CTNF 18/470,734 CTNF 83074 DETAILED ACTION Contents Notice of Pre-AIA or AIA Status 2 Election/Restrictions 2 Claim Rejections - 35 USC § 103 3 Conclusion 8 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 with traverse of group II, claims 1-16, in the reply filed on 2/18/26 is acknowledged. The traversal is without any argument. This is not found persuasive because there is a naked assertion with no rationale. The requirement is still deemed proper and is therefore made FINAL. Claims 1-20 are currently pending. Claims 17-20 are withdrawn from consideration. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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 claimedinvention is not identically disclosed as set forth in section 102 of this title, 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. 07-21-aia AIA Claim s 1-5, 9-13 is rejected under 35 U.S.C. 103 as being unpatentable over Quan et al (IEEE: “Which images to label for few-shot medical landmark detection?”) in view of Luo et al (MIA: “MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning”) . Regarding claim 1 , Quan teaches a system, comprising: a processor that executes computer-executable components stored in a non-transitory computer-readable memory, wherein the computer- executable components comprise (see section 4.2): an access component that accesses a medical image associated with a medical patient (see section 4.1); and a localization component that generates an ensembled heat map indicating where in the medical image an anatomical structure is likely to be located (see section 3.1, 3.2), by executing an embedder neural network on the medical image (see section 3.1) and on a plurality of example medical images associated with other medical patients (see section 3.1). Quan does not teach expressly wherein respective instantiations of the anatomical structure are flagged in the plurality of example medical images by user-provided clicks. Luo, in the same field of endeavor, teaches wherein respective instantiations of the anatomical structure are flagged in the plurality of example medical images by user-provided clicks (see section 2, 2.1, abstract). It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Quan to utilize the cited limitations as suggested by Luo. The suggestion/motivation for doing so would have been to enable accurate results and generalizes well on unseen objects (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Quan , while the teaching of Luo continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question. Regarding claims 2-5, Quan teaches neural network is executed on the medical image and on the plurality of example medical images in patch-wise fashion (see 3.1); divides the medical image into a set of pixel or voxel patches; and executes the embedder neural network on each of the set of pixel or voxel patches, thereby yielding a set of patch embeddings (see 3.1); identifies, within the example medical image and based on a user-provided click of the example medical image, a flagged pixel or voxel patch that depicts an instantiation of the anatomical structure; executes the embedder neural network on the flagged pixel or voxel patch, thereby yielding a flagged patch embedding; and computes a heat map, based on similarity scores computed between the flagged patch embedding and each of the set of patch embeddings, thereby yielding a plurality of heat maps respectively corresponding to the plurality of example medical images (see 3.1, 3.2); similarity scores are cosine similarities (see 3.1). Regarding claims 9-13, the claims are analyzed as a method that implements the limitations of claims 1-5 (see rejection of claims 1-5) . 07-21-aia AIA Claim s 6, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Quan et al (IEEE: “Which images to label for few-shot medical landmark detection?”) in view of Luo et al (MIA: “MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning”) and further in view of Sun et al (CV: “Deep High-Resolution Representation Learning for Human Pose Estimation”) . Regarding claim 6 , Quan with Luo teaches all elements as mentioned above in claim 5. Quan with Luo does not teach expressly generates the ensembled heat map by aggregating the plurality of heat maps together. Sun, in the same field of endeavor, teaches generates the ensembled heat map by aggregating the plurality of heat maps together (see section 2, 3). It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Quan with Luo to utilize the cited limitations as suggested by Sun. The suggestion/motivation for doing so would have been to enable superior pose estimation (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Quan with Luo , while the teaching of Sun continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question. Regarding claim 14, the claim is analyzed as a method that implements the limitations of claim 6 (see rejection of claim 6) . 07-21-aia AIA Claim s 7, 15 is rejected under 35 U.S.C. 103 as being unpatentable over Quan et al (IEEE: “Which images to label for few-shot medical landmark detection?”) in view of Luo et al (MIA: “MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning”) and further in view of Lints et al (US 2020/0160122 A1) . Regarding claim 7 , Quan with Luo teaches all elements as mentioned above in claim 1. Quan with Luo does not teach expressly a display component that visually renders the ensembled heat map on an electronic display. Lints, in the same field of endeavor, teaches a display component that visually renders the ensembled heat map on an electronic display (see abstract). It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Quan with Luo to utilize the cited limitations as suggested by Lints. The suggestion/motivation for doing so would have been to enhance the interactivity between the system and user (see 0035). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Quan with Luo , while the teaching of Lints continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question. Regarding claim 15, the claim is analyzed as a method that implements the limitations of claim 7 (see rejection of claim 7) . 07-21-aia AIA Claim s 8, 16 is rejected under 35 U.S.C. 103 as being unpatentable over Quan et al (IEEE: “Which images to label for few-shot medical landmark detection?”) in view of Luo et al (MIA: “MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning”) and further in view of Caron et al (IEEE: “Emerging Properties in Self-Supervised Vision Transformers”) . Regarding claim 8 , Quan with Luo teaches all elements as mentioned above in claim 1. Quan with Luo does not teach expressly trained in unsupervised fashion in an encoder-decoder deep learning pipeline or via a self-distillation-no-labels technique, or wherein the embedder neural network is an encoder of a pre-trained vision transformer. Caron, in the same field of endeavor, teaches trained in unsupervised fashion in an encoder-decoder deep learning pipeline or via a self-distillation-no-labels technique, or wherein the embedder neural network is an encoder of a pre-trained vision transformer (see abstraction, section 1 and 3). It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Quan with Luo to utilize the cited limitations as suggested by Caron. The suggestion/motivation for doing so would have been to enable high performance (see conclusion). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Quan with Luo , while the teaching of Caron continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question. Regarding claim 16, the claim is analyzed as a method that implements the limitations of claim 8 (see rejection of claim 8). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD PARK. The examiner’s contact information is as follows: Telephone: (571)270-1576 | Fax: 571.270.2576 | Edward.Park@uspto.gov For email communications, please notate MPEP 502.03, which outlines procedures pertaining to communications via the internet and authorization. A sample authorization form is cited within MPEP 502.03, section II. The examiner can normally be reached on M-F 9-6 CST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer, can be reached on (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /EDWARD PARK/ Primary Examiner, Art Unit 2666 Application/Control Number: 18/470,734 Page 2 Art Unit: 2675 Application/Control Number: 18/470,734 Page 3 Art Unit: 2675 Application/Control Number: 18/470,734 Page 4 Art Unit: 2675 Application/Control Number: 18/470,734 Page 5 Art Unit: 2675 Application/Control Number: 18/470,734 Page 6 Art Unit: 2675 Application/Control Number: 18/470,734 Page 7 Art Unit: 2675 Application/Control Number: 18/470,734 Page 8 Art Unit: 2675
Prosecution Insights