CTNF 18/728,209 CTNF 71793 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. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement (IDS) submitted on 09/26/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 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 (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. 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 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-3, 11-13, 15-16, 18-19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over CN112818820A, hereinafter “CN’820” in view of US PGPub 2021/0209464 to Bala et al , hereinafter “Bala.” Note that the below citations to pages, paragraphs, and line numbers to CN’820 are in relation to the supplied English translation of CN’820 . With regard to claim 1, CN’820 discloses an image processing method, comprising: generating a first image generator by training (bottom of page 1 - “first original image generator”), and generating a second image generator by training (bottom of page 1 - “second original image generator”), wherein the first image generator is configured to process an input random feature vector to generate a target object image with a first style (bottom of page 1 - “random vector to the first original image generator….to obtain a first output image”), and the second image generator is configured to process the input random feature vector to generate a target object image with a second style (bottom of page 1 - the same “random vector” is applied also to the second original image generator to generate a second output image); processing an input sample feature vector (bottom of page 1 - the “first sample image” and “second sample image”) based on the first image generator and the second image generator to generate a sample image with the first style and a sample image with the second style as paired sample data (see top of page 2 - “the first image generated by the first image generator and the first sample image have the same style” and also “the second image generated by the second image generator has the same style as the second sample image” and this creates paired images, i.e. “first image” and “second image” (see also last two lines of page 5)). However, CN’820 fails to teach “training a preset model based on the paired sample data to generate a target image generator, wherein the target image generator is configured to process an input image with the first style to generate an output image with the second style.” In the same field of endeavor, Bala teaches an image style generator in [0030]-[0033] that trains a GAN using supplied paired images to create a target image model that inputs from an end user an input image in a first style and outputs a synthesized output image in a second style. Therefore, it would have been obvious before the effective filing date of the claimed invention to have provided the paired image generator of CN’820 with the target image generator taught by Bala as doing so would enhance the functionality of CN’820 by including a final model (the target image generator of Bala) that is able to be trained based upon paired images created by CN’820. In addition, as noted by CN’820 at the middle of page 1, training a network requires a lot of paired data, but that obtaining paired images is difficult to obtain in reality. CN’820 solves the paired image issue, while Bala teaches building from paired images a useful end model that takes an image in a first style and outputs a synthesized image in a second style to the end user. With regard to claim 2, CN’820 in view of Bala discloses the image processing method according to claim 1, wherein the generating of the first image generator by training comprises: randomly collecting first object image data with the first style based on a plurality of first preset indicators; and training a parameter of a generative adversarial network based on the first object image data to obtain the first image generator (See CN’820 at middle of page 2 where a discriminator of a GAN take as input the first output image and the first sample image (preset indicators) and create a first sub-discriminator result that it iteratively processed to train the first image generator). With regard to claim 3, CN’820 in view of Bala discloses the image processing method according to claim 2, wherein the plurality of first preset indicators correspond to a plurality of feature dimensions of a target object (See CN’820 at middle of page 3 - the target object is a face with various hair coloring). With regard to claim 11, CN’820 in view of Bala discloses the image processing method according to claim l, further comprising: performing deformation compensation on a difference part of a facial key point between the sample image with the first style and the sample image with the second style in the paired sample data; and/or performing mapping compensation on a difference part of a non-facial key point between the sample image with the first style and the sample image with the second style in the paired sample data (see CN’820 at the top of page 8, hair texture, hair coloring, clothes coloring, background, and posture can be the same in the first and second images which inherently requires mapping between the two images). With regard to claim 12, CN’820 in view of Bala discloses the image processing method according to claim 1, wherein the training of the preset model based on the paired sample data to generate the target image generator comprises: training a parameter of a generative adversarial network based on the paired sample data by supervised learning to generate the target image generator, wherein image textures of the sample image with the first style and the sample image with the second style are weighted and fused according to preset weights during a training process to adjust an image texture of the output image (See Bala at [0050]-[0058] with adjustable weighting that enable facial textures/features to be seamlessly transferred from the input image to the output image without significant modification to the rest of the target image [0059]). With regard to claim 13, CN’820 in view of Bala discloses the image processing method according to claim 1, wherein the input random feature vector comprises at least one of a contour feature or a pixel color feature (See CN’820 at the top of page 8 with different hair colors being the goal of the paired images (with feature vectors being a hair color)). Claims 15 and 16 are rejected for reasoning, mutatis mutandis , as that of claim 1 above. In addition, for the claimed non-transitory computer-readable storage medium and program stored thereon in claim 16, see CN’820 at page 5, half-way down the page. Claims 18 and 21 are rejected for reasoning, mutatis mutandis , as that of claim 2 above. Claims 19 and 22 are rejected for reasoning, mutatis mutandis , as that of claim 3 above . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 4-10 and 20 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. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art cited concerns the general state of the art surrounding the style conversion of one image style to a second image style via use of machine learning and input/target images in order to output an image in the second style . Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID OMETZ whose telephone number is (571)272-7593. The examiner can normally be reached M-F, 8am-4pm. 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, Sumati Lefkowitz can be reached at 571-272-3638. 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. DAVID OMETZ Primary Examiner Art Unit 2672 /DAVID OMETZ/Primary Examiner, Art Unit 2672 Application/Control Number: 18/728,209 Page 2 Art Unit: 2672 Application/Control Number: 18/728,209 Page 3 Art Unit: 2672 Application/Control Number: 18/728,209 Page 4 Art Unit: 2672 Application/Control Number: 18/728,209 Page 5 Art Unit: 2672 Application/Control Number: 18/728,209 Page 6 Art Unit: 2672 Application/Control Number: 18/728,209 Page 7 Art Unit: 2672 Application/Control Number: 18/728,209 Page 8 Art Unit: 2672
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