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 .
Status of Claims
Claims 1-20 are presently pending and under examination.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 22 July 2024 and 23 June 2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1
Claims 1-20 are directed to statutory subject matter as the claims recite a device and a method.
Step 2A, Prong One
The Examiner has identified device claim 1 as the claim that represents the claimed invention for analysis and is similar to the method of claim 14.
Claim 1 recites the limitations of:
A medical device system, comprising:
a signal receiver circuit configured to receive physiologic information of a patient; and
an assessment circuit configured to:
determine indications of atrial fibrillation of the patient in respective detection windows of a day using the received physiologic information;
record first physiologic information of the patient at a first sampling frequency for the determined indications of atrial fibrillation of the patient up to and not exceeding a first threshold of the medical device system for transmission to a remote device; and
determine and record one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation of the patient at a second sampling frequency lower than the first sampling frequency without regard to the first threshold.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as “mental processes” because the steps of determine indications of atrial fibrillation of the patient in respective detection windows of a day using the received physiologic information; record first physiologic information of the patient at a first sampling frequency for the determined indications of atrial fibrillation of the patient up to and not exceeding a first threshold of the medical device system for transmission to a remote device; and determine and record one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation of the patient at a second sampling frequency lower than the first sampling frequency without regard to the first threshold are akin to a doctor gathering physiological information and determining indications of an atrial fibrillation at different sampling rates. Therefore, these steps may be performed mentally by a human actor. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation as a mental process, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See also MPEP 2106.04 (a)(2) III C where use of a generic computer (i.e. processor, memory, computer) to perform a judicial exception has been shown to be abstract. Claim 14 is also abstract for similar reasons.
Step 2A, Prong Two
This judicial exception is not integrated into a practical application because there are no improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05(a)). There is no improvement to the signal receiver circuit and/or recited assessment circuit. These additional elements are generic and perform conventional manner whether considered individually or in combination. There is no application or use of the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, but merely diagnosis of a generic fibrillation (see Vando Memo). The judicial exception is not applied with, or used by, a particular machine (see MPEP 2106.05(b)). Claim 1 and 14 only recite the additional limitations “a signal receiver circuit”, “assessment circuit”, and “remote device”, which function in their usual capacity and are cited with a high level of generality and amount to nothing more than parts of a generic computer (i.e. most generic computer and medical systems would be known to have these components for general computation, processing, sensing data, and output data) which is well-understood, routine, and conventional in the field of data processing and computing technology. There is no transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05 (c)), as data is simply acquired and assesses. There is no application or use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to the atrial fibrillation analysis environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and the Vanda Memo). Instructions to implement the abstract idea with a circuit(s) merely uses the circuit as a tool to perform the abstract idea (see MPEP 2106.05(f)). The acquisition of physiological information is considered insignificant extra-solution activity to the judicial exception as this additional element would require in all uses of the judicial exception (see MPEP 2106.05 (g)). Acquisition of physiological information represents mere data gathering and is well-known in atrial fibrillation art and poses no meaningful limits on the claims.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of all the reasons already elaborated above. The additional elements of the signal receiver circuit, the assessment circuit, and remote device individually and in combination are all WURC in the art. The applicant discloses that the circuitry may take many forms including personal computers, tablet PCs, PDAs and any other machine that is capable of executing instructions to perform any one or more of the methodologies discussed (see for example par. [0089], [0091], [0097])
Related comments apply to substantially similar and patentably indistinct claim 14.
The mentally performable limitations of claims 2-13 and 15-20 do not contain any additional elements outside of the abstract idea other than those already discussed above (i.e., the signal receiver circuit, assessment circuit, and remote device).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 –
(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.
(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.
Claim(s) 1-3, 5-8, 10, 12, 14-15, 17 and 19is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Szabados et al. (US 2021/0244339 A1), hereinafter Szabados.
Regarding claim 1, Szabados discloses a medical device system (Abstract: “a non-invasive cardiac monitoring device”), comprising:
A signal receiver circuit (physiological monitoring device 100) configured to receive physiological information of a patient ([0148] “The electrodes are configured to sense heart rhythm signals from a patient to which monitoring device 100 is attached.”, [0003] “, the device is specifically designed to sense and record cardiac rhythm (for example, electrocardiogram, ECG) data, although in various alternative embodiments one or more additional physiological parameters may be sensed and recorded”, [0178] “The wearable device can comprise sensors for detecting bio-signals of a user, such as ECG signals.”); and
An assessment circuit (hardware processor) configured to:
Determine indications of atrial fibrillation of the patient in respective detection windows of a day using the received physiological information ([0180] “There has been a lot of recent activity exploring the usage of wearable sensors to detect cardiac arrhythmias like atrial fibrillation (AFib)… detect AFib more accurately using heart rate data derived from ECG signals recorded using a wearable cardiac monitor like the ECG monitoring devices”, [0010] “a hardware processor configured to process the detected cardiac signals through a first subset of layers of a neural network; and a transmitter, the transmitter configured to transmit the output of the first subset to a computing system, the computing system configured to infer a likelihood of an occurrence of cardiac arrhythmia by processing the output through a second subset of layers of the neural network”)
record first physiological information of the patient at a first sampling frequency for the determined indications of atrial fibrillation of the patient ([0182] “a patient's ECG signal recorded using a cardiac monitoring device is typically segmented into episodes of varying durations and of different rhythm types…Various heart conditions can manifest as patterns of irregular/abnormal heart beats known as cardiac arrhythmias which in turn translate into irregular/abnormal patterns in the ECG signal. There are various types of cardiac arrhythmias such as atrial fibrillation.”, Since the monitoring device is recording the ECG signal, Examiner notes that the cardiac monitoring device records the ECG signal and thus the frequency at which the signal is recorded is interpreted to be the first sampling frequency.) up to and not exceeding a first threshold of the medical device system for transmission to a remote device ([0185] “The ECG signal recorded from a patient can be segmented into episodes of different rhythm types and then the AFib burden can be compute by calculating the fraction of time spent in the AFib state. However, if the computation is based only on the extracted sequence of R-peak locations (and not the whole ECG signal), estimating the AFib burden becomes a different algorithmic problem. A scenario where such an algorithm could be useful is when we might want to avoid excessive computations on the monitoring device itself (using the entire ECG signal) or when we might want to limit the amount of data that is transmitted from the monitoring device using wireless communications to computer servers where the AFib burden is calculated.”, Examiner notes that the prior art reference recognizes placing a limit on the amount of data that is transmitted and interpreted this limit to read on the threshold for transmission.)
determine and record one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation of the patient ([0183] “A key signature of AFib episodes is high RR-interval variability.”) at a second sampling frequency lower than the first sampling frequency without regard to the first threshold ([0224] “The patch 2281 can transmit data of a smaller data dimension to server 2282 for the server 2282 to recreate the original input signal, or a derivative thereof, such as the original input signal sampled at a lower frequency, lower quality signal, signal with a certain threshold for reconstruction error, and/or the like”, [0239], [0188]-[0189] “segregated processing of the data can have many technical benefits. For example, such selective transmission of data that is partially processed can decrease power consumption for the wearable path. The wearable patch is not required to store the entire data processing algorithm (such as the entire neural network), but may only need to retrieve a subset of the neural network layers to process. Thus, the data processing on the wearable device can be much faster, require less computations, and use less battery via the processing.”)
Regarding claim 2, Szabados teaches the medical device system of claim 1 (as shown above), wherein to determine and record the one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation of the patient without regard to the first threshold comprises to determine and record one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation without regard to a limit, including to determine and record one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation up to and exceeding the first threshold ([0185] “f the computation is based only on the extracted sequence of R-peak locations (and not the whole ECG signal), estimating the AFib burden becomes a different algorithmic problem. A scenario where such an algorithm could be useful is when we might want to avoid excessive computations on the monitoring device itself (using the entire ECG signal) or when we might want to limit the amount of data that is transmitted from the monitoring device using wireless communications to computer servers where the AFib burden is calculated.”, [0188]-[0189] “In some embodiments, the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows… For the purposes of explaining the concept, w was chosen to be 30 seconds, s chosen to be 25 seconds and M chosen to be 42. The portion of the neural network (e.g., a first subset of layers of the neural network) on the monitor can process the detected cardiac signals to generate the RR-interval data (and/or the RR-interval sub-sequences), and transmit the RR-interval data to a remote computing device. The remote computing device can process the RR-interval data (such as by processing through a second portion of the neural network (e.g., a second subset of layers of the neural network) to make inferences, such as a likelihood of arrythmia…segregated processing of the data can have many technical benefits. For example, such selective transmission of data that is partially processed can decrease power consumption for the wearable path.”, [0183])
Regarding claim 3, Szabados discloses the medical device system of claim 1 (as shown above), wherein the first threshold is representative of a first data limit for storage or transmission by the assessment circuit for a determined indication of atrial fibrillation ([0263], [0185] “A scenario where such an algorithm could be useful is when we might want to avoid excessive computations on the monitoring device itself (using the entire ECG signal) or when we might want to limit the amount of data that is transmitted from the monitoring device using wireless communications to computer servers where the AFib burden is calculated.”).
Regarding claim 5, Szabados discloses the medical device system of claim 1 (as shown above), wherein the assessment circuit is configured to determine and record the one or more key metrics of atrial fibrillation for one or more detection windows of each of the determined indications of atrial fibrillation of the patient for transmission to the remote device, wherein each determined indication of atrial fibrillation comprises one or more detection windows ([0183] “key signature of AFib episodes is high RR-interval variability”, [0187] “the algorithm (e.g., a neural network) can estimate AFib burden over shorter analysis windows (e.g., duration in the order of a half hour or one hour).”, [0188]-[0189] “In some embodiments, the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows. For example, the burden can be accumulated over non-overlapping analysis windows of half-hour duration. Given the R-peak sequence over an analysis window of half-hour, the RR-interval sequence within the analysis window is derived.”).
Regarding claim 6, Szabados discloses the medical device system of claim 1 (as shown above), wherein the assessment circuit is configured to determine and record the one or more key metrics of atrial fibrillation for each detection window of the determined indications of atrial fibrillation, wherein each determined indication of atrial fibrillation comprises one or more detection windows ([0183] “key signature of AFib episodes is high RR-interval variability”, [0187] “the algorithm (e.g., a neural network) can estimate AFib burden over shorter analysis windows (e.g., duration in the order of a half hour or one hour).”, [0188]-[0189] “In some embodiments, the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows. For example, the burden can be accumulated over non-overlapping analysis windows of half-hour duration. Given the R-peak sequence over an analysis window of half-hour, the RR-interval sequence within the analysis window is derived.”).
Regarding claim 7, Szabados discloses the medical device system of claim 1 (as shown above), wherein the assessment circuit is configured to determine and record the one or more key metrics of atrial fibrillation for each detection window of the determined indications of atrial fibrillation and at least one detection window preceding or following the determined indications of atrial fibrillation, wherein each determined indication of atrial fibrillation comprises one or more detection windows ([0183] “key signature of AFib episodes is high RR-interval variability”, [0187] “the algorithm (e.g., a neural network) can estimate AFib burden over shorter analysis windows (e.g., duration in the order of a half hour or one hour).”, [0188]-[0189] “In some embodiments, the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows. For example, the burden can be accumulated over non-overlapping analysis windows of half-hour duration. Given the R-peak sequence over an analysis window of half-hour, the RR-interval sequence within the analysis window is derived.”).
Regarding claim 8, Szabados discloses the medical device system of claim 1 (as shown above), wherein the assessment circuit is configured to determine and record the one or more key metrics of atrial fibrillation for each detection window of the day, including detection windows with and without determined indications of atrial fibrillation ([0185], [0188]-[0189] “the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows. For example, the burden can be accumulated over non-overlapping analysis windows of half-hour duration. Given the R-peak sequence over an analysis window of half-hour, the RR-interval sequence within the analysis window is derived… The temporal sequence feeds into a neural network model for making burden predictions”, [0178]-[0181] “AFib is a fraction of time that a patient spends in the AFib state”, Examiner notes that it is inherent to record key metrics during detection windows that do not indications of atrial fibrillation as AFib Burden is fraction between AFib occurring and not occurring.).
Regarding claim 10, Szabados discloses the medical device system of claim 1 (as shown above), wherein each determined indication of atrial fibrillation comprises one or more detection windows, wherein the one or more key metrics of atrial fibrillation comprise, for the one or more detection windows of each determined indication of atrial fibrillation, a measure or value of one or more of: R-R variability; timing intervals between successive valid R waves; R-wave morphology; P-wave presence; and heart rate ([0183] “Some embodiments detect R-peak signals from the ECG signal. Once R-peaks are detected, the instantaneous heart rate can be estimated as the inverse of the duration between two consecutive R-peaks (referred to as the RR-interval). A key signature of AFib episodes is high RR-interval variability.”, [0188]-[0190]).
Regarding claim 12, Szabados discloses the medical device system of claim 1 (as shown above), wherein the assessment circuit is configured to determine an indication of atrial fibrillation burden for the patient using the one or more recorded key metrics of atrial fibrillation ([0183] “detect R-peak signals from the ECG signal. Once R-peaks are detected, the instantaneous heart rate can be estimated as the inverse of the duration between two consecutive R-peaks (referred to as the RR-interval). A key signature of AFib episodes is high RR-interval variability.”, [0185] “the computation is based only on the extracted sequence of R-peak locations (and not the whole ECG signal), estimating the AFib burden becomes a different algorithmic problem”).
Regarding claim 14, Szabados discloses a method ([0235]), comprising: receiving physiologic information of a patient using a signal receiver circuit (physiological monitoring device 100, [0148] “The electrodes are configured to sense heart rhythm signals from a patient to which monitoring device 100 is attached.”, [0003] “the device is specifically designed to sense and record cardiac rhythm (for example, electrocardiogram, ECG) data, although in various alternative embodiments one or more additional physiological parameters may be sensed and recorded”, [0178] “The wearable device can comprise sensors for detecting bio-signals of a user, such as ECG signals.”); and using an assessment circuit (hardware processor): determining indications of atrial fibrillation of the patient in respective detection windows of a day using the received physiologic information ([0180] “There has been a lot of recent activity exploring the usage of wearable sensors to detect cardiac arrhythmias like atrial fibrillation (AFib)… detect AFib more accurately using heart rate data derived from ECG signals recorded using a wearable cardiac monitor like the ECG monitoring devices”, [0010] “a hardware processor configured to process the detected cardiac signals through a first subset of layers of a neural network; and a transmitter, the transmitter configured to transmit the output of the first subset to a computing system, the computing system configured to infer a likelihood of an occurrence of cardiac arrhythmia by processing the output through a second subset of layers of the neural network”); recording first physiologic information of the patient at a first sampling frequency for the determined indications of atrial fibrillation of the patient ([0182] “a patient's ECG signal recorded using a cardiac monitoring device is typically segmented into episodes of varying durations and of different rhythm types…Various heart conditions can manifest as patterns of irregular/abnormal heart beats known as cardiac arrhythmias which in turn translate into irregular/abnormal patterns in the ECG signal. There are various types of cardiac arrhythmias such as atrial fibrillation.”, Since the monitoring device is recording the ECG signal, Examiner notes that the cardiac monitoring device records the ECG signal and thus the frequency at which the signal is recorded is interpreted to be the first sampling frequency.) up to and not exceeding a first threshold for transmission to a remote device ([0185] “The ECG signal recorded from a patient can be segmented into episodes of different rhythm types and then the AFib burden can be compute by calculating the fraction of time spent in the AFib state. However, if the computation is based only on the extracted sequence of R-peak locations (and not the whole ECG signal), estimating the AFib burden becomes a different algorithmic problem. A scenario where such an algorithm could be useful is when we might want to avoid excessive computations on the monitoring device itself (using the entire ECG signal) or when we might want to limit the amount of data that is transmitted from the monitoring device using wireless communications to computer servers where the AFib burden is calculated.”, Examiner notes that the prior art reference recognizes placing a limit on the amount of data that is transmitted and interpreted this limit to read on the threshold for transmission.); and determining and recording one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation of the patient ([0183] “A key signature of AFib episodes is high RR-interval variability.”) at a second sampling frequency lower than the first sampling frequency without regard to the first threshold ([0224] “The patch 2281 can transmit data of a smaller data dimension to server 2282 for the server 2282 to recreate the original input signal, or a derivative thereof, such as the original input signal sampled at a lower frequency, lower quality signal, signal with a certain threshold for reconstruction error, and/or the like”, [0239], [0188]-[0189] “segregated processing of the data can have many technical benefits. For example, such selective transmission of data that is partially processed can decrease power consumption for the wearable path. The wearable patch is not required to store the entire data processing algorithm (such as the entire neural network), but may only need to retrieve a subset of the neural network layers to process. Thus, the data processing on the wearable device can be much faster, require less computations, and use less battery via the processing.”).
Regarding claim 15, Szabados discloses the method of claim 14 (as shown above), wherein determining and recording the one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation of the patient without regard to the first threshold comprises determining and recording one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation without regard to a limit, including determining and recording one or more key metrics of atrial fibrillation for the determined indications of atrial fibrillation up to and exceeding the first threshold ([0185] “If the computation is based only on the extracted sequence of R-peak locations (and not the whole ECG signal), estimating the AFib burden becomes a different algorithmic problem. A scenario where such an algorithm could be useful is when we might want to avoid excessive computations on the monitoring device itself (using the entire ECG signal) or when we might want to limit the amount of data that is transmitted from the monitoring device using wireless communications to computer servers where the AFib burden is calculated.”, [0188]-[0189] “In some embodiments, the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows… For the purposes of explaining the concept, w was chosen to be 30 seconds, s chosen to be 25 seconds and M chosen to be 42. The portion of the neural network (e.g., a first subset of layers of the neural network) on the monitor can process the detected cardiac signals to generate the RR-interval data (and/or the RR-interval sub-sequences), and transmit the RR-interval data to a remote computing device. The remote computing device can process the RR-interval data (such as by processing through a second portion of the neural network (e.g., a second subset of layers of the neural network) to make inferences, such as a likelihood of arrythmia…segregated processing of the data can have many technical benefits. For example, such selective transmission of data that is partially processed can decrease power consumption for the wearable path.”, [0183]).
Regarding claim 17, Szabados discloses the method of claim 14 (as shown above), wherein determining and recording the one or more key metrics of atrial fibrillation comprises determining and recording the one or more key metrics of atrial fibrillation for one or more detection windows of each of the determined indications of atrial fibrillation of the patient for transmission to the remote device, wherein each determined indication of atrial fibrillation comprises one or more detection windows ([0183] “key signature of AFib episodes is high RR-interval variability”, [0187] “the algorithm (e.g., a neural network) can estimate AFib burden over shorter analysis windows (e.g., duration in the order of a half hour or one hour).”, [0188]-[0189] “In some embodiments, the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows. For example, the burden can be accumulated over non-overlapping analysis windows of half-hour duration. Given the R-peak sequence over an analysis window of half-hour, the RR-interval sequence within the analysis window is derived.”)..
Regarding claim 19, Szabados discloses the method of claim 14 (as shown above), wherein each determined indication of atrial fibrillation comprises one or more detection windows, wherein the one or more key metrics of atrial fibrillation comprise, for the one or more detection windows of each determined indication of atrial fibrillation, a measure or value of one or more of: R-R variability; timing intervals between successive valid R waves; R-wave morphology; P-wave presence; and heart rate ([0183] “Some embodiments detect R-peak signals from the ECG signal. Once R-peaks are detected, the instantaneous heart rate can be estimated as the inverse of the duration between two consecutive R-peaks (referred to as the RR-interval). A key signature of AFib episodes is high RR-interval variability.”, [0188]-[0190]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Szabados in view of Wang et al. (US 2002/0065473 A1), hereinafter Wang.
Regarding claim 4, Szabados discloses the medical device system of claim 1 (as shown above). Szabados fails to explicitly disclose wherein the first threshold comprises a daily threshold for determined indications of atrial fibrillation, wherein the assessment circuit is configured to cease recording the first physiologic information of the patient for a remainder of the day after an amount of the recorded first physiologic information for the day has met or exceeded the first threshold.
However, Wang teaches a method and apparatus for the detection of atrial fibrillation (Abstract, [0002]) wherein “Because the memory size is preferably quite limited a time record or first-in-first-out pool record should be kept on order that the newest triggers record only over the oldest events segments. An additional preferred feature allows for a mode that prevents recording over any triggered event segment. This is preferably implemented by a counter, which fills for each segment used and has storage for the set number of looping segments. When the counter is full, recording of new events stops.” ([0063]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Szabados to incorporate the teachings of Wang to have the first threshold comprises a daily threshold for determined indications of atrial fibrillation, wherein the assessment circuit is configured to cease recording the first physiologic information of the patient for a remainder of the day after an amount of the recorded first physiologic information for the day has met or exceeded the first threshold, as these prior art references and the instant application are directed to detecting atrial fibrillation. One would be motivated to do this as the memory size is limited, as recognized by Wang ([0063]).
Regarding claim 16, Szabados discloses the method of claim 14 (as shown above), wherein determining and recording the one or more key metrics of atrial fibrillation comprises determining and recording the one or more key metrics of atrial fibrillation for each detection window of the day, including detection windows with and without determined indications of atrial fibrillation ([0185], [0188]-[0189] “the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows. For example, the burden can be accumulated over non-overlapping analysis windows of half-hour duration. Given the R-peak sequence over an analysis window of half-hour, the RR-interval sequence within the analysis window is derived… The temporal sequence feeds into a neural network model for making burden predictions”, [0178]-[0181] “AFib is a fraction of time that a patient spends in the AFib state”, Examiner notes that it is inherent to record key metrics during detection windows that do not indications of atrial fibrillation as AFib Burden is fraction between AFib occurring and not occurring.).
Szabados fails to explicitly disclose wherein the first threshold comprises a daily threshold for determined indications of atrial fibrillation, wherein the method includes, using the assessment circuit, ceasing recording the first physiologic information of the patient for a remainder of the day after an amount of the recorded first physiologic information for the day has met or exceeded the first threshold.
However, Wang teaches a method and apparatus for the detection of atrial fibrillation (Abstract, [0002]) wherein “Because the memory size is preferably quite limited a time record or first-in-first-out pool record should be kept on order that the newest triggers record only over the oldest events segments. An additional preferred feature allows for a mode that prevents recording over any triggered event segment. This is preferably implemented by a counter, which fills for each segment used and has storage for the set number of looping segments. When the counter is full, recording of new events stops.” ([0063]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Szabados to incorporate the teachings of Wang to have the first threshold comprises a daily threshold for determined indications of atrial fibrillation, wherein the method includes, using the assessment circuit, ceasing recording the first physiologic information of the patient for a remainder of the day after an amount of the recorded first physiologic information for the day has met or exceeded the first threshold, as these prior art references and the instant application are directed to detecting atrial fibrillation. One would be motivated to do this as the memory size is limited, as recognized by Wang ([0063]).
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Szabados in view of Saha et al. (US 2020/0323459 A1), hereinafter Saha.
Regarding claim 4, Szabados discloses the medical device system of claim 1 (as shown above). Szabados fails to explicitly disclose wherein the first threshold comprises a daily threshold for determined indications of atrial fibrillation, wherein the assessment circuit is configured to cease recording the first physiologic information of the patient for a remainder of the day after an amount of the recorded first physiologic information for the day has met or exceeded the first threshold.
However, Saha teaches systems, devices, and methods for long-duration arrhythmia detection wherein “after a threshold time period (e.g., after 10 minutes of consecutive periods of detected AF) or a number of positive event windows, morphology evaluation of the received cardiac electrical information (e.g., comparison of the received cardiac electrical information to a template, etc.) may cease” ([0047]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Szabados to incorporate the teachings of Saha to have the first threshold comprises a daily threshold for determined indications of atrial fibrillation, wherein the assessment circuit is configured to cease recording the first physiologic information of the patient for a remainder of the day after an amount of the recorded first physiologic information for the day has met or exceeded the first threshold, as these prior art references and the instant application are directed to detecting atrial fibrillation. One would be motivated to do this reducing required resources to maintain event detection, as recognized by ([0047], [0057]).
Regarding claim 16, Szabados discloses the method of claim 14 (as shown above), wherein determining and recording the one or more key metrics of atrial fibrillation comprises determining and recording the one or more key metrics of atrial fibrillation for each detection window of the day, including detection windows with and without determined indications of atrial fibrillation ([0185], [0188]-[0189] “the burden over a longer period of time (e.g., longer than the shorter analysis windows) can be determined by accumulating the burden predictions over the analysis windows. For example, the burden can be accumulated over non-overlapping analysis windows of half-hour duration. Given the R-peak sequence over an analysis window of half-hour, the RR-interval sequence within the analysis window is derived… The temporal sequence feeds into a neural network model for making burden predictions”, [0178]-[0181] “AFib is a fraction of time that a patient spends in the AFib state”, Examiner notes that it is inherent to record key metrics during detection windows that do not indications of atrial fibrillation as AFib Burden is fraction between AFib occurring and not occurring.).
Szabados fails to explicitly disclose wherein the first threshold comprises a daily threshold for determined indications of atrial fibrillation, wherein the method includes, using the assessment circuit, ceasing recording the first physiologic information of the patient for a remainder of the day after an amount of the recorded first physiologic information for the day has met or exceeded the first threshold.
However, Saha teaches systems, devices, and methods for long-duration arrhythmia detection wherein “after a threshold time period (e.g., after 10 minutes of consecutive periods of detected AF) or a number of positive event windows, morphology evaluation of the received cardiac electrical information (e.g., comparison of the received cardiac electrical information to a template, etc.) may cease” ([0047]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Szabados to incorporate the teachings of Saha to have the first threshold comprises a daily threshold for determined indications of atrial fibrillation, wherein the method includes, using the assessment circuit, ceasing recording the first physiologic information of the patient for a remainder of the day after an amount of the recorded first physiologic information for the day has met or exceeded the first threshold, as these prior art references and the instant application are directed to detecting atrial fibrillation. One would be motivated to do this reducing required resources to maintain event detection, as recognized by ([0047], [0057]).
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Szabados in view of Ben-David et al. (US 2020/0170526 A1), hereinafter Ben-David.
Regarding claim 9, Szabados discloses the medical device system of claim 1 (as shown above), comprising an implantable or an ambulatory medical device (Figure 1A-1B: physiological monitoring device 100) including the signal receiver circuit and the assessment circuit ([0010] “the wearable device comprising: an adhesive assembly comprising a housing and a wing, the wing comprising an electrode configured to detect cardiac signals from a user; a hardware processor configured to process the detected cardiac signals through a first subset of layers of a neural network;”), wherein the detection window has a duration between 30 seconds and 2 minutes ([0188] “sliding windows of duration w (contained with the analysis window)…w was chosen to be 30 seconds”).
Szabados fails to disclose wherein the first physiologic information of the patient at the first sampling frequency comprises an ECG signal of the patient at a sampling frequency greater than 100 samples per second for the determine indications of atrial fibrillation up to and not exceeding the first threshold of the medical device system, wherein the one or more key metrics of atrial fibrillation comprise one or more measures or values representative of one or more detection windows of the determined indications at the second sampling frequency having fewer than 5 samples per detection window.
However, Ben-David an implantable device for analyzing a high frequency electrogram signal (Abstract) for the detection of atrial fibrillation ([0325]) wherein to facilitate the saving the device is configured to sample the electrogram is sampled at a high sampling rate, e.g. 1000 samples per second ([0365]) and at other times, the signal may be sampled at a lower frequency, for example fewer than 150 samples per second ([0365]) or even lower than 60 samples per second ([0377]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Szabados to incorporate the teachings of Ben-David to have the first physiologic information of the patient at the first sampling frequency comprises an ECG signal of the patient at a sampling frequency greater than 100 samples per second for the determine indications of atrial fibrillation up to and not exceeding the first threshold of the medical device system, wherein the one or more key metrics of atrial fibrillation comprise one or more measures or values representative of one or more detection windows of the determined indications at the second sampling frequency having fewer than 5 samples per detection window, as these prior art references are directed to detecting atrial fibrillation while conserving power. One would be motivated to do this for facilitating saving power, as recognized by Ben-David ([0364]-[0365]).
Regarding claim 18, Szabados discloses the method of claim 14 (as shown above), wherein the detection window has a duration between 30 seconds and 2 minutes ([0188] “sliding windows of duration w (contained with the analysis window)…w was chosen to be 30 seconds”).
Szabados fails to disclose wherein the first physiologic information of the patient at the first sampling frequency comprises an ECG signal of the patient at a sampling frequency greater than 100 samples per second for the determine indications of atrial fibrillation up to and not exceeding the first threshold of the medical device system, wherein the one or more key metrics of atrial fibrillation comprise one or more measures or values representative of one or more detection windows of the determined indications at the second sampling frequency having fewer than 5 samples per detection window.
However, Ben-David an implantable device for analyzing a high frequency electrogram signal (Abstract) for the detection of atrial fibrillation ([0325]) wherein to facilitate the saving the device is configured to sample the electrogram is sampled at a high sampling rate, e.g. 1000 samples per second ([0365]) and at other times, the signal may be sampled at a lower frequency, for example fewer than 150 samples per second ([0365]) or even lower than 60 samples per second ([0377]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Szabados to incorporate the teachings of Ben-David to have the first physiologic information of the patient at the first sampling frequency comprises an ECG signal of the patient at a sampling frequency greater than 100 samples per second for the determine indications of atrial fibrillation up to and not exceeding the first threshold of the medical device system, wherein the one or more key metrics of atrial fibrillation comprise one or more measures or values representative of one or more detection windows of the determined indications at the second sampling frequency having fewer than 5 samples per detection window, as these prior art references are directed to detecting atrial fibrillation while conserving power. One would be motivated to do this for facilitating saving power, as recognized by Ben-David ([0364]-[0365]).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Szabados in view of Thakur et al. (US Patent 9,999,359 B2), hereinafter Thakur.
Regarding claim 11, Szabados discloses the medical device system of claim 10 (as shown above) and further discloses “the Rules Database 14060 may, in some embodiments, store data (for example, instructions, preferences, profile) that establish parameters for the thresholds for analyzing the feature data.” ([0283]). Szabados fails to explicitly teach wherein the measure or value of one or more of R-R variability, timing intervals between successive valid R waves, and heart rate comprises an indication that one or more of R-R variability, timing intervals between successive valid R waves, and heart rate is above or below a patient-specific or population threshold for the one or more detection windows of each determined indication of atrial fibrillation.
However, Thakur teaches a system and method for detecting atrial fibrillation (Abstract) wherein the measure or value of one or more of R-R variability, timing intervals between successive valid R waves, and heart rate comprises an indication that one or more of R-R variability, timing intervals between successive valid R waves, and heart rate is above or below a patient-specific or population threshold for the one or more detection windows of each determined indication of atrial fibrillation (Column 18, lines 11-15: “In an example, one or more of the heart rate criteria, hemodynamic status criteria, or the activity or exertion criteria can include respective threshold values that can be determined based on population-based statistics or patient-specific statistics.”, Column 22, lines 13-22 : “If the sensed HR does not exceed the threshold value HR.sub.TH1 and the sensed HRV does not exceed the threshold HRV.sub.TH1, no initial AF onset event is deemed detected; and the AF onset detection process continues by receiving a cardiac activity signal at 801. However, if the sensed HR exceeds the threshold value HR.sub.TH1 or the sensed HRV exceeds the threshold HRV.sub.TH1, an AF onset event is deemed initially detected in the first stage detection, and a second stage AF onset event confirmation can by initiated by receiving a heart sound (HS) signal at 805.”, Claim 5)
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Szabados to incorporate the teachings of Thakur to measure or value of one or more of R-R variability, timing intervals between successive valid R waves, and heart rate comprises an indication that one or more of R-R variability, timing intervals between successive valid R waves, and heart rate is above or below a patient-specific or population threshold for the one or more detection windows of each determined indication of atrial fibrillation, as these prior art references are directed to detecting atrial fibrillation. One would be motivated to do this to indicate when a parameter is indicative of an atrial fibrillation.
Claim(s) 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Szabados as applied to claims 12 and 14 above, and further in view of Babaeizadeh et al. (US Patent 8,560,058 B2), hereinafter Babaeizadeh.
Regarding claim 13 and 20, Szabados discloses the medical device system of claim 12 and the method of claim 14 (as shown above). Szabados fails to explicitly disclose wherein the assessment circuit is configured to determine the indication of atrial fibrillation burden for the patient using a comparison of the one or more recorded key metrics to commensurate metrics for adjudicated episodes
However, Babaeizadeh teaches an atrial fibrillation monitor wherein “a beat classifier 48 compares each new beat with previous beats and classifies beats as normal (regular) for the individual or abnormal (irregular).” (Column 3, lines 24-26) and “the foregoing P wave and R-R interval features may be combined to identify AF by AF=[(irregular rhythm) AND (no P wave OR irregular P-R interval OR poor P wave template match)]” (Column 4, lines 45-49) which is used by an AF burden calculator 94 to determine “the calculated AF burden is a statistical calculation representing the frequency and duration of AF episodes. AF burden may also or alternatively be reported as the percentage of some previous time period (e.g., the past 24 hours or the complete monitored time) that the patient's heart has been in atrial fibrillation.” (Column 6, lines 40-45).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Szabados to incorporate the teachings of Babaeizadeh to have the assessment circuit is configured to determine the indication of atrial fibrillation burden for the patient using a comparison of the one or more recorded key metrics to commensurate metrics for adjudicated episodes, as these prior art references are directed to determining atrial fibrillation burden. One would be motivated to do this to determine atrial fibrillation burden based on an individuals regularity.
Conclusion
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/ATTIYA SAYYADA HUSSAINI/ Examiner, Art Unit 3792
/NIKETA PATEL/ Supervisory Patent Examiner, Art Unit 3792
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