DETAILED ACTION
The Applicant’s response, received 03 March 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Election/Restrictions
Applicant’s election without traverse of Group VI (claim 25) in the reply filed on 03 March 2026 is acknowledged.
Claims 1, 3-24, and 26 have been cancelled in the reply filed on 03 March 2026, having been drawn to:
a nonelected method for generating an arrhythmia model library for modeling a heart (Group I, claims 1 and 3-6); a nonelected computing system for generating a model library of models of an electromagnetic source within a body (Group I, claims 7-14); a nonelected method for generating a model library of models of an electromagnetic source within a body (Group I, claims 15-20);
a nonelected computing system for converting a first polyhedral model of a body part to a second polyhedral model of the body part (Group II, claim 21);
a nonelected computing system for generating an electrocardiogram for a heart (Group III, claim 22);
a nonelected computing system for bootstrapping generation of modeled electromagnetic output of a heart (Group IV, claim 23);
a nonelected method for generating a classification for a patient based on patient electromagnetic data representing electromagnetic output of an electromagnetic source within the patient (Group V, claim 24);
a nonelected computing system for displaying a representation of electrical activation of a heart of a patient (Group VII, claim 26);
a nonelected computing system for displaying a representation of a vectorcardiogram (Group VIII, claim 27); and
a nonelected one or more computing systems for learning weights for a machine learning model for identifying a value of a source parameter of a source configuration of an electromagnetic source (Group IX, claim 28).
Status of the Claims
Claims 25 and 29-47 are pending.
Claims 25 and 29-47 are rejected.
Priority
This application claims benefit of 62/663,049, filed 26 April 2018.
Therefore, the effective filing date of the claimed invention is 26 April 2018.
Information Disclosure Statement
The information disclosure statements (IDS) received 02 November 2018, 13 March 2019, 13 August 2019, 30 September 2019, 27 October 2020, and 19 August 2022 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The Petition to Accept Color Drawing received 23 July 2018 has been granted, as noted in the Petition Decision received 05 March 2021.
The drawings received 23 July 2018 are not accepted, and are objected to as noted below.
The drawings are objected to because the sheets are not numbered in consecutive Arabic numerals, starting with 1, as required by 37 C.F.R. 1.84(t) (see MPEP 608.02 V.).
In particular:
These numbers should be placed in the middle of the top of the sheet, but not in the margin. The numbers can be placed on the right-hand side if the drawing extends too close to the middle of the top edge of the usable surface. The drawing sheet numbering must be clear and larger than the numbers used as reference characters to avoid confusion. The number of each sheet should be shown by two Arabic numerals placed on either side of an oblique line, with the first being the sheet number and the second being the total number of sheets of drawings, with no other marking.
The drawings are further objected to because they include the following reference character(s) not mentioned in the description, as required by 37 C.F.R. 1.84(t) (see MPEP 608.02 V.).
In particular:
Reference # 2406 in Fig. 24;
Reference #2503 in Fig. 25;
Reference #2800 in Fig. 28;
Reference #3001 in Fig. 30;
Reference #3806 in Fig. 38; and
Reference #3909 in Fig. 39.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 29, 34-36, and 40-44 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.
Claim 29 recites the limitations "the heart configuration of the patients" in line three. There is insufficient antecedent basis for this limitation in the claim, because the claim previously only recites “a target heart configuration of the target patient,” and further because it is not clear as to whether “the patients” is referring to “each of a plurality of patients.”
Claim 34 recites the limitation "of the patient" in line three. There is insufficient antecedent basis for this limitation in the claim, because the claim previously only recites “each of a plurality of patients.”
Claim 35 recites the limitation "the cluster" in line three. There is insufficient antecedent basis for this limitation in the claim, because the claim previously only recites “a plurality of clusters.”
Claim 36 is indefinite for depending from claim 35 and not remedying the indefiniteness of claim 35.
Claim 40 recites the limitation "the generated labels" in line fourteen. There is insufficient antecedent basis for this limitation in the claim, because the claim previously only recites “generating a label.”
Claims 41-44 are indefinite for depending from claim 40 and not remedying the indefiniteness of claim 40.
Claim 41 recites the limitation "the labels" in lines three and four. There is insufficient antecedent basis for this limitation in the claim, because it is not clear as to whether this limitation is referring to the limitation “a label” in line twelve of claim 40 or referring to the limitation “the generated labels” in line fourteen of claim 40.
Claims 42-44 are indefinite for depending from claim 41 and not remedying the indefiniteness of claim 41.
Claim 44 recites the limitation "the patient" in line one. There is insufficient antecedent basis for this limitation in the claim, because claim 43 only recites “the target patient.”
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 25 and 29-47 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Claim Interpretations
Claim 25 is interpreted to recite a product-by-process limitation of “based on patient training data that includes cardiograms of patients and based on a transference from a model classifier generated based on model training data that includes modeled cardiograms, the modeled cardiograms generated based on a computational model of a heart and model heart configurations,” with the product being the “patient training data” and further interpreted to not require the active steps of performing the processes of generating the “patient training data” (i.e., obtaining and/or generating cardiograms of patients and/or a transference from a model classifier generated based on model training data that includes modeled cardiograms, the modeled cardiograms generated based on a computational model of a heart and model heart configurations).
Claim 30 is interpreted to recite a product-by-process limitation of “generated based on patient training data that includes patient cardiograms collected from patients and patient source locations associated with the patients,” with the product being the “weights of a machine learning algorithm,” and further interpreted to not require the active steps of performing the processes of generating the “weights of a machine learning algorithm” (i.e., training a machine learning algorithm and/or accessing and/or obtaining patient training data that includes patient cardiograms collected from patients and patient source locations associated with the patients).
Claim 45 is interpreted to recite a product-by-process limitation of “the weights being learned using training data that includes training cardiograms labeled with training source locations of arrhythmias, the training data derived from clinical data collected from patients, the patients being identified based on similarity of patient cardiac characteristics of the patients to target cardiac characteristics of the target patient,” with the product being the “weights of a trained neural network,” and further interpreted to not require the active steps of performing the processes of generating the “weights of a trained neural network” (i.e., training the model to learn the weights using training data that includes training cardiograms labeled with training source locations of arrhythmias, and/or deriving the training data from clinical data collected from patients, and/or identifying patients based on similarity of patient cardiac characteristics of the patients to target cardiac characteristics of the target patient).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 25 and 29 are directed to a method performed by a computing system for generating a classification for a target patient based on a target cardiogram of the target patient (i.e., a process); claims 30-39 are directed to a method performed by one or more computing systems for generating a target source location for a target patient based on a target cardiogram of the target patient (i.e., a process); claims 40-44 are directed to a method performed by one or more computing systems for generating a patient-specific neural network that inputs derived electromagnetic data derived from electromagnetic output of an electromagnetic source within a body and that outputs a label relating to the electromagnetic source (i.e., a process); and claims 45-47 are directed to a method for treating an arrhythmia of a target patient (i.e., a process).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 25 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a patient classifier (i.e., mental processes and mathematical concepts)
based on patient training data that includes cardiograms of patients and based on a transference from a model classifier generated based on model training data that includes modeled cardiograms, the modeled cardiograms generated based on a computational model of a heart and model heart configurations (i.e., mental processes); and
applying the patient classifier to the target cardiogram to generate a target classification for the target patient (i.e., mental processes and mathematical concepts).
Independent claim 30 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
accessing weights of a machine learning algorithm (i.e., mental processes)
generated based on patient training data that includes patient cardiograms collected from patients and patient source locations associated with the patients; and
applying the machine learning algorithm to the target cardiogram to generate a target source location for the target patient (i.e., mental processes and mathematical concepts)
wherein treatment for the patient is informed based on the target source location (i.e., mental processes).
Independent claim 40 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
identifying models of the electromagnetic source that are similar to a target patient (i.e., mental processes);
for each identified model,
applying a computational model of the electromagnetic source to generate modeled electromagnetic output of the electromagnetic source based on a model source configuration for that model (i.e., mental processes and mathematical concepts);
deriving modeled derived electromagnetic data from the generated modeled electromagnetic output for that model (i.e., mental processes and mathematical concepts); and
generating a label for that model (i.e., mental processes); and
training the neural network using the modeled derived electromagnetic data and the generated labels as training data to generate weights for the neural network (i.e., mental processes and mathematical concepts).
Independent claim 45 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
accessing weights of a trained neural network (i.e., mental processes),
the weights being learned using training data that includes training cardiograms labeled with training source locations of arrhythmias, the training data derived from clinical data collected from patients, the patients being identified based on similarity of patient cardiac characteristics of the patients to target cardiac characteristics of the target patient;
applying the weights of the trained neural network to the target cardiogram to determine a target source location (i.e., mathematical concepts); and
outputting an indication of the target source location (i.e., mathematical concepts).
Dependent claims 29, 31-39, 41-43, and 46 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 29 further recites:
for each of a plurality of patients, assessing similarity between a target heart configuration of the target patient and the heart configuration of the patients (i.e., mental processes); and
identifying patients based on the assessment of similarity wherein the generating of the patient classifier is based on patient training data of the identified patients (i.e., mental processes).
Dependent claim 31 further recites:
generating the machine learning algorithm includes initializing weights of the machine learning algorithm based on a transference from a model machine learning algorithm generated using model training data that includes modeled cardiograms (i.e., mental processes and mathematical concepts).
Dependent claim 32 further recites:
the model training data is generated based on simulations (i.e., mental processes and mathematical concepts).
Dependent claim 33 further recites:
the machine learning algorithm comprises a neural network (i.e., mathematical concepts).
Dependent claim 34 further recites:
for each of a plurality of patients, assessing similarity between a target heart configuration of the target patient and a heart configuration of the patient (i.e., mental processes); and
identifying patients based on the assessed similarity (i.e., mental processes)
wherein the weights are learned using patient training data of the identified patients (i.e., mental processes and mathematical concepts).
Dependent claim 35 further recites:
for each of a plurality of clusters of similar patients, training a cluster machine learning algorithm using patient cardiograms of the similar patients with the cluster (i.e., mental processes and mathematical concepts).
Dependent claim 36 further recites:
identifying a cluster including patients similar to the target patient (i.e., mental processes); and
applying the cluster machine learning algorithm for the identified cluster to the target cardiogram to generate the target source location (i.e., mental processes and mathematical concepts).
Dependent claim 37 further recites:
identifying patients based on similarity to the target patient wherein the machine learning algorithm is trained using patient cardiograms and patient source locations of the identified patients (i.e., mental processes and mathematical concepts).
Dependent claim 38 further recites:
similarity is based on scar locations (i.e., mental processes).
Dependent claim 39 further recites:
similarity is based on demographic information (i.e., mental processes).
Dependent claim 41 further recites:
the electromagnetic source is a heart, the model source configurations include geometrical and electrophysiological parameters, the modeled derived electromagnetic data comprise cardiograms, and the labels comprise source locations of an arrhythmia (i.e., mental processes).
Dependent claim 42 further recites:
each of the models is derived from clinical data of patients (i.e., mental processes and mathematical concepts).
Dependent claim 43 further recites:
identify a target source location (i.e., mathematical concepts).
Dependent claim 46 further recites:
the training data includes simulated cardiograms labeled with simulated source locations, the simulated cardiograms being derived from simulations of electrical activity of a simulated heart having simulated cardiac characteristics, the simulated cardiac characteristics being selected based on an assessment of similarity of simulated cardiac characteristics to the target cardiac characteristics (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., identifying models of the electromagnetic source that are similar to a target patient), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., applying a computational model of the electromagnetic source to generate modeled electromagnetic output of the electromagnetic source) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 25 and 29-47 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 29, 31-39, 41, 42, and 46 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 25 include:
a computing system.
The additional elements in independent claim 30 include:
one or more computing systems.
The additional elements in independent claim 40 include:
one or more computing systems.
The additional elements in independent claim 45 include:
one or more computing systems;
accessing a target cardiogram of the target patient (i.e., accessing data); and
performing an ablation procedure on the target patient based on the target source location.
The additional elements in dependent claims 43, 44, and 47 include:
collecting a target cardiogram from the target patient (claim 43);
inputting the target cardiogram into the neural network (i.e., inputting data) (claim 43);
the patient is treated with an ablation procedure based on the target source location (claim 44); and
the target cardiogram is collected during the ablation procedure (claim 47).
The additional elements of a computing system (claim 25) and one or more computing systems (claims 30, 40, and 45); invoke a computer and/or computer-related components merely as tools for use in the claimed process, such that they amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional elements of accessing a target cardiogram of the target patient (i.e., accessing data) (claim 45); and inputting the target cardiogram into the neural network (i.e., inputting data) (claim 43); are merely pre-solution activities of gathering data and inputting data for use in the claimed process – nominal additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of collecting a target cardiogram from the target patient (claim 43); and the target cardiogram is collected during the ablation procedure (claim 47); are merely pre-solution activities of gathering data for use in the claimed process – nominal additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of performing an ablation procedure on the target patient based on the target source location (claim 45); and the patient is treated with an ablation procedure based on the target source location (claim 44); do not recite a particular treatment of prophylaxis for a disease or medical condition in accordance with the MPEP at section 2106.04(d)(2) (e.g., factors a., b., and c.).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and/or do not recite a particular treatment of prophylaxis for a disease or medical condition; and as such, when all limitations in claims 25 and 29-47 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 25 and 29-47 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 29, 31-39, 41, 42, and 46 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 25, 30, 40, and 45 and dependent claims 43, 44, and 47 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a computing system (claim 25); one or more computing systems (claims 30, 40, and 45); accessing data (claim 45); and inputting data (claim 43); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of collecting a target cardiogram from the target patient (claim 43); and the target cardiogram is collected during the ablation procedure (claim 47); are conventional. Evidence of conventionality is shown by:
Shah et al. (“Non-Invasive ECG Mapping to Guide Catheter Ablation.” Journal of Atrial Fibrillation, 2014, vol. 7, issue 3, pp. 31-38).
Shah et al. reviews the clinical experience obtained using non-invasive techniques in mapping cardiac electrical disorders and guiding the catheter ablation of atrial arrhythmias (premature atrial beat, atrial tachycardia, atrial fibrillation), ventricular arrhythmias (premature ventricular beats) and ventricular pre-excitation (Wolff-Parkinson-White syndrome) (Abstract). Shah et al. shows a more than 100-year history of 12-lead electrocardiography (ECG) as the standard-of-care tool, which involves measuring electrical potentials from limited sites on the body surface to diagnose cardiac disorder, its possible mechanism and the likely site of origin (Abstract).
The additional elements of performing an ablation procedure on the target patient based on the target source location (claim 45); and the patient is treated with an ablation procedure based on the target source location (claim 44); are conventional. Evidence of conventionality is shown by:
Shah et al. (as cited above)
Shah et al. further shows clinical cases where ablation of single target region terminated persistent atrial fibrillation (page 33, Figure 4); and further shows that various atrial and ventricular arrhythmias including complex fibrillatory processes can be mapped non-invasively to guide catheter ablation (page 36, col. 2, Conclusion).
Therefore, when taken alone, all additional elements in claims 25 and 29-47 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 25 and 29-47 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
Claims 25 and 29 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Krummen et al. (US 2017/0178403 (published 22 June 2017, as cited in the Information Disclosure Statement received 13 March 2019).
The applied reference has a common inventor with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Independent claim 25 is directed to generating a classification for a target patient based on a target cardiogram of the target patient.
Krummen et al. is directed to a system for computational localization of fibrillation sources, wherein the system performs operations comprising generating a representation of electrical activation of a patient’s heart and comparing, based on correlation, the generated representation against one or more stored representations of hearts to identify at least one matched representation of a heart.
Regarding independent claim 25, Krummen et al. shows machine-learning algorithms can be trained based upon patient-specific models to generate algorithms for detecting ventricular fibrillation/atrial fibrillation (VF/AF) within a patient based upon receiving EKG sensor data, CT scan data, vectorcardiograms (VCGs) and/or identifying VF/AF mechanisms and loci using statistical classification and/or machine-learning techniques to compare vectorcardiograms (VCGs) computed from surface EKGs of patients against diagnostic templates, e.g., from a VCG library (para. [0047]); and further shows that the VCG library can include VCG data for the computational models of the model library and/or the patient-specific models, e.g., VCG models can be simulated based on the computational models in the model library (e.g., based upon EKG data associated with each model) (paras. [0043] & [0083]).
Regarding dependent claim 29, Krummen et al. shows systems and methods for identifying fibrillation mechanisms in patients with VF/AF (para. [0037]) and further shows using VCG models for similarity measures (para. [0079]).
Therefore, Krummen et al. anticipates the instant claims.
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.
Claims 25 and 29-47 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (“Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network From 12-Lead ECG.” IEEE Transactions on Biomedical Engineering, 2018 (published 24 August 2017), vol. 65, no. 7, pp. 1662-71) and Prakosa et al. (“Cardiac Electrophysiological Activation Pattern Estimation from Images Using a Patient-Specific Database of Synthetic Image Sequences.” IEEE Transactions on Biomedical Engineering, 2014, vol. 61, no. 2, pp. 235-245).
Independent claim 25 is broadly directed to generating a classification for a target patient based on a target cardiogram of the target patient.
Dependent claim 29 further defines the classifier, e.g., identifying patients based on the assessment of similarity.
Independent claim 30 generally recites applying a machine learning algorithm to the target cardiogram to generate a target source location for the target patient.
Dependent claims 31-39 further define aspects of the machine learning model, e.g., characteristics of the training data, the type of machine learning algorithm, and steps in training the algorithm.
Independent claim 40 is broadly directed to generating (i.e., training) a patient-specific neural network that uses derived electromagnetic data of an electromagnetic source within a body to output a label relating to the electromagnetic source.
Dependent claims 41-44 further define aspects of deriving the models used to train the neural network and using the output of the model to inform a treatment procedure.
Independent claim 45 is broadly directed to using a trained neural network to output an indication of a target source location and using the output to inform a treatment procedure.
Dependent claims 46-67 further define characteristics of the training data and the collection of the target cardiogram.
Yang et al. is broadly directed to a method to localize origins of premature ventricular contractions (PVCs) from 12-lead electrocardiography (ECG) using a convolutional neural network (CNN) and a realistic computer heart model (Abstract).
Prakosa et al. is broadly directed to a patient-specific database of synthetic time series of the cardiac images using simulations of a personalized cardiac electromechanical model, and then using this database to train a machine-learning algorithm and then using this learned algorithm to generate patient-specific estimates using acquired clinical images (Abstract).
Regarding independent claim 25 and dependent claim 29, Yang et al. shows a method to classify and localize origins of cardiac arrhythmias (page 1663, col. 1, para. 2) using the rationale that PVCs are generated by focal sources, and if a CNN is trained with all the possible 12-lead ECGs resulting from a single-site pacing covering the ventricular volume with a certain level of noise, the CNN will be able to identify which segment the origin of PVC lies in and whether it is an epicardial or endocardial source given a set of 12-lead ECGs, and depending on the probability distribution of CNN output, could also give an estimation of source location based on the classification information (page 1663, col. 2, para. 3). Yang et al. further shows using simulated 12-lead ECGs to train and test the CNNs (page 1667, col. 1, para. 2); and further shows the application of the method to a plurality of PVC patients (page 1670, col. 1, para. 3).
Regarding independent claim 25 and dependent claim 29, Yang et al. does not show the exact steps for generating and applying a patient classifier to a target cardiogram to generate a target classification for the target patient, and in particular, does not show using patient training data that includes cardiograms of patients and based on a transference from a model classifier generated based on model training data that includes modeled cardiograms, the modeled cardiograms generated based on a computational model of a heart and model heart configurations.
Regarding independent claim 25 and dependent claim 29, Prakosa et al. shows using a model of the heart to produce synthetic but visually realistic image sequences for which the electrical stimulation is known, extracting descriptors from each sequence and then feeding them into a machine-learning algorithm which estimates the electrical pattern from the kinematic descriptors during the cardiac cycle (page 236, col. 1, para. 4; and Fig. 1). Prakosa et al. further shows that many medical image analysis studies are motivated by machine learning, and further shows that as the electrokinematic relationship is very complex, Prakosa et al. preferred to generate a patient-specific database, so that the learning could be done on cases relatively close to the patient’s condition (Ibid.).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Yang et al. by incorporating methods for using a database of synthetic data derived from cardiac images as training data, as shown by Prakosa et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Yang et al. with the methods of Prakosa et al., because Prakosa et al. shows that abnormal patterns of cardiac electrophysiological activation are at the origin of important cardiovascular diseases, e.g., arrhythmia, but clinically available methods for detailed observation are often through invasive catheter mapping, however, important information could be alternatively deduced from the motion patterns of cardiac image sequences (Abstract). This modification would have had a reasonable expectation of success given that both Yang et al. and Prakosa et al. disclose methods for assessing cardiac functions using machine-learning algorithms trained with non-invasive cardiac imaging data.
Regarding independent claim 30 and dependent claim 31-39, Yang et al. shows that neural networks are well known for recognizing patterns and classification, and that accuracy can be higher than 90% given ample training samples, and that the input to the neural network can be the original time course of the 12-lead ECG, features extracted from time domain, statistical features extracted from frequency domain, components resulting from different transforms, and outputs from some clustering algorithms, and further shows that the application of neural networks can be the classification among certain arrhythmias and/or all arrhythmias (p. 1663, col. 1, para. 1). Yang et al. further shows using patient statistics to inform model building (page 1666, col. 1, para. 2; and Table I), and further shows that radiofrequency catheter ablation is a minimally invasive procedure that by delivering energy to the sections of the heart that are prone to producing arrhythmias, the arrhythmias are terminated and the patient is treated (page 1662, col. 2, para. 2).
Regarding independent claim 30 and dependent claims 31-39, Yang et al. does not explicitly show transfer learning (e.g., using a pre-trained model), however Yang et al. does show that once the CNNs are trained on a patient, they are applicable to all the other PVCs and focal VTs from the same patient, and that the structures and parameters of both CNNs all remain the same across different subjects and noise levels, and further discusses that further training and testing of the CNNs (i.e., transfer learning) could be done off-line in about 20 minutes (page 1670, col. 1, para. 4).
Regarding independent claim 30 and dependent claims 31-39, Yang et al. does not show generating training data based on simulations, or basing similarity on scar locations.
Regarding independent claim 30 and dependent claims 31-39, Prakosa et al. shows using a training set derived from simulations (page 240, col. 1, para. 2l; and page 243, col. 1, para. 2); and further shows factoring in a label for an ablation scar in the computational model (page 237, col. 1, para. 2; and page 238, col. 2, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Yang et al. by incorporating methods for using a database of synthetic data derived from cardiac images as training data, and further derived through simulations, as shown by Prakosa et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Yang et al. with the methods of Prakosa et al., because Prakosa et al. shows that abnormal patterns of cardiac electrophysiological activation are at the origin of important cardiovascular diseases, e.g., arrhythmia, but clinically available methods for detailed observation are often through invasive catheter mapping, however, important information could be alternatively deduced from the motion patterns of cardiac image sequences (i.e., data derived from the images) (Abstract). This modification would have had a reasonable expectation of success given that both Yang et al. and Prakosa et al. disclose methods for assessing cardiac functions using machine-learning algorithms trained with non-invasive cardiac imaging data.
Regarding independent claim 40 and dependent claims 41-44, Yang et al. shows generating a patient-specific convolutional neural network for determining cardiac source localization of origin of arrhythmias (Abstract); and training and testing of the convolutional neural network (page 1664); building models from clinical data of patients (page 1666, col. 1, para. 2) and successful ablation sites (i.e., the patient is treated) (page 1667, col. 1, para. 1).
Regarding independent claim 40 and dependent claims 41-44, Yang et al. does not explicitly show deriving modeled derived electromagnetic data from the generated modeled electromagnetic output for the models; or generating a label for the models.
Regarding independent claim 40 and dependent claims 41-44, Prakosa et al. shows labelling aspects of a computational model (page 237, col. 1, para. 2); and shows that for each patient, a database of synthetic sequences was built for the learning process (page 241, col. 2, paras. 2-3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Yang et al. by incorporating methods for using a database of synthetic data with labels and derived from cardiac images as training data, and further derived through simulations, as shown by Prakosa et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Yang et al. with the methods of Prakosa et al., because Prakosa et al. shows that abnormal patterns of cardiac electrophysiological activation are at the origin of important cardiovascular diseases, e.g., arrhythmia, but clinically available methods for detailed observation are often through invasive catheter mapping, however, important information could be alternatively deduced from the motion patterns of cardiac image sequences (i.e., data derived from the images) (Abstract). This modification would have had a reasonable expectation of success given that both Yang et al. and Prakosa et al. disclose methods for assessing cardiac functions using machine-learning algorithms trained with non-invasive cardiac imaging data.
Regarding independent claim 45 and dependent claims 46-47, Yang et al. shows using a patient-specific convolutional neural network for determining cardiac source localization of origin of arrhythmias (Abstract); and depending on the probability distribution of CNN output, could also give an estimation of source location based on the classification information (page 1663, col. 2, para. 3); and further shows successful ablation sites (i.e., the patient is treated) (page 1667, col. 1, para. 1).
Regarding independent claim 45 and dependent claims 46-47, Yang et al. does not explicitly show applying the weights of a trained neural network to a target cardiogram to determine a target source location; or the weights being learned using training data that includes training cardiograms labeled with training source locations of arrhythmias, the training data derived from clinical data collected from patients, the patients being identified based on similarity of patient cardiac characteristics of the patients to target cardiac characteristics of the target patient; or collecting a target cardiogram during the ablation procedure.
However, Yang et al. does discuss collecting data from 9 PVC patients none of whom have undergone ablation procedure before the study (page 1666, col. 1, para. 2), but who had patient statistics for successful ablation sites after the study (page 1667, col. 1, para. 1) which suggests that it would have been obvious to collect a target cardiogram during the ablation procedure.
Regarding independent claim 45 and dependent claims 46-47, Prakosa et al. shows labelling aspects of a computational model (page 237, col. 1, para. 2); and shows that for each patient, a database of synthetic sequences was built for the learning process (page 241, col. 2, paras. 2-3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Yang et al. by incorporating methods for using a pre-trained model (i.e., with learned weights) trained using a database of synthetic data with labels and derived from cardiac images, and further derived through simulations, as shown by Prakosa et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Yang et al. with the methods of Prakosa et al., because Prakosa et al. shows that abnormal patterns of cardiac electrophysiological activation are at the origin of important cardiovascular diseases, e.g., arrhythmia, but clinically available methods for detailed observation are often through invasive catheter mapping, however, important information could be alternatively deduced from the motion patterns of cardiac image sequences (i.e., data derived from the images) (Abstract). This modification would have had a reasonable expectation of success given that both Yang et al. and Prakosa et al. disclose methods for assessing cardiac functions using machine-learning algorithms trained with non-invasive cardiac imaging data.
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 25 and 29-47 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 21-22, 28, and 52 of copending Application No. 17/110,101 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 21-22, 28, and 52 of the reference application recite essentially the same limitations as instant claim 45, and therefore the reference claims encompass and anticipate instant claim 45.
The claims as a whole provide methods of classification based on a patient cohort, and applying the gained information on treatment when appropriate.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN W. BAILEY whose telephone number is (571)272-8170. The examiner can normally be reached Mon - Fri. 1000 - 1800.
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, KARLHEINZ SKOWRONEK can be reached at (571) 272-9047. 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.
/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687
Prosecution Insights