CTNF 18/798,424 CTNF 99961 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-4, 6-15, and 17-20 are rejected under 35 U.S.C. 102( a)(1) and 35 U.S.C 102(a)(2 ) as being anticipated by Li et al. (hereinafter ‘Li’, U.S. PGPub No. 2022/0104750) . In regards to claim 1 , Li discloses a medical-device system for verifying cardiac pause episodes detected by a medical device in a patient, the system comprising: a cardiac pause verification circuit configured to: receive information about a device-detected cardiac pause episode detected by the medical device using a device-based detection algorithm (see para. [0017]), generate one or more metrics, including at least one entropy metric, from the received information about the device-detected cardiac pause episode ([0084]: "The parameters of the network may then be modified so as to decrease a cost function comparing the known delineation and the output of the network. A cross-entropy error function is used so as to allow for multi-labeling (i.e., allowing for multiple waves at a given instant)."), and verify a presence or absence of cardiac pause in the device-detected cardiac pause episode using the generated one or more metrics and a verification algorithm different from the device-based detection algorithm ([0112]: "For example, fifth section 185 may include pause information (count and/or longest R-R interval), atrioventricular block information, atrial fibrillation/flutter information, ventricular tachycardia information, other supraventricular tachycardia information, and/or any other information concerning any episodes or abnormalities."), and an output circuit configured to provide the verified presence or absence of the cardiac pause to a user or a process executable by the medical-device system ([0089]: "As is shown in FIG. 4, the information generated on back end 46 by ECG platform 37 in steps 54, 56, 58 and 61, and optionally, 63, may be communicated by ECG platform 37 to ECG application 29 on front end 45. ECG application 29 may cause the foregoing information to be displayed on display 17 of system device 14."). In regards to claim 2, Li discloses that the received information about the device-detected cardiac pause episode includes a cardiac signal and event markers generated by and stored in the medical device ([0084]: "Training the neural networks involves applying numerous datasets containing cardiac signals and known outputs to the neural networks. A database of the datasets containing cardiac signals collected across a plurality of patients using the systems and methods described herein may be stored on server 15 and/or drive 16 (e.g., in the cloud)."). In regards to claims 3 and 4, Li discloses that the event markers include a pause onset marker marking a beginning of the device-detected cardiac pause episode, and a pause end marker marking an end of the device-detected cardiac pause episode, the pause onset marker and the pause end marker partitioning the cardiac signal into a pre-pause segment prior to the pause onset marker, an inner pause segment between the pause onset marker and the pause end marker, and a post-pause segment following the pause end marker and that the event markers further include type or timings of heart beats or noise events in the cardiac signal recognized by the medical device ([0092]: "In this exemplary display, secondary labels 112 include beat label PVC (premature ventricular complex) and PSVC (premature supraventricular complex), though it is understood that other secondary labels may be included. The points in the plot 110 associated with the label PVC and PSVC are colored, as shown in FIG. 9 by the presence of points of color different from black."). In regards to claim 6, Li discloses that the cardiac pause verification circuit is configured to verify the presence or absence of cardiac pause in the device-detected cardiac pause episode using the sample entropy of the inner pause segment and the first amplitude ratio ([0071]: "Using the scores assigned to each time point corresponding to each wave type (e.g., P-wave, QRS complex, T-wave, etc.), delineator 39 may post-process the cardiac signal. Post-processing involves, assigning to each time point, none, one, or several waves, calculating the onset and offset of each of the identified waves, and optionally determining the characterization of the waves. Waves may be assigned to each time point by determining that a wave exists at that time point if a certain value is achieved."). In regards to claim 7, Li discloses that the cardiac pause verification circuit is configured to verify the presence or absence of cardiac pause in the device-detected cardiac pause episode further using one or more of: a count of noise events detected by the medical device from a specific portion of the pre-pause segment, an average heart rate in the pre-pause segment, 'or a second amplitude ratio of a representative amplitude of cardiac events in the post-pause segment to a representative amplitude of the inner pause segment ([0072]: "After computing the onset and offset of each wave type in the cardiac signal, delineator 39 may calculate global measurements. Global measurements are derived from the onset and offset of each wave type and may relate to features and characteristics of the cardiac signal such as intervals between waves and wave durations. For example, global measurements may include, but are not limited to, PR interval, P-wave duration, QRS complex duration, QRS axis, QT interval, corrected QT interval (Qtc), T-wave duration, JT interval, corrected JT interval, heart rate, ST elevation, Sokolov index, number of premature ventricular complexes, number of premature atrial complexes, ratio of non-conducted P waves, and/or ratio of paced waves."). In regards to claim 8, Li discloses that the cardiac signal includes an electrocardiogram (ECG), the device-based detection algorithm includes a first R wave detector, the verification algorithm includes a second R wave detector different from and more sensitive than the first R wave detector in detecting R wave timings in the ECG, wherein the cardiac pause verification circuit is configured to verify the presence or absence of cardiac pause in the device-detected cardiac pause episode further based on a comparison timings of R waves detected by the second R wave detector and timings of R waves detected by the first R wave detector ([0098]: "At step 141, ECG platform 37 computes R-R intervals in the cardiac signal (i.e., ECG data). For example, ECG platform 37 may apply the cardiac signal to the delineation neural network to determine the RR intervals, as described above. At step 142, ECG platform 37 may generate the heart rate plot over time. An exemplary heart rate plot, HRDP 150, is illustrated in FIG. 12."). In regards to claim 9, Li discloses that the verification algorithm includes a decision tree taking the one or more metrics as decision nodes, or neural network model taking the one or more metrics as input layer parameters ([0065]: "The first neural network may provide as an output, values corresponding to the likelihood of the presence of or one or more waves at a plurality of time points in the cardiac signal."). In regards to claim 10, Li discloses an ambulatory medical device (AMD) and an external system operatively in communication with the AMD, wherein the AMD includes circuitry configured to sense physiological information in a patient, to detect the cardiac pause episode using the sensed physiological information, and to store the information about the detected cardiac pause episode, wherein the external system including the cardiac pause verification circuit (see [0077] and [0078]: "Abnormalities and conditions may include any physiological abnormality or condition which may be identifiable on the cardiac signal. Today about 150 measurable abnormalities may be identified on cardiac signal recordings."). In regards to claim 11, Li discloses that the output circuit is configured to: present the information about the device-detected cardiac pause episode on a display in response to the verified presence of cardiac pause in the device-detected cardiac pause episodes and withhold display of the information about the device-detected cardiac pause episode in response to the verified absence of cardiac pause in the device-detected cardiac pause episodes ([0051]: "For example, ECG application 29 may cause system device 14 to receive ECG data from sensing device 13, to record ECG data from sensing device 13, to communicate ECG data to server 15, to instruct server 15 to process and analyze ECG data, to receive processed and/or analyzed ECG data from server 15, to communicate user input regarding report generation to server, and to generate a graphic user interface suitable for displaying raw, analyzed and/or processed ECG data and data related thereto."). In regards to claim 12, Li discloses an event detector circuit configured to determine a risk of syncope in the patient based at least in part on the verified presence or absence of the cardiac pause ([0017]: "The system for analyzing ECG data may further involve a second plurality of instructions designed to, when executed, cause the at least one server to receive the ECG data of the patient, analyze the ECG data of the patient using at least one algorithm trained from a plurality of ECG data sets from different patients, quantify a likelihood of a presence of one or more abnormalities, conditions, or descriptors, or any combination thereof, and transmit information corresponding to the presence of the one or more abnormalities, conditions, or descriptors, or any combination thereof, to a computer remote from the at least one server for display."). In regards to claim 13, Li discloses a method of verifying cardiac pause episodes detected by a medical device in a patient, the method comprising: receiving information about a device-detected cardiac pause episode detected by the medical device using a device-based detection algorithm ([0017]: "A system for analyzing ECG data of a patient may, in one example, involve a first plurality of instructions designed to, when executed, obtain ECG data of the patient over a plurality of time points and may further cause transmission of the ECG data to at least one server. The ECG data may be sampled at a predetermined sampling rate such as a rate of at least 20 samples per second. The system for analyzing ECG data may further involve a second plurality of instructions designed to, when executed, cause the at least one server to receive the ECG data of the patient, analyze the ECG data of the patient using at least one algorithm trained from a plurality of ECG data sets from different patients, quantify a likelihood of a presence of one or more abnormalities, conditions, or descriptors, or any combination thereof, and transmit information corresponding to the presence of the one or more abnormalities, conditions, or descriptors, or any combination thereof, to a computer remote from the at least one server for display."), generating one or more metrics, including at least one entropy metric, from the received information about the device-detected cardiac pause episode ([0112]: "For example, fifth section 185 may include pause information (count and/or longest R-R interval), atrioventricular block information, atrial fibrillation/flutter information, ventricular tachycardia information, other supraventricular tachycardia information, and/or any other information concerning any episodes or abnormalities."), verifying a presence or absence of cardiac pause in the device-detected cardiac pause episode using the generated one or more metrics and a verification algorithm different from the device-based detection algorithm, and providing the verified presence or absence of the cardiac pause to a user or a process ([0089]: "As is shown in FIG. 4, the information generated on back end 46 by ECG platform 37 in steps 54, 56, 58 and 61, and optionally, 63, may be communicated by ECG platform 37 to ECG application 29 on front end 45. ECG application 29 may cause the foregoing information to be displayed on display 17 of system device 14."). In regards to claim 14, Li discloses that the received information about the device-detected cardiac pause episode includes a cardiac signal and event markers generated by and stored in the medical device ([0084]: "Training the neural networks involves applying numerous datasets containing cardiac signals and known outputs to the neural networks. A database of the datasets containing cardiac signals collected across a plurality of patients using the systems and methods described herein may be stored on server 15 and/or drive 16 (e.g., in the cloud)."). In regards to claim 15, Li discloses that the event markers include a pause onset marker marking a beginning of the device-detected cardiac pause episode, and a pause end marker marking an end of the device-detected cardiac pause episode, the pause onset marker and the pause end marker partitioning the cardiac signal into a pre-pause segment prior to the pause onset marker, an inner pause segment between the pause onset marker and the pause end marker, and a post-pause segment following the pause end marker ([0092]: "In this exemplary display, secondary labels 112 include beat label PVC (premature ventricular complex) and PSVC (premature supraventricular complex), though it is understood that other secondary labels may be included. The points in the plot 110 associated with the label PVC and PSVC are colored, as shown in FIG. 9 by the presence of points of color different from black."). In regards to claim 17, Li discloses that the at least one entropy metric includes a sample entropy of the inner pause segment of the cardiac signal, the one or more metrics include a first amplitude ratio of a representative cardiac event amplitude in the pre-pause segment to a representative amplitude of the inner pause segment, wherein verifying the presence or absence of cardiac pause in the device-detected cardiac pause episode includes using the sample entropy of the inner pause segment and the first amplitude ratio ([0071]: "Using the scores assigned to each time point corresponding to each wave type (e.g., P-wave, QRS complex, T-wave, etc.), delineator 39 may post-process the cardiac signal. Post-processing involves, assigning to each time point, none, one, or several waves, calculating the onset and offset of each of the identified waves, and optionally determining the characterization of the waves. Waves may be assigned to each time point by determining that a wave exists at that time point if a certain value is achieved."). In regards to claim 18, Li discloses that verifying the presence or absence of cardiac pause in the device-detected cardiac pause episode includes using one or more of: a count of noise events detected by the medical device from a specific portion of the pre-pause segment, an average heart rate in the pre-pause segment, or a second amplitude ratio of a representative amplitude of cardiac events in the post-pause segment to a representative amplitude of the inner pause segment ([0072]: "After computing the onset and offset of each wave type in the cardiac signal, delineator 39 may calculate global measurements. Global measurements are derived from the onset and offset of each wave type and may relate to features and characteristics of the cardiac signal such as intervals between waves and wave durations. For example, global measurements may include, but are not limited to, PR interval, P-wave duration, QRS complex duration, QRS axis, QT interval, corrected QT interval (Qtc), T-wave duration, JT interval, corrected JT interval, heart rate, ST elevation, Sokolov index, number of premature ventricular complexes, number of premature atrial complexes, ratio of non-conducted P waves, and/or ratio of paced waves."). In regards to claim 19, Li discloses that the cardiac signal includes an electrocardiogram (ECG), the device-based detection algorithm includes a first R wave detector, the verification algorithm includes a second R wave detector different from and more sensitive than the first R wave detector in detecting R wave timings in the ECG, wherein verifying the presence or absence of cardiac pause in the device-detected cardiac pause episode is based at least in part on a comparison of timings of R waves detected by the second R wave detector and timings of R waves detected by the first R wave detector ([0098]: "At step 141, ECG platform 37 computes R-R intervals in the cardiac signal (i.e., ECG data). For example, ECG platform 37 may apply the cardiac signal to the delineation neural network to determine the RR intervals, as described above. At step 142, ECG platform 37 may generate the heart rate plot over time. An exemplary heart rate plot, HRDP 150, is illustrated in FIG. 12."). In regards to claim 20, Li discloses presenting the information about the device-detected cardiac pause episode on a display in response to the verified presence of cardiac pause in the device-detected cardiac pause episodes and withholding display of the information about the device-detected cardiac pause episode in response to the verified absence of cardiac pause in the device-detected cardiac pause episodes ([0051]: "For example, ECG application 29 may cause system device 14 to receive ECG data from sensing device 13, to record ECG data from sensing device 13, to communicate ECG data to server 15, to instruct server 15 to process and analyze ECG data, to receive processed and/or analyzed ECG data from server 15, to communicate user input regarding report generation to server, and to generate a graphic user interface suitable for displaying raw, analyzed and/or processed ECG data and data related thereto.") . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Burton (U.S. PGPub No. 2007/0032733) . In regards to claims 5 and 16, Li discloses the invention substantially as disclosed in claims 3 and 15. However, Li does not disclose at least one entropy metric including a Shannon entropy or using the Shannon entropy to verify the presence or absence of a cardiac pause. Burton teaches a device for detecting cardiac events which uses Shannon entropy in its ECG analysis in an interval preceding the R-wave ([0272]: "Proportional Shannon entropy (H(RI-1)) of RI(-1) interval (interval between inspiration and preceding ECG R wave) as a measure of coupling, no correlation between H(RI-1) and either the fractal dimension or approximate entropy of the heart rate time series..."). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the cardiac pause detector to use Shannon entropy in the analysis of the pre-pause segment, as taught by Burton, as doing so would provide one of several ways to analyze cardiac measurements such as an ECG or for detecting possible cardiac pause irregularities or patterns ([0227]: "Breath by breath respiration or ECG analysis may include a combination of real-time on-line analysis during recording or post acquisition analysis including a combination of one or more of the following…"). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN M LEE whose telephone number is (703)756-1789. The examiner can normally be reached 9:00 am - 6:00 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, Carl Layno can be reached at (571) 272-4949. 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. /B.M.L./Examiner, Art Unit 3796 /CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796 Application/Control Number: 18/798,424 Page 2 Art Unit: 3796 Application/Control Number: 18/798,424 Page 3 Art Unit: 3796 Application/Control Number: 18/798,424 Page 4 Art Unit: 3796
