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 .
This is a Non-Final Office Action Correspondence in response to U.S. Application No. 19/473,882 filed on 10/09/2025.
Claims 1-20 are pending. Claims 1, 11 and 17 are independent claims.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged, which is for Provisional Application 63/503,556 filed on 05/22/2023.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 01/12/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-8, and 10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ruiz et al. (NPL, “DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation”, hereinafter “Ruiz”).
Regarding claim 1, Ruiz teaches a computing system for image generation, the system comprising:
one or more processors (Ruiz page 1, Abstract, i.e., an AI system includes processor(s)); and
one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations (Ruiz page 1, Abstract, i.e., an AI system includes memory and programming codes), the operations comprising:
obtaining input data, wherein the input data comprises one or more input images, wherein the one or more input images are descriptive of a particular object (Ruiz page 1, Abstract and Figure 1);
processing the input data with an image generation model to generate output data, wherein the image generation model has been trained to generate a set of model-generated images based on an input, wherein the output data comprises a plurality of output model-generated images, wherein the plurality of output model-generated images are descriptive of the particular object in one or more environments (Ruiz page 1, Abstract and Figure 1), wherein processing input data with the image generation model to generate the output data comprises:
tuning a set of parameters of the image generation model based on the one or more input images (Ruiz page 1, Abstract);
embedding an association between a model-readable token and the set of parameters within the image generation model (Ruiz page 2, left column, second paragraph);
generating a plurality of prompts that comprise the model-readable token (Ruiz page 1, Figure 1, and page 2, second paragraph); and
generating, by processing the plurality of prompts with the image
generation model, the plurality of output model-generated images based at least in part on the set of parameters tuned based on the one or more input images, wherein the set of parameters render the particular object based on the image generation model processing the model-readable token (Ruiz page 1, Figure 1, and page 2, third paragraph); and
providing at least a subset of the plurality of output model-generated images as output (Ruiz page 1, Abstract and Figure 1).
As to claim 2, Ruiz also teaches the system of claim 1, wherein the one or more environments differ from one or more background environments of the one or more input images (Ruiz page 1, Abstract and Figure 1).
As to claim 3, Ruiz also teaches the system of claim 1, wherein the operations further comprise:
processing the plurality of output model-generated images and the one or more input images to determine an object metric for each of the plurality of output model-generated images, wherein the object metric is descriptive of a determined similarity between the particular object as depicted in the one or more input images and the particular object as depicted in a respective output model-generated image of the plurality of output model-generated images (Ruiz page 7, right column, last paragraph, i.e., diversity metric); and
determining the subset of the plurality of output model-generated images to output based on the plurality of object metrics images (Ruiz page 7, right column, last paragraph) .
As to claim 4, Ruiz also teaches the system of claim 1, wherein the operations further comprise:
processing the plurality of output model-generated images to determine a realism metric for each of the plurality of output model-generated images, wherein the realism metric is descriptive of a determined realism of a respective output model-generated image of the plurality of output model-generated images (Ruiz page 6, left column, last paragraph, Section Evaluation Metrics); and
determining the subset of the plurality of output model-generated images to output based on the plurality of realism metrics (Ruiz page 7, right column, last two paragraphs).
As to claim 6, Ruiz also teaches the system of claim 1, wherein tuning the set of parameters comprises:
generating a first model-generated image with the image generation model (Ruiz page 1, Abstract and Figure 1);
evaluating a loss function that evaluates a difference between the first model-generated image and the one or more input images (Ruiz page 2, left column, fourth paragraph); and
adjusting one or more parameters of the set of parameters based at least in part on the loss function (Ruiz page 2, left column, fourth paragraph).
As to claim 7, Ruiz also teaches the system of claim 1, wherein the operations further comprise:
obtaining a search query (Ruiz page 3, right column, last paragraph);
determining the search query is associated with the particular object (Ruiz page 3, right column, last paragraph); and
providing one or more output model-generated images of the plurality of output model-generated images for display in a search results interface (Ruiz page 3, right column, last paragraph).
As to claim 8, Ruiz also teaches the system of claim las in one of claims 17, wherein the operations further comprise:
obtaining content data from a user computing system, wherein the content data is descriptive of one or more content items accessed by the user computing system (Ruiz page 1, Abstract and Figure 1);
determining the content data is associated with the particular object (Ruiz page 1, Abstract and Figure 1); and
providing one or more output model-generated images of the plurality of output model-generated images to the user computing system (Ruiz page 1, Abstract and Figure 1).
As to claim 10, Ruiz also teaches the system of claim 1, wherein the image generation model comprises at least one of a diffusion model or a token-based transformer model (Ruiz page 1, Abstract and Figure 1).
Claim Rejections - 35 USC § 103
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) 5, 9, and 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ruiz in view of Zong et al. (U.S. PG Pub. No. 2021/0234816 A1, hereinafter “Zong”).
As to claim 5, Ruiz also teaches the system of claim 1, wherein the image generation model comprises a plurality of multi-layer perceptron blocks to perform color prediction (Ruiz page 9, right column, last paragraph) but fails to explicitly teaches density prediction for object rendering. However, in the same field of endeavor, Zong teaches density prediction for object rendering (Zong ¶0056). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruiz by incorporating the teachings of Zong. The motivation would be for distinguishing close-together pixels that correspond to the same object from the close-together pixels that correspond to edges of different objects—that is, parts of an image where two different objects touch one another (Zong ¶0056).
As to claim 9, Ruiz teaches the system of claim 1, but fails to explicitly teach wherein the input data comprises a first set of input images associated with a first object and a second set of images associated with a second object, and wherein the plurality of output model-generated images are descriptive of the first object and the second object in the one or more environments. However, in the same field of endeavor, Zong teaches wherein the input data comprises a first set of input images associated with a first object and a second set of images associated with a second object, and wherein the plurality of output model-generated images are descriptive of the first object and the second object in the one or more environments (Zong ¶0056, i.e., multiple objects in the images). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruiz by incorporating the teachings of Zong. The motivation would be for identifying objects within an image for training the caption generator (Zong ¶0056).
Regarding claim 11, Ruiz teaches a computer-implemented method for generating model-generated images with an object, the method comprising:
obtaining, by a computing system comprising one or more processors, a plurality of input images, wherein each of the plurality of input images depict the object (Ruiz page 1, Abstract and Figure 1);
obtaining, by the computing system, a pre-trained image generation model, wherein the pre-trained image generation model was trained to process an input and generate an output image
Ruiz fails to explicitly teaches the output image comprises predicted pixels. However in the same field of endeavor, Zong teaches the output image comprises predicted pixels (Zong ¶0056). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruiz by incorporating the teachings of Zong. The motivation would be for identifying objects within an image for training the caption generator (Zong ¶0056).
Ruiz as modified by Zong also teaches adjusting, by the computing system, a set of parameters based on the plurality of images and the pre-trained image generation model (Ruiz page 1, Abstract);
associating, by the computing system, the set of parameters with a unique identifier (Ruiz page 1, Abstract);
generating, by the computing system, a plurality of prompts comprising the unique identifier and a plurality of different terms associated with a plurality of different settings (Ruiz page 1, Figure 1);
generating, by the computing system, a plurality of model-generated images with the set of parameters and the pre-trained image generation model based on processing the plurality of prompts with the pre-trained image generation model and the set of parameters, wherein the plurality of model-generated images comprise predicted image data that depicts the object in one or more environments (Ruiz page 1, Abstract and Figure 1); and
providing, by the computing system, at least a subset of the plurality of model-generated images as output (Ruiz page 1, Abstract and Figure 1).
As to claim 12, Ruiz as modified by Zong also teaches the method of claim 11, wherein the set of parameters are different from a plurality of pre-tuned parameters of the pre-trained image generation model (Ruiz page 1, Abstract and Figure 1, i.e., “By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views and lighting conditions that do not appear in the reference images”).
As to claim 13, Ruiz as modified by Zong also teaches the method of claim 11, wherein adjusting, by the computing system, the set of parameters based on the plurality of images and the pre-trained image generation model comprises:
generating a plurality of training output images with the pre-trained image generation model and the set of parameters (Ruiz page 1, Abstract and Figure 1);
evaluating a loss function that evaluates a difference between one or more respective training output images of the plurality of training output images and one or more of the plurality of input images (Ruiz page 1, Abstract and Figure 1); and
adjusting one or more parameters of the set of parameters based at least in part on the loss function (Ruiz page 1, Abstract and Figure 1)
As to claim 14, Ruiz as modified by Zong also teaches the method of claim 11, wherein generating the plurality of training output images with the pre-trained image generation model and the set of parameters comprises:
obtaining a prompt associated with the object, wherein the prompt comprises text data and the unique identifier (Ruiz page 1, Abstract and Figure 1); and
processing the prompt with the pre-trained image generation model and the set of parameters to generate the plurality of training output images, wherein the object is rendered based on the set of parameters in response to the pre-trained image generation model processing the unique identifier (Ruiz page 1, Abstract and Figure 1).
As to claim 15, Ruiz as modified by Zong also teaches the method of claim 11, wherein the plurality of input images depict the object from different perspectives (Ruiz page 1, Abstract and Figure 1).
As to claim 16, Ruiz as modified by Zong also teaches the method of claim 11, wherein the plurality of input images depict the object in different environments (Ruiz page 1, Abstract and Figure 1).
Claim 17 recites the limitations substantially similar to those of claim 11 and is similarly rejected.
As to claim 18, Ruiz as modified by Zong also teaches the one or more non-transitory computer-readable media of claim 17, wherein the text data is descriptive of a request for a particular type of environment be rendered (Ruiz page 1, Abstract and Figure 1), and wherein the environment of the one or more model-generated images is of the particular type of environment (Ruiz page 1, Abstract and Figure 1, i.e., “By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views and lighting conditions that do not appear in the reference images”).
As to claim 19, Ruiz as modified by Zong also teaches the one or more non-transitory computer-readable media of claim 17 as in claim 17 or 18, wherein the unique identifier comprises at least one of a set of characters or one or more embedding values (Ruiz page 1, Abstract and Figure 1).
As to claim 20, Ruiz as modified by Zong also teaches the one or more non-transitory computer-readable media of claim 17, wherein the one or more model-generated images comprise the particular object depicted in a different perspective from the one or more particular perspectives of the plurality of input images (Ruiz page 1, Abstract and Figure 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER KHONG whose telephone number is (571)270-7127. The examiner can normally be reached Mon-Fri 8am-5pm EST.
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/ALEXANDER KHONG/Primary Examiner, Art Unit 2168
