CTNF 18/406,739 CTNF 79463 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. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-aia AIA Claim(s) 1-6, 9-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Mikhailiuk et al. (U.S. Pub. 2025/0150414 A1) . With respect to claims 1, 13 and 17, Mikhaliuk et al. discloses a data processing system comprising: a processor, and a machine-readable storage medium storing executable instructions which, when executed by the processor, cause the processor alone or in combination with other processors to perform the following operations: receiving, via a user interface of a client device, a first prompt requesting an output visual content item to be generated( i.e.,” The system receives an image post from the user and generates a description of the image using an image-to-text model. User intent is determined based on the image and description . .If responding with an image is appropriate based on the user intent, the system ge nerates a prompt using the image description and passes it to a text generation model to create an image description and caption. The image description and caption are used to synthesize a new image ”(abstract) ), the first prompt including a style visual content item and a topic content item ( i.e., “a chatbot system responds to user posts comprising images with posts comprising images on an interaction system . The chatbot system leverages a large language model to support conversations on various topics and extend its capabilities to properly reply to image post.’(0021) and topic and properly is topic and style as claimed invention ); constructing a second prompt ( fig. 4A shows step 408 is second prompt as claimed invention ) by a prompt construction unit as an input to a first generative model, by appending the style visual content item and the topic content item to a first instruction string (i.e., “a chatbot system generates the prompt by appending style instructions to the post description . The style instructions include generating the image in an interactive platform post style.”(0027) description is topic of content as claimed invention and “In operation 410 , in response to determining to respond with a chatbot interaction system post 444 , the chatbot system 232 generates a prompt (e.g., prompt 502 of FIG. 5 A) used o prompt a generative component 426 to generate an image description 440 (e.g., image description 506 of FIG. 5 B) and image caption 450 (e.g., image caption 508 of FIG. 5 B) using the user intent 436 .”(0083) ), the first instruction string comprising instructions to the first generative model to generate a textual description combining a topic in the topic content item with a style in the style visual content item as a third prompt i.e.,” The chatbot system generates a description of the post using an image-to-text model and determines a user intent based on the post and description . The chatbot system decides to respond with an image post based on the user intent . The chatbot system generates a prompt using the post description, and generates an image description and caption using the prompt . The chatbot system creates the image post using the image description and caption , and provides the image post to the user's client device..”(0026) and “ the prompt 438 comprises a detailed text description of the image content to be generated such as, but not limited to, objects, scenes, people, colors, textures, styles, and the like. I n some examples, the prompt 438 specifies the size, resolution and level of realism needed for an image.”(0083) ); inputting the third prompt into a second generative model to generate the output visual content item by including the topic in the output visual content item and replacing one or more visual elements of the style visual content item based on the topic while preserving a style of the style visual content item ( i.e., “ the prompt 438 comprises a detailed text description of the image content to be generated such as, but not limited to, objects, scenes, people, colors, textures, styles, and the like. I n some examples, the prompt 438 specifies the size, resolution and level of realism needed for an image.”(0083) and “The SDK stored on the interaction server system 110 effectively provides the bridge between an external resource (e.g., applications 106 or applets) and the interaction client 104 . This gives the user a seamless experience of communicating with other users on the interaction client 104 while also preserving the look and feel of the interaction client 104 … the prompt 438 gives guidance on the graphic design, framing, lighting and composition. In some examples, the prompt 438 provides example images or artistic styles to emulate. In some examples, the prompt 438 may include keywords to make the image feel more humanmade rather than computer generated. By receiving such details in the prompt 438 , the generative component 426 can produce a customized image description 440 and image caption 450 as requested by the chatbot system 232 .”(0061) ); providing the output visual content item to the client device (i .e., “The prompt 438 primes the generative component 426 to output an image description 440 and image caption 450 tailored to the user intent, conversation history, and expected tone. By providing a customized prompt t 438, the chatbot system 232 can steer the generative component 426 to generate an image description 440 and image caption 450 that will lead to an image and caption that aligns with the user intent 436 and interaction goals.”(0084) ). ; and causing the user interface to present the output visual content item ( fig. 3 shows user interface present the output visual content item or “he graphical user interface, presenting the modification performed by the transform system, may supply the user with additional interaction options. Such options may be based on the interface used to initiate the content capture and selection of a particular computer animation model (e.g., initiation from a content creator user interface). I”(0178) ) With respect to claims 2, 14 and 18, Mikhaliuk et al. discloses wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: receiving at least one user feedback on the output visual content item via the user interface (i .e.,. “interaction systems (e.g., social platforms, social media platforms, interactive platforms, extended reality platforms, messaging platforms, systems with which a user interacts, and the like) may provide ways for users to perform various functions, access information, and access entertainment.”(0019) and user interacts is feedback as claimed invention). With respect to claims 3, 15 and 19, Mikhaliuk et al. discloses the data processing system of claim 2, wherein the instructions to the first generative model further comprise instructions to construct a fourth prompt as an input to the first generative model, by appending the feedback and the output visual content item to another instruction string (i. e., “In some examples, a chatbot system generates the prompt by Appending style instructions to the post description . The style instructions include generating the image in an interactive platform post style.”(0027 ) and Examiner asserts that post description is instruction string as claimed invention, further indicate that the platform is interform for user interacts so can be fourth prompt or fifth prompt etc., ), the other instruction string comprising instructions to the first generative model to generate another textual description combining the feedback and the output visual content item as a fifth prompt ((i .e.,. “interaction systems (e.g., social platforms, social media platforms, interactive platforms, extended reality platforms, messaging platforms, systems with which a user interacts, and the like) may provide ways for users to perform various functions, access information, and access entertainment.”(0019) and user interacts is feedback as claimed invention and Fig. 4B shows the interaction or prompt with textual description with model 454, 456, 458, 460 and 462., and “ The image-to-text model applies this learning to generate a textual image description 430 summarizing the contents of the image 446 from the user interaction system post 428 . The image description 430 is a textual summary reflecting the objects, people, actions, and scene captured in the image 446 . The final textual image description 430 generated by the image processing component 202 provides a concise description of what the input image 446 depicts”(0072) ) , and to input the fifth prompt into the second generative model to generate a subsequent output visual content item by replacing one or more visual elements of the output visual content item based on the feedback while preserving the topic and the style of the style visual content item ( fig. 4B shows the subsequent output visual content item from 428 to 444 with user interacts feedback (0019) and “This gives the user a seamless experience of communicating with other users on the interaction client 104 while also preserving the look and feel of the interaction client 104”(0061) ). With respect to claims 4, 16 and 20, Mikhaliuk et al. discloses the data processing system of claim 3, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: providing the subsequent output visual content item to the client device (i .e., “The prompt 438 primes the generative component 426 to output an image description 440 and image caption 450 tailored to the user intent, conversation history, and expected tone. By providing a customized prompt t 438, the chatbot system 232 can steer the generative component 426 to generate an image description 440 and image caption 450 that will lead to an image and caption that aligns with the user intent 436 and interaction goals.”(0084) ).; and causing the user interface to present the subsequent output visual content item ( fig. 3 shows user interface present the output visual content item or “he graphical user interface, presenting the modification performed by the transform system, may supply the user with additional interaction options. Such options may be based on the interface used to initiate the content capture and selection of a particular computer animation model (e.g., initiation from a content creator user interface). I”(0178) ) With respect to claims 5 , Mikhaliuk et al. discloses the data processing system of claim 2, wherein the user feedback is collected via a user selection of at least one of a thumbs-up tab, a thumbs-down tab, a neutral tab, or a generating-more-image tab, a textual input, or a combination thereof ( i.e., “ the image-to-text model is trained on large datasets of images labeled with textual captions and descriptions. This allows the image-to-text model to learn associations between visual patterns/features in images and corresponding textual descriptions. The image-to-text model applies this learning to generate a textual image description 430 summarizing the contents of the image 446 from the user interaction system post 428. The image description 430 is a texttual summary reflecting the objects, people, actions, and scene captured in the image 446. The final textual image description 430 generated by the image processing component 202 provides a concise description of what the input image 446 depicts”(0072) ). With respect to claim 6, Mikhaliuk et al. discloses wherein the instructions to the first generative model further comprise instructions to check whether the third prompt contains the topic and the style, and to input the third prompt into the second generative model when the third prompt contains the topic and the style ( fig. 7 B shows feature such as attributes, content, concept are model that training by machine-learning and “If responding with an image is appropriate based on the user intent, the system generates a prompt using the image description and passes it to a text generation model to create an image description and caption. ”(abstract) ). With respect to claim 9, Mikhaliuk et al. discloses wherein the output visual content item is a photo ( fig. 3 ), a diagram, a chart, an image, an infographic, a video, an animation, a screenshot, a meme, a slide deck, a pictogram, an ideogram, or a software application background. With respect to claim 10, Mikhaliuk et al. discloses wherein the topic content item comprises at least one of a visual content item or a textual content item ( i.e., “ interaction with an interaction system may be enhanced by interacting with a chatbot system designed to simulate human conversation through voice commands or text chats”(0020) or “The image description 430 is a textual summary reflecting the objects, people, actions, and scene captured in the image 446 ”(0072) ). With respect to claim 11, Mikhaliuk et al. discloses wherein the first generative model is a language model or a multi-modal model (i .e., “A chatbot system may employ Natural Language Processing (NLP) and Machine Learning (ML)/artificial intelligence methodologies to understand and interpret a user's input and generate a response.”(0020 )). With respect to claim 12, Mikhaliuk et al. discloses wherein the second generative model is a text-to-image model or a vision model ( i.e., “the collection management system 222 employs machine vision (or image recognition technology) and content rules to curate a content collection automatically.”(0056) ). Allowable Subject Matter Claims 7-8 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, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the claimed wherein the instructions to the first generative model further comprise instructions to construct a sixth prompt as an input to the first generative model when the third prompt misses at least one of the topic or the style, by appending the missed at least one of the topic or the style and the third prompt to another instruction string, the other instruction string comprising instructions to the first generative model to generate another textual description combining the missed at least one of the topic or the style and the third prompt as a seventh prompt, and to input the seventh prompt into the second generative model to generate a subsequent output visual content item by replacing one or more visual elements of the style visual content item based on the topic while preserving the style; wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: providing the subsequent output visual content item to the client device; and causing the user interface to present the subsequent output visual content item. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUNG T VY whose telephone number is (571)272-1954. The examiner can normally be reached M-F 8-5. 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, Tony Mahmoudi can be reached at (571)272-4078. 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. /HUNG T VY/Primary Examiner, Art Unit 2163 June 12, 2026 Application/Control Number: 18/406,739 Page 2 Art Unit: 2163 Application/Control Number: 18/406,739 Page 3 Art Unit: 2163 Application/Control Number: 18/406,739 Page 4 Art Unit: 2163 Application/Control Number: 18/406,739 Page 5 Art Unit: 2163 Application/Control Number: 18/406,739 Page 6 Art Unit: 2163 Application/Control Number: 18/406,739 Page 7 Art Unit: 2163 Application/Control Number: 18/406,739 Page 8 Art Unit: 2163 Application/Control Number: 18/406,739 Page 9 Art Unit: 2163 Application/Control Number: 18/406,739 Page 10 Art Unit: 2163
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