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 .
Election/Restrictions
Applicant’s election without traverse of Group I (claims 1-11 and 13-15) in the reply filed on July 6, 2026 is acknowledged.
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.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim is NOT directed to a process, machine, manufacture or composition of matter. The claimed “computer program product” are non-structural per se, and the specification does not exclude the “computer program product” from being software (see pages 14-15). Therefore, a reasonable interpretation in light of the specification leads to the conclusion that the claim encompasses pure software, which does not fall within the definition of a process, machine, manufacture or composition of matter.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8, 10-11 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Accurate Segmentation of CT Male Pelvic Organs vis Regression-Based Deformable Models and Multi-Task Random Forests” by Gao et al. (hereinafter ‘Gao’).
In regards to claim 1, Gao teaches a computer implemented method for boundary detection of an object of interest in an image, the method comprising: (See Gao Abstract, Gao teaches object segmentation.)
for a volume of the image corresponding to a portion of a three-dimensional (3D) mesh, representing the object of interest, (See Gao Section II(B), Gao teaches using 3D models of triangle mesh.)
predicting, by a regression network, at least one predicted distance from the portion of the 3D mesh to a boundary of the object of interest in the image, the at least one predicted distance corresponding to a class; and determining a distance of the portion of the 3D mesh to the boundary based on at least one probability of the volume corresponding to a class and the at least one predicted distance. (See Gao Section III(B), Gao teaches regression and classification networks. In particular, the Gao teaches determining object boundary using regression displacement field and classification map.)
In regards to claim 2, Gao teaches wherein the method further comprises determining, by a classification network, at least one probability of the volume corresponding to a class, and wherein the distance of the portion of the 3D mesh to the boundary is determined based on the at least one probability determined by the classification network. (See Gao Figure 3 and Section III(B)).
In regards to claim 3, Gao teaches wherein a class corresponds to at least one property of tissue. (See Gao Abstract.).
In regards to claim 4, Gao teaches wherein the method further comprises adjusting the 3D mesh based on the determined distance. (See Gao Section III(D), Gao teaches 3D deformable models.)
In regards to claim 5, Gao teaches wherein the method further comprises assigning a label corresponding to a class to at least one of the portion of the 3D mesh and the volume based on at least one probability of the volume corresponding to a class. (See Gao Section III(B), Gao teaches assigning labels.)
In regards to claim 6, Gao teaches wherein a predicted distance of the portion of the 3D mesh to the boundary is predicted for each class of a plurality of classes, and the probability of the volume corresponding to each class of the plurality of classes is determined. (See Gao Section II(B), Gao teaches predicting class/label and displacement for the portions of 3D mesh).
In regards to claim 7, Gao teaches wherein the determined distance of the portion of the 3D mesh to the boundary is determined based on a sum for all classes of the predicted distance corresponding to a class multiplied by the probability of the volume corresponding to the class; or wherein the determined distance of the portion of the 3D mesh to the boundary is the distance corresponding to a class for which the determined probability of the volume corresponding to the class is the highest. (See Gao Section III(B)).
In regards to claim 8, Gao teaches wherein the method is performed for each volume corresponding to each of a plurality of portions of the 3D mesh. (See Gao Figure 5).
In regards to claim 10, Gao teaches wherein a portion of a classification network is shared with the regression network. (See Gao Figure 2.)
In regards to claim 11, Gao teaches wherein the 3D mesh is defined by a plurality of polygons and the portion of the 3D mesh corresponds to a polygon of the 3D mesh. (See Gao Section II(B).)
In regards to claim 13, Gao teaches a method of training a regression network for use in predicting a distance from a portion of a three-dimensional (3D) mesh to a boundary of an object of interest in an image, the distance corresponding to a class, the method comprising: (See Gao Section III(B), Gao teaches regression and classification networks. In particular, the Gao teaches determining object boundary using regression displacement field and classification map
training the regression network in parallel with a classification network, wherein the classification network is trained to determine at least one probability of a volume of an image corresponding to a class, and wherein the regression network and the classification network share weights. (See Gao Figure 5 and Section III(C), Gao teaches training of the network.)
Claims 14-15 recite limitations that are similar to that of claim 1. Therefore, claims 14-15 are rejected similarly as claim 1.
Allowable Subject Matter
Claim 9 is 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.
The following is a statement of reasons for the indication of allowable subject matter:
In regards to claim 9, Gao does not teach or suggest wherein the regression network comprises a layer comprising a portion specific weighting for each class, and the regression network is configured to output one distance per class for each of the plurality of portions of the 3D mesh; or wherein the regression network comprises a layer comprising one convolutional kernel per class, and the regression network is configured to output one distance per class for each of the plurality of portions of the 3D mesh.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UTPAL D SHAH whose telephone number is (571)272-5729. The examiner can normally be reached M-F: 7:30-5:30.
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, Vu Le can be reached at (571) 272-7332. 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.
/UTPAL D SHAH/Primary Examiner, Art Unit 2668
Prosecution Insights