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 .
Allowable Subject Matter
Claims 3 – 8 and 16-18 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 § 101
Claims 14-18 are patent eligible under 101.
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) 1, 9-14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (US 20220122305 A1).
Regarding claim 1, Smith teaches a system comprising (See abstract: system):
at least one processor (See ¶12, “In some embodiments, a computing system includes a processor […]”); and
a memory storing instructions that, when executed by the at least processor (¶14, “Various embodiments are described herein, including methods, systems, non-transitory computer-readable storage media storing programs, code, or instructions executable by one or more processors, and the like.”), configure the system to perform operations comprising (¶12, “ In some embodiments, a computing system includes a processor and a non-transitory computer-readable medium comprising instructions which, when executed by the processor, perform processing including […]”):
accessing a first image of a first image domain (See ¶53, “In another example, the image editing system uses an optimization technique to modify a latent space representation of an input image in a first domain, […] For example, the first domain is cartoons of people and the second domain is photorealistic images of people. A cartoon image of a person is used to generate a photorealistic image of a person that looks similar to the cartoon image.”);
determining a latent space value for a generative neural network (GNN) to generate the first image (See abstract, “An improved system architecture uses a pipeline including an encoder and a Generative Adversarial Network (GAN) including a generator neural network to generate edited images with improved speed, realism, and identity preservation. The encoder produces an initial latent space representation of an input image by encoding the input image. The generator neural network generates an initial output image by processing the initial latent space representation of the input image. The system generates an optimized latent space representation of the input image using a loss minimization technique that minimizes a loss between the input image and the initial output image. The loss is based on target perceptual features extracted from the input image and initial perceptual features extracted from the initial output image. The system outputs the optimized latent space representation of the input image for downstream use.” The examiner notes the GAN, is considered to broadly read on a GNN.); in the primary embodiment, but doesn’t explicitly disclose
inputting, into the GNN, the latent space value and a condition value to generate a second image of a second image domain, the condition value indicating the second image domain, wherein the GNN is trained to generate images of the first image domain and the second image domain.
Outside the primary embodiment of Smith, Smith teaches inputting, into the GNN, the latent space value and a condition value to generate a second image of a second image domain, the condition value indicating the second image domain, wherein the GNN is trained to generate images of the first image domain and the second image domain (¶79 and ¶188).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the primary embodiment of Smith with an embodiment outside the primary embodiment of Smith as a score of confidence can provide a framework for evaluating the GNN/GAN’s performance, thus providing the option to quantify the quality of the output.
Regarding claim 9, Smith teaches the system of claim 1 (See the rejection of claim 1), wherein the condition value indicates comprises one or more of a smile (¶43, smile), a gender, an age, a frown, a cartoon image, a face type, a hair type (¶71, hair color), an expression (¶47, facial expression, See MPEP 2173.05(h).
Regarding claim 10, Smith teaches the system of claim 1, wherein the determining the latent space value further comprises:
inputting a first value into the GNN to generate a third image (¶153);
determining a difference between the first image and the third image (¶153); and
determining a second value based on the difference (¶153-¶154).
Regarding claim 11, Smith teaches the system of claim 1, wherein the operations further comprise:
presenting an interface on a display of the system for a user to select the condition value (¶155); and
in response of a selection of the condition value by the user, presenting the second image on the display (¶155, a first and second image can be shown simultaneously.).
Regarding claim 12, Smith teaches the system of claim 11, wherein the operations further comprise:
capturing the first image (¶54: obtain(s) a first image).
Regarding claim 13, Smith teaches the system of claim 1, wherein a plurality of image domains comprises the first image domain and the second image domain, and wherein the GNN is trained for each of the plurality of image domains (¶153-154).
Claim 14 recites similar limitations to that of claim 1 but doesn’t explicitly disclose a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising.
Smith teaches a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising (¶12, “ In some embodiments, a computing system includes a processor and a non-transitory computer-readable medium comprising instructions which, when executed by the processor, perform processing including […]”. ¶14, “Various embodiments are described herein, including methods, systems, non-transitory computer-readable storage media storing programs, code, or instructions executable by one or more processors, and the like.”).
Claim 19, recites similar limitations to that of claim 1 but doesn’t explicitly disclose a method comprising.
Smith teaches a method comprising ((¶14, “Various embodiments are described herein, including methods, systems, non-transitory computer-readable storage media storing programs, code, or instructions executable by one or more processors, and the like.”).
Thus claim 19 recites similar limitation to that of claim 1 and is rejected under similar limitations as detailed above.
Claim(s) 2, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (US 20220122305 A1) in view of Kuta et al. (US 20220172517 A1).
. Regarding claim 2, Smith teaches the system of claim 1, wherein the operations are further configured to: but doesn’t explicitly disclose:
determine a warp field based on face landmarks between the first image and the second image; and
adjust the second image in accordance with the warp field.
Kuta teaches determine a warp field based on face landmarks between the first image and the second image; and
adjust the second image in accordance with the warp field (See ¶9, ¶55 and ¶155).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Smith in view of Kuta, as Kuta would increase the privacy available by anonymizing a face, thus increasing privacy.
Claim 15 and 20 recites similar limitations to that of claim 2 and thus is rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J CRADDOCK whose telephone number is (571)270-7502. The examiner can normally be reached Monday - Friday 10:00 AM - 6 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Devona E Faulk can be reached at 571-272-7515. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT J CRADDOCK/Primary Examiner, Art Unit 2618
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