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 .
For the purpose of examination, all references to the as-filed specification have been made using the USPGPub. version of the instant application.
Specification
The disclosure is objected to because of the following informalities: The terms "engineering feature" and "engineered feature" are used interchangeably throughout the specification. Applicant is recommended to use either one for consistency.
Appropriate correction is required.
Claim Objections
Claims 1, 4, 5, 8, 11, 12, 15, 18, & 19 are objected to because of the following informalities: For acronyms, applicant is recommended to provide a definition for each acronym at its first occurrence. For example, WCT is defined multiple times (in claims 8 and 15) when WCT was already defined in claim 1. The terms "engineering feature" and "engineered feature" are used interchangeably throughout these claims. Applicant is recommended to use either one in all claims for consistency.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 5, 8-14, 17, & 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claims 3, 10, and 17, it is unclear how WCT pattern can be considered “indicative of” a probability. It seems that WCT pattern may be indicative of VT or SWCT and determine a probability of VT or SWCT based on WCT pattern according to [0102] of the specification. For the purpose of examination, claims 3, 10, and 17 have been interpreted according to the specification.
Claims 5, 12, & 19 recite the limitation "the transformation" in third line of each claim. There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination, this limitation will be interpreted as referring to the actions “transform the WCT ECG data” in independent Claims 1, “transform the WCT ECG data and baseline ECG data” in independent claim 8, and “transforming the WCT ECG data and baseline ECG data” in independent claim 15, as though these limitations read “perform a transformation on the… data”.
Claim 8 recites the limitation “wherein at least one processor is programmed to”. It is unclear if this is referring to the same “at least one processor in communication with at least one memory device” or a new at least one processor. For the purposes of examination, this limitation has been interpreted as wherein the at least one processor is programmed to”. Dependent claims 9-14 are indefinite for the same reasons set forth above for claim 8.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category
Claims 1-14 recite a computer device and is therefore a machine. Claims 15-31 recite a method and is therefore a process.
Step 2A—Prong 1: Recited Judicial Exception
Claims 1, 8, & 15 recite the following limitations:
receive WCT electrocardiogram (ECG) data indicative of a WCT pattern;
transform the WCT ECG data into at least one engineering feature
execute at least one machine learning model to analyze the at least one engineering feature
determine whether the WCT pattern is indicative of at least one of a ventricular tachycardia (VT) and a supraventricular wide complex tachycardia (SWCT)
select a treatment for the subject based upon the determination
The limitations, as drafted, describe a process that, under its broadest reasonable interpretation, includes performance of the limitation in the mind or a mathematical calculation except for the recitation of “at least one processor in communication with at least one memory device”, which are recited at a high level of generality and is nothing more than parts of generic computer (see [0188]-[0190]). The recitation of “machine learning model” is nothing more than mathematical calculations according to the as-filed specification, which discloses the following in [0095]:
Non-limiting examples of suitable machine learning models include genetic algorithms, linear or logistic regressions, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, dimensionality reduction, and support vector machines. In one exemplary method, a logistic regression model 125 is used to transform the engineered features into a classification or probability 130 of VT or SWCT.
.
That is, other than reciting instructions for and parts of a generic computer performing these tasks and instructions, nothing in the claim precludes the steps from practically being performed in the human mind or being considered as mathematical calculations. MPEP 2106.04(a)(2)(III) states that the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea and MPEP 2106.04(a)(2)(I)(C) states that a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. For example, other aside from the recitation of “performed by a computer”, the claim encompasses a medical professional gathering a patient’s ECG data, classifying cardiac rhythms using mathematical/statistical modeling to determine ventricular tachycardia and supraventricular wide complex tachycardia, and selecting an appropriate treatment for the patient.
Step 2A—Prong 2: Integration into a Practical Application
The claim recites additional element: “at least one processor in communication with at least one memory device” to perform the abstract steps identified in Step 2A, Prong 1 section above. This limitation read on a computer implemented method and is recited at a high level of generality, i.e., as a generic processor, performing a generic computer function of processing and evaluating data for diagnostic. These generic computer limitations are no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional limitation does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: Inventive Concept
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial except into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The specification in [0009]-[0011] does not provide any indication that the computer processor and memory are anything other than generic, off-the-shelf computer components. Likewise, specification in [0012] does not provide any indication that the machine learning model is anything other than a generic machine learning model. Court decisions cited in MPEP 2106.05(d)(II) indicate that computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). Accordingly, a conclusion that the generic computer functions merely being used to implement an abstract idea is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
Dependent claims 2-7, 9-14, & 16-21 further limit the abstract idea already indicated in independent claims 1, 8, & 15 respectively and they are ineligible for the same reasons provided for claims 1, 8, & 15 above.
Claims 2, 9, and 16 recite the additional element “a 12-lead ECG”. The 12-ECG is not claimed as part of the computer device and is not performing any of the recited steps of the method. It is merely the source of the data used by the machine and method and therefore constitutes nothing more than insignificant pre-solution data gathering. The use of a 12-lead ECG to collect electrogram data is well-understood, routine, conventional activity as evidenced by Reilly and Lee (Electrograms (ECG, EEG, EMG, EOG), Technology and Health Care, Vol. 18 (2010), Pg. 448).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-5, 7-12, 14-19, & 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 3, 8, 11, 15, 20, 21, 27, 29, 31, 37, 41, 43, 46, & 47 of U.S. Patent No. 11154233. Although the claims at issue are not identical, they are not patentably distinct from each other as laid out below.
With respect to instant claims 1-5, 7-12, 14-19, & 21, see Table 1 below.
Table 1
Instant Application18/980157
Conflicting Patent
US 11154233 B2
Conflicting Application
18/706109
Differences
From 18/706109
Claim 1 & 8
A computer device for classifying a wide complex tachycardia (WCT) pattern of a subject, the computer device comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to: receive WCT electrocardiogram (ECG) data indicative of a WCT pattern and baseline ECG data;
Claim 27
An apparatus for classifying a wide complex heart beat(s) comprising: an input/output interface; a memory; [[and]] one or more processors communicably coupled to the input/output interface and the memory, wherein the one or more processors: receive one or more wide complex heart beat waveform amplitudes and/or time- voltage areas[[,]] and one or more baseline heart beat waveform amplitudes and/or time- voltage areas via the input/output interface or the memory, determine a classification probability signal change between the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas and the one or more baseline heart beat waveform amplitudes and/or time-voltage areas using the one or more processors,
Claim 20
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 patten
Claim 30
The system of claim 30 (from 28, from 20), wherein the at least one processor is further configured to: a. receive baseline ECG data indicative of a baseline cardiac pattern;
in communication with at least one memory device
transform the WCT ECG data and baseline ECG data into at least one engineering feature;
Claim 43
The apparatus of claim 27 wherein the one or more processors determine the classification probability… by: receiving a wide complex heart beat waveform duration via the input/output interface or the memory; determining a percent amplitude change (PAC) based on the one or more wide complex heart beat waveform amplitudes and the one or more baseline wide complex heart beat waveform amplitudes, and/or a percent time-voltage area change (PTVAC) based on the one or more wide complex heart beat waveform time-voltage areas and the one or more baseline wide complex heart beat waveform time-voltage areas;
Claim 20
b. transform the WCT ECG data into at least one engineered feature
execute at least one machine learning model to analyze the at least one engineering feature and to output a classification of the WCT pattern;
Claim 46
The apparatus of claim 43 (and 27) wherein the one or more processors determine the classification probability… by: determining a VT probability using a statistical or machine learning process based on the WCT QRS duration and (1) the frontal PAC and the horizontal PAC, and/or (2) the frontal PTVAC and the horizontal PTVAC
Claim 20
c. transform the at least one engineered feature into an assigned classification of the WCT pattern using a machine learning model
based upon the classification of the WCT pattern, determine whether the WCT pattern is indicative of at least one of a ventricular tachycardia (VT) and a supraventricular wide complex tachycardia (SWCT);
Claim 29
The apparatus of claim 27, wherein: the wide complex heart beat(s) comprise a wide complex tachycardia (WCT); the ventricular source comprises a ventricular tachycardia (VT); and the supraventricular aberrant condition comprises a supraventricular wide complex tachycardia (SWCT).
Claim 20
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 select a treatment for the subject based upon the determination.
Claim 27
wherein a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change is provided,
Claim 20
d. 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 SWOT.
Claim 2 & 9
wherein the at least one processor is further programmed to receive the WCT ECG data from a 12 lead ECG device.
Claim 41
The apparatus of claim 40 (from 37 from 27), wherein the one or more sensors or devices comprise a 12-lead ECG device
Claim 22
The system of any one of claim 20, wherein the ECG device comprises one of a 12-lead ECG device,
Claim 3 & 10
wherein the at least one processor is further programmed to determine whether the WCT pattern is indicative of at least one of a ventricular tachycardia, a supraventricular wide complex tachycardia, a probability of a VT, and a probability of an SWCT.
Claim 27
wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular aberrant condition;
Claim 31
The apparatus of claim 30 (from 27), wherein: the classification probability 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.
Claim 20
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;
Claim 4 & 11
wherein the at least one engineered feature includes at least one of WCT QRS duration (ms), PMonoTVA (%), WCT Polarity Code (WCT-PC) for each lead of the ECG data, and any combination thereof.
Claim 46
The apparatus of claim 43 (from 27), wherein the one or more processors determine the classification probability… by: receiving a WCT QRS duration via the input/output interface or the memory;
Claim 20
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 a combination thereof;
Claim 5 & 12
wherein the at least one processor is further programmed to select at least one engineering feature for the transformation.
Claim 37
The apparatus of claim 27, wherein the one or more processors… determining the one or more wide complex heart beat waveform amplitudes and/or time- voltage areas from the ECG QRS data, the EMG data, and/or the VCG data;
Claim 20
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 a combination thereof;
Claim 7 & 15
wherein the machine learning model is selected from a logistic regression (LR) model, an artificial neural network (ANN), a Random Forests (RF) model, a support vector machine (SVM), and an ensemble learning (EL) model.
Claim 47
The apparatus of claim 46 (from 43, from 27) wherein the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naïve Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
Claim 28
The system of (claim 20?) 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.
Claim 15
A method for classifying a wide complex tachycardia (WCT) pattern of a subject, the method implemented by a computer device comprising at least one processor in communication with at least one memory device, wherein the method comprises: receiving WCT ECG data indicative of the WCT pattern and baseline ECG data;
Claim 1
A computerized method of classifying a wide complex heart beat(s) comprising: providing a computing device having an input/output interface, one or more processors, and a memory; receiving one or more wide complex heart beat waveform amplitudes and/or time-voltage areas[[,]] and one or more baseline heart beat waveform amplitudes and/or time-voltage areas via the input/output interface or the memory;
Claim 2
The method of claim 1, wherein the signal change further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing.
Claim 1
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;
comprising at least one processor in communication with at least one memory device,
transforming the WCT ECG data and baseline ECG data into at least one engineered feature;
Claim 11
The method of claim 1, wherein: determining the one or more wide complex heart beat waveform amplitudes and/or time- voltage areas from the ECG QRS data, the EMG data, and/or the VCG data using the one or more processors
Claim 1
b. transforming, using the computing device, the WCT ECG data into at least one engineered feature[[;]],
executing at least one machine learning model to analyze the at least one engineering feature and to output a classification of the WCT pattern;
Claim 20
The method of claim 1. Wherein determining the classification probability signal change between the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas and the one or more baseline heart beat waveform amplitudes and/or time-voltage areas comprises: determining a VT probability using a statistical or machine learning process based on the WCT QRS duration and (1) the frontal PAC and the horizontal PAC, and/or (2) the frontal PTVAC and the horizontal PTVAC
Claim 1
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,
based upon the classification of the WCT pattern, determining whether the WCT pattern is indicative of at least one of a ventricular tachycardia (VT) and a supraventricular wide complex tachycardia (SWCT);
Claim 1
wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular aberrant condition;
Claim 1
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 selecting a treatment for the subject based upon the determination.
Claim 8
wherein providing the signal change comprises providing a "shock" signal, a "no shock" signal
Claim 1
transforming the assigned classification of the WCT pattern into a treatment recommendation using at least one treatment rule,
Claim 16
The method of claim 15 further comprising receiving the WCT ECG data from a 12 lead ECG device.
Claim 15
The method of claim 14 (including claim 11 and claim 1), wherein the one or more sensors or devices comprise a 12-lead ECG device
Claim 3
The method of claim 1, wherein receiving the WCT ECG data indicative of the WCT pattern comprises receiving WCT ECG data from an ECG device comprising a 12-lead ECG device
Claim 17
The method of claim 15 further comprising determining whether the WCT pattern is indicative of at least one of a ventricular tachycardia, a supraventricular wide complex tachycardia, a probability of a VT, and a probability of an SWCT.
Claim 1
a classification probability based on signal change between the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas and one or more the baseline heart beat waveform amplitudes and/or time-voltage areas using the one or more processors;
Claim 3
The method of claim 1, wherein: the wide complex heart beat(s) comprise a wide complex tachycardia (WCT); the ventricular source comprises a ventricular tachycardia (VT); and the supraventricular aberrant condition comprises a supraventricular wide complex tachycardia (SWCT).
Claim 1
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;
Claim 18
The method of claim 15, wherein the at least one engineered feature includes at least one of WCT QRS duration (ms), PMonoTVA (%), QRS-PS for each lead of the ECG data, and any combination thereof.
Claim 20
The method of claim 1 wherein… determining a VT probability using a statistical or machine learning process based on the WCT QRS duration and (1) the frontal PAC and the horizontal PAC, and/or (2) the frontal PTVAC and the horizontal PTVAC;
Claim 6
The method of claim 1, wherein transforming the WCT ECG data into the at least one engineered feature further comprises:a. transforming the WCT ECG data into the a WCT QRS duration using automated data analysis software and receiving, using the computing device, the wide complex tachycardia (WCT) WCT QRS duration from the automated data analysis software; b. transforming the WCT ECG data into the PMonoTVA using a first transform
Claim 19
The method of claim 18 further comprising selecting at least one engineering feature for the transformation.
Claim 11
The method of claim 1, wherein receiving… comprises determining the one or more wide complex heart beat waveform amplitudes and/or time- voltage areas from the ECG QRS data, the EMG data, and/or the VCG data
Claim 1
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 a combination thereof;
Claim 21
The method of claim 15, wherein the machine learning model is selected from a logistic regression (LR) model, an artificial neural network (ANN), a Random Forests (RF) model, a support vector machine (SVM), and an ensemble learning (EL) model.
Claim 21
The method of claim 20 (from claim 1), wherein the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
Claim 9
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 machine learning 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.
Therefore, the claims of U.S. Patent No. 11504046 anticipate all of instant Claims 1-5, 7-12, 14-19, & 21.
Claims 1-5, 7-12, 14-19, & 21 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 3, 6, 9, 20, 22, 28, 30 of copending Application No. 18/706109 in view of May (US 2022/0054069, published Feb-24 2022).
The claims of copending Application No. 18/706109 recites all of instant Claims 1-5, 7-12, 14-19, & 21 as set forth in Table 1 above except in communication with at least one memory device and comprising at least one processor in communication with at least one memory device.
Attention is brought to the May reference, which teaches an apparatus and method for wide complex beat differentiation that can be automatically implemented using data provided by contemporary ECG, EMG and/or VCG interpretation software ([0008]). The present invention provides a computerized method of classifying a wide complex heart beat(s) comprising: … one or more processors and a memory ([0009]). May also teaches that manual interpretation methods do not perform well when used be less experienced ECG interpreters. In fact, few clinicians, aside from expert electrocardiographers, are able use manual methods with reliable accuracy.
It would have been obvious to one of ordinary skill in the art at the time of filing of the instant application to use the at least one processor in communication with at least one memory device taught by May to implement the computing device and computer aided method recited by copending Application No. 18/706109 for the purpose of allow less experienced ECG interpreters to differentiate VT and SWCT with greater reliable accuracy.
This is a provisional nonstatutory double patenting rejection.
Claims 6, 13, & 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 8, 11, 20, 27, 29, 43, & 46 of U.S. Patent No. 11154233 in view of Sandler & Marriott (The Differential Morphology of Anomalous Ventricular Complexes of RBBB-Type in Lead V1 Ventricular Ectopy versus Aberration. Published 1965, hereinafter Sandler, cited in IDS dated 4/15/2025) as evidenced by Meek & Morris (ABC of clinical electrocardiography: Introduction. I—Leads, rate, rhythm, and cardiac axis. Published 2002, hereinafter Meek).
With respect to instant claims 6, 13, & 20, see Table 2 below.
Table 2
Instant Application18/980157
Conflicting Patent
US 11154233 B2
Conflicting Application
18/706109
Differences
The computer device of claim 1, wherein each WCT-PC for each lead of the ECG data is selected from positive, negative, or equiphasic.
The computer device of claim 1, wherein each WCT-PC for each lead of the ECG data is selected from positive, negative, or equiphasic.
The computer device of claim 11, wherein each QRS-PS for each lead of the ECG data is selected from equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-).
The computer device of claim 11, wherein each QRS-PS for each lead of the ECG data is selected from equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-).
The method of Claim 18, wherein each QRS-PS for each lead of the ECG data is selected from equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-).
The method of Claim 18, wherein each QRS-PS for each lead of the ECG data is selected from equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-).
The claims of U.S. Patent No. 11154233 do not recite wherein each WCT-PC for each lead of the ECG data is selected from positive, negative, or equiphasic or wherein each QRS-PS for each lead of the ECG data is selected from equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-).
However, attention is brought to the Sandler reference which teaches morphological differences between the QRS complex in right bundle branch block (RBBB) aberrant conduction (which can manifest as supraventricular tachycardia) and ventricular extrasystoles, (which can manifest as ventricular tachycardia). Sandler explains that in RBBB type beats, the initial vector of the beat is usually identical with that of normally conducted beats, since in RBBB the earliest ventricular activation is undisturbed. When aberrant conduction is present alongside RBBB, the initial vector differed in 56% of cases. However, in ventricular extrasystoles, 51% of beats had an initial deflection opposite to the flanking sinus beats, and 45% had a similar, but obviously different inclination, with only 4% having an identical vector (Results, Pg. 55 ¶3 – ¶4). Although a changed initial vector (WCT-PC) alone does not clearly distinguish between ventricular ectopy and RBBB with aberration of conduction, an identical initial vector provides strong evidence in favor of RBBB with or without aberration of conduction (Summary and Conclusion, Pg 556 ¶3) The attached annotated figures shows examples of the 9 possible combination deflection change (equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-)).
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Sandler does not teach positive, negative, or equiphasic QRS deflections by those names, however it is well understood in the field of electrocardiogram (ECG) interpretation that the direction of ECG tracing deflection (WCT-PS) is a result of the electrical impulse vector, as evidenced by Meek. Meek teaches that the direction of the deflection on the electrocardiogram depends on whether the electrical impulse is travelling towards or away from a detecting electrode. By convention, an electrical impulse travelling directly towards the electrode produces an upright (“positive”) deflection relative to the isoelectric baseline, whereas an impulse moving directly away from an electrode produces a downward (“negative”) deflection relative to the baseline. When the wave of depolarization is at right angles to the lead, an equiphasic deflection is produced (Pg 415, ¶7).
It would have been obvious to one of ordinary skill in the art at the time of filing of the instant application to apply the classification of a polarity code (WCT-PC) as positive, negative or equiphasic and to define a shift in those polarity (WCT-PS) in terms of positive, negative or equiphasic taught by Sandler as evidenced by Meek to the computer apparatus and method recited by U.S. Patent No. 11504046 for the purpose of distinguishing between aberrant conduction and presence of ventricular ectopic activity.
Claims 6, 13, & 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, 20, 30 of copending Application No. 18/706109 in view of May, in further view of Sandler as evidenced by Meek.
With respect to instant claims 6, 13, & 20, see the Table 2 above.
The claims of copending Application No. 18/706109 in view of May do not recite wherein each WCT-PC for each lead of the ECG data is selected from positive, negative, or equiphasic or wherein each QRS-PS for each lead of the ECG data is selected from equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-).
However, attention is brought to the Sandler reference, which teaches morphological differences between the QRS complex in right bundle branch block (RBBB) aberrant conduction (which can manifest as supraventricular tachycardia) and ventricular extrasystoles, and Meek, which teaches that the direction of the deflection on the electrocardiogram depends on whether the electrical impulse is travelling towards or away from a detecting electrode, as set forth above.
It would have been obvious to one of ordinary skill in the art at the time of filing of the instant application to apply the classification of a polarity code (WCT-PC) as positive, negative or equiphasic and to define a shift in those polarity (WCT-PS) in terms of positive, negative or equiphasic taught by Sandler as evidenced by Meek to the computer apparatus and method recited by copending Application No. 18/706109 for the purpose of distinguishing between aberrant conduction and presence of ventricular ectopic activity.
This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
A rejection on this statutory basis (35 U.S.C. 102(g) as in force on March 15, 2013) is appropriate in an application or patent that is examined under the first to file provisions of the AIA if it also contains or contained at any time (1) a claim to an invention having an effective filing date as defined in 35 U.S.C. 100(i) that is before March 16, 2013 or (2) a specific reference under 35 U.S.C. 120, 121, or 365(c) to any patent or application that contains or contained at any time such a claim.
Claims 1-5, 7-12, 14-19, & 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by May (US 2019/0387992). US 2019/0387992 is a USPGPub. of US 11154233, which is a conflicting patent under the nonstatutory type double patenting rejection set forth above.
Regarding claims 1-4, 8-11, & 15-19 May teaches a computer device and method for classifying a wide complex tachycardia (WCT) pattern of a subject (“a computerized method of classifying a wide complex heart beat(s)” in [0009] and “In another aspect, the computing device comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device. Moreover, the method can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method” in [0014]), the computer device comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to: receive WCT electrocardiogram (ECG) data indicative of a WCT pattern and baseline ECG data (“comprising: providing a computing device having an input/output interface, one or more processors and a memory; receiving one or more wide complex heart beat waveform amplitudes and/or time-voltage areas and one or more baseline heart beat waveform amplitudes and/or time-voltage areas” in ([0009]); transform the WCT ECG data into at least one engineering feature (“receiving a WCT QRS duration” in [0012]); execute at least one machine learning model to analyze the at least one engineering feature and to output a classification of the WCT pattern (“determining a VT probability using a statistical or machine learning process based on the WCT QRS duration” in [0012]); based upon the classification of the WCT pattern, determine whether the WCT pattern is indicative of at least one of a ventricular tachycardia (VT) and a supraventricular wide complex tachycardia (SWCT) (“differentiate discrete ventricular depolarizations due to premature ventricular contractions, ventricular pacing, and supraventricular aberrant conduction” in [0058]); and select a treatment for the subject based upon the determination (“providing a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change” in [0014]), wherein the at least one processor is further programmed to receive the WCT ECG data from a 12 lead ECG device (“the one or more sensors or devices comprise a 12-lead ECG device” in [0017]), wherein the at least one processor is further programmed to determine whether the WCT pattern is indicative of at least one of a ventricular tachycardia, a supraventricular wide complex tachycardia (“the one or more sensors or devices comprise a 12-lead ECG device” in [0017]), a probability of a VT, and a probability of an SWCT (“wherein the signal change comprises the classification probability, and the classification probability comprises a VT probability, a SWCT probability, or a ventricular pacing probability” in [0017] ), wherein the at least one engineered feature includes at least one of WCT QRS duration (ms) (“receiving a WCT QRS duration” in [0012] and “differentiate discrete ventricular depolarizations due to premature ventricular contractions, ventricular pacing, and supraventricular aberrant conduction” in [0058]).
Regarding claim 5 & 12 May teaches the computer device of Claim 4 and The computer device of Claim 11, wherein the at least one processor is further programmed to select at least one engineering feature for the transformation (“determining the one or more waveform amplitudes and/or time-voltage areas from the ECG QRS data, the EMG data and/or the VCG data; and determining the one or more baseline waveform amplitudes and/or time-voltage areas from the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data” in [0017]).
Regarding claim 7, 14, &21 May teaches the computer device of Claim 1, the computer device of Claim 8, and the method of claim 15, wherein the machine learning model is selected from a logistic regression (LR) model, an artificial neural network (ANN), a Random Forests (RF) model, a support vector machine (SVM), and an ensemble learning (EL) model (“the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm” in [0012]).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 6, 13, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over May in view of Sandler, as evidenced by Meek.
With regard to claims 6, 13, & 20 May teaches the computer device of claim 1, the computer device of claim 11, and the method of claim 18 as set forth above.
May does not teach wherein each WCT-PC for each lead of the ECG data is selected from positive, negative, or equiphasic or wherein each QRS-PS for each lead of the ECG data is selected from equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-).
However, Sandler teaches morphological differences between the QRS complex in right bundle branch block (RBBB) aberrant conduction (which can manifest as supraventricular tachycardia) and ventricular extrasystoles, (which can manifest as ventricular tachycardia). Sandler explains that in RBBB type beats, the initial vector of the beat is usually identical with that of normally conducted beats, since in RBBB the earliest ventricular activation is undisturbed. When aberrant conduction is present alongside RBBB, the initial vector differed in 56% of cases. However, in ventricular extrasystoles, 51% of beats had an initial deflection opposite to the flanking sinus beats, and 45% had a similar, but obviously different inclination, with only 4% having an identical vector (Results, Pg. 55 ¶3 – ¶4). Although a changed initial vector (WCT-PC) alone does not clearly distinguish between ventricular ectopy and RBBB with aberration of conduction, an identical initial vector provides strong evidence in favor of RBBB with or without aberration of conduction (Summary and Conclusion, Pg 556 ¶3) The attached annotated figure shows examples of the 9 possible combination deflection change (equiphasic (=) → equiphasic (=), positive (+) → positive (+), negative (-) → negative (-), positive (+) → negative (-), positive (+) → equiphasic (=), negative (-) → positive (+), negative (-) → equiphasic (=), equiphasic (=) → positive (+), or equiphasic (=) → negative (-)).
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Sandler does not teach positive, negative, or equiphasic QRS deflections by those names, however it is well understood in the field of electrocardiogram (ECG) interpretation that the direction of ECG tracing deflection (WCT-PS) is a result of the electrical impulse vector, as evidenced by Meek. Meek teaches that the direction of the deflection on the electrocardiogram depends on whether the electrical impulse is travelling towards or away from a detecting electrode. By convention, an electrical impulse travelling directly towards the electrode produces an upright (“positive”) deflection relative to the isoelectric baseline, whereas an impulse moving directly away from an electrode produces a downward (“negative”) deflection relative to the baseline. When the wave of depolarization is at right angles to the lead, an equiphasic deflection is produced (Pg 415, ¶7).
It would have been obvious to one of ordinary skill in the art at the time of filing of the instant application to apply the classification of a polarity code (WCT-PC) as positive, negative or equiphasic and to define a shift in those polarity (WCT-PS) in terms of positive, negative or equiphasic taught by Sandler as evidenced by Meek to the systems and methods taught by May for the purpose of distinguishing between aberrant conduction and presence of ventricular ectopic activity.
Conclusion
The prior art made of record in IDS dated 4/15/2025 and not relied upon is considered pertinent to applicant's disclosure.
Kashou et al. Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias (Pub. Nov 2022, cited in IDS dated 4/15/2025)
Abstract
Background: Accurate automated wide QRS complex tachycardia (WCT) differentiation
into ventricular tachycardia (VT) and supraventricular wide complex tachycardia
(SWCT) can be accomplished using calculations derived from computerized electrocardiogram
(ECG) data of paired WCT and baseline ECGs.
Methods: We developed and trialed WCT differentiation models comprised of novel
and previously described parameters derived from WCT and baseline ECG data. In
Part 1, a derivation cohort was used to evaluate five different classification models:
logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support
vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation
cohort was used to prospectively evaluate the performance of two LR models using
parameters generated from the WCT ECG alone (Solo Model) and paired WCT and
baseline ECGs (Paired Model).
Results: Of the 421 patients of the derivation cohort (Part 1), a favorable area under
the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96),
ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation
cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103
patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo
Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac
device recording.
SYSTEMS AND METHODS FOR RESTRICTING RIGHTS TO AN ELECTROCARDIOGRAM PROCESSING SYSTEM (US 2022/0218259)
Abstract
Systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using a substantial amount of ECG data. 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. The processed ECG data is used to generate a graphic user interface that is communicated from the server(s) to a computer for display in a user-friendly and interactive manner with enhanced accuracy. The systems may restrict access to certain ECG data, analyses, reports, and/or functionality to different entities, devices and/or users.
Summary
Provided herein are systems and methods for analyzing ECG data using machine learning algorithms and medical grade artificial intelligence with enhanced accuracy and efficiency. Specifically, systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using artificial intelligence and a substantial amount of ECG data. 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 into smart technology (e.g., a smartwatch). The system may analyze ECG data sampled from the patient to accurately and efficiently detect and/or predict cardiac events such as such as cardiac arrhythmias and/or abnormalities including atrial fibrillation (AFib).
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/W.P.A./ Examiner, Art Unit 3792
/UNSU JUNG/ Supervisory Patent Examiner, Art Unit 3792
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