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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/6/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to parent Application No. EP24212072.3, filed on 11/11/2024 Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Salehi et al. (WO 2024/088836A1 published on 5/2/2024, see IDS of 11/6/2025; hereinafter "Salehi").
With regards to Claim 1, Salehi discloses a computer-implemented method for guidance support for guiding a medical device, the method comprising:
receiving image data that represents the medical device inside a hollow organ, wherein the medical device extends along a part of the hollow organ (at operation 102 of method 100, the imaging device 30 performs interventional imaging during an interventional procedure to acquire a time sequence of images 35 (or imaging frames) of the movement of the interventional instrument 12 (with tip 14) {i.e. medical device} via robot 16during the procedure while disposed in a vessel {i.e. hollow organ} to a ROI {i.e. target position}; see Salehi ¶ [0029]);
determining a diameter and/or a curvature of the hollow organ at a target position or in a target region for the medical device depending on the image data (the processor 20 may predict the traversal time by detecting anatomical features of the portion of the anatomy (e.g., vessel tortuosity, bifurcations, vessel branching, vessel bends {i.e. curvature}, small vessel diameter, calcification, lesions, etc. in the portion of the anatomy) and locations of the anatomical features in the anatomy in a series of imaging frames in the time sequence of images 35 in the prediction of such time; see Salehi ¶ [0032]); and
generating guidance support information for guiding the medical device at least to the target position or the target region depending on the diameter and/or the curvature and depending on at least one geometric property of the medical device (the output of the machine-learning model {i.e. guidance support} may be used to control the autonomous robot 16. For instance, identified areas of slowdown may be used to signal to the robot 16 to reduce translation speed since areas of slowdown may be areas with highly tortuous or narrow vessels that must be navigated carefully; see Salehi ¶ [0058]; and wherein the machine-learning model relies on a visible curvature {i.e. geometric property} of the interventional device based on the image; see Salehi ¶ [0055]).
Claims 12 & 14 recite similar limitations and are rejected under the same rationale as Claim 1.
With regards to Claim 21, wherein the at least one geometric property of the medical device is determined at least in part depending on the image data (wherein the machine-learning model relies on a visible curvature {i.e. geometric property} of the interventional device based on the image; see Salehi ¶ [0055]).
With regards to Claim 31, wherein the image data comprises at least two projection images representing the medical device inside the hollow organ according to at least two respective projection directions (wherein the images are acquired via C-arm imaging device or a CT scanner; see Salehi ¶ [0037]; it should be appreciated that one of ordinary skill in the art would recognize that a C-arm or CT scanner acquires tomographic data radially, i.e. at least two projection images in at least two respective directions).
With regards to Claim 41, wherein the image data comprises a plurality of projection images representing the medical device inside the hollow organ according to respective projection directions, the method further comprising:
generating a three-dimensional reconstruction depending on the plurality of projection images (wherein the pre-op or intra-op images are 3D images of the vasculature; see Salehi ¶ [0037 & 0042]); and
determining the diameter and/or the curvature based further on the three-dimensional reconstruction (the processor identifies anatomical features, such as vessel diameter, based on the input images; see Salehi ¶ [0031-0032]).
With regards to Claim 51, wherein the at least one geometric property of the medical device comprises (claimed in the alternative) a curvature of the medical device or of the part of the medical device and/or a length of the medical device or of the part of the medical device (wherein the machine-learning model relies on a visible curvature {i.e. geometric property} of the interventional device based on the image; see Salehi ¶ [0055]) (claimed in the alternative).
With regards to Claim 67, wherein the guidance support information is generated depending on at least one mechanical property of the medical device (wherein the machine-learning model predicts traversal time based on the interventional device’s capability of adapting to the anatomical features (e.g., flexibility to traverse a bend in a vessel; see Salehi ¶ [0032 & 0034]).
With regards to Claim 76, wherein the at least one mechanical property comprises a stiffness of the medical device or of a part of the medical device and/or an elasticity of the medical device or of the part of the medical device and/or a surface lubricity of the medical device or of the part of the medical device (wherein the machine-learning model predicts traversal time based on the interventional device’s capability of adapting to the anatomical features (e.g., flexibility {i.e. stiffness} to traverse a bend in a vessel; see Salehi ¶ [0034]).
With regards to Claim 81, wherein the guidance support information or a part of the guidance support information is output to a user of the medical device (when 3D information is also available, the distance to target can be computed, and this distance is displayed on the GUI 28; see Salehi ¶ [0059]).
With regards to Claim 91, wherein the guidance support information or a part of the guidance support information is provided to a control system for automatically controlling a navigation of the medical device (the output of the machine-learning model {i.e. guidance support} may be used to control the autonomous robot 16. For instance, identified areas of slowdown may be used to signal to the robot 16 to reduce translation speed since areas of slowdown may be areas with highly tortuous or narrow vessels that must be navigated carefully; see Salehi ¶ [0058]).
Claim 13 recites similar limitations and are rejected under the same rationale as Claim 9.
With regards to Claim 101, wherein the medical device comprises a catheter and/or a vessel prosthesis and/or a vessel implant and/or a flow diverter and/or a stent and/or a guide wire (the interventional instrument 12 includes a catheter, a probe, a needle, an electrode, and so forth; see Salehi ¶ [0021]).
With regards to Claim 111, wherein the guidance support information comprises:
(claimed in the alternative)
a recommendation or specification of a navigation maneuver to move the medical device to the target position or the target region or beyond the target position or the target region (the output of the machine-learning model {i.e. guidance support} may be used to control the autonomous robot 16. For instance, identified areas of slowdown may be used to signal to the robot 16 to reduce translation speed since areas of slowdown may be areas with highly tortuous or narrow vessels that must be navigated carefully; see Salehi ¶ [0058]; one of ordinary skill in the art would recognize autonomous control amounts to a recommended navigation maneuver); and/or
(claimed in the alternative).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHISH S. JASANI whose telephone number is (571) 272-6402. The examiner can normally be reached M-F 9:00 am - 5:00 pm (CST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Keith Raymond can be reached on (571) 270-1790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHISH S. JASANI/Examiner, Art Unit 3798
/KEITH RAYMOND/Supervisory Patent Examiner, Art Unit 3798
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