technical improvement in diagnostic accuracy by employing a machine learning model specifically trained to correlate ECG waveforms with broader patient context (vital signs, medical history) to distinguish pathological patterns from noise.” (Remarks, page 8). The Application, acquiring specific 12-lead ECG data, storing the data with contextual metadata, applying a trained machine learning model to generate a likelihood metric, implementing a conditional, threshold-based trigger to retrieve multi-modal patient data, and updating and outputting a refined diagnostic result for clinical use - is part of the abstract idea and the abstract idea cannot be used to integrate itself into a practical application. Here, the additional elements, including “an electrocardiogram evaluation apparatus”, “at least one processor”, “at least one memory”, do not result in a practical application or technical improvement, as they are recited at an apply it level, as stated above. The application addressing a data reliability problem inherent in ECG signal processing and improves upon conventional, isolated waveform analysis is an improvement to the abstract idea. Applicant also state, “the amended claims improve computer functionality and optimizes processing resources.” (Remarks, page 8). Examiner respectfully disagrees. There is no nexus between Applicant’s arguments that the Application, reduces unnecessary network queries, decreases database load, and conserves computational resources and the claims. Here, the Application is an improvement to the abstract idea and does not improve any computer element. All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea. Here, individually and in combination, the additional elements do not provide significantly more than the abstract idea. The claims recite features that are "well-understood, routine, conventional activities”. There is no technological improvement to any additional element. For the reasons stated above, claims 9 and 17 similarly fail to overcome the 35 U.S.C. 101 rejection. Rejections under 35 U.S.C. 102: The amendment overcomes the 102 rejection. Applicant’s arguments with regard to 102 are amended for Claims 9 and 17 and are moot for Claim 1-8 and 18-20. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:15PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached on (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DAWN T. HAYNES/ Art Unit 3686 /RACHELLE L REICHERT/Primary Examiner, Art Unit 3686 Application/Control Number: 18/849,592 Page 2 Art Unit: 3686 Application/Control Number: 18/849,592 Page 3 Art Unit: 3686 Application/Control Number: 18/849,592 Page 4 Art Unit: 3686 Application/Control Number: 18/849,592 Page 5 Art Unit: 3686 Application/Control Number: 18/849,592 Page 6 Art Unit: 3686 Application/Control Number: 18/849,592 Page 7 Art Unit: 3686 Application/Control Number: 18/849,592 Page 8 Art Unit: 3686 Application/Control Number: 18/849,592 Page 9 Art Unit: 3686 Application/Control Number: 18/849,592 Page 10 Art Unit: 3686 Application/Control Number: 18/849,592 Page 11 Art Unit: 3686 Application/Control Number: 18/849,592 Page 12 Art Unit: 3686 Application/Control Number: 18/849,592 Page 13 Art Unit: 3686 Application/Control Number: 18/849,592 Page 14 Art Unit: 3686 Application/Control Number: 18/849,592 Page 15 Art Unit: 3686 Application/Control Number: 18/849,592 Page 16 Art Unit: 3686 Application/Control Number: 18/849,592 Page 17 Art Unit: 3686 Application/Control Number: 18/849,592 Page 18 Art Unit: 3686 Application/Control Number: 18/849,592 Page 19 Art Unit: 3686 Application/Control Number: 18/849,592 Page 20 Art Unit: 3686 Application/Control Number: 18/849,592 Page 21 Art Unit: 3686 Application/Control Number: 18/849,592 Page 22 Art Unit: 3686 Application/Control Number: 18/849,592 Page 23 Art Unit: 3686 Application/Control Number: 18/849,592 Page 24 Art Unit: 3686 Application/Control Number: 18/849,592 Page 25 Art Unit: 3686 Application/Control Number: 18/849,592 Page 26 Art Unit: 3686
Prosecution Insights