METHODS AND SYSTEMS TO CONFIRM DEVICE CLASSIFIED ARRHYTHMIAS UTILIZING MACHINE LEARNING MODELS

§103 §112 §DP

DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/11/2026 has been entered. Status of Claims Claims 1-3, 5-7, 26-31, 33, 36, and 41-43 are rejected. Claims 4, 8-25, 32, 34-35, and 38-40 are canceled. Claim 37 is objected to. Response to Arguments Claim Objections The previous claim objection has been withdrawn in view of the amendment. Rejection under 35 U.S.C. § 112 Some of the previous 112(a) rejections have been withdrawn in view of the amendment. Applicant asserts that there is support for the limitation “wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, based on the recommendation, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD” in ¶116-118. However, the Examiner disagrees. A recommendation for an adjustment is not the same as performing an adjustment. This limitation is neither recited in the originally filed claims, shown in the originally filed drawings, nor disclosed in the originally filed specification. Therefore, it is new matter and fails to comply with the written description requirement. Regarding claim 5, Applicant asserts that a person of skill would readily understand from the specification that “sensing parameters” are utilized to analyze CA signals. However, the specification does not disclose analyzing the CA signals utilizing the sensing parameter. These limitations are neither recited in the originally filed claims, shown in the originally filed drawings, nor disclosed in the originally filed specification. Therefore, it is new matter and fails to comply with the written description requirement. Regarding claim 36, Applicant cites to various ¶ for support. However, these are not the same as the limitation as claimed. The specification does not disclose the IMD memory storing the adjustment to reprogram the sensing parameter to reduce the number of false positive candidate arrhythmias episodes declared by the IMD. This limitation is neither recited in the originally filed claims, shown in the originally filed drawings, nor disclosed in the originally filed specification. Therefore, it is new matter and fails to comply with the written description requirement. The previous 112(b) rejection has been withdrawn in view of the amendment. Rejection under 35 U.S.C. § 101 Applicant’s arguments, see Remarks, filed 6/1/26, with respect to claims 1-3, 5-7, 26-31, 33, 36-37, and 41-43 have been fully considered and are persuasive. The 101 rejection of claims 1-3, 5-7, 26-31, 33, 36-37, and 41-43 has been withdrawn. Rejection under 35 U.S.C. § 103 Applicant’s arguments with respect to claims 1-3, 5-7, 26-31, 33, 36, and 41-43 have been considered but are moot in view of the new ground of rejection. Claim Objections Claim 5 is objected to because of the following informalities: the extra space before “.” should be removed. Appropriate correction is required. Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 1-3, 5-7, 26-31, 33, 36-37, and 41-43 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites “wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, based on the recommendation, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD.” While the specification discloses: ¶106-additionally or alternatively, the information may include observations or recommendations regarding potential adjustments that may be made to the parameters of the IMD to reduce the number of false positives, additionally or alternatively, the ML model may output recommendations for how to reprogram parameters of the IMD, such as how to reprogram sensitivity threshold utilized in connection with identifying P waves, R waves and T waves. This is not the same as the limitations as claimed. The specification does not disclose wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, based on the recommendation, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD. A recommendation for an adjustment is not the same as performing an adjustment. This limitation is neither recited in the originally filed claims, shown in the originally filed drawings, nor disclosed in the originally filed specification. Therefore, it is new matter and fails to comply with the written description requirement. Claim 5 recites “analyze the CA signals, utilizing the sensing parameter.” While ¶11 of the specification discloses: The IMD may include a combination of subcutaneous electrodes configured to collect the CA signals. The IMD memory may be configured to store program instructions. One or more IMD processors may be configured to execute the program instructions to: analyze the CA signals and based on the analysis declare candidate arrhythmias episodes, generate the DCA data sets including the corresponding CA signals and the corresponding DD markers and a transceiver configured to wirelessly transmit the DCA data sets to an external device. this is not the same as the limitation as claimed. The specification does not disclose analyzing the CA signals utilizing the sensing parameter. These limitations are neither recited in the originally filed claims, shown in the originally filed drawings, nor disclosed in the originally filed specification. Therefore, it is new matter and fails to comply with the written description requirement. Claim 36 recites “wherein the IMD comprises IMD memory configured to store the adjustments to reprogram the sensing parameter to reduce the number of false positive candidate arrhythmias episodes declared by the IMD.” While ¶106 of the specification discloses: the ML model may output recommendations for how to reprogram parameters of the IMD, such as how to reprogram sensitivity threshold utilized in connection with identifying P waves, R waves and T waves this is not the same as the limitation as claimed. The specification does not disclose the IMD memory storing the adjustment to reprogram the sensing parameter to reduce the number of false positive candidate arrhythmias episodes declared by the IMD. This limitation is neither recited in the originally filed claims, shown in the originally filed drawings, nor disclosed in the originally filed specification. Therefore, it is new matter and fails to comply with the written description requirement. Claim 42 recites “wherein the external device is configured to transmit, to the IMD, an adjustment of the sensing parameter for the IMD based on the recommendation.” While the specification discloses: ¶59-the external device 154 is in place to receive or transmit data to the microcontroller 121 through the telemetry circuits 164; ¶106-additionally or alternatively, the information may include observations or recommendations regarding potential adjustments that may be made to the parameters of the IMD to reduce the number of false positives This is not the same as the limitations as claimed. The specification does not disclose wherein the external device is configured to transmit, to the IMD, an adjustment of the sensing parameter for the IMD based on the recommendation. This limitation is neither recited in the originally filed claims, shown in the originally filed drawings, nor disclosed in the originally filed specification. Therefore, it is new matter and fails to comply with the written description requirement. 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. Claim 43 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 43 recites the limitation "the sensing parameter." There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination, the Examiner is interpreting the limitation as “a sensing parameter.” Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1, 6-7, 36, and 41-43 are rejected under 35 U.S.C. 103 as being unpatentable over Dani (US 20200357519 filed on 4/17/20) in view of Dawoud (US 20190336026 filed on 5/7/18). Regarding claim 1, Dani teaches a system for declaring arrhythmias in cardiac activity, comprising: an implantable medical device (IMD) configured to collect cardiac activity (CA) signals (¶9-an IMD, senses cardiac electrogram data of a patient) and to declare candidate arrhythmia episodes (¶7-detecting or classifying an episode cardiac arrhythmia); and an external device (¶29-external device 12) comprising: memory (¶34-memory) to store specific executable instructions (¶44-instructions from external device 12) and a machine learning (ML) model (¶37-a machine learning model); one or more processors (¶31-a processor) configured to execute the specific executable instructions to: obtain device classified arrhythmia (DCA) data sets generated by the IMD for corresponding candidate arrhythmia episodes declared by the IMD (¶37-a medical device, such as IMD 10…uses feature delineation to make a preliminary detection of cardiac arrhythmia in patient 4), the DCA data sets including the CA signals for one or more beats sensed by the IMD (¶9-a medical device, such as an IMD, senses cardiac electrogram data of a patient. The medical device performs feature-based delineation of the cardiac electrogram data to obtain cardiac features indicative of an episode of arrhythmia in the patient) and one or more device documented (DD) markers that are generated by the IMD (¶65-IMD 10 further applies feature delineation to the cardiac electrogram data to detect one or more episodes of arrhythmia. For example, IMD 10 may apply QRS detection delineation and noise flagging to the cardiac electrogram data to provide arrhythmia characteristics and/or cardiac features for detected episodes of arrhythmia); and apply the ML model to the DCA data sets to identify a valid sub-set of the DCA data sets that correctly characterize the corresponding CA signals (¶37-verify that feature delineation of the cardiac electrogram data has correctly detected an episode of cardiac arrhythmia. In some examples, the medical device applies a machine learning model to cardiac electrogram data of patient 2 to verify that feature delineation of the cardiac electrogram data has correctly classified an episode of cardiac arrhythmia as a particular type of arrhythmia) and to identify an invalid sub-set of the DCA data sets that incorrectly characterize the corresponding CA signals (¶90-feature delineation incorrectly determines that an episode of cardiac arrhythmia has occurred in patient 4); wherein the ML model outputs (¶54-machine learning system 250 may output); and a display configured to present the output of the ML model as information concerning at least one of the valid sub-set or invalid sub-set of the DCA data sets (¶8-use of a machine learning model, such as a neural network or deep-learning model, that is trained on training datasets to detect cardiac arrhythmia from cardiac electrogram data; ¶104-outputting, by the medical device and for display, the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia). However, Dani does not explicitly teach a recommendation for how to reprogram a sensing parameter utilized in connection with identifying at least one of i) P waves, ii) R waves, or T waves to avoid over sensing or under sensing the at least one of i) P waves, ii) R waves, or T waves; wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, based on the recommendation, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD. Dawoud relates generally to implantable medical devices, and more particularly to detection and discrimination of arrhythmic patterns of interest (¶6). Dawoud further teaches the invention using the following steps: a recommendation for how to reprogram a sensing parameter utilized in connection with identifying at least one of i) P waves, ii) R waves, or T waves to avoid over sensing or under sensing the at least one of i) P waves, ii) R waves, or T waves (¶133-FIG. 4 illustrates a flow chart for classifying AF detection and developing recommendations for sensitivity profile parameter settings; ¶21-identifying a T-wave characteristic of interest (COI) and an R-wave COI from the CA signals and adjusting profile parameters of a sensitivity profile based on the T-wave COI and R-wave COI); wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, based on the recommendation, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD (¶21-identifying a T-wave characteristic of interest (COI) and an R-wave COI from the CA signals and adjusting profile parameters of a sensitivity profile based on the T-wave COI and R-wave COI; ¶192-the process of FIG. 9A exhibited an improvement of positive predictive value to >99% and >88%, improvement of relative sensitivity >99% and >96% and reduction of false detection rate of >99% and >95% in bradycardia and asystole, respectively, relative to conventional ORI processes; ¶141-the sensitivity profile parameter adjustments may be automatically pushed and uploaded to the ICM at the conclusion of the operations of FIG. 4, such as when the adjustment is within a predetermined limit). Therefore, 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 invention of Dani to include a recommendation for how to reprogram a sensing parameter utilized in connection with identifying at least one of i) P waves, ii) R waves, or T waves to avoid over sensing or under sensing the at least one of i) P waves, ii) R waves, or T waves; wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, based on the recommendation, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD of Dawoud in order to improve physiologic signal sensing in connection with arrhythmia episodes (Dawoud, ¶160) which improves positive predictions and reduces false detections (Dawoud, ¶192). Regarding claim 6, the combination of Dani and Dawoud teaches the system of claim 1, wherein the external device represents a local external device that includes the memory and the one or more processors and a transceiver (Dani, ¶47-external device 12 , or by using another local or networked computing device configured to communicate with processing circuitry 50 via communication circuitry 54; ¶29-wireless telemetry; ¶34-memory), the transceiver configured to wirelessly receive the DCA data sets from the IMD (Dani, ¶29-external device 12 may be a computing device configured for use in settings such as a home, clinic, or hospital, and may further be configured to communicate with IMD 10 via wireless telemetry; ¶34). Regarding claim 7, the combination of Dani and Dawoud teaches the system of claim 1, wherein the external device represents a server that includes the memory and the one or more processors (Dani, ¶36-transmit the signals from the electrodes or other sensors to another device (e.g., external device 12 ) or server; ¶34-memory; ¶31-processor), the memory configured to store the collection of the DCA data sets (Dani, ¶34), the one or more processors configured to apply the ML model to the collection of the DCA data sets (Dani, ¶12-applying, by the medical device, the machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to verify, based on the machine learning model, that the episode of arrhythmia has occurred in the patient). Regarding claim 36, the combination of Dani and Dawoud teaches the system of claim 1, wherein the IMD comprises IMD memory configured to store the adjustments to reprogram the sensing parameter to reduce the number of false positive candidate arrhythmias episodes declared by the IMD (Dani, ¶34-external device 12 may be used to interrogate IMD 10 to retrieve data, including device operational data as well as physiological data accumulated in IMD memory; ¶30-program IMD 10 , e.g., select or adjust values for operational parameters of IMD 10). Regarding claim 41, the combination of Dani and Dawoud teaches the system of claim 1, wherein the recommendation for how to reprogram the sensing parameter includes an indication of a sensitivity level to be utilized by the IMD to identify R waves in the CA signals (Dawoud, ¶34-developing recommendations for sensitivity profile parameter settings; ¶87-the confirmatory feature detection process 137 may implement one or more of the R-wave detection processes; ¶141-the sensitivity profile parameter adjustments, in the confirmation log, may be presented on a display; ¶160-CA signal, detection algorithm). Therefore, 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 invention of Dani to include wherein the recommendation for how to reprogram the sensing parameter includes an indication of a sensitivity level to be utilized by the IMD to identify R waves in the CA signals of Dawoud in order to detect an arrhythmia within the beat segment of interest based on a presence or absence of the one or more R-waves (Dawoud, ¶14). Regarding claim 42, the combination of Dani and Dawoud teaches the system of claim 1, wherein the external device is configured to transmit, to the IMD, an adjustment of the sensing parameter for the IMD based on the recommendation (Dawoud, ¶92-the local external device and/or remote server may then return, to the ICM 100, adjustments to the sensitivity profile parameters; ¶133-FIG. 4 illustrates a flow chart for classifying AF detection and developing recommendations for sensitivity profile parameter settings; ¶21-identifying a T-wave characteristic of interest (COI) and an R-wave COI from the CA signals and adjusting profile parameters of a sensitivity profile based on the T-wave COI and R-wave COI). Therefore, 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 invention of Dani to include wherein the external device is configured to transmit, to the IMD, an adjustment of the sensing parameter for the IMD based on the recommendation of Dawoud in order to improve physiologic signal sensing in connection with arrhythmia episodes (Dawoud, ¶160) which improves positive predictions and reduces false detections (Dawoud, ¶192). Regarding claim 43, Dani teaches a system for declaring arrhythmias in cardiac activity, comprising: an implantable medical device (IMD) configured to collect cardiac activity (CA) signals (¶9-an IMD, senses cardiac electrogram data of a patient) and to declare candidate arrhythmia episodes (¶7-detecting or classifying an episode cardiac arrhythmia); and an external device (¶29-external device 12) comprising: memory (¶34-memory) to store specific executable instructions (¶44-instructions from external device 12) and a machine learning (ML) model (¶37-a machine learning model); one or more processors (¶31-a processor) configured to execute the specific executable instructions to: obtain device classified arrhythmia (DCA) data sets generated by the IMD for corresponding candidate arrhythmia episodes declared by the IMD (¶37-a medical device, such as IMD 10…uses feature delineation to make a preliminary detection of cardiac arrhythmia in patient 4), the DCA data sets including the CA signals for one or more beats sensed by the IMD (¶9-a medical device, such as an IMD, senses cardiac electrogram data of a patient. The medical device performs feature-based delineation of the cardiac electrogram data to obtain cardiac features indicative of an episode of arrhythmia in the patient) and one or more device documented (DD) markers that are generated by the IMD (¶65-IMD 10 further applies feature delineation to the cardiac electrogram data to detect one or more episodes of arrhythmia. For example, IMD 10 may apply QRS detection delineation and noise flagging to the cardiac electrogram data to provide arrhythmia characteristics and/or cardiac features for detected episodes of arrhythmia); and apply the ML model to the DCA data sets to identify a valid sub-set of the DCA data sets that correctly characterize the corresponding CA signals (¶37-verify that feature delineation of the cardiac electrogram data has correctly detected an episode of cardiac arrhythmia. In some examples, the medical device applies a machine learning model to cardiac electrogram data of patient 2 to verify that feature delineation of the cardiac electrogram data has correctly classified an episode of cardiac arrhythmia as a particular type of arrhythmia) and to identify an invalid sub-set of the DCA data sets that incorrectly characterize the corresponding CA signals (¶90-feature delineation incorrectly determines that an episode of cardiac arrhythmia has occurred in patient 4), wherein the ML model outputs (¶54-machine learning system 250 may output) an indication that a particular DCA data set is unduly noisy and should not be characterized as a normal sinus rhythm, nor an arrhythmia (¶77-the use of filtered cardiac electrogram data may allow IMD 10 to discard these periods of noisy data which may be subject to signal artifacts such as amplitude level changes); and a display configured to present the output of the ML model as information concerning at least one of the valid sub-set or invalid sub-set of the DCA data sets (¶8-use of a machine learning model, such as a neural network or deep-learning model, that is trained on training datasets to detect cardiac arrhythmia from cardiac electrogram data; ¶104-outputting, by the medical device and for display, the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia). However, Dani does not explicitly teach wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD. Dawoud teaches wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD (¶21-identifying a T-wave characteristic of interest (COI) and an R-wave COI from the CA signals and adjusting profile parameters of a sensitivity profile based on the T-wave COI and R-wave COI; ¶192-the process of FIG. 9A exhibited an improvement of positive predictive value to >99% and >88%, improvement of relative sensitivity >99% and >96% and reduction of false detection rate of >99% and >95% in bradycardia and asystole, respectively, relative to conventional ORI processes; ¶141-the sensitivity profile parameter adjustments may be automatically pushed and uploaded to the ICM at the conclusion of the operations of FIG. 4, such as when the adjustment is within a predetermined limit). Therefore, 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 invention of Dani to include wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD of Dawoud in order to improve physiologic signal sensing in connection with arrhythmia episodes (Dawoud, ¶160) which improves positive predictions and reduces false detections (Dawoud, ¶192). Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Dani in view of Dawoud as applied to claim 1 above, and further in view of Lu (US 20200178825 filed on 12/5/18). Regarding claim 2, the combination of Dani and Dawoud teaches the system of claim 1. However, the combination of Dani and Dawoud does not teach wherein the ML model represents a convolutional neural network comprising sub-layers and including one or more rectified linear unit activation functions and batch normalization. Lu teaches wherein the ML model represents a convolutional neural network (¶30-convolutional neural network (CNN)) comprising sub-layers (¶30-include one or more additional convolutional layers) and including one or more rectified linear unit activation functions (101 in Fig. 6 and ¶35-activation function of leaky relu) and batch normalization (101 in Fig. 6 and ¶35-batch normalization). Lu relates generally to cardiac monitoring, such as electrocardiography, and more particularly, to automatic detection of cardiac abnormalities from cardiac waveforms with deep neural networks (¶1). Lu further teaches the use of batch normalization and an activation function of leaky relu in the first layer of the convolutional neural network (¶35). A novel deep neural network system and training structure of Lu requires minimal pre-processing of ECG waveforms and does not require feature extraction or waveform parameter identification prior to providing the ECG data to the neural network system (¶17). Therefore, 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 invention of Dani to include wherein the ML model represents a convolutional neural network comprising sub-layers and including one or more rectified linear unit activation functions and batch normalization of Lu in order to identify arrhythmias based on cardiac waveforms while minimizing pre-processing of ECG waveforms (Lu, ¶17). Claims 3, 5, and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Dani in view of Dawoud as applied to claim 1 above, and further in view of Pedalty (US 20200352462 filed on 4/16/20). Regarding claim 3, the combination of Dani and Dawoud teaches the system of claim 1, wherein the CA signals represent subcutaneous electrocardiogram (EGM) signals for a series of beats over a predetermined period of time (Dani, ¶27-IMD 10 may be implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1); ¶50-an electrocardiogram of patient 4; ¶50-analyzes patient data that represents one or more values that are averaged over a short-term period of time (e.g., about 3 minutes)), and the ML model applied to the one or more features of interest for the DCA data sets to identify the invalid sub-set of the DCA data sets (Dani, ¶37-applies a machine learning model to cardiac electrogram data of patient 2 to verify that feature delineation of the cardiac electrogram data has correctly detected an episode of cardiac arrhythmia). However, the combination of Dani and Dawoud does not teach wherein the one or more processors configured to identify one or more features of interest based in part on the CA signals aligned in time with the corresponding DD markers. Pedalty teaches the one or more processors configured to identify one or more features of interest based in part on the CA signals aligned in time with the corresponding DD markers (¶70-each ECG waveform is labeled with one or more episodes of arrhythmia of one or more types. For example, a training ECG waveform may include a plurality of segments, each segment labeled with a descriptor that specifies an absence of arrhythmia or a presence of an arrythmia of a particular classification (e.g., bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block)). Pedalty relates to medical devices (¶2). Therefore, 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 invention of Dani to include wherein the one or more processors configured to identify one or more features of interest based in part on the CA signals aligned in time with the corresponding DD markers of Pedalty in order for detecting or classifying cardiac arrhythmia in new cardiac electrogram data (Pedalty, ¶70). Regarding claim 5, the combination of Dani and Dawoud teaches the system of claim 1, the IMD comprising: a combination of subcutaneous electrodes configured to collect the CA signals (Dani, ¶27-IMD 10 may be implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1); ¶36-one or more sensors (e.g., electrodes) are described herein as being positioned on a housing of IMD 10); IMD memory (Dani, memory 56 in Fig. 2) configured to store program instructions; and one or more IMD processors (Dani, ¶43-processing circuitry 50 may include any one or more of a microprocessor) configured to execute the program instructions to: analyze the CA signals, utilizing the sensing parameter, and based on the analysis declare the candidate arrhythmia episodes (Dani, ¶11-analysis of cardiac features that have been identified by feature delineation as likely presenting an episode of arrhythmia in the patient); and a transceiver configured to wirelessly transmit the DCA data sets to the external device (Dani, ¶29-external device 12 may be a computing device configured for use in settings such as a home, clinic, or hospital, and may further be configured to communicate with IMD 10 via wireless telemetry; ¶34; ¶37). However, the combination of Dani and Dawoud does not teach to generate the DCA data sets including the corresponding CA signals and the corresponding DD markers. Pedalty teaches to generate the DCA data sets including the corresponding CA signals and the corresponding DD markers (¶70-each ECG waveform is labeled with one or more episodes of arrhythmia of one or more types. For example, a training ECG waveform may include a plurality of segments, each segment labeled with a descriptor that specifies an absence of arrhythmia or a presence of an arrythmia of a particular classification (e.g., bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block)). Therefore, 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 invention of Dani to include generating the DCA data sets including the corresponding CA signals and the corresponding DD markers of Pedalty in order for detecting or classifying cardiac arrhythmia in new cardiac electrogram data (Pedalty, ¶70). Regarding claim 31, the combination of Dani and Dawoud teaches the system of claim 1. However, the combination of Dani and Dawoud does not teach wherein the display is further configured to display the CA signals and corresponding DD markers from the valid sub-set. Pedalty wherein the display is further configured to display the CA signals and corresponding DD markers from the valid sub-set (Fig. 10B- see 1020 which is an episode of arrhythmia in patient 4; Fig. 10C-display 1003 colors segments for which computing system 24 has determined a high likelihood that atrial fibrillation has occurred in patient 4 in green ( 1012 ), segments for which computing system 24 has made an uncertain determination of whether atrial fibrillation has occurred in patient 4 in yellow ( 1014 ), and segments for which computing system 24 has determined a low likelihood that atrial fibrillation has occurred in patient 4 in red ( 1016 ); Fig. 10D; ¶118-121). Therefore, 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 invention of Dani to include wherein the display is further configured to display the CA signals and corresponding DD markers from the valid sub-set of Pedalty in order to provide clear explainability and simple arrhythmia visualization of a machine learning system (Pedalty, ¶28). Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Dani in view of Dawoud as applied to claim 1 above, and further in view of Perschbacher (US 20170290550 filed on 4/4/17), hereinafter referred to as Persch. Regarding claim 26, the combination of Dani and Dawoud teaches the system of claim 1, wherein the ML model is further configured to output, in connection with each of a plurality of the DCA data sets (Dani, ¶8-use of a machine learning model, such as a neural network or deep-learning model, that is trained on training datasets to detect cardiac arrhythmia from cardiac electrogram data). However, the combination of Dani and Dawoud does not explicitly teach a confidence indicator indicative of a degree of confidence that the corresponding DCA data set represents a true positive or false positive designation of an arrhythmia of interest, the confidence indicator representing at least a portion of the information displayed. Persch teaches a confidence indicator indicative of a degree of confidence that the corresponding DCA data set (Persch, ¶8-a confidence indicator indicating a confidence level of the detection of the arrhythmic event) represents a true positive or false positive designation of an arrhythmia of interest (Persch, ¶32-confidence-based arrhythmia detection, false positive, false determinations),the confidence indicator representing at least a portion of the information displayed (Persch, ¶70-the output module 252 may include a display for displaying the information. The information may be presented in a table, a chart, a diagram, or any other types of textual, tabular, or graphical presentation formats, for displaying to a system user; ¶50). Therefore, 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 invention of Dani to include a confidence indicator indicative of a degree of confidence that the corresponding DCA data set represents a true positive or false positive designation of an arrhythmia of interest, the confidence indicator representing at least a portion of the information displayed of Persch in order for detecting and managing cardiac arrhythmias (Persch, ¶2). Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Dani in view of Dawoud, and further in view of Persch as applied to claim 26 above, and further in view of Nakar (US 20180042510 filed on 7/11/17). Regarding claim 27, the combination of Dani, Dawoud, and Persch teaches the system of claim 26, wherein the one or more processors are further configured to: compare the confidence indicator for the corresponding DCA data set to a detection threshold (Persch, ¶64-generate the confidence indicator based on a comparison of the signal metrics 222 and one or more thresholds). Therefore, 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 invention of Dani to include wherein the one or more processors are further configured to: compare the confidence indicator for the corresponding DCA data set to a detection threshold of Persch in order for detecting and managing cardiac arrhythmias (Persch, ¶2). However, the combination of Dani and Dawoud does not teach to add the corresponding DCA data set to the valid sub-set or invalid sub-set based on the comparison. Nakar teaches to add the corresponding DCA data set to the valid sub-set or invalid sub-set based on the comparison (¶75-add beat information, arrhythmia, comparison). Nakar relates generally to electrocardiograph (ECG) signals, and specifically to detecting ECG signals having similar morphologies (¶2). Nakar further teaches a comparison to a threshold value (¶75). If the comparison returns positive, the beat is assumed to represent the same arrhythmia as the morphology pattern. In this case processor 40 may add this beat information into collective information of map 82 of the heart (¶75). Therefore, 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 invention of Dani to include adding the corresponding DCA data set to the valid sub-set or invalid sub-set based on the comparison of Nakar in order to detect ECG signals having similar morphologies (Nakar, ¶2). Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Dani in view of Dawoud as applied to claim 1 above, and further in view of An (US 20130116578 filed on 12/26/12). Regarding claim 28, the combination of Dani and Dawoud teaches the system of claim 1. However, the combination of Dani and Dawoud does not teach wherein the one or more processors are further configured to adjust the detection threshold to increase or decrease a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets. An teaches wherein the one or more processors are further configured to adjust the detection threshold to increase or decrease a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets (¶56-a user may set the CAS threshold to some arbitrary high value and then dynamically or manually adjust the CAS threshold, such as to fine tune false positive or false negative rates (e.g., specificity or sensitivity); ¶174-a WHF detection algorithm include, but are not limited to, a neural network algorithm, a fuzzy logic algorithm, a linear regression model algorithm, a decision tree algorithm, a Hidden Markov Model algorithm, a k-nearest neighbor algorithm, and a support vector machine; ¶185-a measured increase in the Q-S1 time duration may indicate an increased risk of the subject experiencing a WHF event; ¶175-prediction by the WHF detection algorithm is adjusted according to the determined HF risk score. For example, detection thresholds may be adjusted to be more easily satisfied in order to increase the sensitivity of HF detection). An relates generally to implantable medical devices, and more particularly, but not by way of limitation, to systems and methods for between-patient comparisons for risk stratification of future heart failure decompensation (¶3). Therefore, 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 invention of Dani to include wherein the one or more processors are further configured to adjust the detection threshold to increase or decrease a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets of An in order to fine tune false positive or false negative rates (e.g., specificity or sensitivity) (An, ¶56). Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Dani in view of Dawoud, and further in view of Persch as applied to claim 26 above, and further in view of An. Regarding claim 29, the combination of Dani, Dawoud, and Persch teaches the system of claim 26, wherein, in response to the confidence indicator (Persch, ¶8-a confidence indicator indicating a confidence level of the detection of the arrhythmic event) indicating a high confidence (¶8-indicates a relatively high confidence). Therefore, 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 invention of Dani to include wherein, in response to the confidence indicator indicating a high confidence of Persch in order for detecting and managing cardiac arrhythmias (Persch, ¶2). However, the combination of Dani and Dawoud does not teach the true positive designation, the one or more processors are further configured to decrease a detection threshold to increase a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets. An teaches the true positive designation (¶57-correctly characterizes more true positives), the one or more processors are further configured to decrease a detection threshold to increase a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets (¶53-by increasing the sensitivity of these sensors (e.g., decreasing a threshold value); ¶185-a measured increase in the Q-S1 time duration may indicate an increased risk of the subject experiencing a WHF event; ¶175-prediction by the WHF detection algorithm is adjusted according to the determined HF risk score. For example, detection thresholds may be adjusted to be more easily satisfied in order to increase the sensitivity of HF detection; ¶174). Therefore, 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 invention of Dani to include the true positive designation, the one or more processors are further configured to decrease a detection threshold to increase a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets of An in order to fine tune false positive or false negative rates (e.g., specificity or sensitivity) (An, ¶56). Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Dani in view of Dawoud as applied to claim 1 above, and further in view of Sirendi (US 20200008696 filed on 3/7/18). Regarding claim 30, the combination of Dani and Dawoud teaches the system of claim 1. However, the combination of Dani and Dawoud does not teach wherein the ML model represents a model that is trained utilizing an augmented collection of DCA data sets, wherein the augmented collection of the DCA data sets includes reference DCA data sets from patients and synthetic DCA data sets that are generated based on the reference DCA data sets. Sirendi teaches wherein the ML model represents a model that is trained utilizing an augmented collection of DCA data sets, wherein the augmented collection of the DCA data sets includes reference DCA data sets from patients (¶205-threshold values are determined based upon past data from multiple sources, for example a database containing physiological data for patients alongside occurrences of cardiac events may be used for training a classifier) and synthetic DCA data sets that are generated based on the reference DCA data sets (¶39; ¶42-Gaussian process, improved classifier; ¶139). Sirendi relates to the analysis of cardiac data, in particular, the invention relates to a system and method of determining the probability of a cardiac event occurring. This may enable timely preventative action to be taken, or may enable the determination of periods where increased monitoring of a person may be beneficial (¶1). Sirendi further teaches threshold values being determined based upon past data from multiple sources, for example a database containing physiological data for patients alongside occurrences of cardiac events may be used for training a classifier (¶199). Additionally, Sirendi teaches a Gaussian process for training (¶139). Therefore, 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 invention of Dani to include wherein the ML model represents a model that is trained utilizing an augmented collection of DCA data sets, wherein the augmented collection of the DCA data sets includes reference DCA data sets from patients and synthetic DCA data sets that are generated based on the reference DCA data sets of Sirendi in order to determine the probability of a cardiac event occurring. This may enable timely preventative action to be taken, or may enable the determination of periods where increased monitoring of a person may be beneficial (Sirendi, ¶1). Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Dani in view of Dawoud as applied to claim 1 above, and further in view of Krause (US 20080300497 filed on 6/4/07). Regarding claim 33, the combination of Dani and Dawoud teaches the system of claim 1. However, the combination of Dani and Dawoud does not teach wherein the ML model is further configured to output an alert to recommend reprogramming the IMD. Krause teaches wherein the ML model is further configured to output an alert to recommend reprogramming the IMD (¶103-alert the physician that corrective action may be required, arrhythmia detection, reprograming, implanted device). Krause relates generally to implantable medical devices (IMDs), and, more particularly, the present invention relates to detecting noisy physiologic data intervals (¶1). Krause additionally teaches to alert the physician that corrective action may be required in order to ensure proper sensing and arrhythmia detection. Such action may include reprogramming sensing or detection parameters or repositioning an electrode or an implanted device (¶103). Therefore, 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 invention of Dani to include wherein the ML model is further configured to output an alert to recommend reprogramming the IMD of Krause in order to ensure proper sensing and arrhythmia detection (Krause, ¶103). Double Patenting This double patenting rejection is being issued in view of 19/177513 not being commensurate in scope to the previous restriction requirement. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 1, 6-7, 36, and 41-43 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani (US 20200357519) and Dawoud (US 20190336026). This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani and Dawoud as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) 1 1, 4, 9, 14, 15 Dani teaches an external device (¶29-external device 12); obtain device classified arrhythmia (DCA) data sets generated by the IMD for corresponding candidate arrhythmia episodes declared by the IMD (¶37-a medical device, such as IMD 10…uses feature delineation to make a preliminary detection of cardiac arrhythmia in patient 4), the DCA data sets including the CA signals for one or more beats sensed by the IMD (¶9-a medical device, such as an IMD, senses cardiac electrogram data of a patient. The medical device performs feature-based delineation of the cardiac electrogram data to obtain cardiac features indicative of an episode of arrhythmia in the patient) and one or more device documented (DD) markers that are generated by the IMD (¶65-IMD 10 further applies feature delineation to the cardiac electrogram data to detect one or more episodes of arrhythmia. For example, IMD 10 may apply QRS detection delineation and noise flagging to the cardiac electrogram data to provide arrhythmia characteristics and/or cardiac features for detected episodes of arrhythmia); and apply the ML model to the DCA data sets to identify a valid sub-set of the DCA data sets that correctly characterize the corresponding CA signals (¶37-verify that feature delineation of the cardiac electrogram data has correctly detected an episode of cardiac arrhythmia. In some examples, the medical device applies a machine learning model to cardiac electrogram data of patient 2 to verify that feature delineation of the cardiac electrogram data has correctly classified an episode of cardiac arrhythmia as a particular type of arrhythmia) and to identify an invalid sub-set of the DCA data sets that incorrectly characterize the corresponding CA signals (¶90-feature delineation incorrectly determines that an episode of cardiac arrhythmia has occurred in patient 4); wherein the ML model outputs (¶54-machine learning system 250 may output); and a display configured to present the output of the ML model as information concerning at least one of the valid sub-set or invalid sub-set of the DCA data sets (¶8-use of a machine learning model, such as a neural network or deep-learning model, that is trained on training datasets to detect cardiac arrhythmia from cardiac electrogram data; ¶104-outputting, by the medical device and for display, the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia). Therefore, 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 invention of the copending application to include an external device; obtain device classified arrhythmia (DCA) data sets generated by the IMD for corresponding candidate arrhythmia episodes declared by the IMD, the DCA data sets including the CA signals for one or more beats sensed by the IMD and one or more device documented (DD) markers that are generated by the IMD; and apply the ML model to the DCA data sets to identify a valid sub-set of the DCA data sets that correctly characterize the corresponding CA signals and to identify an invalid sub-set of the DCA data sets that incorrectly characterize the corresponding CA signals; wherein the ML model outputs; and a display configured to present the output of the ML model as information concerning at least one of the valid sub-set or invalid sub-set of the DCA data sets of Dani in order to accurately detect arrhythmias in a patient (Dani, ¶24). a recommendation for how to reprogram a sensing parameter utilized in connection with identifying at least one of i) P waves, ii) R waves, or T waves to avoid over sensing or under sensing the at least one of i) P waves, ii) R waves, or T waves (¶133-FIG. 4 illustrates a flow chart for classifying AF detection and developing recommendations for sensitivity profile parameter settings; ¶21-identifying a T-wave characteristic of interest (COI) and an R-wave COI from the CA signals and adjusting profile parameters of a sensitivity profile based on the T-wave COI and R-wave COI); wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, based on the recommendation, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD (¶21-identifying a T-wave characteristic of interest (COI) and an R-wave COI from the CA signals and adjusting profile parameters of a sensitivity profile based on the T-wave COI and R-wave COI; ¶192-the process of FIG. 9A exhibited an improvement of positive predictive value to >99% and >88%, improvement of relative sensitivity >99% and >96% and reduction of false detection rate of >99% and >95% in bradycardia and asystole, respectively, relative to conventional ORI processes; ¶141-the sensitivity profile parameter adjustments may be automatically pushed and uploaded to the ICM at the conclusion of the operations of FIG. 4, such as when the adjustment is within a predetermined limit). Therefore, 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 invention of the copending application to include a recommendation for how to reprogram a sensing parameter utilized in connection with identifying at least one of i) P waves, ii) R waves, or T waves to avoid over sensing or under sensing the at least one of i) P waves, ii) R waves, or T waves; wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, based on the recommendation, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD of Dawoud in order to improve physiologic signal sensing in connection with arrhythmia episodes (Dawoud, ¶160) which improves positive predictions and reduces false detections (Dawoud, ¶192). 6 Dani teaches wherein the external device represents a local external device that includes the memory and the one or more processors and a transceiver (Dani, ¶47-external device 12 , or by using another local or networked computing device configured to communicate with processing circuitry 50 via communication circuitry 54; ¶29-wireless telemetry; ¶34-memory), the transceiver configured to wirelessly receive the DCA data sets from the IMD (Dani, ¶29-external device 12 may be a computing device configured for use in settings such as a home, clinic, or hospital, and may further be configured to communicate with IMD 10 via wireless telemetry; ¶34). Therefore, 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 invention of the copending application to include wherein the external device represents a local external device that includes the memory and the one or more processors and a transceiver, the transceiver configured to wirelessly receive the DCA data sets from the IMD of Dani in order to accurately detect arrhythmias in a patient (Dani, ¶24). 7 Dani teaches wherein the external device represents a server that includes the memory and the one or more processors (Dani, ¶36-transmit the signals from the electrodes or other sensors to another device (e.g., external device 12 ) or server; ¶34-memory; ¶31-processor), the memory configured to store the collection of the DCA data sets (Dani, ¶34), the one or more processors configured to apply the ML model to the collection of the DCA data sets (Dani, ¶12-applying, by the medical device, the machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to verify, based on the machine learning model, that the episode of arrhythmia has occurred in the patient). Therefore, 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 invention of the copending application to include wherein the external device represents a server that includes the memory and the one or more processors, the memory configured to store the collection of the DCA data sets, the one or more processors configured to apply the ML model to the collection of the DCA data sets of Dani in order to accurately detect arrhythmias in a patient (Dani, ¶24). 36 Dani teaches wherein the IMD comprises IMD memory configured to store the adjustments to reprogram the sensing parameter to reduce the number of false positive candidate arrhythmias episodes declared by the IMD (Dani, ¶34-external device 12 may be used to interrogate IMD 10 to retrieve data, including device operational data as well as physiological data accumulated in IMD memory; ¶30-program IMD 10 , e.g., select or adjust values for operational parameters of IMD 10). Therefore, 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 invention of the copending application to include wherein the IMD comprises IMD memory configured to store the adjustments to reprogram the sensing parameter to reduce the number of false positive candidate arrhythmias episodes declared by the IMD of Dani in order to accurately detect arrhythmias in a patient (Dani, ¶24). 41 Dawoud teaches wherein the recommendation for how to reprogram the sensing parameter includes an indication of a sensitivity level to be utilized by the IMD to identify R waves in the CA signals (Dawoud, ¶34-developing recommendations for sensitivity profile parameter settings; ¶87-the confirmatory feature detection process 137 may implement one or more of the R-wave detection processes; ¶141-the sensitivity profile parameter adjustments, in the confirmation log, may be presented on a display; ¶160-CA signal, detection algorithm). Therefore, 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 invention of the copending application to include wherein the recommendation for how to reprogram the sensing parameter includes an indication of a sensitivity level to be utilized by the IMD to identify R waves in the CA signals of Dawoud in order to detect an arrhythmia within the beat segment of interest based on a presence or absence of the one or more R-waves (Dawoud, ¶14). 42 Dawoud teaches wherein the external device is configured to transmit, to the IMD, an adjustment of the sensing parameter for the IMD based on the recommendation (Dawoud, ¶92-the local external device and/or remote server may then return, to the ICM 100, adjustments to the sensitivity profile parameters; ¶133-FIG. 4 illustrates a flow chart for classifying AF detection and developing recommendations for sensitivity profile parameter settings; ¶21-identifying a T-wave characteristic of interest (COI) and an R-wave COI from the CA signals and adjusting profile parameters of a sensitivity profile based on the T-wave COI and R-wave COI). Therefore, 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 invention of the copending application to include wherein the external device is configured to transmit, to the IMD, an adjustment of the sensing parameter for the IMD based on the recommendation of Dawoud in order to improve physiologic signal sensing in connection with arrhythmia episodes (Dawoud, ¶160) which improves positive predictions and reduces false detections (Dawoud, ¶192). 43 1, 4, 9, 14, 15 Dani teaches an external device (¶29-external device 12); obtain device classified arrhythmia (DCA) data sets generated by the IMD for corresponding candidate arrhythmia episodes declared by the IMD (¶37-a medical device, such as IMD 10…uses feature delineation to make a preliminary detection of cardiac arrhythmia in patient 4), the DCA data sets including the CA signals for one or more beats sensed by the IMD (¶9-a medical device, such as an IMD, senses cardiac electrogram data of a patient. The medical device performs feature-based delineation of the cardiac electrogram data to obtain cardiac features indicative of an episode of arrhythmia in the patient) and one or more device documented (DD) markers that are generated by the IMD (¶65-IMD 10 further applies feature delineation to the cardiac electrogram data to detect one or more episodes of arrhythmia. For example, IMD 10 may apply QRS detection delineation and noise flagging to the cardiac electrogram data to provide arrhythmia characteristics and/or cardiac features for detected episodes of arrhythmia); and apply the ML model to the DCA data sets to identify a valid sub-set of the DCA data sets that correctly characterize the corresponding CA signals (¶37-verify that feature delineation of the cardiac electrogram data has correctly detected an episode of cardiac arrhythmia. In some examples, the medical device applies a machine learning model to cardiac electrogram data of patient 2 to verify that feature delineation of the cardiac electrogram data has correctly classified an episode of cardiac arrhythmia as a particular type of arrhythmia) and to identify an invalid sub-set of the DCA data sets that incorrectly characterize the corresponding CA signals (¶90-feature delineation incorrectly determines that an episode of cardiac arrhythmia has occurred in patient 4), wherein the ML model outputs (¶54-machine learning system 250 may output) an indication that a particular DCA data set is unduly noisy and should not be characterized as a normal sinus rhythm, nor an arrhythmia (¶77-the use of filtered cardiac electrogram data may allow IMD 10 to discard these periods of noisy data which may be subject to signal artifacts such as amplitude level changes). Therefore, 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 invention of the copending application to include an external device; obtain device classified arrhythmia (DCA) data sets generated by the IMD for corresponding candidate arrhythmia episodes declared by the IMD, the DCA data sets including the CA signals for one or more beats sensed by the IMD and one or more device documented (DD) markers that are generated by the IMD; and apply the ML model to the DCA data sets to identify a valid sub-set of the DCA data sets that correctly characterize the corresponding CA signals and to identify an invalid sub-set of the DCA data sets that incorrectly characterize the corresponding CA signals, wherein the ML model outputs an indication that a particular DCA data set is unduly noisy and should not be characterized as a normal sinus rhythm, nor an arrhythmia of Dani in order to accurately detect arrhythmias in a patient (Dani, ¶24). Dawoud teaches wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD (¶21-identifying a T-wave characteristic of interest (COI) and an R-wave COI from the CA signals and adjusting profile parameters of a sensitivity profile based on the T-wave COI and R-wave COI; ¶192-the process of FIG. 9A exhibited an improvement of positive predictive value to >99% and >88%, improvement of relative sensitivity >99% and >96% and reduction of false detection rate of >99% and >95% in bradycardia and asystole, respectively, relative to conventional ORI processes; ¶141-the sensitivity profile parameter adjustments may be automatically pushed and uploaded to the ICM at the conclusion of the operations of FIG. 4, such as when the adjustment is within a predetermined limit). Therefore, 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 invention of the copending application to include wherein the IMD is further configured to adjust the sensing parameter utilized by the IMD, to reduce a number of false positive candidate arrhythmia episodes declared by the IMD of Dawoud in order to improve physiologic signal sensing in connection with arrhythmia episodes (Dawoud, ¶160) which improves positive predictions and reduces false detections (Dawoud, ¶192). Claim 2 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani, Dawoud, and Lu (US 20200178825). This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani, Dawoud, and Lu as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) Secondary Reference Lu (US 20200178825) 2 Lu teaches wherein the ML model represents a convolutional neural network (¶30-convolutional neural network (CNN)) comprising sub-layers (¶30-include one or more additional convolutional layers) and including one or more rectified linear unit activation functions (101 in Fig. 6 and ¶35-activation function of leaky relu) and batch normalization (101 in Fig. 6 and ¶35-batch normalization). Therefore, 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 invention of the copending application to include wherein the ML model represents a convolutional neural network comprising sub-layers and including one or more rectified linear unit activation functions and batch normalization of Lu in order to identify arrhythmias based on cardiac waveforms while minimizing pre-processing of ECG waveforms (Lu, ¶17). Claims 3, 5, and 31 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani, Dawoud, and Pedalty. This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani, Dawoud, and Pedalty as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) Secondary Reference Pedalty (US 20200352462) 3 4 Dani teaches wherein the CA signals represent subcutaneous electrocardiogram (EGM) signals for a series of beats over a predetermined period of time (Dani, ¶27-IMD 10 may be implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1); ¶50-an electrocardiogram of patient 4; ¶50-analyzes patient data that represents one or more values that are averaged over a short-term period of time (e.g., about 3 minutes)), and the ML model applied to the one or more features of interest for the DCA data sets to identify the invalid sub-set of the DCA data sets (Dani, ¶37-applies a machine learning model to cardiac electrogram data of patient 2 to verify that feature delineation of the cardiac electrogram data has correctly detected an episode of cardiac arrhythmia). Therefore, 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 invention of the copending application to include wherein the CA signals represent subcutaneous electrocardiogram (EGM) signals for a series of beats over a predetermined period of time, and the ML model applied to the one or more features of interest for the DCA data sets to identify the invalid sub-set of the DCA data sets of Dani in order to accurately detect arrhythmias in a patient (Dani, ¶24). Pedalty teaches the one or more processors configured to identify one or more features of interest based in part on the CA signals aligned in time with the corresponding DD markers (¶70-each ECG waveform is labeled with one or more episodes of arrhythmia of one or more types. For example, a training ECG waveform may include a plurality of segments, each segment labeled with a descriptor that specifies an absence of arrhythmia or a presence of an arrythmia of a particular classification (e.g., bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block)). Therefore, 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 invention of the copending application to include wherein the one or more processors configured to identify one or more features of interest based in part on the CA signals aligned in time with the corresponding DD markers of Pedalty in order for detecting or classifying cardiac arrhythmia in new cardiac electrogram data (Pedalty, ¶70). 5 4 Dani teaches a combination of subcutaneous electrodes configured to collect the CA signals (Dani, ¶27-IMD 10 may be implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1); ¶36-one or more sensors (e.g., electrodes) are described herein as being positioned on a housing of IMD 10); IMD memory (Dani, memory 56 in Fig. 2) configured to store program instructions; and one or more IMD processors (Dani, ¶43-processing circuitry 50 may include any one or more of a microprocessor) configured to execute the program instructions to: analyze the CA signals, utilizing the sensing parameter, and based on the analysis declare the candidate arrhythmia episodes (Dani, ¶11-analysis of cardiac features that have been identified by feature delineation as likely presenting an episode of arrhythmia in the patient); and a transceiver configured to wirelessly transmit the DCA data sets to the external device (Dani, ¶29-external device 12 may be a computing device configured for use in settings such as a home, clinic, or hospital, and may further be configured to communicate with IMD 10 via wireless telemetry; ¶34; ¶37). Therefore, 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 invention of the copending application to include a combination of subcutaneous electrodes configured to collect the CA signals; IMD memory configured to store program instructions; and one or more IMD processors configured to execute the program instructions to: analyze the CA signals, utilizing the sensing parameter, and based on the analysis declare the candidate arrhythmia episodes and a transceiver configured to wirelessly transmit the DCA data sets to the external device of Dani in order to accurately detect arrhythmias in a patient (Dani, ¶24). Pedalty teaches to generate the DCA data sets including the corresponding CA signals and the corresponding DD markers (¶70-each ECG waveform is labeled with one or more episodes of arrhythmia of one or more types. For example, a training ECG waveform may include a plurality of segments, each segment labeled with a descriptor that specifies an absence of arrhythmia or a presence of an arrythmia of a particular classification (e.g., bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block)). Therefore, 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 invention of the copending application to include generating the DCA data sets including the corresponding CA signals and the corresponding DD markers of Pedalty in order for detecting or classifying cardiac arrhythmia in new cardiac electrogram data (Pedalty, ¶70). 31 Pedalty wherein the display is further configured to display the CA signals and corresponding DD markers from the valid sub-set (Fig. 10B- see 1020 which is an episode of arrhythmia in patient 4; Fig. 10C-display 1003 colors segments for which computing system 24 has determined a high likelihood that atrial fibrillation has occurred in patient 4 in green ( 1012 ), segments for which computing system 24 has made an uncertain determination of whether atrial fibrillation has occurred in patient 4 in yellow ( 1014 ), and segments for which computing system 24 has determined a low likelihood that atrial fibrillation has occurred in patient 4 in red ( 1016 ); Fig. 10D; ¶118-121). Therefore, 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 invention of the copending application to include wherein the display is further configured to display the CA signals and corresponding DD markers from the valid sub-set of Pedalty in order to provide clear explainability and simple arrhythmia visualization of a machine learning system (Pedalty, ¶28). Claim 26 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani, Dawoud, and Perschbacher (US 20170290550), hereinafter referred to as Persch. This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani, Dawoud, and Persch as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) Secondary Reference Perschbacher (US 20170290550), hereinafter referred to as Persch 26 Dani teaches wherein the ML model is further configured to output, in connection with each of a plurality of the DCA data sets (Dani, ¶8-use of a machine learning model, such as a neural network or deep-learning model, that is trained on training datasets to detect cardiac arrhythmia from cardiac electrogram data). Therefore, 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 invention of the copending application to include wherein the ML model is further configured to output, in connection with each of a plurality of the DCA data sets of Dani in order to accurately detect arrhythmias in a patient (Dani, ¶24). Persch teaches a confidence indicator indicative of a degree of confidence that the corresponding DCA data set (Persch, ¶8-a confidence indicator indicating a confidence level of the detection of the arrhythmic event) represents a true positive or false positive designation of an arrhythmia of interest (Persch, ¶32-confidence-based arrhythmia detection, false positive, false determinations),the confidence indicator representing at least a portion of the information displayed (Persch, ¶70-the output module 252 may include a display for displaying the information. The information may be presented in a table, a chart, a diagram, or any other types of textual, tabular, or graphical presentation formats, for displaying to a system user; ¶50). Therefore, 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 invention of the copending application to include a confidence indicator indicative of a degree of confidence that the corresponding DCA data set represents a true positive or false positive designation of an arrhythmia of interest, the confidence indicator representing at least a portion of the information displayed of Persch in order for detecting and managing cardiac arrhythmias (Persch, ¶2). Claim 27 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani, Dawoud, Persch, and Nakar (US 20180042510). This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani, Dawoud, Persch, and Nakar as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) Secondary Reference Perschbacher (US 20170290550), hereinafter referred to as Persch Secondary Reference Nakar (US 20180042510) 27 Persch teaches compare the confidence indicator for the corresponding DCA data set to a detection threshold (Persch, ¶64-generate the confidence indicator based on a comparison of the signal metrics 222 and one or more thresholds). Therefore, 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 invention of the copending application to include wherein the one or more processors are further configured to: compare the confidence indicator for the corresponding DCA data set to a detection threshold of Persch in order for detecting and managing cardiac arrhythmias (Persch, ¶2). Nakar teaches to add the corresponding DCA data set to the valid sub-set or invalid sub-set based on the comparison (¶75-add beat information, arrhythmia, comparison). Therefore, 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 invention of the copending application to include adding the corresponding DCA data set to the valid sub-set or invalid sub-set based on the comparison of Nakar in order to detect ECG signals having similar morphologies (Nakar, ¶2). Claim 28 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani, Dawoud, and An (US 20130116578). This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani, Dawoud, and An as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) Secondary Reference An (US 20130116578) 28 An teaches wherein the one or more processors are further configured to adjust the detection threshold to increase or decrease a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets (¶56-a user may set the CAS threshold to some arbitrary high value and then dynamically or manually adjust the CAS threshold, such as to fine tune false positive or false negative rates (e.g., specificity or sensitivity); ¶174-a WHF detection algorithm include, but are not limited to, a neural network algorithm, a fuzzy logic algorithm, a linear regression model algorithm, a decision tree algorithm, a Hidden Markov Model algorithm, a k-nearest neighbor algorithm, and a support vector machine; ¶185-a measured increase in the Q-S1 time duration may indicate an increased risk of the subject experiencing a WHF event; ¶175-prediction by the WHF detection algorithm is adjusted according to the determined HF risk score. For example, detection thresholds may be adjusted to be more easily satisfied in order to increase the sensitivity of HF detection). Therefore, 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 invention of the copending application to include wherein the one or more processors are further configured to adjust the detection threshold to increase or decrease a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets of An in order to fine tune false positive or false negative rates (e.g., specificity or sensitivity) (An, ¶56). Claim 29 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani, Dawoud, Persch, and An (US 20130116578). This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani, Dawoud, Persch, and An as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) Secondary Reference Perschbacher (US 20170290550), hereinafter referred to as Persch Secondary Reference An (US 20130116578) 29 Persch teaches wherein, in response to the confidence indicator (Persch, ¶8-a confidence indicator indicating a confidence level of the detection of the arrhythmic event) indicating a high confidence (¶8-indicates a relatively high confidence). Therefore, 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 invention of the copending application to include wherein, in response to the confidence indicator indicating a high confidence of Persch in order for detecting and managing cardiac arrhythmias (Persch, ¶2). An teaches the true positive designation (¶57-correctly characterizes more true positives), the one or more processors are further configured to decrease a detection threshold to increase a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets (¶53-by increasing the sensitivity of these sensors (e.g., decreasing a threshold value); ¶185-a measured increase in the Q-S1 time duration may indicate an increased risk of the subject experiencing a WHF event; ¶175-prediction by the WHF detection algorithm is adjusted according to the determined HF risk score. For example, detection thresholds may be adjusted to be more easily satisfied in order to increase the sensitivity of HF detection; ¶174). Therefore, 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 invention of the copending application to include the true positive designation, the one or more processors are further configured to decrease a detection threshold to increase a sensitivity of the ML model utilized in connection with identifying the at least one of P waves, R waves and T waves in the DCA data sets of An in order to fine tune false positive or false negative rates (e.g., specificity or sensitivity) (An, ¶56). Claim 30 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani, Dawoud, and Sirendi (US 20200008696). This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani, Dawoud, and Sirendi as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) Secondary Reference Sirendi (US 20200008696) 30 8 Sirendi teaches wherein the ML model represents a model that is trained utilizing an augmented collection of DCA data sets, wherein the augmented collection of the DCA data sets includes reference DCA data sets from patients (¶205-threshold values are determined based upon past data from multiple sources, for example a database containing physiological data for patients alongside occurrences of cardiac events may be used for training a classifier) and synthetic DCA data sets that are generated based on the reference DCA data sets (¶39; ¶42-Gaussian process, improved classifier; ¶139). Therefore, 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 invention of the copending application to include wherein the ML model represents a model that is trained utilizing an augmented collection of DCA data sets, wherein the augmented collection of the DCA data sets includes reference DCA data sets from patients and synthetic DCA data sets that are generated based on the reference DCA data sets of Sirendi in order to determine the probability of a cardiac event occurring. This may enable timely preventative action to be taken, or may enable the determination of periods where increased monitoring of a person may be beneficial (Sirendi, ¶1). Claim 33 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/177513 in view of Dani, Dawoud, and Krause (US 20080300497). This is a provisional nonstatutory double patenting rejection. 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 invention of the copending application to include the subject matter in Dani, Dawoud, and Krause as shown below. Claims of the Present Application (17/341436) Claims of the Copending Application (19/177513) Secondary Reference Dani (US 20200357519) Secondary Reference Dawoud (US 20190336026) Secondary Reference Krause (US 20080300497) 33 Krause teaches wherein the ML model is further configured to output an alert to recommend reprogramming the IMD (¶103-alert the physician that corrective action may be required, arrhythmia detection, reprograming, implanted device). Therefore, 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 invention of the copending application to include wherein the ML model is further configured to output an alert to recommend reprogramming the IMD of Krause in order to ensure proper sensing and arrhythmia detection (Krause, ¶103). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20210345900: relate generally to implantable medical devices, and more particularly to detection of low-level P-waves and discrimination of noise in cardiac activity signals (¶1). Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA HODGE whose telephone number is (571) 272-7101. The examiner can normally be reached M-F: 8:00 am-5:00 pm. 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, UNSU JUNG can be reached at (571) 272-8506. 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. /L.N.H./Examiner, Art Unit 3792 /AMANDA L STEINBERG/Examiner, Art Unit 3792