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 .
Response to Arguments
Applicant’s arguments, filed 01/20/2026, with respect to the objection of claim 12 have been fully considered and are persuasive. The objection of claim 12 has been withdrawn.
Applicant’s arguments, filed 01/20/2026, with respect to claims 1 and 3-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Additionally, since the amendments to independent claims 1, 12, and 16 change the scope of claims 1 and 3-20, and do not merely incorporate limitations from previous dependent claims, a new grounds of rejection is made in view of previously applied references as well as new reference Liu et al. (U.S. Pub. No. 2011/0077538 A1) as explained in further detail below. Furthermore, Applicant amended to add new claim 21, which is rejected in view of previously applied references as explained in further detail below.
Applicant contends that the combination of Azevedo and Chen does not teach or suggest at least “generating additional data from piecewise splines of portions of the first ECG data where the candidate pace pulses are identified, wherein the additional data comprises waveform descriptors of the piecewise splines characterizing the candidate pace pulses,” as amended claim 1 recites. These arguments refer to the added claim limitation of “generating additional data from piecewise splines of portions of the first ECG data where the candidate pace pulses are identified, wherein the additional data comprises waveform descriptors of the piecewise splines characterizing the candidate pace pulses” such that the arguments are moot in light of the new scope of the claims.
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 1, 3, 5, 12, 14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Azevedo et al. (hereinafter “Azevedo”) (U.S. Pub. No. 2021/0330216 A1) in view of Chen et al. (hereinafter “Chen”) (U.S. Pub. No. 2023/0190169 A1) and Liu et al. (hereinafter “Liu”) (U.S. Pub. No. 2011/0077538 A1).
Regarding claim 1, Azevedo teaches a system (Abstract, where “A system, a wearable device, and a method are provided which can increase the accuracy of physiological metrics”) comprising:
a backend device (¶[0076], where “The various algorithms … can be … distributed across a wearable device and a remote device (e.g., a paired mobile device, a backend server, the cloud).” Examiner interprets that the remote device is a backend device.); and
a frontend device (¶[0081], where “FIG. 1 illustrates a system diagram of an example of a wearable device 102 … The wearable device 102 can comprise at least two components including a disposable component 110 and an electronics module 120 … electronics module 120 can comprise a computing platform 108, … and sensor interfaces 116,” ¶[0083], where “The computing platform 108 can comprise circuits designed to interface with various sensors and combinations of components of the wearable device 102. For example, the computing platform 108 can provide a combination of analog front-end,” ¶[0090], where “The sensor interface 116 can be disposed between electrodes 150 and a band pass filter or channel. The sensor interface 116 can provide an analog front end”) comprising:
one or more processors (¶[0081], where “¶[0081], where “wearable device 102 can comprise at least two components including a disposable component 110 and an electronics module 120 … The electronics module 120 can comprise … a processor 111 (e.g., a microcontroller unit (MCU))”);
multiple sensors in communication with the one or more processors (¶[0081], where “The wearable device 102 can comprise at least two components including a disposable component 110 and an electronics module 120 … electronics module 120 can comprise … sensor interfaces 116,” ¶[0090], where “The sensor interface 116 can be disposed between electrodes 150 … The sensor interface 116 may comprise active signal conditioning circuits including strain gauge measurement circuits, for example. One channel can receive low frequency information associated with the physiological data of the patient (e.g., user, subject) and another channel can receive high frequency information associated with an electronic device within the patient. The high frequency channel can receive DC data of the patient. The high frequency channel data can be passed to a digital signal processor (DSP) implemented in the computing platform 108 and then to processor 111 for decompression and decoding, or passed directly to the processor 111”) and configured to detect an analog electrocardiogram (ECG) signal over time (¶[0224], where “FIG. 23 illustrates a flowchart for receiving and process ECG data. Namely, ECG signal is received from electrodes of the wearable device that are placed on a patient. The raw ECG signal can be processed in an analog processing block 2302 and output as raw ECG data”);
an analog-to-digital converter (ADC) (¶[0086], where “The processor 111 can comprise, … an analog-to-digital converter (ADC) … The processor 111 can receive a signal from each of the sensors by operating the analog front end for analog sensors and by receiving digital data from sensors with the ADC converter”) configured to convert the analog ECG signal to first ECG data (¶[0224], where “The raw ECG signal can be processed in an analog processing block 2302 and output as raw ECG data”), the first ECG data being associated with a first sampling frequency (¶[0227], where “For example, the vector co-processor 2306 can receive raw ECG data sampled at a first frequency”); and
one or more non-transitory computer-readable media storing computer- executable instructions that, when executed, cause the one or more processors to perform operations (¶[0089], where “The memory 112 can be non-transitory memory and can comprise machine executable instructions that when executed by the processor 111 can cause the processor 111 and/or a processor on a remote device to perform the functions of the various algorithms and other innovations”) comprising:
generating second ECG data by down-sampling the first ECG data (¶[0230], where “to enhance the raw ECG data, the raw ECG data can be processed through a high pass filter by the vector co-processor, 2402,” ¶[0231], where “the high pass filtered ECG data can be processed through a low pass filter,” ¶[0232], where “the low pass filtered ECG data can be processed through a down-sampling filter, 2306. The down-sampling filter can reduce the sampling rate of the ECG data which can reduce processing cycles required to process the ECG data and the size of the ECG data in memory”), the second ECG data being associated with a second sampling frequency that is lower than the first sampling frequency (¶[0227], where “the vector co-processor 2306 can receive raw ECG data sampled at a first frequency (e.g., 256 Hz) and produce enhanced ECG data … at a different second frequency less than the first frequency (e.g., 64 Hz). By utilizing the vector co-processor 2306 to create the enhance ECG data, efficiency can be increased in downstream filtering operations, the quantity of data moved can be less, and the efficiency of memory utilization in the processor 2308 can be increased”);
generating additional data (¶[0224], where “The raw ECG signal can be processed in an analog processing block 2302 and output as raw ECG data. The raw ECG data can be input to a processing block 2304 for processing by the HR algorithm. … The processing block 2304 can extract features from the data and output metrics, such as, for example, heat rate and/or other metrics”); and
transmitting the second ECG data and the additional data to the backend device (¶[0081], where “wearable device 102 can comprise at least two components including a disposable component 110 and an electronics module 120 … electronics module 120 can comprise … a wireless communication circuit 106,” ¶[0087], where “The wireless communication circuit 106 may be a low power mobile chipset and can be configured to connect to the cellular network as well as other remote devices (e.g., wireless devices such as, cell-phones, smart phones, tablet computers, laptop computers, gateway devices, among others). The wireless communication circuit 106 can comprise an antenna to receive and transmit wireless signals, a transmitter circuit, a receiver circuit, and a link master controller that includes a mechanism to connect (establish a link) to another, external, wireless device and transfer data, as described in more detail herein below. The link master controller can establish connection to an external device such as a mobile device. As a master of the link, the link master controller can perform control of data transmission over the link to the external device … link master controller can send a signal to a remote device with an instruction that gives a number of data records stored in memory (a total number of all data records and a total number of records of each data type),” ¶[0302], where “Raw ECG data, enhanced ECG data, and metrics can be stored in an ECG data record in memory such as, for example, the memory of the wearble device and/or the memory of a remote device”).
Although Azevedo teaches analysis of an ECG signal which inherently contains pace pulses, Azevedo does not explicitly teach identifying candidate pace pulses in the first ECG data; nor generating additional data from piecewise splines of portions of the first ECG data where the candidate pace pulses are identified, wherein the additional data comprises waveform descriptors of the piecewise splines characterizing the candidate pace pulses.
Chen teaches a system for pace pulse detection (Abstract, where “An apparatus and method for detecting a pace pulse signal are described. The apparatus and the method include receiving a plurality of sample signals from one ECG lead,” ¶[0020], where “the system includes a physiological monitoring device 7 capable of receiving physiological data from various sensors 17 connected to a patient 1, and a monitor mount 10 to which the physiological monitoring device 7 is removably mounted or docked”) from measured signals from multiple ECG leads (¶[0038], where “the pace pulse detection may be performed on more than one ECG lead (e.g., Lead I and Lead II). Performing pace pulse detection process on multiple ECG leads may further improve detection sensitivity by e.g., identifying pulse signals with very small amplitude and narrow width. Furthermore, it may improve detection specificity by rejecting noise”), and further teaches identifying candidate pace pulses in the first ECG data (¶[0043], where “If one or more sample signals exceed the dynamic thresholds and are identified as edge points of a pace pulse, the pace pulse candidate is found (Y in step 306)”); and
that the additional data comprises waveform descriptors characterizing the candidate pace pulses (¶[0059], where “to further improve the sensitivity and specificity of the pace pulse detection, the identified pace pulse candidate after the pace search step 306 may further be screened in pace candidate screening step 308. In one embodiment, one or more features in the morphology of the candidate may be analyzed. The features may include symmetry, slope direction, start slope (or onset slope), end slope (or offset slope), amplitude and width”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Chen, which teaches identifying candidate pace pulses in the first ECG data; and that the additional data comprises waveform descriptors characterizing the candidate pace pulses, with the invention of Azevedo in order to improve detection specificity by rejecting noise (Chen ¶[0038]) and to further improve the sensitivity and specificity of the pace pulse detection (Chen ¶[0059]).
Neither Azevedo nor Chen teaches generating additional data from piecewise splines of portions of the first ECG data where the candidate pace pulses are identified, wherein the additional data comprises waveform descriptors of the piecewise splines characterizing the candidate pace pulses.
Liu teaches an electrocardiogram signal processing system (Abstract), and further teaches generating additional data from piecewise splines of portions of the first ECG data where the candidate pace pulses are identified (¶[0062], where “The wavelet transformation unit 102 decomposes an input signal electrocardiogram signal 152 into different scales with different bandwidth. The quadratic spline wavelet function is normally used for ECG signal analysis.” Examiner interprets that since the piecewise splines are portions of the ECG data derived from the ECG signal, and since the candidate pace pulses are identified from the ECG data, that the piecewise splines are portions of the ECG data where candidate pace pulses are identified, where identification of the candidate pace pulses are taught by Chen.), wherein the additional data comprises waveform descriptors of the piecewise splines characterizing the candidate pace pulses (¶[0062], where “The wavelet transformation unit 102 decomposes an input signal electrocardiogram signal 152 into different scales with different bandwidth. The quadratic spline wavelet function is normally used for ECG signal analysis.” Examiner interprets that since Chen teaches waveform descriptors characterizing the candidate pace pulses, and since the piecewise splines are portions of the ECG data derived from the ECG signal, that the combination of Chen and Liu teaches waveform descriptors of the piecewise splines as they are decomposed ECG signals.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Liu, which teaches generating additional data from piecewise splines of portions of the first ECG data where the candidate pace pulses are identified, wherein the additional data comprises waveform descriptors of the piecewise splines characterizing the candidate pace pulses, with the modified invention of Azevedo in order to achieve high power and area efficiency (Liu ¶[0055]).
Regarding claim 3, Azevedo in combination with Chen and Liu teaches all limitations of claim 1 as described in the rejection above.
Chen teaches that the waveform descriptors characterize at least one of: shapes of the candidate pace pulses; frequency components of the candidate pace pulses; or amplitudes of the candidate pace pulses (¶[0059], where “to further improve the sensitivity and specificity of the pace pulse detection, the identified pace pulse candidate after the pace search step 306 may further be screened in pace candidate screening step 308. In one embodiment, one or more features in the morphology of the candidate may be analyzed. The features may include symmetry, slope direction, start slope (or onset slope), end slope (or offset slope), amplitude and width”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Chen, which teaches that the waveform descriptors characterize at least one of: shapes of the candidate pace pulses; frequency components of the candidate pace pulses; or amplitudes of the candidate pace pulses, with the modified invention of Azevedo in order to further improve the sensitivity and specificity of the pace pulse detection (Chen ¶[0059]).
Regarding claim 5, Azevedo in combination with Chen and Liu teaches all limitations of claim 1 as described in the rejection above.
Chen teaches that the waveform descriptor comprises at least one of: a portion of the first ECG data (¶[0043], where “If one or more sample signals exceed the dynamic thresholds and are identified as edge points of a pace pulse, the pace pulse candidate is found (Y in step 306),” ¶[0059], where “one or more features in the morphology of the candidate may be analyzed. The features may include symmetry, slope direction, start slope (or onset slope), end slope (or offset slope), amplitude and width.” Examiner takes the position that the waveform descriptor, here the amplitude, will comprise a portion of the first ECG data since the pace pulse candidate, which the amplitude is determined from, is identified from a portion of the first ECG data.); or a representation of the portion of the first ECG data, the representation having a lower fidelity than the portion of the first ECG data.
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Chen, which teaches that the waveform descriptor comprises at least one of: a portion of the first ECG data; or a representation of the portion of the first ECG data, the representation having a lower fidelity than the portion of the first ECG data, with the modified invention of Azevedo in order to further improve the sensitivity and specificity of the pace pulse detection (Chen ¶[0059]).
Regarding claim 12, see the rejection of claim 1 above. However, claim 12 adds the limitations of “the one or more processors performing operations comprising: obtaining first electrocardiogram (ECG) data associated with a first sampling frequency; presenting multiple ECG waveforms based on the second ECG data; and presenting the waveform descriptors characterizing the one or more candidate pace pulses based on the additional data”.
Azevedo teaches the one or more processors performing operations comprising: obtaining first electrocardiogram (ECG) data associated with a first sampling frequency (¶[0224], where “The raw ECG signal can be processed in an analog processing block 2302 and output as raw ECG data,” ¶[0227], where “For example, the vector co-processor 2306 can receive raw ECG data sampled at a first frequency”); and generating second ECG data by down-sampling the first ECG data (¶[0230], where “to enhance the raw ECG data, the raw ECG data can be processed through a high pass filter by the vector co-processor, 2402,” ¶[0231], where “the high pass filtered ECG data can be processed through a low pass filter,” ¶[0232], where “the low pass filtered ECG data can be processed through a down-sampling filter, 2306. The down-sampling filter can reduce the sampling rate of the ECG data which can reduce processing cycles required to process the ECG data and the size of the ECG data in memory”).
Chen teaches the one or more processors performing operations (¶[0014], where “apparatus includes one or more processors, and a computer-readable medium storing instructions that when executed by the one or more data processors, performs operations”) comprising: presenting multiple ECG waveforms based on the second ECG data (¶[0038], where “the pace pulse detection may be performed on more than one ECG lead (e.g., Lead I and Lead II). Performing pace pulse detection process on multiple ECG leads may further improve detection sensitivity by e.g., identifying pulse signals with very small amplitude and narrow width. Furthermore, it may improve detection specificity by rejecting noise”); and presenting the waveform descriptors characterizing the one or more candidate pace pulses based on the additional data (¶[0059], where “to further improve the sensitivity and specificity of the pace pulse detection, the identified pace pulse candidate after the pace search step 306 may further be screened in pace candidate screening step 308. In one embodiment, one or more features in the morphology of the candidate may be analyzed. The features may include symmetry, slope direction, start slope (or onset slope), end slope (or offset slope), amplitude and width”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Chen, which teaches the one or more processors performing operations comprising: presenting multiple ECG waveforms based on the second ECG data; and presenting the waveform descriptors characterizing the one or more candidate pace pulses based on the additional data, with the modified invention of Azevedo in order to improve detection specificity by rejecting noise (Chen ¶[0038]) and to further improve the sensitivity and specificity of the pace pulse detection (Chen ¶[0059]).
Regarding claim 14, Azevedo in combination with Chen and Liu teaches all limitations of claim 12 as described in the rejection above. Furthermore, regarding claim 14, see the rejection of claim 3 above.
Regarding claim 16, see the rejection of claims 1 and 12 above. However, claim 16 adds the limitation of a method.
Azevedo teaches a method (Abstract, which teaches “a method … which can increase the accuracy of physiological metrics”).
Regarding claim 18, Azevedo in combination with Chen and Liu teaches all limitations of claim 16 as described in the rejection above. Furthermore, regarding claim 18, see the rejection of claim 3 above.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Azevedo, Chen, and Liu as applied to claim 1 above, and further in view of Segman et al. (hereinafter “Segman”) (U.S. Pub. No. 2020/0170518 A1).
Regarding claim 4, Azevedo in combination with Chen and Liu teaches all limitations of claim 1 as described in the rejection above.
None of Azevedo, Chen, nor Liu teaches that identifying the candidate pace pulses in the first ECG data comprises identifying, in the first ECG data, a recovery pulse.
Segman teaches an apparatus generates a pulse (or other hemodynamic) signal from an ECG signal, and displays a waveform of that signal (Abstract), and further teaches that identifying the candidate pace pulses in the first ECG data comprises identifying, in the first ECG data, a recovery pulse (¶[0003], where “one or more hardware processors configured to constantly apply an integral function to the digital ECG signal and initiate a display signal to a digital display device to display a waveform of the integral function during the period of time, wherein the waveform of the integral function has a shape of a pulse signal waveform of the subject taken during the period of time, wherein the integral function comprises: PWF(t)T =A∫ t t+T F(ECG(u))du wherein PWF(t)T is the recovered pulse waveform from the ECG signal”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Segman, which teaches that identifying the candidate pace pulses in the first ECG data comprises identifying, in the first ECG data, a recovery pulse, with the modified invention of Azevedo in order to reduce or eliminate noise before integration or to smooth or sharpen the digital ECG signal before integration (Segman ¶[0003]).
Claims 6-7 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Azevedo, Chen, and Liu as applied to claim 1 above, and further in view of Herleikson (U.S. Pat. No. 5,682,902 A, IDS Reference 4 from IDS dated 09/28/2023).
Regarding claim 6, Azevedo in combination with Chen and Liu teaches all limitations of claim 1 as described in the rejection above.
Although Azevedo teaches that the backend device is configured to determine that features extracted from an ECG waveform contain an artifact by identifying a spike, or accentuated peaks, in the ECG waveform (¶[0076], where “The various algorithm can be executed by … a processor on the remote device,” ¶[0225], where “the HR algorithm according to the present disclosure processes the ECG data in the vector co-processor 2306. Namely, the vector co-processor 2306 enhances the raw ECG data received from the analog processing block 2302 and outputs enhanced ECG data to the processor 2308 where features can be extracted from the enhanced ECG data. That is, the processing of the raw ECG data can be split across the vector co-processor 2306 and the processor 2308. Namely, the vector co-processor 2306 can de-noise the raw ECG data by converting the raw ECG data into an enhanced ECG data with accentuated R-wave peaks that can be more facile to identify and/or locate while calculating a heart rate metric”), and Chen teaches a waveform descriptor, such as an amplitude, none of Azevedo, Chen, nor Liu teaches that the backend device is configured to determine that a waveform descriptor among the waveform descriptors comprises an artifact by: identifying a spike in the waveform descriptor; nor determining that a paced rhythm is absent within a threshold time period after the spike in the waveform descriptor.
Herleikson teaches a flexible ECG monitoring system including pace pulse detection that includes an A/D converter and a digital signal processor, where ECG signals are digitized and the digital signal processor derives a signal that is an estimate of the slope of the ECG signal (Abstract), and further teaches determining that a waveform descriptor among the waveform descriptors comprises an artifact by: identifying a spike in the waveform descriptor (Col. 10, lines 61-64, where “When a pace pulse is detected 608, the pace pulse amplitude is measured 610 by taking the difference between the peak value of the pace pulse and the average of 2 milliseconds of signal data just prior to the pace pulse,” Col. 11, lines 17-22, 33-38, where “When a pace pulse is detected, the current threshold is stored in a location known as the `delayed threshold`. If, during the pace pulse removal period, a slope is detected that exceeds the delayed threshold, then the removal period is extended so that it continues 12 milliseconds after this detected slope … Updating the delayed threshold when something does exceed the delayed threshold prevents the following undesirable situation from occurring: if the detector initially triggers in a period of high frequency noise, the removal period could continue to be extended until the noise ends.” Examiner takes the position that since noise, which is an artifact, is being removed based on whether there is a slope is detected that exceeds the delayed threshold, or a spike in the amplitude above a threshold, that the spike determines the existence of an artifact in a waveform descriptor, such as an amplitude.); and determining that a paced rhythm is absent within a threshold time period after the spike in the waveform descriptor (Col. 11, lines 12-17, where “Pace pulses from certain types of pacemakers have a long repolarization tail. Rather than always removing a long enough period of time to remove such long pace pulses, the illustrative embodiment starts with a fixed 12 millisecond removal period, and detects certain conditions when that period should be extended.” Examiner interprets that the period during a repolarization tail is a period where paced rhythm is absent since it is a more flattened portion of the ECG signal.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Herleikson, which teaches determining that a waveform descriptor among the waveform descriptors comprises an artifact by: identifying a spike in the waveform descriptor and determining that a paced rhythm is absent within a threshold time period after the spike in the waveform descriptor, with the modified invention of Azevedo in order to prevent noise if the detector initially triggers in a period of high frequency noise as the removal period could continue to be extended until the noise ends (Herleikson Col. 11, lines 33-38).
Regarding claim 7, Azevedo in combination with Chen, Liu, and Herleikson teaches all limitations of claim 6 as described in the rejection above.
Herleikson teaches that the backend device is further configured to: modify the additional data by removing the waveform descriptor comprising the artifact (Col. 11, lines 6-11, where “If pace pulses are to be removed 612 from the ECG signal, then that removal is done 614 on the 4 KHz data. Removal is accomplished by replacing (starting just prior to the pace pulse) 12 milliseconds of signal. This interval is replaced with a flat signal level that is the average of 2 milliseconds of signal just prior to the pace pulse,” Col. 11, lines 14-28, where “the illustrative embodiment starts with a fixed 12 millisecond removal period, and detects certain conditions when that period should be extended, as follows. When a pace pulse is detected, the current threshold is stored in a location known as the 'delayed threshold'. If, during the pace pulse removal period, a slope is detected that exceeds the delayed threshold, then the removal period is extended so that it continues 12 milliseconds after this detected slope. Also, if such a slope is detected, at that time the delayed threshold is updated-in other words the then current threshold is again stored in the delayed threshold. This method results in certain pace pulse repolarization waves being detected; in that case the removal period is extended so that the repolarization wave is removed”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Herleikson, which teaches that the backend device is further configured to: modify the additional data by removing the waveform descriptor comprising the artifact, with the modified invention of Azevedo in order to prevent noise if the detector initially triggers in a period of high frequency noise as the removal period could continue to be extended until the noise ends (Herleikson Col. 11, lines 33-38).
Regarding claim 21, Azevedo in combination with Chen and Liu teaches all limitations of claim 1 as described in the rejection above.
Chen teaches determining a portion of the first ECG data that corresponds to the candidate pace pulses (¶[0043], where “If one or more sample signals exceed the dynamic thresholds and are identified as edge points of a pace pulse, the pace pulse candidate is found (Y in step 306).” Examiner interprets that since the candidate pace pulses correspond to a portion of the ECG signal that this corresponds to a portion of the first ECG data.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Chen, which determining a portion of the first ECG data that corresponds to the candidate pace pulses, with the modified invention of Azevedo in order to improve detection specificity by rejecting noise (Chen ¶[0038]) and to further improve the sensitivity and specificity of the pace pulse detection (Chen ¶[0059]).
None of Azevedo, Chen, nor Liu teaches determining, based at least in part on the portion of the first ECG data, a time window to utilize with respect to the first ECG data; nor determining a bounding range to apply to the first ECG data utilizing the time window, wherein the bounding range includes additional portions of the first ECG data bounding the portion of the first ECG data, and wherein generating the second ECG data is performed on the portion of the first ECG data and the additional portions of the first ECG data.
Herleikson teaches determining, based at least in part on the portion of the first ECG data, a time window to utilize with respect to the first ECG data (Col. 9, lines 24-26, where “In simplest terms, this pace pulse detector looks for positive and negative edges that occur within a certain time window”); and
determining a bounding range to apply to the first ECG data utilizing the time window (Col. 9, lines 24-26, where “In simplest terms, this pace pulse detector looks for positive and negative edges that occur within a certain time window.” Examiner interprets that a time window inherently includes a bounding range as the time window includes a starting and ending time.), wherein the bounding range includes additional portions of the first ECG data bounding the portion of the first ECG data (Col. 9, lines 27-29, where “The time window is set to be longer than the expected width of pace pulses to be detected”), and wherein generating the second ECG data is performed on the portion of the first ECG data and the additional portions of the first ECG data (Examiner interprets that since Azevedo teaches generating the second ECG data, where the second ECG data is created by processing the raw ECG data, that this includes the portion of the first ECG data as well as the additional portions since the time window as taught by Herleikson includes an ECG signal that can be processed through the down-sampling as taught in Azevedo.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Herleikson, which teaches determining, based at least in part on the portion of the first ECG data, a time window to utilize with respect to the first ECG data; and determining a bounding range to apply to the first ECG data utilizing the time window, wherein the bounding range includes additional portions of the first ECG data bounding the portion of the first ECG data, and wherein generating the second ECG data is performed on the portion of the first ECG data and the additional portions of the first ECG data, with the modified invention of Azevedo in order to reject false detection of any signal that is not a pace pulse (Herleikson Col. 9, lines 18-20).
Claims 8-9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Azevedo and Chen as applied to claim 1 above, and further in view of Johnson et al. (hereinafter “Johnson”) (U.S. Pub. No. 2012/0053473 A1).
Regarding claim 8, Azevedo in combination with Chen and Liu teaches all limitations of claim 1 as described in the rejection above.
Although Azevedo teaches the backend device (¶[0076], where “The various algorithm can be executed by … a processor on the remote device”) and Chen teaches identifying candidate pace pulses (¶[0043], where “If one or more sample signals exceed the dynamic thresholds and are identified as edge points of a pace pulse, the pace pulse candidate is found (Y in step 306)”), none of Azevedo, Chen, nor Liu teaches that the backend device is further configured to: generate an average pace pulse by performing signal averaging on the candidate pace pulses; nor compare the average pace pulse to an individual candidate pace pulse.
Johnson teaches a heart monitor that computes ST segment deviations and stores the results in heart rate based histograms and excludes beats from the computation of the time series if their ST deviations both varies too far from the long term median value and varies too far from the then current time series value (Abstract), and further teaches that the backend device is further configured to: generate an average pace pulse by performing signal averaging on the candidate pace pulses (¶[0007], where “An ST deviation time series is generated by a recursive filter that is preferably an exponential average filter whose output is a weighted sum of the then existing ST time series value and current ST deviation values of analyzable beats,” ¶[0063], where “Block 234 then transfers control to block 236, which updates the adaptive parameter ST_int according to the number of samples between ST_ind and Rpeak. ST_int is preferably updated according to an exponential average filter. The exponential average of a variable (V) is expavg(V, Δ, α, min, max, mindelt, maxdelt), which means that the variable V is updated by the current value A, with an update weighting ½α, subject to constraints on the maximum and minimum allowable value for the variable and changes in that variable”); and compare the average pace pulse to an individual candidate pace pulse (¶[0063], where “Block 234 then transfers control to block 236, which updates the adaptive parameter ST_int according to the number of samples between ST_ind and Rpeak. ST_int is preferably updated according to an exponential average filter. The exponential average of a variable (V) is expavg(V, Δ, α, min, max, mindelt, maxdelt), which means that the variable V is updated by the current value A, with an update weighting ½α, subject to constraints on the maximum and minimum allowable value for the variable and changes in that variable.” Examiner takes the position that by comparing the current value to the previous average that this is equivalent to comparing an average to an individual candidate.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Johnson, which teaches that the backend device is further configured to: generate an average pace pulse by performing signal averaging on the candidate pace pulses; and compare the average pace pulse to an individual candidate pace pulse, with the modified invention of Azevedo since if a segment is too abnormal, or appears to correspond to a rapid change in physiological state (e.g. a change in heart rate), its deviation cannot be relied on for ischemia detection or for contributing to ST/RR statistics (Johnson ¶[0068]).
Regarding claim 9, Azevedo in combination with Chen, Liu, and Johnson teaches all limitations of claim 8 as described in the rejection above.
Johnson teaches that the backend device is further configured to: determine a discrepancy between the average pace pulse and the individual candidate pace pulse (¶[0067], where “Stdev is corrected for QRS amplitude in block 254 according to the method described in FIG. 11. Control passes to block 255, which fetches the long term median ST deviation value associated with the average RR interval of the current segment (mPavg). Control then passes to block 256, which checks whether the difference between both (i) STDN and the median ST deviation at the heart rate associated with the current segment (median); and (ii) STDN and the current long term ST deviation exponential average (mStDevN)_xavg), exceed a threshold”); determine that the discrepancy is greater than a threshold (¶[0067], where “Control then passes to block 256, which checks whether the difference between both (i) STDN and the median ST deviation at the heart rate associated with the current segment (median); and (ii) STDN and the current long term ST deviation exponential average (mStDevN)_xavg), exceed a threshold”); and based on determining that the discrepancy is greater than the threshold, modify the additional data by removing the individual candidate pace pulse (¶[0070], where “In block 306. STFOM is compared to a threshold. If the threshold is exceeded, control passes to block 332 of FIG. 10, which results in the exclusion of the segment's ST deviation from ischemia detection/statistics,” ¶[0071], where “Block 310 (FIG. 9) checks whether the difference between mStDevNavg and the current long term, filtered ST time series (mStDevN_xavg) exceeds a threshold. If so, mQMorphcnt is incremented in block 312 and the segment is not added to the ST/RR histograms”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Johnson, which teaches that the backend device is further configured to: determine a discrepancy between the average pace pulse and the individual candidate pace pulse, determine that the discrepancy is greater than a threshold; and based on determining that the discrepancy is greater than the threshold, modify the additional data by removing the individual candidate pace pulse, with the modified invention of Azevedo since if a segment is too abnormal, or appears to correspond to a rapid change in physiological state (e.g. a change in heart rate), its deviation cannot be relied on for ischemia detection or for contributing to ST/RR statistics (Johnson ¶[0068]).
Regarding claim 20, Azevedo in combination with Chen and Liu teaches all limitations of claim 16 as described in the rejection above. Furthermore, regarding claim 20, see the rejection of claims 8 and 9 above, where the limitation of generating an average pace pulse by performing signal averaging on the candidate pace pulses is rejected in claim 8, and the limitations of determining a discrepancy between the average pace pulse and individual candidate pace pulses; determining that the discrepancy is greater than a threshold; and based on determining that the discrepancy is greater than the threshold, modifying the additional data by removing the individual candidate pace pulse are rejected in claim 9.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Azevedo and Chen as applied to claim 1 above, and further in view of Raj et al. (hereinafter “Raj”) (U.S. Pat. No. 5,956,013 A).
Regarding claim 10, Azevedo in combination with Chen and Liu teaches all limitations of claim 1 as described in the rejection above.
Although Azevedo teaches the backend device (¶[0076], where “The various algorithm can be executed by … a processor on the remote device”) and Chen teaches a display device (¶[0025], where “display/GUI 4 is for displaying various patient data and hospital or patient care information and includes a user interface implemented for allowing communication between a user and the physiological monitoring device 7. The display/GUI 4 includes, but is not limited to, a keyboard, a liquid crystal display (LCD), cathode ray tube (CRT), thin film transistor (TFT), light-emitting diode (LED), high definition (HD) or other similar display devices with touch screen capabilities. The patient information displayed can, for example, relate to the measured physiological parameters of the patient 1 (e.g., blood pressure, heart related information, pulse oximetry, respiration information, etc.)”), none of Azevedo, Chen, nor Liu teaches that the backend device is configured to: present multiple ECG waveforms respectively corresponding to multiple leads; and present the waveform descriptors in a user interface element that is separate from the multiple ECG waveforms or visually overlapping the multiple ECG waveforms.
Raj teaches a computer that controls a video display having a display screen for displaying ECG heartbeats superimposed over one another (Abstract), and further teaches that the backend device is configured to: present multiple ECG waveforms respectively corresponding to multiple leads (Col. 2, lines 66-67 – Col. 3, line 1, where “The stored ECG data to be analyzed is collected by a Holter monitor which is fitted to a patient for detecting and storing a continuous ECG waveforms for many hours,” Col. 1, lines 15-18, where “In a Holter ECG monitoring system, a patient is fitted with a monitor which detects and stores continuous ECG waveforms. Sometimes several leads are connected to the patient so that two or more such waveforms are recorded”); and present the waveform descriptors in a user interface element that is separate from the multiple ECG waveforms or visually overlapping the multiple ECG waveforms (Col. 3, lines 42-51, where “The continuous ECG waveform on display 12 is made up of a plurality of successive heartbeat waveforms, like heartbeat waveforms 24, 26, 28, 30, 32, 34. Each of these pulses is known in the field of cardiology as QRS. The heartbeat waveforms are also referred to herein simply as heartbeats. The normal heartbeat waveforms, and some abnormal waveforms, are characterized by a vertical pulse having an upper tip which comprises the maximum amplitude of each heartbeat waveform. This pulse is referred to herein as an R-wave,” Col. 3, lines 61-64, where “Scan window 14 includes five consecutive heartbeat waveforms from the stored ECG data superimposed over one another as shown. All of the heartbeats in window 14 are aligned at the peaks of their respective R-waves”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Raj, which teaches that the backend device is configured to: present multiple ECG waveforms respectively corresponding to multiple leads; and present the waveform descriptors in a user interface element that is separate from the multiple ECG waveforms or visually overlapping the multiple ECG waveforms, with the modified invention of Azevedo since such a display allows a clinician to see beat-to-beat changes in the patient's ECG complex which may be significant (Raj Col. 1, lines 29-31).
Regarding claim 11, Azevedo in combination with Chen, Liu, and Raj teaches all limitations of claim 10 as described in the rejection above.
Raj teaches that the backend device comprises a display (Col. 3, lines 34-35, where “Display screen 10 includes a first display 12 and a second display or scan window 14.”) and is configured to: receive an input signal indicating a zoom-in instruction (Col. 3, lines 65-67, where “The various buttons surrounding display 14 control the manner in which heartbeat waveforms are displayed thereon. Each button is actuated by using a mouse,” Figure 1, which shows .5x, 1x, and 2x buttons to the right of fast-forward button 44, where Examiner interprets that the 2x button is a button that zooms in on the displayed waveforms.); and based on receiving the input signal, present, on the display, a zoomed-in view of at least one of the multiple ECG waveforms (Col. 3, lines 65-67, where “The various buttons surrounding display 14 control the manner in which heartbeat waveforms are displayed thereon. Each button is actuated by using a mouse,” Figure 1, which shows .5x, 1x, and 2x buttons to the right of fast-forward button 44, where Examiner interprets that the 2x button is a button that zooms in on the displayed waveform or waveforms.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Raj, which teaches that the backend device comprises a display and is configured to: receive an input signal indicating a zoom-in instruction; and based on receiving the input signal, present, on the display, a zoomed-in view of at least one of the multiple ECG waveforms, with the modified invention of Azevedo since such a display allows a clinician to see beat-to-beat changes in the patient's ECG complex which may be significant (Raj Col. 1, lines 29-31) and to control how the waveforms are displayed (Raj Col. 3, lines 65-67).
Claims 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Azevedo, Chen, and Liu as applied to claims 12 and 16 above, and further in view of Felblinger et al. (hereinafter “Felblinger”) (EP 3,643,228 A1).
Regarding claim 13, Azevedo in combination with Chen and Liu teaches all limitations of claim 12 as described in the rejection above.
None of Azevedo, Chen, nor Liu teaches that the first sampling frequency is between 8 kHz and 40 kHz, nor wherein the second sampling frequency is between 500 Hz and 1000 Hz.
Felblinger teaches that that the first sampling frequency is between 8 kHz and 40 kHz (¶[0024], where “first sampling frequency … is thus chosen to be between 10 kHz and 40 kHz, preferably of the order of 16 kHz”), and wherein the second sampling frequency is between 500 Hz and 1000 Hz (¶[0025], where “second sampling frequency … may be between 250 Hz and 2 kHz, and preferably be of the order of 1 kHz”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Felblinger, which teaches that the first sampling frequency is between 8 kHz and 40 kHz, and wherein the second sampling frequency is between 500 Hz and 1000 Hz, with the modified invention of Azevedo in order to make it possible to precisely retain the start and end of an artifact (Felblinger ¶[0024]).
Regarding claim 17, Azevedo in combination with Chen and Liu teaches all limitations of claim 16 as described in the rejection above. Furthermore, regarding claim 17, see the rejection of claim 13 above.
Claims 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Azevedo, Chen, and Liu as applied to claims 12 and 16 above, and further in view of Mehta (U.S. Pub. No. 2020/0289063 A1).
Regarding claim 15, Azevedo in combination with Chen and Liu teaches all limitations of claim 12 as described in the rejection above.
Chen teaches generating the additional data comprises: identifying a portion of the first ECG data comprising a candidate pace pulse among the candidate pace pulses (¶[0043], where “If one or more sample signals exceed the dynamic thresholds and are identified as edge points of a pace pulse, the pace pulse candidate is found (Y in step 306),” ¶[0059], where “one or more features in the morphology of the candidate may be analyzed. The features may include symmetry, slope direction, start slope (or onset slope), end slope (or offset slope), amplitude and width.” Examiner takes the position that the waveform descriptor, here the amplitude, will comprise a portion of the first ECG data since the pace pulse candidate, which the amplitude is determined from, is identified from a portion of the first ECG data.); and generating a waveform descriptor among the waveform descriptors based on the portion of the first ECG data (¶[0059], where “to further improve the sensitivity and specificity of the pace pulse detection, the identified pace pulse candidate after the pace search step 306 may further be screened in pace candidate screening step 308. In one embodiment, one or more features in the morphology of the candidate may be analyzed. The features may include symmetry, slope direction, start slope (or onset slope), end slope (or offset slope), amplitude and width”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Chen, which teaches generating the additional data comprises: identifying a portion of the first ECG data comprising a candidate pace pulse among the candidate pace pulses; and generating a waveform descriptor among the waveform descriptors based on the portion of the first ECG data, with the modified invention of Azevedo in order to improve detection specificity by rejecting noise (Chen ¶[0038]) and to further improve the sensitivity and specificity of the pace pulse detection (Chen ¶[0059]).
None of Azevedo, Chen, nor Liu teaches the waveform descriptor comprising a representation of the portion of the first ECG data, the representation having a lower fidelity than the portion of the first ECG data.
Mehta teaches techniques for determining reliability of electrocardiogram (ECG) data (Abstract), and further teaches the waveform descriptor comprising a representation of the portion of the first ECG data (¶[0075], where “At block 802 the monitoring application identifies the sample interval for the ECG data,” ¶[0076], where “At block 804, the monitoring application sets confidence thresholds for the sample … the monitoring application can use two thresholds: a per-heartbeat threshold and a per-sample threshold. These thresholds can relate to SNR values used to determine confidence in the ECG data … per-heartbeat threshold can include a single value (e.g., a minimum threshold) or multiple values (e.g., minimum, average, and high confidence thresholds),” ¶[0077], where “the per-sample threshold can include SNR values to determine the confidence in a given sample of ECG data … the per-sample threshold can act similarly to the per-heartbeat threshold, for multiple heartbeats in an ECG sample (e.g., by averaging the SNR for multiple heartbeats in a sample, or by considering the minimum or maximum SNR for multiple heartbeats in a sample).” Examiner interprets that confidence in the ECG data is a representation of a portion of the ECG data.), the representation having a lower fidelity than the portion of the first ECG data (¶[0078], where “At block 806 the monitoring application determines the per-heartbeat confidence for a given heartbeat in the ECG data sample. For example, the per-heartbeat threshold can include a minimum threshold, an average confidence threshold, and a high confidence threshold. If the SNR for a particular heartbeat falls below the minimum threshold, the monitoring application assigns little or no confidence to the ECG data for that heartbeat. Any detection and classification associated with that heartbeat could be thrown out or excluded from any diagnostic applications.” Examiner takes the position that a low confidence is equivalent to a low fidelity, or accuracy, since a confidence is a degree of accuracy such that a low confidence is equivalent to a low accuracy.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Mehta, which teaches the waveform descriptor comprising a representation of the portion of the first ECG data, the representation having a lower fidelity than the portion of the first ECG data, with the modified invention of Azevedo in order to analyze the ECG data for a heartbeat in diagnostic applications, to provide data to patients and care providers by providing providers with a confidence level (Mehta ¶[0079]), and so that ECG data with lower confidence can be excluded from consideration when deciding medical treatment (Mehta ¶[0086]).
Regarding claim 19, Azevedo in combination with Chen and Liu teaches all limitations of claim 16 as described in the rejection above. Furthermore, regarding claim 19, see the rejection of claim 15 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SEFRA D. MANOS/ Examiner, Art Unit 3792
/UNSU JUNG/ Supervisory Patent Examiner, Art Unit 3792