DETAILED ACTION
Election/Restrictions
Applicant’s election without traverse of Group I (claims 7-22) in the reply filed on 06/01/2026 is acknowledged.
Allowable Subject Matter
Claims 8, 9 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 14-22 are allowed.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 8 (and dependent 9) the prior art of record, alone or in combination, fails to fairly teach or suggest these limitations, including the concept of an image reconstruction method, comprising: obtaining an implicit signed distance function image of an object; updating the implicit signed distance function image to produce an updated implicit signed distance function image that matches acquired imaging data; and converting the updated implicit signed distance function image into an explicit signed distance function image of the object, wherein obtaining the implicit signed distance function image of the object includes: generating, from a filtered back projection image of the object, a binary classification image of the object; and converting the binary classification image into the implicit signed distance function image.
Regarding claim 14 (and dependents 15-22) the prior art of record, alone or in combination, fails to fairly teach or suggest these limitations, including the concept of an image reconstruction method, comprising: performing an image segmentation on an initial reconstructed image to obtain binary classification images for identifying an object of interest in the initial reconstructed image; converting the binary classification images into a first set of signed distance function (SDF) images configured to explicitly represent the object of interest; converting the first set of signed distance function images into a second set of SDF images configured to implicitly represent the object of interest; training the second set of SDF images to match acquired sinogram data; and converting the trained second set of SDF images into a third set of SDF images configured to explicitly represent the object of interest..
For example, Tang teaches a technique for organ reconstruction/segmentation by collecting a set of digital images and predicting signed distance functions. ¶ 0069, teaches regressing the Signed Distance Function (SDF) directly from the input images through a 3D convolutional neural network. ¶ 0100 teaches converting the SDF/SDM of the organ into a segmentation mask which is located at the level set, or zero crossing, of the signed distance function. Park teaches converting an implicit signed distance function an explicit signed distance function image of the object. The sampled zero level-set surface provides explicit sampled locations on the zero level-set surface/segmentation edge. None of the closest prior art teaches or suggests all of the limitations of the independent claims. The claim language goes beyond the similarities of these devices and Applicant’s invention and a combination could not reasonably be made without impermissible hindsight. The differences here are viewed as allowable over the prior art.
Claim Rejections - 35 USC § 103
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.
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) 7, 10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang (US PGPub 2021/0350528) in view of Park (“DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation”).
Regarding claim 7, Tang discloses an image reconstruction method, comprising: (See Abstract and Fig. 3, Tang teaches a technique for organ reconstruction/segmentation by collecting a set of digital images and predicting signed distance functions.)
obtaining an implicit signed distance function image of an object; (¶ 0069, “regress the Signed Distance Function (SDF) directly from the input images through a 3D convolutional neural network”)
updating the implicit signed distance function image to produce an updated implicit signed distance function image that matches acquired imaging data; and (See Figs. 1 and 3, the initial SDF/SDM prediction is trained/updated, see ¶ 0102-0103 and 0121.)
converting the updated implicit signed distance function image into a segmentation image of the object. (¶ 0100, converting the SDF/SDM of the organ into a segmentation mask which is located at the level set, or zero crossing, of the signed distance function.)
In the field of signed distance function representation Park teaches converting an implicit signed distance function an explicit signed distance function image of the object. (Pg. 3, right column, last paragraph, “Our key idea is to directly regress the continuous SDF from point samples using deep neural networks. The resulting trained network is able to predict the SDF value of a given query position, from which we can extract the zero level-set surface by evaluating spatial samples.” The sampled zero level-set surface provides explicit sampled locations on the zero level-set surface/segmentation edge.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Tang’s signed distance function representation with Park’s signed distance function representation. Tang teaches a technique for organ reconstruction/segmentation by predicting signed distance functions and finally predicting segmentation of the organ edge which are at the level set, or zero crossing, of the signed distance function. Tang does not explicitly define this conversion as determining the explicit signed distance function. Park teaches a technique for modelling 3D representation using a signed distance function and sampling the zero level-set surface to provide explicit sampled locations of the signed distance function at the segmentation boundary. The combination constitutes the repeatable and predictable result of simply applying Park’s technique here for performing segmentation via conversion to an explicit signed distance function. This cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 10, the above combination discloses the method of claim 7, wherein the implicit signed distance function image is represented by a neural network. (See Tang Fig. 1)
Regarding claim 13, the above combination discloses the method of claim 7, wherein converting the updated implicit signed distance function image into the explicit signed distance function image of the object includes sampling the updated implicit signed distance function image over a grid at a predetermined spatial resolution to generate a spatiotemporal intensity image of the object. (Pg. 12, right column, last paragraph, “for 25,000 points we uniformly sample within the unit sphere”)
Claim(s) 11 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang (US PGPub 2021/0350528) in view of Park (“DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation”) and Sun (“CoIL: Coordinate-Based Internal Learning for Tomographic Imaging”).
Regarding claim 11, the above combination discloses the method of claim 7, wherein the acquired imaging data includes acquired CT data. (Tang ¶ 0001, “the disclosure is related to using an AI neural network to perform organ segmentation for use in medical imaging technology, such as, computed tomography (CT) scans (generating digital x-ray images using x-ray beams aimed at portions of a patient (e.g., organs)).”)
In the field of CT image analysis Sun teaches that the acquired imaging data includes acquired CT data. (Sun teaches a technique for neural network-based CT image analysis which operates on sinogram CT data, see Fig. 5)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Tang’s CT image analysis with Sun’s CT image analysis. Tang teaches a technique for CT organ reconstruction/segmentation via CT input. Park teaches using raw sinogram CT data as input. Simply modifying the imaging modality to accept sinogram CT imaging cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 12, the above combination discloses the method of claim 11, wherein the acquired sinogram data includes data that is acquired by a computed tomography (CT) scanner. (See rejection of claim 11.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Raphael Schwartz whose telephone number is (571)270-3822. The examiner can normally be reached Monday to Friday 9am-5pm CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vincent Rudolph can be reached at (571) 272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
Prosecution Insights