DETAILED ACTION
Applicant's amendments and remarks, filed 2/3/26, are fully acknowledged by the Examiner. Currently, claims 1-20 are pending with claims 1, 5, 10-11, and 20 amended. Applicant's amendment to claims 10 and 20 has overcome the previously filed 35 USC 112(b) rejection. The following is a complete response to the 2/3/26 communication.
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 .
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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 8, 11-14, and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rodriguez (US 2020/0022649).
Regarding claim 1, Rodriguez teaches a system for improving a cardiac ablation procedure, the
system comprising: a recommendation unit configured to provide an initial recommendation for at least one proposed ablation line for an ablation procedure on an anatomy of a patient (lesion planning system for providing an ablation line as in par. [0144]), said system displaying said at least one proposed ablation line on an anatomical map of said anatomy (par. [0145]); wherein the recommendation unit comprises a first trained machine-learning model configured to segment the anatomical map (par. [0381] machine learning for segment model); and a second trained machine-learning model configured to propose the at least one proposed ablation line based, at least in part, on one or more segments output from the first trained machine learning model (par. [0324] machine learning applied to ablation line parameters as in 1803 and fig. 6, Fig. 9 with parameters based on other modeled values), wherein both said first and second trained machine-learning models have a same structure (multiple machine learning methods as in par. [0233]).
Regarding claim 2, Rodriguez teaches further comprising an ablation line editor configured to
enable a physician to modify a selected one of said at least one proposed ablation line (par. [0150]).
Regarding claim 3, Rodriguez teaches further comprising a treatment determiner configured to
determine a recommended energy delivery per segment of a chosen ablation line (par. [0131] energy delivered as parameter).
Regarding claim 4, Rodriguez teaches further comprising a guidance unit to display a next
ablation site on said anatomy based at least on a chosen ablation line and on a catheter location in said anatomy (par. [0146]).
Regarding claim 8, Rodriguez teaches wherein the recommendation unit is further configured to
recommend which one of the at least one proposed ablation line is most suitable for a procedure for said patient (par. [0376]).
Regarding claim 11, Rodriguez teaches a computer-implemented method for improving a cardiac
ablation procedure, the method comprising: providing an initial recommendation for at least one proposed ablation line for an ablation procedure on an anatomy of a patient (providing an ablation line as in par. [0144]), comprising displaying said at least one proposed ablation line on an anatomical map of said anatomy (par. [0145]); wherein the initial recommendation utilizes a first trained machine-learning model configured to segment the anatomical map (par. [0381] machine learning for segment model); anda second trained machine-learning model configured to propose the at least one proposed ablation line, at least in part, on one or more segments output from the first trained machine learning model (par. [0324] machine learning applied to ablation line parameters as in 1803 and fig. 6, Fig. 9 with parameters based on other modeled values), wherein both said first and second trained machine-learning models have the same structure (multiple machine learning methods as in par. [0233]).
Regarding claim 12, Rodriguez teaches the method further comprising enabling a physician to
modify a selected one of said at least one proposed ablation line (par. [0150]).
Regarding claim 13, Rodriguez teaches a recommended energy delivery per segment of a chosen
ablation line (par. [0131] energy delivered as parameter).
Regarding claim 14, Rodriguez teaches displaying a next ablation site on said anatomy based at
least on a chosen ablation line and on a catheter location in said anatomy (par. [0146]).
Regarding claim 18, Rodriguez teaches recommending which one of the at least one proposed
ablation line is most suitable for a procedure for said patient (par. [0376]).
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.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rodriguez in view of Xu (US 2019/0259493).
Regarding claim 5, Rodriguez teaches wherein said recommendation unit comprises:
an ablation line trainer to train said second trained machine-learning model to output proposed ablation lines (par. [0324]); and
an ablation line proposer utilizing said second trained machine-learning model to propose said at least one proposed ablation line for said anatomy (par. [0324]-[0325]).
Rodriguez is not explicit regarding a map segmenter to segment said anatomical map to output parts of said anatomy, said map segmenter utilizing said first trained machine-learning model.
However, Xu teaches a map segmenter to segment said anatomical map to output parts of said
anatomy, said map segmenter utilizing a trained machine-learning model (par. [0031]);
It would have been obvious to one of ordinary skill in the art to modify Rodriguez with a map segmenter and utilizing a machine learning model for the map segmenter, as in Xu, as a known use of machine learning in tissue treatment.
Regarding claim 15, Rodriguez teaches wherein said providing comprises:
training said second trained machine-learning model to output proposed ablation lines (par. [0325]); and
proposing said at least one proposed ablation line for said anatomy, said proposed utilizing said second trained machine-learning model (par. [0324]-[0325]).
Rodriguez is silent regarding segmenting said anatomical map to output parts of said anatomy, said segmenting utilizing said first trained machine-learning model.
However, Xu teaches a map segmenter to segment said anatomical map to output parts of said
anatomy, said map segmenter utilizing a trained machine-learning model (par. [0031]);
It would have been obvious to one of ordinary skill in the art to modify Rodriguez with a map segmenter and utilizing a machine learning model for the map segmenter, as in Xu, as a known use of machine learning in tissue treatment.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rodriguez in view of Mahmood (WO 2021/062366).
Regarding claim 6, Rodriguez is not explicit wherein said same structure is a graph convolutional neural network (GCN), but teaches neural networks (par. [0233]).
Mahmood teaches graph convolution networks as a known subset of machine learning that may be used (par. [0033]).
It would have been obvious to one of ordinary skill in the art to modify Rodriguez with using graph convolutional neural network as a machine learning model, as a known model of machine learning.
Regarding claim 16, Rodriguez is not explicit wherein said same structure is a graph
convolutional neural network (GCN), but teaches neural networks (par. [0233]).
Mahmood teaches graph convolution networks as a known subset of machine learning that may be used (par. [0033]).
It would have been obvious to one of ordinary skill in the art to modify Rodriguez with using graph convolutional neural network as a machine learning model, as a known model of machine learning.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rodriguez in view of Mansi (US 2014/0296842).
Regarding claim 7, Rodriguez is not explicit wherein said same structure is a classifier.
Mansi teaches machine learning with a classifier structure (par. [0076]).
It would have been obvious to one of ordinary skill in the art to modify Rodriguez with machine learning with a classifier structure as in Mansi, as known way of machine learning in an ablation procedure.
Regarding claim 17, Rodriguez is not explicit wherein said same structure is a classifier.
Mansi teaches machine learning with a classifier structure (par. [0076]).
It would have been obvious to one of ordinary skill in the art to modify Rodriguez with machine learning with a classifier structure as in Mansi, as known way of machine learning in an ablation procedure.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rodriguez in view of Ganesan (WO 2013/123549).
Regarding claim 9, Rodriguez is not explicit wherein said at least one proposed ablation line is a wide antral circumferential ablation (WACA) line.
However, Ganesan teaches wide antral circumferential ablation (WACA) as a linear ablation technique (par. [00309]).
It would have been obvious to one of ordinary skill in the art to modify Rodriguez with wide antral circumferential ablation as a linear ablation.
Regarding claim 19, Rodriguez is not explicit wherein said at least one proposed ablation line is a
wide antral circumferential ablation (WACA) line.
However, Ganesan teaches wide antral circumferential ablation (WACA) as a linear ablation technique (par. [00309]).
It would have been obvious to one of ordinary skill in the art to modify Rodriguez with wide antral circumferential ablation as a linear ablation.
Allowable Subject Matter
Claims 10 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 10 and 20 are objected to for the same reasons as in the action dated 11/3/25.
Response to Arguments
Applicant's arguments filed 2/3/26 have been fully considered but they are not persuasive. Applicant argues that Rodriguez does not teach the claims as amended. However, different portions of Rodriguez teach the segmentation and ablation lines with machine learning, such as in at least par. [0337].
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 BO OUYANG whose telephone number is (571)272-8831. The examiner can normally be reached M-F 8-5 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joanne Rodden can be reached at 303-297-4276. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BO OUYANG/Examiner, Art Unit 3794
/MICHAEL F PEFFLEY/Primary Examiner, Art Unit 3794