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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/27/2026 has been entered.
Response to Arguments
Applicant’s arguments filed 9/4/2025, with respect to claims 1-11 and 17-20 have been fully considered but are moot in view new ground(s) of rejection.
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) 1-3, 7, 8 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (PGPUB Document No. US 2023/0230198) in view of sightengine et al. (“The Leading Image and Video Moderation API”, URL: https://web.archive.org/web/20220318113445/https://sightengine.com/).
Regarding claim 1, Zhang teaches a computer-implemented method, comprising:
Obtaining a first input for an image generative model (text input in a text-to-image generation system (Zhang: 0021));
Iteratively executing the image generative model to obtain an output image satisfying at least one criterion, the iteratively executing including (This process can be iterative (e.g., until a user is satisfied with the resultant digital image (Zhang: 0021)):
Obtaining, via the image generative model, an image generated based on an input (text-to-image generation system (Zhang: 0021));
Determining that the image generated based on the input does not satisfy the at least one criterion (repeating the process until the user is satisfied implies at least one iteration not satisfying at least one criterion (Zhang: 0021));
Responsive to determining that the image generated based on the input does not satisfy the at least one criterion, modifying the input, wherein the iteratively executing is repeated until an image is obtained based on the first input that satisfies the at least one criterion (the additional text feedback, wherein the process repeats until the user is satisfied (Zhang: 0021)).
However, Zhang does not expressly teach but sightengine teaches determining that the image generated based on the input contains at least one defined anomaly associated with a pose or structure features of a subject depicted in the generated image (image and video moderation comprising moderating content comprising nudity, offensive & hate signs, weapons, drugs, etc. (refer to sightengine)).
Responsive to determining that the image generated based on the input contains the at least one defined anomaly modifying the input, wherein the iteratively executing is repeated until the first output image satisfying the at least one criterion is obtained based on the first input (applying the teaching of sightengine to Zhang enables the user satisfied criterion further include avoiding inappropriate or offensive content. Therefore, the combined teachings enable repeating the process of Zhang until the one defined anomaly (inappropriate or offensive content) is removed/reduced)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of an ordinary skill in the art to modify Zhang such that the criterion to satisfy further include reducing the risk of inappropriate or offensive content as taught by sightengine, because this enables the resulting image to be more appropriate/presentable to the user.
Regarding claim 2, the combined teachings above teach the method of claim 1, wherein the obtained image that satisfies the at least one criterion is provided as the output image (the resulting modified image that satisfies the user (Zhang: 0021)).
Regarding claim 3, the combined teachings above teach the method of claim 1, wherein determining that the image generated based on the input does not satisfy the at least one criterion includes using a machine learning model to analyze the image (the interactive image generation system utilizes a generative neural network (Zhang: 0035)).
Regarding claim 7, the combined teachings above teach the method of claim 1, wherein modifying the input comprises at least one of modifying a text prompt or changing a seed value associated with the image generative model (the additional text feedback, wherein the process repeats until the user is satisfied (Zhang: 0021)).
Regarding claim 8, the combined teachings above teach the method of claim 7, wherein modifications to the text prompt are determined based on at least one anomaly associated with the image generated based on the input (repeating the process until the user is satisfied implies at least one iteration not satisfying at least one criterion (Zhang: 0021) corresponds to there being at least one anomaly with respect to what the user has expected).
Claim(s) 17-19 are corresponding system claim(s) of claim(s) 1-3. The limitations of claim(s) 17-19 are substantially similar to the limitations of claim(s) 1-3. Therefore, it has been analyzed and rejected substantially similar to claim(s) 17-19. Note, the combined teachings teach a system comprising a processor and memory (Zhang: 0157).
Claim(s) 20 are corresponding non-transitory processor-readable medium (Zhang: 0157) claim(s) of claim(s) 1. The limitations of claim(s) 20 are substantially similar to the limitations of claim(s) 1. Therefore, it has been analyzed and rejected substantially similar to claim(s) 20.
Claim(s) 4 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of sightengine as applied to claim(s) above, and further in view of Saraee et al. (PGPUB Document No. US 2022/0383615).
Regarding claim 4, the combined teachings above contains a “base” process of a text-to-image generation system, which the claimed invention can be seen as an “improvement” in that the image generating process is carried out by, the machine learning model providing an evaluation of an input image corresponding to the at least one criterion.
Saraee teaches a known technique of a content evaluation system utilizing neural network for using the retrieved images as input to generate performance scores for each of the images (Saraee: 0815)) that is applicable to the “base” process.
Saraee’s known technique would have been recognized by one skill in the art as applicable to the “base” process of the combined teachings above and the results would have been predictable and resulted in outputting images in a text-to-image generation system, which results in an improved process.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 6, the combined teachings teach the method of claim 4, wherein the machine learning model is trained to assign aesthetics scores to generated images (content evaluation system utilizing neural network for using the retrieved images as input to generate performance scores for each of the images (Saraee: 0815)).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of sightengine in view of Saree as applied to claim(s) above, and further in view of Shen et al. (PGPUB Document No. US 2019/0295223).
Regarding claim 5, the combined teachings above contain a “base” process of determining at least one of: poses of human subjects in a generated image; an indicator of photorealism associated with the generated image; structural anomalies in subjects in the generated image; or lighting anomalies on the subjects or scene depicted in the generated image (workers determining how realistic the images are (Zhang: 0124)), which the claimed invention can be seen as an “improvement” in that the process is carried out by a machine learning model.
Shen teaches a known technique of training a machine learning model to determine at least one the above (Shen: 0096), which is applicable to the “base” process.
Shen’s known technique would have been recognized by one skill in the art as applicable to the “base” process of the combined teachings above and the results would have been predictable and resulted in determining the output image, which results in an improved process.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of sightengine as applied to claim(s) above, and further in view of Shen in view of Saree.
Regarding claim 10, the combined teachings above contain a “base” process comprising: obtaining, via the image generative model, one or more further output images that are associated with detected anomalies (workers determining how realistic the images are (Zhang: 0124));
However, the combined teachings above do not expressly teach,
(1) The above process being carried out by the system
(2) And determining a pre-processing filter for applying to training image sets that are inputted to the image generative model, the pre-processing filter being constructed based on the further output images.
(1) Shen teaches a known technique of training a machine learning model to determine at least one the above (Shen: 0096), which is applicable to the “base” process.
Shen’s known technique would have been recognized by one skill in the art as applicable to the “base” process of the combined teachings above and the results would have been predictable and resulted in determining the output image, which results in an improved process.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
(2) Saraee teaches “determining a pre-processing filter for applying to training image sets that are inputted to the image generative model, the pre-processing filter being constructed based on the further output images” (a known technique of a content evaluation system utilizing neural network for using the retrieved images as input to generate performance scores for each of the images, wherein the requirement for meeting a score corresponds to the pre-processing filter as presently claimed (Saraee: 0815)). The teachings of Saree are applicable to the “base” process.
Saraee’s known technique would have been recognized by one skill in the art as applicable to the “base” process of the combined teachings above and the results would have been predictable and resulted in outputting images in a text-to-image generation system, which results in an improved process.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 11, the combined teachings teach the method of claim 10, wherein the pre-processing filter comprises an aesthetics scoring model for assigning aesthetics scores to images of a training image set (content evaluation system utilizing neural network for using the retrieved images as input to generate performance scores for each of the images (Saraee: 0815)).
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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David H Chu whose telephone number is (571)272-8079. The examiner can normally be reached M-F: 9:30 - 1:30pm, 3:30-8:30pm.
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/DAVID H CHU/Primary Examiner, Art Unit 2616