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 .
The action is responsive to the following communication: a response to a restriction/election filed on 07/16/2026 where:
• Applicant provisionally elects to prosecute Species I (claims 1-18) without traverse.
Allowable Subject Matter
Claims 2, 3, 7, 13 and 14 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.
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, 4-6, 8-12, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chunduru et al. (US 2025/0157632, hereinafter Chunduru) in view of Amiranashvili (“Learning Shape Reconstruction from Sparse Measurements with Neural Implicit Functions”).
Regarding claim 1, Chunduru teaches: A method ([0017]) for three dimensional organ reconstruction from two dimensional sparse imaging data, the method comprising:
acquiring a plurality of two dimensional medical images of a patient ([0018 and 0024], require medical images as input);
generating organ contours in the plurality of two dimensional medical images ([0070], synthetic ICE frames 308 may include a simplified version e.g., an image illustrating heart anatomy via a plurality of lines indicating contours of heart's structure as shown in FIG. 3.).
Chunduru does not explicitly teach: training a neural implicit shape function model with the organ contours to regress a three dimensional organ shape; and
generating the three dimensional organ shape using the trained neural implicit shape function model.
However, Amiranashvili teaches: training a neural implicit shape function model with the organ contours to regress a three dimensional organ shape (Abstract, For existing models, the resolution of a learned shape prior is limited to the resolution of the training data. Our method is based on neural implicit shape representations and learns a continuous shape prior only from highly anisotropic segmentations.); and
generating the three dimensional organ shape using the trained neural implicit shape function model (Conclusion and fig. 1, section 2.1, We have posed a novel task of reconstructing high-resolution shapes from sparse measurements without relying on high-resolution data for training.).
Therefore, the Applicant's claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chunduru to include training a neural implicit shape function model with the organ contours to regress a three dimensional organ shape; and generating the three dimensional organ shape using the trained neural implicit shape function model as taught by Amiranashvili. The motivation/suggestion would have been to further enhance/improve the method for three dimensional organ reconstruction since doing so would allow for reconstructing anatomical shapes from sparse or partial measurements; therefore, allowing to successfully reconstruct high-resolution shapes from sparse segmentations, using as few as three orthogonal slices.
Regarding claim 4, Chunduru and Amiranashvili teach: The method of claim 1, wherein the two dimensional medical images comprise Intracardiac Echocardiography (ICE) images (Chunduru, [0045], medical image 152 may include an intracardiac echocardiography (ICE) image).
Regarding claim 5, Chunduru and Amiranashvili teach: The method of claim 1, wherein the two dimensional medical images comprise magnetic resonance (MR) images (Chunduru, [0045-0046], raw data from one or more imaging device e.g., MRI).
Regarding claim 6, Chunduru and Amiranashvili teach: The method of claim 1, wherein generating organ contours comprises segmentation of the plurality of two dimensional medical images using a deep learning method that has been trained with an existing database of Intracardiac Echocardiography images with annotated contours as ground truth (Chunduru, [0063, 0085, 0101], Such large volume of data may be beneficial for training deep learning machine learning models, which typically require extensive datasets to achieve desired performance.).
Regarding claim 8, Chunduru and Amiranashvili teach: The method of claim 1, further comprising aligning the two dimensional medical images in a three dimensional space using image header information (Chunduru, [0025], A “voxel,” for the purpose of this disclosure, is a 3D equivalent of a pixel in 2D imaging. While a pixel represents a point in a 2D image and may include properties such as color and/or brightness).
Regarding claim 9, Chunduru and Amiranashvili teach: The method of claim 1, wherein the trained neural implicit shape function model iterates over every point in a region of interest and predicts a signed distance of each point to a closest boundary (Amiranashvili 2.2, shape prior training: For a batch of randomly chosen voxels, we obtain a set of predicted occupancy values.).
Regarding claim 10, Chunduru and Amiranashvili teach: The method of claim 1, wherein the trained neural implicit shape function model classifies each point in a region of interest as lying inside or outside the three dimensional organ shape (Amiranashvili, 2.1, Shape Representation: in the ambient space, such classifier predicts whether the point x lies inside or outside of the given shape.).
Regarding claim 11, Chunduru and Amiranashvili teach: The method of claim 1, wherein the organ contours comprise three dimensional coordinates in space or voxelized in a three dimensional volume (Amiranashvili, 2.1, Shape Representation: A 3D shape is modelled as a decision boundary of a binary classifier).
Regarding claim 15, Chunduru and Amiranashvili teach: The system of claim 12, wherein the medical imaging device comprises an ultrasound system (Chunduru, [0030] Other exemplary embodiments of set of images 124 may include, without limitation, X-ray images, magnetic resonance imaging (MRI) scans, ultrasound images).
Regarding claim 16, Chunduru and Amiranashvili teach: The system of claim 15, wherein the boundary detection machine learned model is configured to segment the plurality of two dimensional images using a deep learning method that has been trained with an existing database of ultrasound images with annotated contours as ground truth (Chunduru, [0063, 0085, 0101], Such large volume of data may be beneficial for training deep learning machine learning models, which typically require extensive datasets to achieve desired performance.)..
Claim 12 is rejected for reasons similar to claim 1 above.
Claim 17 is rejected for reasons similar to claim 9 above.
Claim 18 is rejected for reasons similar to claim 10 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW H LAM whose telephone number is (571)270-7969 and fax number is 571-270-8969. The examiner can normally be reached on 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Tieu can be reached on 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW H LAM/
Primary Examiner, Art Unit 2682
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