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 .
Claim Objections
Claim 11 is objected to because of the following informalities: Claim 11 is dependent on claim 10, however claim 10 is canceled. Appropriate correction is required.
Claim 28 is objected to because of the following informalities: Claim 28 is dependent on claim 26, however claim 26 is canceled. Appropriate correction is required.
Claim 39 is objected to because of the following informalities: Claim 39 is dependent on claim 35, however claim 35 is canceled. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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, 20, 22, and 39 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being unpatentable by de Saint Victor(US 20220095982 A1).
Regarding claim 1, de Saint Victor discloses A computer-aided method of classifying a wide complex tachycardia (WCT) pattern of a subject, the method comprising: a. receiving, using a computing device, WCT ECG data indicative of the WCT pattern(The computerized-system may further be designed to analyze the wave information from the delineation algorithm using a classification algorithm to determine a likelihood of a presence of the one or more abnormalities, conditions, or descriptors associated with cardiac events for the patient. The wave information may be inputted into the classification algorithm and separately used to determine that at least two beats of the plurality of beats should be grouped together. The computerized-system may further be designed to, prior to analyzing the ECG data using the delineation algorithm, pre-process the ECG data to remove noise from the ECG data. The computerized-system may assign the ECG data and information based on the ECG data to a user account for review. The computerized may receive user input data regarding the ECG data and information based on the ECG data from the user account based on the review[0021]); b. transforming, using the computing device, the WCT ECG data into at least one engineered feature; wherein the at least one engineered feature is selected from a percent monophasic time-voltage area (PMonoTVA), a percent monophasic amplitude (PMonoAmp), a wide complex tachycardia (WCT) QRS duration, and any combination thereof; c. transforming, using the computing device, the at least one engineered feature into an assigned classification of the WCT pattern using a machine learning model, wherein the classification of the WCT pattern is selected from a ventricular tachycardia (VT), a supraventricular wide complex tachycardia (SWCT), a probability of a VT, a probability of an SWCT, and any combination thereof; and d. transforming the assigned classification of the WCT pattern into a treatment recommendation using at least one treatment rule, wherein the treatment rule is selected from: i. recommending a shock delivery to the heart of the subject if the assigned classification is VT; or ii. recommending no shock delivery if the assigned classification is SWCT(The first neural network may be a delineation neural network having machine learning functionality. The second neural network may be a classification neural network having machine learning functionality. The output of the first and/or second neural networks may be processed by the ECG platform to achieve delineation and classification of the ECG data[0086]. Delineator 39 may further deduce labels solely from the information generated by delineator 39. For example, the following labels may be deduced by delineator 39: short PR interval (i.e., PR interval<120 ms), first degree AV block (e.g., PR interval>200 ms), axis deviations, long QTc, short QTc, wide complex tachycardia, and/or intraventricular conduction blocks[0118]. At step 962, ECG data corresponding to previous events corresponding to arrhythmias may be processed or analyzed to determine a pattern or trend corresponding to the arrhythmias. For example, one or more trained models may be used to detect such patterns and/or trends. At step 964, the patterns and/or trends may be used to determine a time period for which there is an increased risk and/or likelihood of an arrhythmia occurring[0198]. Today about 150 measurable abnormalities may be identified on cardiac signal recordings. Abnormalities and conditions may include but are not limited to, sinoatrial block, paralysis or arrest, atrial fibrillation, atrial flutter, atrial tachycardia, junctional tachycardia, supraventricular tachycardia, sinus tachycardia, ventricular tachycardia[0123]. ECG information 936 may optionally include information about a detected anomaly, descriptor and/or condition. Notification information 938 may include a notice that the user has a notification or message (e.g., from a health care provider and/or from the ECG platform running on the server). In one example, the notification may be a diagnosis or detected abnormality, condition, and/or anomaly determined by the ECG platform and/or the healthcare provider. Alternatively, or additionally, a notification may include a treatment recommendation Information displayed and provided by the ECG platform may have to be reviewed and/or released by a healthcare professional[0192]).
Regarding claim 3, de Saint Victor discloses the method of claim 1, wherein receiving the WCT ECG data indicative of the WCT pattern comprises receiving WCTECG data from an ECG device comprising a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillator (S-ICD), a pacemaker, an automatic implantable cardioverter defibrillator (AICD), an automated external defibrillator (AED),and any combination thereof(FIG. 1A illustrates a recording of a standard 12-lead resting ECG. As is shown in FIG. 1A, each lead generates an electrical signal, resulting in 12 electrical signals[0004]).
Regarding claim 20, de Saint Victor discloses a system for classifying a wide complex tachycardia (WCT) pattern of a subject, the system comprising a computing device comprising at least one processor, the at least one processor configured to: a. receive WCT ECG data indicative of the WCT pattern; b. transform the WCT ECG data into at least one engineered feature, wherein the at least one engineered feature is selected from a percent monophasic time-voltage area (PMonoTVA), a percent monophasic amplitude (PMonoAmp),,a wide complex tachycardia (WCT) QRS duration, and any combination thereof; and c. transform the at least one engineered feature into an assigned classification of the WCT pattern using a machine learning model, wherein the classification of the WCT pattern is selected from a ventricular tachycardia (VT), a supraventricular wide complex tachycardia (SWCT), a probability of a VT, a probability of an SWCT, and any combination thereof; and. transform the assigned classification of the WCT pattern into a treatment recommendation using at least one treatment rule, wherein the treatment rule is selected from: i. recommending a shock delivery to the heart of the subject if the assigned classification is VT; or ii. recommending no shock delivery if the assigned classification is SWCT(The computerized-system may further be designed to analyze the wave information from the delineation algorithm using a classification algorithm to determine a likelihood of a presence of the one or more abnormalities, conditions, or descriptors associated with cardiac events for the patient. The wave information may be inputted into the classification algorithm and separately used to determine that at least two beats of the plurality of beats should be grouped together. The computerized-system may further be designed to, prior to analyzing the ECG data using the delineation algorithm, pre-process the ECG data to remove noise from the ECG data. The computerized-system may assign the ECG data and information based on the ECG data to a user account for review. The computerized may receive user input data regarding the ECG data and information based on the ECG data from the user account based on the review[0021]. The first neural network may be a delineation neural network having machine learning functionality. The second neural network may be a classification neural network having machine learning functionality. The output of the first and/or second neural networks may be processed by the ECG platform to achieve delineation and classification of the ECG data[0086]. Delineator 39 may further deduce labels solely from the information generated by delineator 39. For example, the following labels may be deduced by delineator 39: short PR interval (i.e., PR interval<120 ms), first degree AV block (e.g., PR interval>200 ms), axis deviations, long QTc, short QTc, wide complex tachycardia, and/or intraventricular conduction blocks[0118]. At step 962, ECG data corresponding to previous events corresponding to arrhythmias may be processed or analyzed to determine a pattern or trend corresponding to the arrhythmias. For example, one or more trained models may be used to detect such patterns and/or trends. At step 964, the patterns and/or trends may be used to determine a time period for which there is an increased risk and/or likelihood of an arrhythmia occurring[0198]. Today about 150 measurable abnormalities may be identified on cardiac signal recordings. Abnormalities and conditions may include but are not limited to, sinoatrial block, paralysis or arrest, atrial fibrillation, atrial flutter, atrial tachycardia, junctional tachycardia, supraventricular tachycardia, sinus tachycardia, ventricular tachycardia[0123]. ECG information 936 may optionally include information about a detected anomaly, descriptor and/or condition. Notification information 938 may include a notice that the user has a notification or message (e.g., from a health care provider and/or from the ECG platform running on the server). In one example, the notification may be a diagnosis or detected abnormality, condition, and/or anomaly determined by the ECG platform and/or the healthcare provider. Alternatively, or additionally, a notification may include a treatment recommendation Information displayed and provided by the ECG platform may have to be reviewed and/or released by a healthcare professional[0192]).
Regarding claim 22, de Saint Victor discloses the system of any one of claim 20, wherein the ECG device comprises one of a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillator (S-ICD), a pacemaker, an automated external defibrillator (AED), an automatic implantable cardioverter defibrillator (AICD), and any combination thereof(FIG. 1A illustrates a recording of a standard 12-lead resting ECG. As is shown in FIG. 1A, each lead generates an electrical signal, resulting in 12 electrical signals[0004]).
Regarding claim 39, de Saint Victor discloses the system of claim 35, wherein the system further comprises the ECG device, a treatment device, and any combination thereof operatively coupled to the computing device(The systems receive ECG data from a sensing device positioned on a patient such as one or more ECG leads/electrodes that may be integrated in a smart device. The system may include an application that communicates with an ECG platform running on a server(s) that processes and analyzes the ECG data, e.g., using neural networks, to detect and/or predict various abnormalities, conditions and/or descriptors[Abstract]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6, 9, 11-15, 17, 25, 28, and 30-34 are rejected under 35 U.S.C. 103 as being unpatentable over de Saint Victor(US 20220095982 A1), and further in view of May(US 20190387992 A1).
Regarding claim 6, de Saint Victor discloses the method of claim 1, but fails to disclose wherein transforming the WCT ECG data into the at least one engineered feature further comprises: a. transforming the WCT ECG data into the WCT QRS duration using automated data analysis software and receiving, using the computing device, the wide complex tachycardia (WCT) QRS duration from the automated data analysis software; b. transforming the WCT ECG data into the PMonoTVA using a first transform comprising:(Monophasic TVA) wherein Monophasic TVA comprises a summation of all QRS time- voltage areas from all monophasic QRS complexes from the ECG data and Multiphasic TVA comprises a summation of all QRS time-voltage areas from all multiphasic QRS complexes from the ECG data; c. transforming the WCT ECG data into the PMonoAmp using a second transform comprising:
PNG
media_image1.png
73
586
media_image1.png
Greyscale
wherein Monophasic amplitude comprises a summation of all QRS amplitudes from all monophasic QRS complexes from the ECG data and Multiphasic amplitude comprises a summation of all QRS amplitudes from all multiphasic QRS complexes from the ECG data; and d. any combination thereof.
However, May teaches “Note that contemporary computerized ECG interpretation software also routinely provides standard ECG measurements including QRS duration (ms), QTc duration (ms), and frontal plane R and T wave axes (°). These measurements are typically apparent/reported on the 12-lead ECG paper recording. The difference in QRS duration (ms), frontal plane R wave axis (°) and frontal plane T wave axis (°) between the WCT and baseline ECGs may be automatically calculated by computerized ECG interpretation software[0064]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the WCT QRS duration of the method from differentiating wide complex heart beats of May. Doing so would specify how the data is transformed by the computing device in order to categorize the WCT signals.
Regarding claim 9, de Saint Victor in view of May teaches the method of claim 6, wherein transforming, using the computing device, the at least one engineered feature into a classification of the WCT pattern using a model further comprises using a machine learning model comprising one of a logistic regression model, an artificial neural network, a random forest model, and a support vector machine(de Saint Victor - To implement the ECG processing system, ECG application running on the system device may receive ECG data (i.e., cardiac signal) from a sensing device and may transmit the ECG data to a ECG platform running on the server. The ECG platform may execute a first and second neural network and may apply the ECG data to the first and second neural network. The first neural network may be a delineation neural network having machine learning functionality. The second neural network may be a classification neural network having machine learning functionality. The output of the first and/or second neural networks may be processed by the ECG platform to achieve delineation and classification of the ECG data[0086]).
Regarding claim 11, de Saint Victor in view of May teaches the method of claim 10, but de Saint Victor fails to disclose wherein using the machine learning model comprising the logistic regression model further comprises: a. transforming the at least one engineered feature into a weighted sum of predictors xg using the equation: X¢
PNG
media_image2.png
6
15
media_image2.png
Greyscale
0+i1X1 + fl2X2
PNG
media_image3.png
43
71
media_image3.png
Greyscale
wherein
PNG
media_image4.png
20
59
media_image4.png
Greyscale
and fl2 are constant weighting factors, X1 is PMonoTVA, and X2 is WCT QRS Duration; and b. calculating the probability of the VT (Pyr) using the equation: PVr'.
However, May teaches “As previously mentioned, the amplitude based WCT Formula is a binary outcome logistic regression model that uses select independent WCT predictors: (1) WCT duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%). Each WCT predictor (X.sub.x) was assigned beta coefficients (β.sub.x) according to their influence on the binary outcome (VT vs. non-VT). The “constant” term (B.sub.0) represents the y-intercept of the least squares regression line. Discrete measured or calculated WCT predictor values derived from paired baseline and WCT ECGs are incorporated into the WCT Formula to calculate VT probability (P)[0035]. where: X.sub.β is the weighted sum of the WCT predictors [0084] P.sub.VT is the probability of VT. [Equations 000014, 00015]).
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the logistic regression model of the method from differentiating wide complex heart beats of May. Doing so would specify how the data is transformed by a specific kind of model in order to calculate probability of the heart conditions.
Regarding claim 12, de Saint Victor in view of May teaches the method of claim 11, further comprising: a. receiving, using the computing device, baseline ECG data indicative of a baseline cardiac pattern; and b. transforming, using the computing device, the baseline ECG data and the WCT ECG data into the at least one additional engineered feature selected from a QRS Axis change, a T Axis change, a frontal percent time-voltage area change (PTVAC), a Horizontal PTVAC, a frontal percent amplitude change (PAC), a horizontal PAC, and any combination thereof(de Saint Victor - A computerized-system for analyzing ECG data of a patient may, in another example, analyze the ECG data using a delineation algorithm to determine wave information indicating a likelihood of a presence of at least one wave and analyze the ECG data and wave information using a baseline classification algorithm. The computerized-system may further determine a first value using the baseline classification algorithm, the first value indicating a presence of at least one cardiac event, and may analyze the ECG data and wave information using a desensitized classification algorithm, the desensitized classification algorithm having decreased sensitivity compared to the baseline classification algorithm[0034]. For example, global measurements may include, but are not limited to, PR interval, P-wave duration, QRS complex duration, QRS axis, QT interval, corrected QT interval (Qtc), T-wave duration, JT interval, corrected JT interval, heart rate, ST elevation, Sokolov index, number of premature ventricular complexes, number of premature atrial complexes (PAC), ratio of non-conducted P waves, and/or ratio of paced waves[0117]. Delineator 39 may further deduce labels solely from the information generated by delineator 39. For example, the following labels may be deduced by delineator 39: short PR interval (i.e., PR interval<120 ms), first degree AV block (e.g., PR interval>200 ms), axis deviations, long QTc, short QTc, wide complex tachycardia, and/or intraventricular conduction blocks[0118]).
Regarding claim 13, de Saint Victor in view of May teaches the method of claim 12, but de Saint Victor fails to disclose wherein transforming the baseline ECG data and the WCT ECG data into the at least one additional engineered feature further comprises: a. transforming the WCT ECG data into a WCT QRS axis angle and the baseline ECG data into a baseline QRS axis angle using automated data analysis software, and subtracting, using the computing device, the baseline QRS axis angle from the WCT QRS axis angle to obtain the QRS Axis change; b. transforming the WCT ECG data into a WCT T axis angle and the baseline ECG data into a baseline T axis angle using automated data analysis software, and subtracting, using the computing device, the baseline T axis angle from the WCT T axis angle to obtain the T Axis change; c. transforming the WCT ECG data into a WCT Frontal percent amplitude (PA) and the baseline ECG data into a baseline Frontal PA using automated data analysis software, and subtracting, using the computing device, the baseline Frontal PA from the WCT Frontal PA to obtain the Frontal PAC; and d. transforming the WCT ECG data into a WCT Horizontal percent amplitude (PA) and the baseline ECG data into a baseline Horizontal PA using automated data analysis software, and subtracting, using the computing device, the baseline Horizontal PA from the WCT Horizontal PA to obtain the Horizontal PAC.
However, May teaches “As shown in FIG. 11, WCT predictors included baseline QRS duration (ms) (p=0.05), baseline QTc interval duration (ms) (p=0.05), WCT QRS duration (ms) (p<0.001), change in QRS duration (ms) (p<0.001), change in R wave axis (°) (p<0.001), change in T wave axis (°) (p<0.001), frontal PAC (%) (p<0.001) and horizontal PAC (%) (p<0.001). As shown in FIG. 13, the amplitude based WCT Formula diagnostic performance including (1) WCT QRS duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%) demonstrated favorable VT and SWCT differentiation (AUC of 0.96) using the derivation cohort (collection of paired WCT and baseline ECGs)[0108]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the WCT QRS duration of the method from differentiating wide complex heart beats of May. Doing so would specify how the data is transformed by the computing device in order to categorize the WCT signals.
Regarding claim 14, de Saint Victor in view of May teaches the method of 13, but de Saint Victor fails to disclose wherein using the logistic regression model further comprises: a. transforming the at least one engineered feature into a weighted sum of predictors Xp using the equation:
PNG
media_image5.png
22
22
media_image5.png
Greyscale
=-30+
PNG
media_image6.png
21
86
media_image6.png
Greyscale
32X2+
PNG
media_image7.png
21
87
media_image7.png
Greyscale
X4+
PNG
media_image8.png
21
13
media_image8.png
Greyscale
35X5 +
PNG
media_image9.png
21
42
media_image9.png
Greyscale
wherein ,p,plu,/32, #i3X4 is T Axis change , X5 is Frontal PAC, and X is Horizontal PAC; and b. calculating the probability of the VT (Pv) using the equation:
PNG
media_image10.png
43
81
media_image10.png
Greyscale
PVT.
However, May teaches “As previously mentioned, the amplitude based WCT Formula is a binary outcome logistic regression model that uses select independent WCT predictors: (1) WCT duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%). Each WCT predictor (X.sub.x) was assigned beta coefficients (β.sub.x) according to their influence on the binary outcome (VT vs. non-VT). The “constant” term (B.sub.0) represents the y-intercept of the least squares regression line. Discrete measured or calculated WCT predictor values derived from paired baseline and WCT ECGs are incorporated into the WCT Formula to calculate VT probability (P)[0083],[00014-eq], and [00015-eq]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the logistic regression model of the method from differentiating wide complex heart beats of May. Doing so would specify how the data is transformed by a specific kind of model in order to calculate probability of the heart conditions.
Regarding claim 15, de Saint Victor in view of May teaches the method of claim 14, but de Saint Victor fails to disclose wherein transforming the at least one engineered feature into the classification of the WCT pattern further comprises: a. assigning the classification of VT if PUr is at least equal to a predetermined threshold value; and b. assigning the classification of SWCT if Pvr is less than the predetermined threshold value.
However, May teaches “In another aspect, the signal change comprises a VT probability, the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value, and the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value[0017]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the predetermined thresholds of the method from differentiating wide complex heart beats of May. Doing so would specify values to compare the data too in order to complete the tachycardia classification.
Regarding claim 17, de Saint Victor in view of May teaches the method of claim 15, but de Saint Victor fails to disclose wherein the predetermined threshold value comprises one of 1%, 10%, 25%, 50%, 75%,90%, 95%, and 99%.
However, May teaches “In another aspect, the method further comprises selecting the predetermined value from a range of 0% to 100%. In another aspect, the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%[0010]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the predetermined thresholds of the method from differentiating wide complex heart beats of May. Doing so would specify values to compare the data too in order to complete the tachycardia classification.
Regarding claim 25, de Saint Victor in view of May teaches the system of claim 20, but de Saint Victor fails to disclose wherein the at least one processor is further configured to: a. receive at least a portion of the engineered features from automated data analysis software configured to transform the WCT ECG data into the portion of the engineered features; b. transform the ECG data into PMonoTVA using a first transform comprising:
PNG
media_image11.png
50
385
media_image11.png
Greyscale
x 100wherein Monophasic TiVA comprises a summation of all QRS time- voltage areas from all monophasic QRS complexes from the ECG data and Multiphasic TVA comprises a summation of all QRS time-voltage areas from all multiphasic QRS complexes from the ECG data; c. transform the ECG data into PMonoAmp using a second transform comprising:
PNG
media_image12.png
73
586
media_image12.png
Greyscale
wherein Monophasic amplitude comprises a summation of all QRS amplitudes from all monophasic QRS complexes from the ECG data and Multiphasic amplitude comprises a summation of all QRS amplitudes from all multiphasic QRS complexes from the ECG data; and d. any combination thereof.
However, May teaches “Note that contemporary computerized ECG interpretation software also routinely provides standard ECG measurements including QRS duration (ms), QTc duration (ms), and frontal plane R and T wave axes (°). These measurements are typically apparent/reported on the 12-lead ECG paper recording. The difference in QRS duration (ms), frontal plane R wave axis (°) and frontal plane T wave axis (°) between the WCT and baseline ECGs may be automatically calculated by computerized ECG interpretation software[0064]. the method further comprises obtaining the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas from an electrocardiogram (ECG) QRS signal, a ventricular electrogram (EMG) signal, and/or a vectorcardiogram (VCG) signal. In another aspect, the wide complex heart beat waveform amplitudes and/or time-voltage areas comprise a plurality of measured amplitudes and/or time-voltage areas of a ECG QRS waveform, a EMG waveform and/or a VCG waveform above and below an isoelectric baseline; and the baseline heart beat waveform amplitudes and/or time-voltage areas comprise a plurality of measured amplitudes and/or time-voltage areas of a baseline ECG QRS waveform, a baseline EMG waveform and/or a baseline VCG waveform above and below the isoelectric baseline[0010]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the WCT QRS duration of the method from differentiating wide complex heart beats of May. Doing so would specify how the data is transformed by the computing device in order to categorize the WCT signals.
Regarding claim 28, de Saint Victor in view of May teaches the system of claims 26, wherein the at least one processor is further configured to transform the least one engineered feature into a classification of the WCT pattern using a machine learning model comprising one of a logistic regression model, an artificial neural network, a random forest model, and a support vector machine(de Saint Victor - To implement the ECG processing system, ECG application running on the system device may receive ECG data (i.e., cardiac signal) from a sensing device and may transmit the ECG data to a ECG platform running on the server. The ECG platform may execute a first and second neural network and may apply the ECG data to the first and second neural network. The first neural network may be a delineation neural network having machine learning functionality. The second neural network may be a classification neural network having machine learning functionality. The output of the first and/or second neural networks may be processed by the ECG platform to achieve delineation and classification of the ECG data[0086]).
Regarding claim 30, de Saint Victor in view of May teaches the system of claims 28,wherein the at least one processor is further configured to transform the least one engineered feature into a classification of the WCT pattern using the logistic regression model by: a. transforming the at least one engineered feature into a weighted sum of predictors X1' using the equation: X¢=0+
PNG
media_image13.png
21
40
media_image13.png
Greyscale
+2X2 wherein
PNG
media_image14.png
19
59
media_image14.png
Greyscale
and #2 are constant weighting factors, X1 is PMonoTVA, and X2 is WCT QRS Duration; and b. calculating the probability of the VT (Pv) using the equation:
PNG
media_image15.png
42
119
media_image15.png
Greyscale
However, May teaches “As previously mentioned, the amplitude based WCT Formula is a binary outcome logistic regression model that uses select independent WCT predictors: (1) WCT duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%). Each WCT predictor (X.sub.x) was assigned beta coefficients (β.sub.x) according to their influence on the binary outcome (VT vs. non-VT). The “constant” term (B.sub.0) represents the y-intercept of the least squares regression line. Discrete measured or calculated WCT predictor values derived from paired baseline and WCT ECGs are incorporated into the WCT Formula to calculate VT probability (P)[0035]. where: X.sub.β is the weighted sum of the WCT predictors [0084] P.sub.VT is the probability of VT. [Equations 000014, 00015]).
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the logistic regression model of the method from differentiating wide complex heart beats of May. Doing so would specify how the data is transformed by a specific kind of model in order to calculate probability of the heart conditions.
Regarding claim 31, de Saint Victor in view of May teaches the system of claims 30, wherein the at least one processor is further configured to: a. receive baseline ECG data indicative of a baseline cardiac pattern; and b. transform the baseline ECG data and the WCT ECG data into at least one additional engineered feature selected from a QRS Axis change, a T Axis change, a frontal percent time-voltage area change (PTVAC), a Horizontal PTVAC, a frontal percent amplitude change (PAC), a horizontal PAC, and any combination thereof(de Saint Victor - A computerized-system for analyzing ECG data of a patient may, in another example, analyze the ECG data using a delineation algorithm to determine wave information indicating a likelihood of a presence of at least one wave and analyze the ECG data and wave information using a baseline classification algorithm. The computerized-system may further determine a first value using the baseline classification algorithm, the first value indicating a presence of at least one cardiac event, and may analyze the ECG data and wave information using a desensitized classification algorithm, the desensitized classification algorithm having decreased sensitivity compared to the baseline classification algorithm[0034]. For example, global measurements may include, but are not limited to, PR interval, P-wave duration, QRS complex duration, QRS axis, QT interval, corrected QT interval (Qtc), T-wave duration, JT interval, corrected JT interval, heart rate, ST elevation, Sokolov index, number of premature ventricular complexes, number of premature atrial complexes (PAC), ratio of non-conducted P waves, and/or ratio of paced waves[0117]. Delineator 39 may further deduce labels solely from the information generated by delineator 39. For example, the following labels may be deduced by delineator 39: short PR interval (i.e., PR interval<120 ms), first degree AV block (e.g., PR interval>200 ms), axis deviations, long QTc, short QTc, wide complex tachycardia, and/or intraventricular conduction blocks[0118]).
Regarding claim 32, de Saint Victor in view of May teaches the system of claims 31, but de Saint Victor fails to disclose wherein the at least one processor is further configured to: a. transform the WCT ECG data into a WCT QRS axis angle and the baseline ECG data into a baseline QRS axis angle using automated data analysis software, and subtracting, using the computing device, the baseline QRS axis angle from the WCT QRS axis angle to obtain the QRS Axis change: b. transform the WCT ECG data into a WCT T axis angle and the baseline ECG data into a baseline T axis angle using automated data analysis software, and subtracting, using the computing device, the baseline T axis angle from the WCT T axis angle to obtain the T Axis change; c. transform the WCT ECG data into a WCT Frontal percent amplitude (PA) and the baseline ECG data into a baseline Frontal PA using automated data analysis software, and subtracting, using the computing device, the baseline Frontal PA from the WCT Frontal PA to obtain the Frontal PAC; and d. transform the WCT ECG data into a WCT Horizontal percent amplitude (PA) and the baseline ECG data into a baseline Horizontal PA using automated data analysis software, and subtracting, using the computing device, the baseline Horizontal PA from the WCT Horizontal PA to obtain the Horizontal PAC.
However, May teaches “As shown in FIG. 11, WCT predictors included baseline QRS duration (ms) (p=0.05), baseline QTc interval duration (ms) (p=0.05), WCT QRS duration (ms) (p<0.001), change in QRS duration (ms) (p<0.001), change in R wave axis (°) (p<0.001), change in T wave axis (°) (p<0.001), frontal PAC (%) (p<0.001) and horizontal PAC (%) (p<0.001). As shown in FIG. 13, the amplitude based WCT Formula diagnostic performance including (1) WCT QRS duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%) demonstrated favorable VT and SWCT differentiation (AUC of 0.96) using the derivation cohort (collection of paired WCT and baseline ECGs)[0108]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the WCT QRS duration of the method from differentiating wide complex heart beats of May. Doing so would specify how the data is transformed by the computing device in order to categorize the WCT signals.
Regarding claim 33, de Saint Victor in view of May teaches the system of claims 32, but de Saint Victor fails to disclose wherein the at least one processor is further configured to transform the least one engineered feature into a classification of the WCT pattern using the logistic regression model by: a. transforming the at least one engineered feature into a weighted sum of predictors X, using the equation:
PNG
media_image16.png
22
339
media_image16.png
Greyscale
X4+I
PNG
media_image17.png
20
86
media_image17.png
Greyscale
36X6 wherein po,#1,#2,#3
PNG
media_image18.png
20
69
media_image18.png
Greyscale
and #l are constant weighting factors, X is PMonoTVA, X2 is WCT QRS Duration, X3 is [[T]]QRS Axis change, X4 is T Axis change Frontal-PAC,X5 is Frontal PAC -T-Axis-change, and X6 is Horizontal PAC; and b. calculating the probability of the VT (Pyr) using the equation:
PNG
media_image19.png
43
110
media_image19.png
Greyscale
.
However, May teaches “As previously mentioned, the amplitude based WCT Formula is a binary outcome logistic regression model that uses select independent WCT predictors: (1) WCT duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%). Each WCT predictor (X.sub.x) was assigned beta coefficients (β.sub.x) according to their influence on the binary outcome (VT vs. non-VT). The “constant” term (B.sub.0) represents the y-intercept of the least squares regression line. Discrete measured or calculated WCT predictor values derived from paired baseline and WCT ECGs are incorporated into the WCT Formula to calculate VT probability (P)[0083],[00014-eq], and [00015-eq]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the logistic regression model of the method from differentiating wide complex heart beats of May. Doing so would specify how the data is transformed by a specific kind of model in order to calculate probability of the heart conditions.
Regarding claim 34, de Saint Victor in view of May teaches the system claims 33, but de Saint Victor fails to disclose wherein the at least one processor is further configured to transform the least one engineered feature into a classification of the WCT pattern using the logistic regression model by: a. assigning the classification of VT if Pvr is at least equal to a predetermined threshold value; and b. assigning the classification of SWCT if Pyr is less than the predetermined threshold value.
However, May teaches “In another aspect, the signal change comprises a VT probability, the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value, and the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value[0017]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the system of analyzing ECG data of de Saint Victor with the predetermined thresholds of the method from differentiating wide complex heart beats of May. Doing so would specify values to compare the data too in order to complete the tachycardia classification.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA CATHERINE ANTHONY whose telephone number is (703)756-4514. The examiner can normally be reached 7:30 am - 4:30 pm, EST, M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CARL LAYNO can be reached at (571) 272-4949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARIA CATHERINE ANTHONY/Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796