DETAILED ACTION
Response to Amendment
Applicant’s amendments filed on 24 November 2025 have been entered. Claims 1-10, 12, 14-17, and 19 have been amended. Claims 1-20 are still pending in this application, with claims 1, 8 and 15 being independent.
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 .
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 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 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.
Claim(s) 1-5, 7-12 and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 20250095230 A1), referred herein as Sharma in view of Jampani et al. (US 20240412458 A1), referred herein as Jampani.
Regarding Claim 1, Sharma in view of Jampani teaches one or more processors, comprising (Sharma Abst: systems, methods, and non-transitory computer readable media for generating digital images utilizing a diffusion neural network to preserve color harmony and image composition from a sample digital image while modifying image content; [0067] the components of the image context modification system 102 include one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices (e.g., the computing device 700). When executed by the one or more processors):
receive one or more prompts that indicate a subject and content related to the subject (Sharma [0047] As illustrated in FIG. 4, the client device 402 displays an image generation interface 404 that includes various images and elements for generating and searching digital images based on a sample digital image 408 and a text prompt 406); and
in response to receiving the one or more prompts, generate at least a first image and a second image depicting the subject (Sharma [0049] As further illustrated in FIG. 4, the image generation interface 404 includes three digital images identified or generated based on the sample digital image 408 and the text prompt 406. Particularly, the image generation interface 404 includes a searched digital image 410, a generated digital image 412, and a generated digital image 414), wherein the first image is to depict the subject within a first setting corresponding to the content and the second image is to depict the subject within a second setting corresponding to the content and different from the first setting (Sharma [0051] Sharma [0049] As).
Sharma disclosed processors, but does not teach circuitry, and within two or more different images.
Jampani disclosed methods, systems, and apparatus, including computer programs encoded on computer storage media relate to processing images using neural networks, which is an analogous art.
Jampani teaches circuitry to use one or more neural networks (Jampani [0104] implemented in digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware; a new diffusion-based scoring technique for (i) optimizing an image, e.g., a two-dimensional (2D) rendered image, or (ii) optimizing a parametric model, e.g., a differentiable renderer that includes one or more NeRF neural networks, to generate enhanced rendered images); and
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sharma to incorporate the teachings of Jampani, and applying the digital electronic circuitry of diffusion neural networks into methods and systems relate to improving image generation based on text-based prompts in an iterative manner.
Doing so would provide a system that generates an output image by editing an input image using the DASS losses, i.e., either unconditioned or conditioned on a text description for neural networks to generate objects within different images.
Regarding Claim 2, Sharma in view of Jampani teaches the one or more processors of claim 1, and further teaches wherein the subject within the first image and the second image include a same feature (Sharma [0034] the image context modification system 102 generates the blurred digital image 204 to obscure or obfuscate the content of the sample digital image 202 (e.g., the indication that the sample digital image 202 portrays a cross-section of a citrus fruit, with its individual segments) while preserving or retaining the color harmony and the image composition of the sample digital image 202 (which are thus both reflected in the blurred digital image 204). [0049] [0049] As further illustrated in FIG. 4, the image generation interface 404 includes three digital images identified or generated based on the sample digital image 408 and the text prompt 406).
Regarding Claim 3, Sharma in view of Jampani teaches the one or more processors of claim 1, and further teaches wherein the one or more neural networks include one or more layers that denoise images to generate the first image and the second image (Sharma [0038] the image content modification system 102 utilizes the architecture of the diffusion neural network 206 (e.g., a plurality of denoising layers that remove noise or recreate a digital image) to generate a digital image (e.g., the generated digital image 208) from the noise map/inversion; [0041] As just mentioned, in some embodiments, the image context modification system 102 generates digital images using a diffusion neural network to not only preserve color harmony and image composition of a sample digital image, but also to reflect content indicated by a text prompt).
Regarding Claim 4, Sharma in view of Jampani teaches the one or more processors of claim 1, and further teaches wherein the one or more neural networks include a diffusion neural network (Sharma [0045] the diffusion seed 306 is a number that, when controlled, results in reproducible images generated by the diffusion neural network 312 and allows for testing of other parameters, such as blurring sample digital images and variations in text prompts to generate different digital images).
Regarding Claim 5, Sharma in view of Jampani teaches the one or more processors of claim 1, and further teaches wherein the one or more prompts include one or more input text prompts, wherein the text prompts are generated by one or more users (Sharma [0047] The image context modification system 102 further receives an indication of user input selecting options for generating new digital images for the text prompt 406, searching a database for images based on the text prompt 406, or both).
Regarding Claim 7, Sharma in view of Jampani teaches the processor of claim 1, and further teaches wherein the subject is the same subject and in different poses within the first image and the second image (Jampani [0070] the differentiable renderer 350 can generate a 3D model of an object instance to have a particular (e.g., user-specified) pose…by using the 3D model, the differentiable renderer 350 can generate 2D rendered images of the object instance that has the particular pose, the particular motion, the particular texture, or a combination thereof).
Regarding Claim 8-12 and 14, Sharma in view of Jampani teaches a system comprising: one or more processors to use one or more neural networks to (Sharma [0067] the components of the image context modification system 102 include one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices (e.g., the computing device 700). When executed by the one or more processors; [0014] when generating new or modified digital images using diffusion neural networks):
The metes and bounds of the rest of the limitations of the claims substantially correspond to the limitations set forth in claims 1-5 and 7; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Regarding Claim 15-19, Sharma in view of Jampani teaches a method comprising (Sharma Abst: systems, methods, and non-transitory computer readable media for generating digital images utilizing a diffusion neural network to preserve color harmony and image composition from a sample digital image while modifying image content; [0067] the components of the image context modification system 102 include one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices (e.g., the computing device 700). When executed by the one or more processors):
The metes and bounds of the rest of the limitations of the claims substantially correspond to the limitations set forth in claims 1-5; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Claim(s) 6, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 20250095230 A1), referred herein as Sharma in view of Jampani et al. (US 20240412458 A1), referred herein as Jampani and Mitchell et al. (US 20250022185 A1), referred herein as Mitchell.
Regarding Claim 6, Sharma in view of Jampani teaches the one or more processors of claim 1, and further teaches wherein the one or more neural networks receive only text prompts as inputs (Mitchell [0023] the image tuning system 114 receives an image generation prompt 202. As demonstrated in FIG. 2, the image generation prompt 202 can include a request from a user for a generated image corresponding to a “person wearing a fun hat.”).
Mitchell disclosed a text-to-image model that takes as input a natural language description and produces an image matching that description, which is an analogous art.
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sharma to incorporate the teachings of Mitchell, and applying the image generation prompt 202 into the sample digital image generating process in FIG. 4 of Sharma.
Doing so would provide an improved generated images without regenerating an image, from scratch, with a human-modified prompt. for neural networks to generate objects within different images.
Regarding Claim 13, Sharma in view of Jampani teaches the system of claim 8. The metes and bounds of the claim substantially correspond to the limitations set forth in claim 6; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Regarding Claim 20, Sharma in view of Jampani teaches the method of claim 15. The metes and bounds of the claim substantially correspond to the limitations set forth in claim 6; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Response to Arguments
Applicant's arguments filed on 24 November 2025, with respect to the 103 rejection have been fully considered but are moot in view of the new grounds of rejection.
Examiner notes that independent claims 1, 8 and 15 have been amended to include new limitation. Examiner finds these limitations to be unpatentable as can be found in above detail action.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Samantha (Yuehan) Wang whose telephone number is (571)270-5011. The examiner can normally be reached Monday-Friday, 8am-5pm.
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/Samantha (YUEHAN) WANG/
Primary Examiner
Art Unit 2617
Prosecution Insights