DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/14/2026 has been entered.
Response to Arguments
Applicant's arguments filed 7/14/2026 have been fully considered but they are not persuasive. New grounds for rejection for new claim 22 are presented below, further in view of Dubhasi (U.S. Patent Application Publication No. 2019/0328302).
With respect to the rejections under 35 U.S.C. § 102, the amendments merit new grounds for rejection in view of previously cited Amit reference, consistent with previously presented rejection to claims 7-8.
Applicant argues against the combination in view of Amit on pp. 11-12 of the Remarks dated 7/14/2026 but these arguments are not found persuasive. Applicant’s position is that the Amit reference does not teach training data comprising a “plurality of examples of geometric renal arterial information labeled with a respective one or more of a plurality of locations to apply renal denervation therapy or a plurality of modalities of renal denervation therapy to apply to the patient” because Amit teaches specific ECG ablation data labels, and a general application to renal therapy.
This is not found persuasive, Applicant is reminded that “(a) person of ordinary skill in the art is also a person of ordinary creativity, not an automaton.”KSR, 550 U.S. at 421, 82 USPQ2d at 1397. “[I]n many cases a person of ordinary skill will be able to fit the teachings of multiple patents together like pieces of a puzzle.”Id. at 420, 82 USPQ2d at 1397. Office personnel may also take into account “the inferences and creative steps that a person of ordinary skill in the art would employ.”Id. at 418, 82 USPQ2d at 1396. In the instant rejection, Amit teaches training the disclosed model with “relevant data input” and “considering relevant success criteria” with respect to renal denervation. Amit further teaches in ¶¶[0136-0142] a “set of cases tagged by a trained physician with the following information:” and the information includes “ablation anatomical location”, “ablation type”, and “width and depth of ablation”. The Examiner considers these labels to be sufficiently analogous to “one or more of ap lurality of locations to apply renal denervation therapy” and “a plurality of modalities of renal denervation therapy to apply to the patient.” The only difference between these labels disclosed by Amit and the claimed training labels is whether they are heart arterial information or renal arterial information. Therefore, with the suggestion from Amit to use renal specific information for training, the examiner considers such a modification obvious over Coates in view of the Amit reference.
With respect to the rejections under 35 U.S.C. § 101, on pp. 12-13 of the Remarks, Applicant alleges that the claims are not directed to any abstract ideas as they are directed to limitations that cannot be practically performed mentally or with a pencil and paper.
This is not found persuasive as there is no evidence of record that a PHOSITA is incapable of hand-calculating steps of a machine-learning model. The instant claims do not require any particular machine learning model, data resolution, or real-time processing that would render the calculation outside the capacity of PHOSITA. Applicant is reminded that arguments presented by the applicant cannot take the place of evidence in the record. In re Schulze, 346 F.2d 600, 602, 145 USPQ 716, 718 (CCPA 1965) and In re De Blauwe, 736 F.2d 699, 705, 222 USPQ 191, 196 (Fed. Cir. 1984).
Applicant further alleges that the claims are directed to only a specific practical application that provides a technical improvement to the field of renal denervation therapy selection.
This is not found persuasive as the alleged improvement is apparently provided by only the features identified as comprising the abstract idea.
Applicant further states that the combination of features as claimed is unconventional and provides a specific technical benefit that is not well-understood, routine, or conventional because the alleged improvement is integrated into a particular technology due to the combination with additional elements such as “receive geometric renal arterial anatomy information of a patient based on imaging data” and “output an indication of one or more of the determined location… or the determined modality.”
This is not found persuasive because both of these identified additional elements are not integrated with the abstract idea, as they are either pre-solution or post-solution and further comprise elements that have been identified by the courts as comprising “insignificant extra-solution activity” as they are directed to obtaining pre-existing data (pre-solution) and simple outputting (post-solution). At best, they nominally tie the abstract idea to a technical field, but they are not considered integrated with the abstract idea.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
This analysis in view of 35 U.S.C. § 101 is based on MPEP § 2106, please see
this section of the MPEP for additional information.
First, the broadest reasonable interpretation of the claim as a whole is
established:
Claims 1, 9, and 14 claim a computing device, system, and method comprising a memory, processing circuitry, and (in claim 9) a positive recitation of an imaging device, which determine either a location or modality of renal denervation therapy based on anatomy information received by the processing circuitry, and outputting an indication of either the location or modality, in order for it to be later applied to the patient.
Claims 2-4, 10-12, and 15-17 include additional specificity to location determination and output, modality determination and output, and selection of modality.
Claims 5-6, 13, and 18-21 add specific therapies the determined modality must be chosen from, and a number of alternative anatomical parameters that are received by the processing circuitry.
Claims 7-8 add a machine learning model and training data set.
Step 1 of the analysis is the question: “Is the claim to a process, machine,
manufacture, or composition of matter?” and the answer is determined to be yes, as the
claims as a whole are directed to a manufacture and a method.
For Step 2, the preliminary question is whether the eligibility of the claim is self-
evident. The answer is determined to be no, as the claim is not immediately self-evident
as statutory.
Step 2A Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
A claim is directed to a judicial exception when a law of nature, a natural
phenomenon, or an abstract idea is recited (i.e., set forth or described) in the claim.
While the terms “set forth” and “describe” are thus both equated with “recite”, their
different language is intended to indicate that there are different ways in which an
exception can be recited in a claim. For instance, the claims in Diehr set forth a
mathematical equation in the repetitively calculating step, the claims in Mayo set forth
laws of nature in the wherein clause, meaning that the claims in those cases contained
discrete claim language that was identifiable as a judicial exception. The claims in Alice
Corp., however, described the concept of intermediated settlement without ever explicitly using the words “intermediated” or “settlement.”
Claim 1/9, and 14 recites the following limitations:
determine one or more of a location of renal denervation therapy or a modality of renal denervation therapy to apply to the patient based on the renal arterial anatomy information
The above identified elements comprise an explicit claim recitation of an abstract idea. Therefore, rather than merely involve a judicial exception, the claims are directed to the identified judicial exception.
This claim language is identified as an abstract idea, because in MPEP §
2106.04(a)(2) III B. this language is similar to concepts relating to organizing or
analyzing information in a way that can be performed mentally or are analogous to
human mental work. For example, Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d
1138, 120 USPQ2d 1473 (Fed. Cir. 2016). In Synopsys, the patentee claimed methods
of logic circuit design, comprising converting a functional description of a level sensitive
latch into a hardware component description of the latch. 839 F.3d at 1140; 120 USPQ2d at 1475. Although the patentee argued that the claims were intended to be
used in conjunction with computer-based design tools, the claims did not include any
limitations requiring computer implementation of the methods and thus do not involve
the use of a computer in any way. 839 F.3d at 1145; 120 USPQ2d at 1478-79. The
court therefore concluded that the claims “read on an individual performing the claimed
steps mentally or with pencil and paper,” and were directed to a mental process of
“translating a functional description of a logic circuit into a hardware component
description of the logic circuit.” 839 F.3d at 1149-50; 120 USPQ2d at 1482-83.
In the instant case, the identified abstract idea is similar to Synopsys because the
language reads on an individual receiving renal arterial anatomy information, and determining a location or modality for therapy for a patient mentally or with a pencil and paper. They do not require any particular computer technology and therefore are directed to a mental process of evaluating imaging data determining an appropriate treatment plan.
Yes. The claim is directed to an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
First, the additional elements are identified.
In claim 1 and 9: memory, processing circuitry, receiving renal arterial anatomy information, outputting an indication; In claim 14: receiving renal arterial anatomy, outputting an indication
Claims 2-4, 7-8, 10-12, 15-17: receiving and outputting
Claim 9: imaging device
The imaging device is recited broadly as any imaging device without reference to a particular arrangement or configuration. The imaging sensors are only nominally tied to the abstract idea and the data acquisition is all performed as pre-solution activity to the abstract idea claimed, as no additional data collection or active imaging is claimed. Therefore the claimed sensors amount to mere data gathering and considered an insignificant extra-solution activity.
The processing circuitry, memory, output, and receiving of data appear to be an addition of a general purpose computer post-hoc to an abstract idea and is therefore not considered to transform the abstract idea into patent eligible subject matter. Output or display is considered to be insignificant extra-solution activity as to outputting certain aspects of the abstract idea.
The remaining features in the claims are directed to further specifying the intended use but do not impose further limits to the recited system because they are generally linking the use of the judicial exception to a particular field of use or technological environment.
Step 2B: Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The imaging device is recited broadly as any imaging device without reference to a particular arrangement or configuration. The imaging sensors are only nominally tied to the abstract idea and the data acquisition is all performed as pre-solution activity to the abstract idea claimed, as no additional data collection or active imaging is claimed. Therefore the claimed sensors amount to mere data gathering and considered an insignificant extra-solution activity.
The processing circuitry, memory, output, and receiving of data appear to be an addition of a general purpose computer post-hoc to an abstract idea and is therefore not considered to transform the abstract idea into patent eligible subject matter. Output or display is considered to be insignificant extra-solution activity as to outputting certain aspects of the abstract idea.
The remaining features in the claims are directed to further specifying the intended use but do not impose further limits to the recited system because they are generally linking the use of the judicial exception to a particular field of use or technological environment.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6 and 8-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coates et al. (U.S. Patent Application Publication No. 2019/0223955) hereinafter referred to as Coates; in view of Amit et al. (U.S. Patent Application Publication No. 2021/0085387) hereinafter referred to as Amit.
Regarding claims 1 and 8, Coates teaches a computing device (¶[0101], ¶[0115] computer model, therefore computing device) comprising:
a memory; and processing circuitry coupled to the memory (¶[0102] memory, processor coupled to the memory), the processing circuitry being configured to:
receive geometric renal arterial anatomy information of a patient (¶[0101] takes into consideration patient-specific tissue characteristics and anatomy, including ¶[0245] vessel diameter, length, intima-media thickness, coefficient of friction, tortuousity, distensibility, stiffness, modulus of elasticity, etc. of renal artery as the vessel of interest, e.g. ¶[0246]) based on imaging data (¶[0107] patient-specific image of the region generated using imaging modality, ¶[0115]);
determine one or more of a location of renal denervation therapy (¶[0111] guided) or a modality of renal denervation therapy (¶[0112] amount of energy) to apply to the patient by evaluating the geometric renal arterial anatomy information (¶¶[0112-0114] parameters of therapy dependent on types of tissue in individual patient-specific anatomy, ¶[0115] digital reconstruction and computer modeling of patient anatomy is used to choose location of the therapy, and based on obtained geometric information from imaging); and
output an indication of one or more of the determined location to apply renal denervation therapy to the patient or the determined modality of renal denervation therapy to apply to the patient (¶¶[0117-0118] therapy plan, Figs. 8-9 GUI includes display of locations and therapy parameters, Fig. 4).
Coates further teaches wherein to determine the one or more of a location of renal denervation therapy (¶[0111]) or a modality of renal denervation therapy to apply to the patient based on the geometric renal arterial anatomy information (¶¶[0112-0114] parameters of therapy dependent on types of tissue in individual patient-specific anatomy),
the processing circuitry is configured to generate an output indicating one or more of the determined location to apply renal denervation therapy to the patient or the determined modality of renal denervation therapy to apply to the patient (¶¶[0117-0118] therapy plan, Figs. 8-9 GUI includes display of locations and therapy parameters, Fig. 4) using a plurality of examples of renal arterial anatomy information labeled with a respective one or more of a plurality of locations to apply renal denervation therapy or a plurality of modalities of renal denervation therapy to apply to the patient (¶[0109], ¶[0170], ¶¶[0185-0186]).
Coates does not teach the processing circuitry configured to apply the geometric renal arterial anatomy information to a machine learning model, the machine learning model trained to generate an output indicating one or more of the determined location to apply renal denervation therapy to the patient or the determined modality of renal denervation therapy to apply to the patient using a training set comprising a plurality of examples of renal arterial anatomy information labeled with a respective one or more of a plurality of locations to apply renal denervation therapy or a plurality of modalities of renal denervation therapy to apply to the patient.
Attention is drawn to the Amit reference, which teaches a processing circuitry configured to apply geometric arterial anatomy information (¶[0102], ¶[0124] imaging data) to a machine learning model (¶¶[0130-0133] one or two machine learning models trained and used), the machine learning model trained to generate an output indicating one or more of a determined location to apply therapy to the patient (¶[0078]) or a determined modality of therapy to apply to the patient (¶[0084]) using a training set (¶¶[0036-0037]), comprising a plurality of examples of arterial anatomy information (3D location information) labeled with a respective one or more of a plurality of locations to apply therapy (3D location of ablation points) or a plurality of modalities of therapy (ablation parameters) to apply to the patient (¶¶[0108-0121], ¶[0123], ¶¶[0136-0139]). Further, while this embodiment of Amit is directed primarily to cardiac ablation, Amit contemplates the application of the techniques therein to renal denervation (¶[0143]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the renal denervation system of Coates to include a machine learning algorithm, as taught by Amit, because Amit teaches a benefit to machine learning algorithm inferences, including simple assessment of the quality of ablative treatment, leading to increased accuracy and improvement in ablation procedure outcomes (Amit ¶[0043]).
Regarding claim 2, Coates as modified teaches the computing device of claim 1.
Coates further teaches wherein the processing circuitry is further configured to:
determine the location of renal denervation therapy to apply to the patient based on the geometric renal arterial anatomy information (¶[0111], and ¶[0115] digital reconstruction and computer modeling of patient anatomy is used to choose location of the therapy, and based on obtained geometric information from imaging); and output the indication of the determined location to apply the renal denervation therapy to the patient (¶¶[0117-0118] therapy plan, Figs. 8-9 GUI includes display of locations and therapy parameters, Fig. 4).
Regarding claim 3, Coates as modified teaches the computing device of claim 1.
Coates further teaches wherein the processing circuitry is further configured to:
receive an indication of the determined modality of renal denervation therapy to apply to the patient (¶[0100]);
determine the location of renal denervation therapy based on the geometric renal arterial anatomy information and the determined modality of renal denervation therapy (¶[0106] select a volume of influence that extends to the tissue of interest); and
output the indication of the determined location to apply the renal denervation therapy to the patient (¶¶[0117-0118] therapy plan, Figs. 8-9 GUI includes display of locations and therapy parameters, Fig. 4).
Regarding claim 4, Coates as modified teaches the computing device of claim 1.
Coates further teaches wherein the processing circuitry is further configured to:
determine the modality of renal denervation therapy to apply to the patient based on the geometric renal arterial anatomy information (¶¶[0112-0114] parameters of therapy dependent on types of tissue in individual patient-specific anatomy); and
output the indication of the determined modality of renal denervation therapy to apply to the patient (¶¶[0117-0118] therapy plan, Figs. 8-9 GUI includes display of locations and therapy parameters, Fig. 4).
Regarding claim 5, Coates as modified teaches the computing device of claim 1.
Coates further teaches wherein the modality of renal denervation therapy includes one or more of radiofrequency (¶[0118], ¶[0121]), cryoablation (¶[0121]), ultrasound (¶[0121]), microwave (¶[0121]), radiation (¶[0121]), and chemical (¶[0121]).
Regarding claim 6, Coates as modified teaches the computing device of claim 1.
Coates further teaches wherein the geometric renal arterial anatomy information includes one or more of a length of aorta to hilum, number of branches, location of branches, length of aorta to primary bifurcation point, maximum diameter of main renal artery, minimum diameter of the main renal artery, mean diameter of the main renal artery, diameter of branches, length of branches, length of proximal segment of the main renal artery, length of middle segment of the main renal artery, or length of distal segment of the main renal artery (¶[0245] vessel diameter, length, intima-media thickness, coefficient of friction, tortuousity, distensibility, stiffness, modulus of elasticity, etc. of renal artery as the vessel of interest, e.g. ¶[0246]).
Regarding claim 9, Coates as modified teaches a system comprising:
an imaging device configured to collect the imaging data indicative of the geometric renal arterial anatomy information of a patient (¶[0137]); and
the computing device of claim 1 (see rejection of claim 1, above), wherein the computing device is communicatively coupled to the imaging device (¶[0107]).
Regarding claims 10-13/14-19, the claims are directed to a system/method comprising substantially the same subject matter as claims 2-5/1-6, and are rejected under substantially the same sections of Coates and Amit.
Regarding claim 20, Coates as modified teaches the computing device of claim 1.
Coates teaches wherein evaluating the geometric renal arterial anatomy information comprises evaluating a length of a space from an aorta to a renal hilum to select a distal portion of the space as the location to apply the renal denervation therapy (¶[0246] selecting within 5mm from inner wall of the renal artery).
As above, Amit teaches a processing circuitry configured to apply geometric arterial anatomy information (¶[0102], ¶[0124] imaging data) to a machine learning model (¶¶[0130-0133] one or two machine learning models trained and used)
Regarding claim 21, Coates teaches the computing device of claim 1.
Coates further teaches wherein evaluating the geometric renal arterial anatomy information comprises determining the location based on a convergence of one or more renal nerves toward a main renal artery to minimize a depth of ablation (¶[0246] selecting within 5mm from inner wall of the renal artery).
As above, Amit teaches a processing circuitry configured to apply geometric arterial anatomy information (¶[0102], ¶[0124] imaging data) to a machine learning model (¶¶[0130-0133] one or two machine learning models trained and used).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coates and Amit as applied to claim 1 above, and further in view of Dubhasi (U.S. Patent Application Publication No. 2019/0328302) hereinafter referred to as Dubhasi.
Regarding claims 22, Coates as modified teaches the computing device of claim 1.
Coates further teaches renal denervation therapy results in a reduction in blood pressure (¶[0230] and ¶[0232]) and a score predictive of efficacy of lesioning of a target nerve (¶[0170]).
Coates as modified does not teach the output generated by the machine learning model further comprises a score indicative of a predicted magnitude of reduction in systemic blood pressure for the patient in response to the determined location or the determined modality of renal denervation therapy.
Attention is drawn to the Dubhasi reference, which teaches wherein a machine learning model for evaluation renal denervation therapy patients output further comprises a score indicative of a predicted magnitude of reduction in systemic blood pressure for the patient in response to the determined location or the determined modality of renal denervation therapy (¶[0046-0048]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the renal denervation selection of Coates as modified to screen patients for expected efficacy, as taught by Dubhasi, to avoid ineffective ablation procedures in unsuitable candidates (Dubhasi ¶[0020] specifically screens for/identifies suitable candidates, thereby providing for the avoidance of unsuitable candidates).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA L STEINBERG whose telephone number is (303)297-4783. The examiner can normally be reached Mon-Fri 8-4.
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/AMANDA L STEINBERG/ Examiner, Art Unit 3792
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