DETAILED ACTION
Acknowledgements
This office action is in response to Applicant’s amendment made on November 10, 2025.
Claims 1, 2, 4-7, 11-13, 15-18, and 22-28 claims remain pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment(s)
Claims 1, 2, 4-7, 11-13, 15-18, and 22-28 claims remain pending.
Claim Objection(s)
Claims 24-28 are objected to because of the following informalities: they do not meet the requirements of 37 CFR 1.121(c) because (c) Claims. Amendments to a claim must be made by rewriting the entire claim with all changes (e.g., additions and deletions) as indicated in this subsection, except when the claim is being canceled. Each amendment document that includes a change to an existing claim, cancellation of an existing claim or addition of a new claim, must include a complete listing of all claims ever presented, including the text of all pending and withdrawn claims, in the application. The claim listing, including the text of the claims, in the amendment document will serve to replace all prior versions of the claims, in the application. In the claim listing, the status of every claim must be indicated after its claim number by using one of the following identifiers in a parenthetical expression: (Original), (Currently amended), (Canceled), (Withdrawn), (Previously presented), (New), and (Not entered). Examiner in light of expediting prosecution has examined claims 24-28 as new claims.
Claim Rejection – 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, 2, 4-7, 11-13, 15-18, and 22-28 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter the grounds set out in detail below:
Independent Claims 1, 12, and 23:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent Claim 1 falls within the statutory category of a method
Independent Claim 12 falls within the statutory category of a machine
Independent Claim 23 falls within the statutory category of an article of manufacture
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 12, and 23 (claim 1 being representative) claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea (claim 1 being representative) are:
A method that improves accuracy of cardiac mapping in identifying a source of an arrhythmia in a heart of a patient during a cardiac procedure, the method comprising:
receiving biometric data obtained in the heart of the patient during the cardiac procedure;
extracting a plurality of beats from the biometric data;
deriving a respective time measurement for each of the plurality of beats from the biometric data, wherein the respective time measurement is indicative of a time measured a respective beat among the plurality of the beats;
deriving a respective electrode location when each beat among the plurality of beats was received;
estimating a respective focal origin for each of the plurality of beats based on the respective time measurement and the respective electrode location, wherein the respective focal origin is a location of focal arrhythmogenic activity in the heart;
estimating a respective beat origin and a respective velocity of each of the plurality of beats based on the respective time measurement and the respective electrode location
classifying the plurality of beats into clusters in a feature space that includes the respective focal origin and the respective velocity estimated for each beat, wherein each of the clusters represents an arrhythmia type
generating, during the cardiac procedure, for each cluster, a cardiac map that visualizes data associated with beats of that cluster and identifies the cluster; and when a number of clusters is at least a threshold, outputting an indication of complex arrhythmia.
The abstract idea is following rules and instructions to classify heart beats into a type of arrhythmia a physician should know to follow which falls within the managing personal behavior or interactions between people or following rules or instructions within “certain methods of organizing human activity” as See MPEP § 2106.04(a)(2)(II).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent Claims 1, 12, and 23 this judicial exception is not integrated into a practical application.
In Claim 1 the additional elements are:
one or more electrodes
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, one or more electrodes, is recited in the manner of merely using the word “apply-it” or its equivalent. Such positive recitation is nothing more than using the electrodes as a tool to implement the abstract idea of gathering data.
In Claim 12 the additional elements not already recited in claim 1 are:
a system with a memory a processor
a communication interface that is communicatively coupled to one or more electrodes
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a system with a memory and a process, is performing the abstract idea and stated with a high level of generality and used as a tool to apply the abstract idea and therefore “apply-it”.
The additional element, a communication interface that is communicatively coupled to one or more electrodes, is recited in the manner of merely using the word “apply-it” or its equivalent. Such positive recitation is nothing more than using these generic computer components as a tool to implement the abstract idea of communicating and gathering data.
In Claim 23 the additional elements not already recited in claim 1 and 12 are:
non-transitory computer-readable medium comprising instructions executed by one or more processors
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, non-transitory computer-readable medium comprising instructions executed by one or more processors , is performing the abstract idea and stated with a high level of generality. Such positive recitation is nothing more than using these generic computer components as a tool to implement the abstract idea “apply-it” or its equivalent.
Accordingly, claims 1, 12, and 23 do not integrate the abstract idea into a practical application.
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims 1, 12, and 23 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed above in step 2A prong 2 above, these additional elements, whether viewed individually or as an ordered combination, amount to no more than merely using generic computer components as a tool to implement the abstract idea therefore the additional elements are “apply it” and thus insufficient to provide “significantly more”. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Dependent Claims 2, 4-7, 11, 13, 15-18, and 22, 24-28: Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 2, 4-7, 10, 11, 24, and 25 fall within the statutory category of a method
The dependent claims 13, 15, 16, 17, 18, 22, 26, and 27 fall within the statutory category of a machine.
The dependent claim 28 falls within the statutory category of an article of manufacture.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2, 4-7, 11, 13, 15-18, and 22, 24-28 continue to limit the abstract idea in the independent claims by (1) limiting the types of data, (2) limiting the arrhythmia type, (3) limits how to classify thus, inheriting the same abstract idea of managing personal behavior or relationships or interactions between people within “certain methods of organizing human activity”. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2, 4-7, 11, 13, 15-18, and 22, 24-28this judicial exception is not integrated into a practical application.
The dependent claims recite further additional elements below not previously analyzed in the independent claims:
A catheter
A coronary sinus catheter
machine learning model
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a catheter and a coronary sinus catheter, is recited in the manner of merely “apply-it” to gather data
The additional element, machine learning model, is recited in the manner of merely generally linking the use of data analysis to machine learning.
Eligibility Step 2B (Does the claim amount to significantly more?): The additional elements considered in the dependent claims both alone and in an ordered combination do not more than merely generally link the data analysis to machine learning and thus do not provide “significantly more”. The claims are not patent eligible.
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 1, 2, 4, 10, 12, 13, 15, 16, 21, and 23 are rejected to under 35 U.S.C. 103 as being unpatentable over Harlev et. al (hereinafter Harlev) (US10335051B2) in view of Thakur et. al (hereinafter Thakur) (US10595739B2)
As per claim 1, Harlev teaches:
A method that improves accuracy of cardiac mapping in identifying a source of an arrhythmia in a heart of a patient during a cardiac procedure, the method comprising: (Col. 10 lines 49-54, discloses, cardiac mapping therefore identifying arrhythmia in a heart of a patient and Col. 16 lines 8-10 discloses improving accuracy of beat alignment for cardiac mapping and see Col. 13 lines 4017 )
Receiving biometric data obtained from one or more electrodes in the heart of the patient during the cardiac procedure; (Col. 3 and Col. 4 lines 49-55 and Col. 23 lines 14-20 discloses, receiving the signals from the one or more electrodes on the catheter inserted in to the heart of the patient, where these signals collected can be physiological data signals of ablation data during an ablation procedure)
Extracting a plurality of beats from the biometric data (Col. 16 lines 64-67 and Col. 17 line 1 and 9-13, discloses beat selection from the electrical signals such as the physiological signals previously cited)
deriving a respective time measurement for each of the plurality of beats from the biometric data, wherein the respective time measurement is indicative of a time when each of the one or more electrodes measured a respective beat among the plurality of the beats; (Col. 2 lines 32-41 discloses, calculating an average time offset factor and see Col. 12 lines 61-65 discloses, a time interval calculated between consecutive beats and Col. 18 lines 34-41, discloses, a beat duration time measurement for the beats)
deriving a respective electrode location for each of the one or more electrodes when each beat among the plurality of beats was received by a respective electrode; (Col. 17 lines 1-8 discloses, data inclusive of electrode location for each identified beat and Col. 22 lines 61-66 discloses, location of electrodes)
and generating at least one map of the heart of the patient during the cardiac procedure, …[…]…(Col. 1 lines 59-66 and Col. 11 lines 8-11, discloses generation of a cardiac map in a cardiac mapping procedure)
However, Harlev does not teach the underlined portions:
estimating a respective focal origin for each of the plurality of beats based on the respective time measurement and the respective electrode location, wherein the respective focal origin is a location of focal arrhythmogenic activity in the heart;
estimating a respective velocity of each of the plurality of beats based on the respective time measurement and the respective electrode location
Classifying the plurality of beats into clusters in a feature space that includes on the respective focal origin and the respective velocity estimated for each beat, wherein each of the clusters represents an arrhythmia type
generating at least one map of the heart of the patient during the cardiac procedure, wherein the at least one map for each cluster, a cardiac map that visualizes data associated with beats of that cluster and identifies the cluster; and when a number of clusters is at least a threshold, outputting an indication of complex arrhythmia
However, Thakur does teach the underlined portions:
estimating a respective focal origin for each of the plurality of beats based on the respective time measurement and the respective electrode location, wherein the respective focal origin is a location of focal arrhythmogenic activity in the heart; (see Col. 6 lines 6-67 and see discloses, The processing system 32 identifies a near - field signal component , i.e. activation signals associated with local activation and originating from the tissue adjacent to the mapping electrode 24 , from an obstructive far - field signal component , i.e. activation signals originating from non - adjacent tissue , within the sensed signals / examiner notes that the activation signal is the origin of any heartbeat and the teaches maintenance of atrial fibrillation and see Col. 10 lines 8-15 discloses, foci location of atrial fibrillation “Col. 12 lines 18-20 discloses determining a heart beat location and Col. 9 lines 16-18, discloses determining a velocity for each activation signal in pattern maps which examiner interprets as being each heart beat and Col. 9 lines 44-65 discloses, that the maps for determining beat location are based on electrode location and respective time durations for example of activation signals for example.)
estimating a respective velocity of each of the plurality of beats based on the respective time measurement and the respective electrode location (see Col. 6 lines 18-24 discloses, The processing system 32 identifies a near - field signal component , i.e. activation signals associated with local activation and originating from the tissue adjacent to the mapping electrode 24 , from an obstructive far - field signal component , i.e. activation signals originating from non - adjacent tissue , within the sensed signals / examiner notes that the activation signal is the origin of any heartbeat and see “Col. 12 lines 18-20 discloses determining a heart beat location and Col. 9 lines 16-18, discloses determining a velocity for each activation signal in pattern maps which examiner interprets as being each heart beat and Col. 9 lines 44-65 discloses, that the maps for determining beat location are based on electrode location and respective time durations for example of activation signals for example.)
Classifying the plurality of beats into clusters in a feature space that includes on the respective focal origin and the respective velocity estimated for each beat, wherein each of the clusters represents an arrhythmia type(See Col. 9 line 14-30 , 44-56 teaching velocity or magnitude and propagation of signal location (such as atrial or ventricle) and generate a pattern map and Col. 10, line 2-62 classifies patterns into groups (clusters) such that each group may identify type of fibrillation (arrhythmia) based on foci location that is ventricular or atrial indicating origin of beat and arrhythmia, (also see col. 11, line 1-20) and see Cols. 12-15 where Col. 14 lines 51-61 discloses foci location of arrhythmia)
generating at least one map of the heart of the patient during the cardiac procedure, wherein the at least one map for each cluster, a cardiac map that visualizes data associated with beats of that cluster and identifies the cluster; and when a number of clusters is at least a threshold, outputting an indication of complex arrhythmia (see Col. 4 lines 1-15 and see Col. 6 lines 53-67)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harlev’s teaching of respective electrode locations when each beat was received with Thakur’s teachings of respective beat locations and velocity based on the electrode locations and time measurement, the motivation being it is merely additional data that is gathered for the cardiac mapping which is predictable to gather from catheters and electrodes and can improve the accuracy and precision of targeted ablation procedure in Harlev (see Harlev Col. 1 lines 20-55)
As per claim 2, Harlev further teaches:
The method of claim 1, wherein the biometric data comprise at least one of body surface electrocardiogram data, intracardiac electrocardiogram data, or ablation data (Col. 2 lines 42-45 discloses the data signals can be ECG or intracardiac ECG)
As per claim 4, Harlev further teaches:
The method of claim 1, wherein the biometric data comprise measurements acquired by a catheter and corresponding reference measurements acquired by a Coronary Sinus catheter and wherein the corresponding reference measurements are used to link the measurements acquired by the catheter (Col. 14 lines 47-60 discloses, using data from intracardiac catheter and a coronary sinus catheter for reference measurements such as alignment and selection process and examiner interprets as they both come to link the reference point as relative to one another)
As per claims 13 and 15 they are system claims which repeat the same limitations of claims 2 and 4 the corresponding method claim, as a collection of elements as opposed to a series of process steps. Since the teachings of Harlev and Thakur as well as motivations to combine disclose the underlying process steps that constitute the methods of claims 2 and 4 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well as cited below. As such, the limitations of claim 13 and 15 is rejected for the same reasons given above for claims 2 and 4.
As per claim 12 it is a system claim which repeat the same limitations of claim 1 the corresponding method claim, as a collection of elements as opposed to a series of process steps. Since the teachings of Harlev and Thakur as well as motivations to combine disclose the underlying process steps that constitute the methods of claim 1 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well as cited below. As such, the limitations of claim 12 is rejected for the same reasons given above for claim 1.
A system that improves accuracy of cardiac mapping in identifying a source of an arrhythmia in a heart of a patient, the system comprising: (Col. 10 lines 44-54, discloses, a system and cardiac mapping therefore identifying arrhythmia in a heart of a patient and Col. 16 lines 8-10 discloses improving accuracy of beat alignment for cardiac mapping)
a memory: a communication interface that is communicatively coupled to one or more electrodes; and one or more processors that are communicatively coupled to the memory and the communication interface, wherein the one or more processors are collectively configured to: (Col. 24 lines 1-49 discloses a communication interface such as a workstation or smart device and a processor with memory)
As per claim 23 it is an article of manufacture claim which repeats the same limitations of claim 1 the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Harlev and Thakur as well as motivations to combine the underlying process steps that constitute the method of claim 1 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well as cited below. As such, the limitations of claim 23 is rejected for the same reasons given above for claim 1.
A non-transitory computer-readable medium comprising instructions for improving accuracy of cardiac mapping in identifying a source of an arrhythmia in a heart of a patient, the instructions when executed by one or more processors cause the one or more processors to collectively execute a method comprising: (Col. 23 lines 48-64 discloses, a computer readable medium and processor executing instructions)
Claims 11 and 22 are rejected to under 35 U.S.C. 103 as being unpatentable over Harlev et. al (hereinafter Harlev) (US10335051B2) in view of Thakur et. al (hereinafter Thakur) (US10595739B2) in further view of Lu et. al (hereinafter Lu) (US2020/0178825A1)
In regards to claim 11, Harlev and Thakur do not teach:
The method of claim 1, further comprising: wherein, the classifying utilizes a machine learning model and the method further comprises: receiving a training dataset associated with past patients, wherein the training dataset for each of the past patient comprises: past beat segments extracted from past biometric data obtained from the past patients, and training the machine learning model, based on the training dataset, to predict the arrhythmia type associated with a beat segment of the beat segments obtained from the patient.
Lu, however, does teach:
The method of claim 1, further comprising: wherein, the classifying utilizes a machine learning model (abstract discloses “The processing system is further configured to generate , with the representation neural network , a latent representation from the spectrum image , and then to generate , with the classifier neural network , an arrhythmia classifier from the latent representation”) and the method further comprises: receiving (“receive”) a training dataset associated with past patients (“training data set may comprise databases of ECG records that are annotated and marked where rhythm changes occur and labeled with the arrhythmia type exhibited therein ( i.e. , one of the predetermined list of rhythm types” / predetermined records imply past tense), wherein the training dataset for each of the past patient comprises:([0003] discloses, “For example , a training data set may comprise databases of ECG records that are annotated and marked where rhythm changes occur and labeled with the arrhythmia type exhibited therein ( i.e. , one of the predetermined list of rhythm types )” and [0004] discloses, “One embodiment of a system for identifying arrhythmias based on cardiac waveforms includes a storage system storing a trained deep neural network system , wherein the trained deep neural system includes a trained representation neural network and a trained classifier neural network . A processing system is communicatively connected to the storage system and configured to receive cardiac waveform data for a patient , identify a time segment in the cardiac waveform data , and transform the time segment into a spectrum image . The processing system is further configured to generate , with the representation neural network , a latent representation from the spectrum image , and then to generate , with the classifier neural network , an arrhythmia classifier from the latent representation .”) past beat segments extracted from past biometric data obtained from the past patient (“waveform data for a patient is divided into time segments” waveform data can be a beat and time segments are segments of that waveform /synonymous with electrical activity cited for independent claim 1) time segment is transformed into image which is fed to neural networks that generate an arrhythmia classifier) ([0020] discloses, “….. In the pre - processing module 7 , cardiac waveform data for a patient is divided into time segments 4 , such as one lead of ECG data divided into sequential time segments of a pre - defined length . The time segment 4 of cardiac wave data is transformed into a spectrum image 6 , such as via Fast Fourier transform ( FFT ) , or more specifically , a Short Time window Fast Fourier transform ( SFFT ) . ([0021]) discloses, “The image is then provided to a trained deep neural network system 8 that includes a trained representation neural network 12 that generates a latent representation 15 from the spectrum image 6 and a trained classifier neural network 18 that generates an arrhythmia classifier 20 from the latent representation”
and training the machine learning model, based on the training dataset, to predict the arrhythmia type associated with a beat segment of the beat segments obtained from the patient. (“the presence or absence of a predetermined list of rhythm types for which the deep neural network system is trained to identify . For example , the deep neural network system may be trained to identify various arrhythmias , including asystole , supraventricular tachycardia , ventricular fibrillation , ventricular tachycardia , atrial fibrillation , normal sinus rhythm , or any subset of those rhythms or other known arrhythmia types.”([0019] discloses, “The arrhythmia classifier characterizes the presence or absence of a predetermined list of rhythm types for which the deep neural network system is trained to identify . For example , the deep neural network system may be trained to identify various arrhythmias , including asystole , supraventricular tachycardia , ventricular fibrillation , ventricular tachycardia , atrial fibrillation , normal sinus rhythm , or any subset of those rhythms or other known arrhythmia types . For example , the deep neural network system may output an arrhythmia classifier comprising a classification value indicating the presence or absence of each rhythm type in the pre - determined list of rhythm types for which the deep neural network system is trained.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harlev’s teaching and Thakur’s teaching, with Lu’s explicit teaching of past beat segments being analyzed by machine learning to come to an arrhythmia type for the same reasons given for claims 1 and 5 as additional types of data such as past data would not change the motivations to combine machine learning classification and beat segmentation to come to a specific arrythmia type.
As per claim 22 it is a system claim which repeat the same limitations of claim 11 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Harlev, Thakur, and Lu as well as motivations to combine disclose the underlying process steps that constitute the methods of claim 11 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 22 is rejected for the same reasons given above for claims 11.
Claims 5 and 16 are rejected to under 35 U.S.C. 103 as being unpatentable over Harlev et. al (hereinafter Harlev) (US10335051B2) in view of Thakur et. al (hereinafter Thakur) (US10595739B2) and in further view of Villongco (US2021/0259610A1)
As per claim 5, Harlev and Thakur do not teach:
The method of claim 1, wherein the arrhythmia type comprises a specific arrhythmia type, a normal sinus rhythm, a mixed arrhythmia, or a combination thereof.
However, Villongco does teach:
The method of claim 1, wherein the arrhythmia type comprises a specific arrhythmia type, a normal sinus rhythm, a mixed arrhythmia, or a combination thereof.( [0077] discloses, “Methods and systems are provided for generating data representing electromagnetic states ( e.g. , normal sinus rhythm versus ventricular fibrillation ) of an electromagnetic source ( e.g. , a heart ) within a body for various purposes such as medical , scientific , research , and engineering purposes .”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harlev’s teaching of determining source of arrhythmias (Col. 10 lines 44-54) and Thakur’s teachings of cardiac mapping classifying patterns and identifying respective beat locations and velocity based on the electrode locations and time measurement as previously cited with Villongco’s teaching of a specific arrhythmia type concluded by the mapping and classifying, the motivation being it would improve patient outcomes to understand the specific arrhythmia type and would not be unpredictable to combine the use of the cardiac mapping’s across the references and machine learning data analysis to come to a specific arrhythmia as this should be what a physician does to diagnose the patient.
As per claim 16, it is a system claim which repeat the same limitations of claim 5 the corresponding method claim, as a collection of elements as opposed to a series of process steps. Since the teachings of Harlev, Thakur, and Villongco as well as motivations to combine disclose the underlying process steps that constitute the methods of claim 5 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well as cited below. As such, the limitations of claim 16 is rejected for the same reasons given above for claim 5.
Claims 6, 7, 17, and 18 are rejected to under 35 U.S.C. 103 as being unpatentable over Harlev et. al (hereinafter Harlev) (US10335051B2) in view of Thakur et. al (hereinafter Thakur) (US10595739B2) and in further view of DZIUBINSKI (US2022/0218262A1)
As per claim 6, Harlev does not teach the underlined portions:
The method of claim 1, further comprising dividing the plurality of beats into beat segments, wherein each of the beat segments includes a subset of the plurality of beats; for each of the beat segments, estimating one or more beat characteristics among the subset;
and classifying each of the beat segments into one of the clusters based on their respective one or more beat characteristics
However, Thakur does teach the underlined portion below:
and classifying each of the beat segments into one of the clusters based on their respective one or more beat characteristics (See col. 9 line 14-30 , 44-56 teaching velocity or magnitude and propagation of signal location (such as atrial or ventricle) and generate a pattern map and Col. 10, line 25-classifies patterns into groups (clusters) such that each group may identify type of fibrillation that is ventricular or atrial (arrhythmia) see col. 11, line 1-20)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harlev’s teaching and Thakur’s teachings for the same reasons given above for claim 1.
Harlev and Thakur do not teach the underlined portions:
The method of claim 1, further comprising dividing the plurality of beats into beat segments, wherein each of the beat segments includes a subset of the plurality of beats; for each of the beat segments, estimating one or more beat characteristics among the subset;
However, DZIUBINSKI does teach the underlined portions:
The method of claim 1, further comprising dividing the plurality of beats into beat segments, wherein each of the beat segments includes a subset of the plurality of beats; for each of the beat segments, estimating one or more beat characteristics among the subset; ([0020] discloses, “In some embodiments , a non - overlap between adjacent ECG segments are identified , and an artificial heartbeat marker is included in the ECG data set , wherein the artificial heartbeat marker indicates an ECG fragment between the non - overlapping ECG segments.” And see [0051])
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harlev’s teaching of beat trains (see Col. 4 lines 3-7) and Thakur’s teaching of segmented blocks for each activation signal (see Col. 3 lines 55-63), with DZIUBINSKI, the motivation being improved clarity and accuracy in the mapping using machine learning to clearly delineate between consecutive beats (see DZIUBINSKI [0005]).
In regards to claims 7, Harlev and Thakur do not teach:
The method of claim 6, wherein the one or more beat characteristics comprise one or more of a shape-descriptor, an origin, or a propagation velocity.
However, DZIUBINSKI does teach:
The method of claim 6, wherein the one or more beat characteristics comprise one or more of a shape-descriptor, an origin, or a propagation velocity. ([0038] discloses, “In addition , because the His - Purkinje system coordinates the depolarization of the ventricles , the QRS complex tends to look or appear “ spiked ” rather than rounded due to the increase in conduction velocity” / conduction velocity is interpreted as a type of propagation velocity)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harlev’s teaching and Thakur’s teachings with DZIUBINSKI, the motivation being the same as the one for claim 6.
As per claims 17 and 18 they are system claims which repeat the same limitations of claims 6 and 7 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Harlev, Thakur, and DZIUBINSKI as well as motivations to combine disclose the underlying process steps that constitute the methods of claims 6 and 7 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 17 and 18 are rejected for the same reasons given above for claims 6 and 7.
Claims 24-28 are rejected to under 35 U.S.C. 103 as being unpatentable over Harlev et. al (hereinafter Harlev) (US10335051B2) in view of Thakur et. al (hereinafter Thakur) (US10595739B2) and in further view of Anter, E., Duytschaever, M., Shen, C., Strisciuglio, T., Leshem, E., Contreras-Valdes, F. M., ... & Buxton, A. E. (2018). Activation mapping with integration of vector and velocity information improves the ability to identify the mechanism and location of complex scar-related atrial tachycardias. Circulation: Arrhythmia and Electrophysiology, 11(8), e006536. (hereinafter Duytschaever)
As per claim 24, Harley and Thakur do not teach:
The method of claim 1, wherein estimating the respective focal origin and the respective velocity for each beat comprises minimizing, for that beat, a cost function that measures timing-residual error between measured activation times at the electrodes and model-predicted activation times based on the electrodes' contemporaneous locations and unknown origin and conduction-velocity parameters, the minimizing performed by gradient-descent optimization.
However, Duytschaever teaches:
The method of claim 1, wherein estimating the respective focal origin and the respective velocity for each beat comprises minimizing, for that beat, a cost function that measures timing-residual error between measured activation times at the electrodes and model-predicted activation times based on the electrodes' contemporaneous locations and unknown origin and conduction-velocity parameters, the minimizing performed by gradient-descent optimization. (see page 3 vector map discloses, “The algorithm assigns each triangle on the reconstructed mesh with 3 descriptors: LAT value, conduction vector, and the probability of nonconductivity. Conduction velocities are calculated using the LAT values and the known distance and direction between triangles. The mathematical representation of this is shown in Figure IA in the Data Supplement. These descriptor values are initially known only for triangles with a direct measurement but unknown for all other triangles on the reconstructed mesh. To solve this problem, the following physiologically based assumptions are applied: (1) velocity continuity: in areas of conduction continuity, conduction velocity is kept as similar as possible to the neighboring triangles, and only gradual changes are allowed. This relationship between triangles is performed through mathematical equations looking for the minimum mean square difference of the relations set above. The optimal solution results in minimal differences at most locations, allowing large differences only in areas with enough measurements contradicting continuity; and (2) nonconduction areas are identified by multiple measurements at the same location, indicating that the electrode resides in an area with at least 2 distinct waves. In these areas, the probability of conduction slowing, or complete block, is determined by the vectors of propagation and the calculated conduction velocity (Figure IB in the Data Supplement). In regions with a structural obstacle for conduction, the vectors of propagation must go around the obstruction or conduct through the obstruction at a slow velocity. Conduction block was defined as a value lower than the lowest physiological conduction velocity in human atria (10 cm/s) as determined by Allessie et al.4 The above criteria are used to set the probability of a triangle to be in a nonconductive area. The equations are solved until the resulting LAT, conduction velocity, and probability of nonconductivity are stabilized without further changes, representing the optimal solution.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harlev’s teaching of determining source of arrhythmias (Col. 10 lines 44-54) and Thakur’s teachings of cardiac mapping classifying patterns and identifying respective beat locations and velocity based on the electrode locations and time measurement as previously cited with Duytschaever’s teaching of minimizing cost function and error, the motivation being it would reduce resources and provide the most optimal outcome when determining specific arrhythmia type and would not be unpredictable to combine the use of this math with machine learning data analysis to come to a specific arrhythmia as this improves accuracy.
As per claim 25, Harley and Thakur do not teach:
The method of claim 1, further comprising computing a measurement error for each electrode, eliminating electrode measurements whose error exceeds a predetermined level, and re-estimating the respective focal origin and the respective velocity until convergence.
However, Duytschaever teaches:
The method of claim 1, further comprising computing a measurement error for each electrode, eliminating electrode measurements whose error exceeds a predetermined level, and re-estimating the respective focal origin and the respective velocity until convergence. (see page 3 vector map discloses, “The algorithm assigns each triangle on the reconstructed mesh with 3 descriptors: LAT value, conduction vector, and the probability of nonconductivity. Conduction velocities are calculated using the LAT values and the known distance and direction between triangles. The mathematical representation of this is shown in Figure IA in the Data Supplement. These descriptor values are initially known only for triangles with a direct measurement but unknown for all other triangles on the reconstructed mesh. To solve this problem, the following physiologically based assumptions are applied: (1) velocity continuity: in areas of conduction continuity, conduction velocity is kept as similar as possible to the neighboring triangles, and only gradual changes are allowed. This relationship between triangles is performed through mathematical equations looking for the minimum mean square difference of the relations set above. The optimal solution results in minimal differences at most locations, allowing large differences only in areas with enough measurements contradicting continuity; and (2) nonconduction areas are identified by multiple measurements at the same location, indicating that the electrode resides in an area with at least 2 distinct waves. In these areas, the probability of conduction slowing, or complete block, is determined by the vectors of propagation and the calculated conduction velocity (Figure IB in the Data Supplement). In regions with a structural obstacle for conduction, the vectors of propagation must go around the obstruction or conduct through the obstruction at a slow velocity. Conduction block was defined as a value lower than the lowest physiological conduction velocity in human atria (10 cm/s) as determined by Allessie et al.4 The above criteria are used to set the probability of a triangle to be in a nonconductive area. The equations are solved until the resulting LAT, conduction velocity, and probability of nonconductivity are stabilized without further changes, representing the optimal solution.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harlev’s teachings and Thakur’s teachings with Duytschaever’s teachings for the same reasons given above for claim 24.
As per claims 26-27 they are system claims which repeat the same limitations of claims 24 and 25 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Harlev, Thakur, and Duytschaever as well as motivations to combine disclose the underlying process steps that constitute the methods of claims 24 and 25 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 26 and 27 are rejected for the same reasons given above for claims 24 and 25.
As per claim 28 it is an article of manufacture claim which repeats the same limitations of claim 24 the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Harlev, Thakur, and Duytschaever as well as motivations to combine the underlying process steps that constitute the method of claim 24 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well as cited below. As such, the limitations of claim 28 is rejected for the same reasons given above for claim 24.
Response to Arguments Regarding 35 U.S.C § 101 Rejection
The applicant argues on pages 2-7 of the submitted remarks that the claims which stand rejected under 35 U.S.C. § 101 are eligible for the remarked arguments.
Due to the length of the arguments examiner has not copy and pasted the arguments here but will paraphrase specific arguments and respond accordingly.
First applicant argues that the analysis by examine that the claims are directed to an abstract idea is “is legally and factually incorrect.” As it “ignores the claims' technology-specific computations and outputs” and that applicant’s arguments are confirmed based on examiner’s previous overgeneralized characterization of the claims and response to arguments in relation to CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358, 1368 (Fed. Cir. 2020) and is thus not an abstract idea and patent eligible. Further applicant notes certain methods of organizing human activity following rules and instructions managing personal behavior enumerated grouping is therefore incorrect.
The MPEP § 2106.04(a) The sub-grouping “managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions. An example of a claim reciting managing personal behavior is Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 USPQ2d 1636 (Fed. Cir. 2015). The patentee in this case claimed methods comprising storing user-selected pre-set limits on spending in a database, and when one of the limits is reached, communicating a notification to the user via a device. 792 F.3d. at 1367, 115 USPQ2d at 1639-40. The Federal Circuit determined that the claims were directed to the abstract idea of "tracking financial transactions to determine whether they exceed a pre-set spending limit (i.e., budgeting)", which "is not meaningfully different from the ideas found to be abstract in other cases before the Supreme Court and our court involving methods of organizing human activity." 792 F.3d. at 1367-68, 115 USPQ2d at 1640
MPEP 2106.04(a)(2) (II) states, “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping.”
Examiner appreciates applicants argument but respectfully does not find it persuasive. Examiner notes that there is no nexus between the positive recitation of claims in CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358, 1368 (Fed. Cir. 2020) and the instant applications positively recited claims. To qualify as “a patent-eligible improvement,” the invention must be directed to a specific improvement in the computer’s functionality, not simply to use of the computer “as a tool” to implement an abstract idea. Unlike CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358, 1368 (Fed. Cir. 2020) the instant application claim language and the specification make clear, the invention is directed to the abstract idea of improving erroneous heart mapping by analyzing biometric data (instant app. paragraphs [0002]-[0006]). And such analysis is still directed to an abstract idea. The instant application focuses on using a general-purpose processor to carry out the abstract idea of analyzing data to generate a cardiac mapping (e.g. [0020] and [0027] of instant application) unlike CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358, 1368 (Fed. Cir. 2020) which was directed to a specific technological improvement in a computerized device that was linked to a specific problem and solution of improvement to that device by way of clinically significant detection. The instant application claim construction does not reflect an improvement upon existing technology confined to a general purpose computer as the claims are by providing specific and tangible technological advancements, particularly in real-time monitoring and analysis of cardiac signals but rather collecting data and analyzing this data at the high level steps recited to generate a cardiac mapping with no “real-time” wording claimed but rather “during a cardiac procedure” which is not a sole reason for eligibility nor does it make it dispositive of being directed to an abstract idea just as the machine or transformation test is not a sole reason and does not make claims dispositive of being directed to an abstract idea. Furthermore, examiner notes based on the claim amendments the claims are certain methods of organizing human activity as they recite following rules or instructions to generate a cardiac map. There is no specific technologically improvement within the general confines of a processor which is performing the steps claimed and the abstract idea cannot bring about a practical application or provide significantly more and outside of general purpose computer elements the claims recite machine learning and one or more electrodes. The machine learning is broadly claimed and thus generally linking the abstract idea to artificial intelligence and the one or more electrodes are merely used to gather biometric data thus apply-it. Neither are being technological improved alone or in combination with the abstract idea but rather an abstract idea of cardiac mapping even if improved is still abstract.
Applicant continues to argue that The Office labels components "generic" and concludes there is no "significantly more." But the proper inquiry examines the ordered combination: per-beat origin/velocity from per-beat time and contemporaneous pose ->clustering in the origin/velocity feature space ->cluster-identified maps generated during the procedure with a complexity gate. The Office Action offers no evidence that this combination was routine; indeed, its § 103 analysis relies on stitching together multiple disparate references to try to supply pieces, which underscores that the combination was not conventional. See Non-Final Office Action, pp. 6-13 (collecting Harlev, Thakur, Villongco, Dziubinski, and Lu for different fragments).
Examiner appreciates applicants argument but does not find it persuasive. Firstly “apply-it” used by examiner to characterize generic computer components was used by examiner under the practical application step of analysis under 101 for additional elements. No further analysis is required or was argued by examiner under “well understood routine and conventional” within the significantly more consideration for these additional elements. The abstract idea of ordered combination elements argued are abstract. For sake of response the ordered combination noted in argument by applicant has several arguments not even within the claim construction therefore was not considered by examiner as examiner considers claim constructions and words recited within the claim. Finally, MPEP 2106.05 makes clear novelty is not a consideration within patent eligible subject matter.
Applicant continues to argue the abstract idea is "following rules and instructions to classify heart beats ... within 'certain methods of organizing human activity."' Is incorrect.
That characterization does not fit claims that compute per-beat focal origin and conduction velocity from time-stamped and pose-registered catheter measurements, cluster in a feature space that includes those computed physiologic parameters, and generate cluster-identified maps during the cardiac procedure-a device-implemented improvement to EP mapping fidelity. Nothing in the Action identifies any element that can be performed mentally as claimed (particularly the joint time/pose-based per-beat estimation and the intra-procedural cluster-map outputs).
Examiner does not find this argument persuasive. Examiner will not repeat aforementioned arguments of why the claim construction is directed to an abstract idea and categorized as certain methods of organizing human activity. First Mental process was not noted as the category under the abstract idea by examiner. But further still elements and words noted by applicant here cannot be found in the claim construction. Examiner examines the recited claim construction for what is abstract and additional elements. The use of a computer to execute a task does not make the task dispositive of being directed to an abstract idea, nor does number of people required, and it is not would a human but could a human.
Applicant continues to argue that "electrodes," "processor/memory/communication interface," and "CRM," and labels each "apply-it." But integration into a practical application is shown by the claim-required sequence and relationships: computations based on the union of time and pose; clustering in a feature space that includes origin and velocity (not generic waveform features); generation, during the procedure, of a map for each cluster that identifies the cluster; and output of a complexity indicator when cluster count meets a threshold. Those are claim-specific constraints that tie the exception to an EP-system improvement.
Examiner does not find applicant’s argument persuasive. The practical application and technology improvement come from the additional elements not the abstract idea as stated in the MPEP. Therefore, the claims give no improvement to an EP system rather improvement to the abstract idea thus cannot be the practical application. The additional elements were “parsed” out as required and are generic computer components.
Applicant continues to argue the "machine learning model" is just a general link to data analysis. But even setting ML aside, eligibility stands on the independently sufficient, non-ML pipeline in the independents (per-beat time/pose -- origin/velocity ->cluster-identified maps during the procedure with complexity gating). The ML embodiments are an alternative, not the essence of eligibility.
Examiner does not find applicant’s argument persuasive. Machine learning is broadly claimed within the claim construction as recited and the specific technology of machine learning is not improved rather tied to an environment thus generally linking. Remainder of “independents” pointed out by applicant are abstract thus cannot bring about the practical application or improvement. Further again some elements noted are not even within the claim construction.
Finally applicant argues In addressing Applicant's CardioNet, Thales, and Ex parte Hannun points, the Action asserts there is "no nexus" and that the claims "collect/analyze data ... during a cardiac procedure" with no device improvement. Non-Final Office Action, pp. 28-30. The amended claims, however, expressly require device-implemented computations (per-beat origin and velocity from time/pose), a physiology-grounded clustering space (including those estimates), and device-level mapping outputs keyed to clusters and a complexity threshold. That is a specific technological solution to morphology mixing and mapping fidelity-squarely within the kind of technology- focused improvements that courts have deemed eligible when claims specify how the system processes signals to produce a new, more reliable output. Under Step One, the amended claims are directed to a specific improvement in EP mapping systems: computing per-beat focal origin and conduction velocity from time-and-pose data, clustering in a feature space that includes those physiologic parameters, and generating intra-procedural, cluster-identified maps with a complexity gate. Under Step Two, the Office identifies no evidence that this ordered combination was well-understood, routine, and conventional; the multi-reference § 103 combinations point the other way. Applicant respectfully requests withdrawal of the § 101 rejection. See Non-Final Office Action, p. 2 (introducing § 101 grounds),pp. 3-5 (Step 2A-1/2A-2/2B analysis), and pp. 28-30 (responses to Applicant's prior § 101 arguments).
Examiner does not find applicant’s arguments persuasive. First the abstract idea cannot bring about the improvement the additional elements do any improvement present is improvement to the abstract idea. Well-understood, routine, and conventional was not argued or noted in any office by examiner thus there is no response to this argument as there was no on the record analysis needed or made to this effect. Novelty as aforementioned is of no relevance under 2106.05 of the MPEP to eligibility. Finally, again certain elements argued here are not present in the claim construction. Also, examiner notes previous arguments in previous office actions as made by examiner are still considered applicable by examiner.
Examiner analyzed the claims as required by the MPEP and concludes that the 35 U.S.C § 101 rejection is maintained.
Response to Arguments Regarding 35 U.S.C § 103 Rejections
Applicant argues on pages 7-14 of the remarks applicant respectfully traverses the §103 rejections in this application and submits that independent claims 1, 12, and 23 recite features not taught, suggested, or otherwise yielded by any proper combination of the cited references. Accordingly, the Applicant submits that the amended claims are patentable for at least the following reasons.
Applicant argues, As amended, independent claims 1, 12, and 23 recite a specific intra-procedural mapping pipeline with three defining relationships and a constrained output. First, for each beat, the system derives activation times per electrode together with the contemporaneous electrode locations and, from those beat-specific inputs, estimates a respective focal origin and a respective conduction velocity. Second, the system classifies beats into clusters in a feature space that includes those two per-beat physiologic estimates-focal origin and conduction velocity-so that each cluster represents an arrhythmia type in physiologic rather than purely morphological terms. Third, the system generates, during the procedure, a cardiac map for each cluster that identifies the cluster and outputs an indication of complex arrhythmia when the number of clusters meets a threshold. New dependent claims 24-29 further specify how the origin and velocity are computed by minimizing a timing-residual cost function between measured activation times and model-predicted times given the electrodes' contemporaneous locations and unknown origin/velocity parameters, solved by gradient descent with per-electrode error computation, outlier-electrode elimination, and re-estimation until convergence. None of the cited art, alone or in the proposed combinations, teaches or suggests this per-beat, time-and-pose-driven estimation followed by origin/velocity-space clustering and cluster-identified intra-procedural mapping with a complexity gate, nor the robust inverse-problem loop recited in the dependents.
Harlev addresses beat alignment, selection, and synchronization using templates, cross-correlation, and grading metrics to decide which beats are admitted for mapping. Its contribution is gating and stabilizing inputs, not computing, for each beat, a focal origin and a conduction velocity from joint time stamps and contemporaneous electrode pose. Harlev does not classify beats within a feature space defined by those physiologic parameters and it does not generate per-cluster maps that identify clusters. In short, Harlev may influence when to include beats, but it does not disclose how to invert beat-level time-and-pose data into origin and velocity or how to use those estimates as the axes for clustering and map generation during the case.
Thakur focuses on activation-pattern prevalence, including dominant-frequency analysis, wavefront vectors, and pattern maps, and then classifies patterns and provides summary plots. That approach is not the claimed pipeline. It does not teach computing, for each individual beat, a global focal origin and a global conduction velocity from the ensemble of electrode activation times and poses, using an explicit error-minimization formulation. Nor does it teach clustering beats in a feature space that includes those per-beat origin and velocity parameters, or generating per-cluster maps identified by cluster during the procedure. Its "vector" concept is a local descriptor, not a global inverse solution for per-beat origin and velocity, and there is no robust outlier-pruning, re-estimation loop aligned with the dependents.
Villongco presents a model-based and machine-learning pipeline centered on vectorcardiograms. It trains classifiers using simulated or labeled VCGs and applies them for patient-specific inference. The unit of analysis is the VCG signal space, not a catheter-based, per-beat inversion from electrode time-of-arrival and contemporaneous position to physiologic parameters. Villongco does not disclose clustering beats by origin and velocity or producing cluster-identified LAT/voltage maps intra-procedurally; its visuals concern VCG surfaces, simulated cardiograms, and classifier outputs.
Lu converts ECG time segments into spectrum images for classification with deep neural networks. It does not use intracardiac electrode pose, does not compute per-beat focal origin and conduction velocity, and does not perform cluster-identified EP mapping during a procedure.
Dziubinski describes ECG segmentation and dataset construction for remote monitoring, including fixed-size segments, labeling, and feature vectors. It is not an EP-lab mapping workflow, and it does not disclose per-beat time-and-pose inversion to origin and velocity, clustering based on those estimates, or cluster-identified intra- procedural mapping.
Combining Harlev and Thakur at most yields beat selection and synchronization followed by pattern-prevalence summaries. That composite does not teach the per- beat estimation of focal origin and conduction velocity from joint activation time and electrode pose, it does not teach classification of beats in a feature space that includes those parameters, and it does not teach generating, during the procedure, a map for each cluster that identifies the cluster together with a threshold-based complexity indicator. Neither reference teaches the timing-residual cost-function inversion in unknown origin and velocity parameters solved by gradient descent with electrode- level error computation and outlier removal. To reach the dependents would require importing Applicant's inverse-problem architecture and robust re-estimation loop, which is not suggested by Harlev's correlation gating or Thakur's pattern summaries.
Adding Villongco, Lu, or Dziubinski does not cure these gaps. Villongco's VCG- trained classification operates in a different data regime and does not transform beat- wise intracardiac time-and-pose measurements into per-beat origin and velocity used for clustering and mapping. Lu contributes only general spectral image classification of ECGs. Dziubinski supplies segmentation logistics for remote monitoring. None of these references supplies the claimed estimation-to-clustering-to-mapping chain or the explicit complexity gate.
For independent claims 1, 12, and 23, the requirement to estimate, for each beat, both a respective focal origin and a respective conduction velocity based on that beat's activation times and the contemporaneous locations of the electrodes is unmet. Harlev does not invert time-and-pose to global per-beat parameters; Thakur does not either, and its emphasis is on pattern-level summaries, not per-beat global physiologic parameters. Villongco's VCG classification, Lu's spectrum-image DNN, and Dziubinski's segmentation each lack the time-and-pose inversion and the per-beat parameter estimation altogether. The further requirement to classify the plurality of beats into clusters in a feature space that includes those per-beat origin and velocity parameters is also absent. Thakur's classifications are driven by activation-pattern maps, not per-beat origin/velocity features; Villongco's are VCG-based; Lu's are image-based; Dziubinski's are segment-based. Finally, the output requirement to generate, during the cardiac procedure, for each cluster, a cardiac map that visualizes data associated with beats of that cluster and identifies the cluster, with an additional complexity indication when the number of clusters meets a threshold, is not taught or suggested. Thakur's summary plots do not correspond to cluster-specific LAT/voltage maps keyed to clusters defined by origin and velocity, and the remaining references are even further afield. With respect to dependent claims 24 through 29, none of the references discloses or suggests minimizing, on a per-beat basis, a timing-residual cost function between measured electrode activation times and model-predicted times given electrode pose and unknown origin and conduction-velocity parameters, solved by gradient descent. Nor do they teach computing a per-electrode measurement error, removing outlier electrodes that exceed a predetermined error level, and re-estimating until convergence. Those steps reflect a particular robust inverse problem solution for beat-level physiologic parameter estimation that is not present in the cited art's synchronization, pattern prevalence, high-level ML, or segmentation approaches.
Harlev's template-correlation and beat-grading framework is about deciding which beats to include for conventional mapping, not about reconstructing physiologic parameters via model-based inversion. Converting it into the claimed per-beat origin/velocity solver with robust re-estimation would fundamentally repurpose Harlev's gating into a physiologic parameter-estimation engine; the references provide no articulated reason to make that redesign, and Harlev's reliance on correlation- based morphology matching teaches away from replacing it with a model-fit inverse solution. Thakur's pattern-prevalence agenda is orthogonal. Even if combined with Harley, the result would be beat-quality-gated pattern surveys rather than the claimed origin/velocity feature-space clustering and per-cluster intra-procedural mapping. To arrive at Applicants' approach, the combination would need to be re-architected to compute per-beat global parameters from time-and-pose measurements and then to cluster beats by those parameters-steps not suggested by the references. The remaining references operate in different data domains and do not provide a predictable path to the claimed pipeline without hindsight.
Examiner disagrees with applicant and does not find the arguments persuasive. The cited references teach what are in the claim construction. First, much of the noted argument includes elements or limitations not positively recited in the claim construction. Further, no specific teachings of any of the art are argued just generalizations on what the prior art discloses and generalizations on what it doesn’t teach not specific recitations of limitations not taught by each reference or combinations, thus examiner finds the argument to be conclusory in nature. Finally, examiner notes no new claim 29 exists. For sake of response, the argument that Harlev does not teach “how to invert beat-level time-and-pose data into origin and velocity or how to use those estimates as the axes for clustering and map generation during the case.” This is not a recitation of any limitation in the claim construction and Thakur is cited in combination with Harlev when teaching the classifying limitations as recited in the claim. It is stated that Thakur does not teach “computing, for each individual beat, a global focal origin and a global conduction velocity from the ensemble of electrode activation times and poses, using an explicit error-minimization formulation. Nor does it teach clustering beats in a feature space that includes those per-beat origin and velocity parameters, or generating per-cluster maps identified by cluster during the procedure. Its "vector" concept is a local descriptor, not a global inverse solution for per-beat origin and velocity, and there is no robust outlier-pruning, re-estimation loop aligned with the dependents” This is not an explicit recitation of any of the claim construction. Further if this is referring to some of the newly added dependent claims this is a moot point as a new combined reference is used to teach the newly added dependent claim limitations. Further Villongco. Lu, and Dziubinski is not cited as teaching any of the recited wordage used by applicants within the claim construction of dependent claims as recited in the argument. Finally, there is no positively recited claim language of “estimation-to-clustering-to-mapping chain or the explicit complexity gate.” And it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Examiner maintains the 35 U.S.C § 103 rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687