DETAILED ACTION
A. This action is in response to the following communications: Transmittal of New Application filed 12/05/2023.
B. Claims 1-20 remains pending.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Parasnis, Abhay et al. (US Pub. 2024/0265274 A1), herein referred to as “Parasnis”.
As for claims 1, 11 and 16, Parasnis teaches. A computerized system and corresponding one or more computer-storage media of 11 and computer-implemented method of 16, specifically for claim 11 “having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to perform operations, the operations comprising”; specifically for claim 1 “one or more computer processors; and computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations, the operations comprising” (par.269-271 describe the hardware and software environment to implement interactive template for multimodal content generation);
Par. 47 The content-generation tool provides a powerful and versatile interface for generating multimodal content, which means that the same user interface (UI) is used for generating any combination of text, images, videos, etc. Further, generated content may be used to generate additional content, such as generated text may be used to generate images for an advertisement.
accessing a request associated with image data in an item listing system (fig. 4; par. 67- 71 create a catalog; this is a website depicted on figure 5 which is composed of generative AI images (text to image) to create a user interface with created products to sell based upon user prompts; par. 73);
based on the request, accessing composite image data associated with a generative AI model, the composite image data comprising a generative AI image element and a generative AI item listing interface (i.e. catalog interface, par.73) element, the generative AI model is associated with presentation training operations and a presentation data structure that support a presentation mapping and rotation system for composite image data for the item listing system (par. 53 Generative Artificial Intelligence (herein GAI) is used to create new content by utilizing images in addition to text and audio files as well; by using a detection of underlying pattern related to image input to product similar image output using Generative Adversarial Networks (GANs);
par. 51 The content-generation tool is a platform that can generate multiple types of generative content that are customized for the user and the user's particular environment (e.g., assets, products, services, voice, style, company of the user);
par. 73 Further, the user may generate and image for the asset using the prompt tool and then save the asset image for later use. Further yet, the user can access all the assets from the asset catalog. For example, all generated images for a product will show up under the product view; Fig. 6 shows a rotated view for mapping the image to a catalog/listing page layout of Fig. 5, note emphasized picture below;
Case Example par. 87 images generated through GAI by means of awareness of context for the user the created image “composites” the original plush toys by appending this image to the ad image by means of individually created models such that these models are being “aware” of actual look and properties of user products (e.g. images) so that the generated images match perfectly the plush toys a company is selling to the ad image it is creating based upon stable diffusion);
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communicating the composite image data to an item listing system client to cause display of the composite image data via the item listing system client (fig. 5 and 6 shows examples user interface where the generative ai image is used in the catalog layout);
accessing a composite image data instruction associated with the composite image data (par. 166 is an example where the user can select the generated composite image data (fig. 16) for changing the image (asset));
based on the composite image data instruction, accessing updated composite image data (par. 167 set of instructions on how to change image (asset) through editing commands and Ai prompts; note fig. 15 1504 where user selects portion of asset to change);
communicate the updated composite image data to cause display of the updated composite image data on the item listing system client (fig. 15, 1518 par. 176 present updated image with new asset; the new asset has an updated image that the user edited to change visual characteristics of said image (asset)).
As for claims 2, 12 and 17, Parasnis teaches. The system of claim 1, further comprising a generative AI presentation engine that integrates with one or more of the following: item listing service, a search service (par. 209 search service), a recommendation service (par. 134 recommendation), and image composition service that support a plurality of item listing system interfaces for presenting instances of composite image data for requests processed using the generative AI presentation engine (par. 126 selection and editing with a template).
As for claims 3 and 18, Parasnis teaches. The system of claim 1, further comprising a generative AI presentation engine the is associated with a machine learning engine associated with training the generative AI model that supports image generation and text generation for instances of composite image data (par. 142 use of a custom model trained on user specific product images; par. 153 The menu 1304 is then presented, with the options to use the selection to generate new text, use the selection to generate an image, regenerate text in line (e.g., give me another option to replace the selected text), and generate more text like this (to show in the results panel selectable options)).
As for claims 4, 13 and 19, Parasnis teaches. The system of claim 1, wherein the presentation training operations support training generative AI models based on training data comprising user data, image data, text data and item listing interfaces data, wherein the training operations support generating instances of composite image data for a plurality of item listing interfaces of the item listing system (par. 142 use of a custom model trained on user specific product images; par. 153 The menu 1304 is then presented, with the options to use the selection to generate new text, use the selection to generate an image, regenerate text in line (e.g., give me another option to replace the selected text), and generate more text like this (to show in the results panel selectable options)).
As for claims 5, 14 and 20, Parasnis teaches. The system of claim 1, wherein the presentation data structure comprises a presentation logic that includes instructions for mapping composite image data to portions of a corresponding item listing interface (par. 121-125 further details on the use of template wherein the templates have a prestored logic that includes a layout and desired type of content inside sub sections of the layout to be filled and pre-generated based upon generative ai and user prompts).
As for claim 6, Parasnis teaches. The system of claim 1, wherein the composite image data corresponds to a user associated with the request, wherein the composite image data is generated along with presentation logic using the generative AI model for one or more item listing interfaces of the item listing system (par. 126 use of out of the box pre-made templates and the ability to create/program your own template along with the use of generative ai content to fill said template upon use of selection of said template during normal use. The content-generation tool provides some out-of-the box templates, such as the basic ones to create a text, create an image, etc., or more complex ones like creating an Instagram ad or a landing page for a website. As discussed above, the user may also create custom templates without having to programmatically create the templates, although, in some embodiments, an option to programmatically create a template is also provided, e.g., by the use of an Application Programming Interface (API)).
As for claims 7 and 15, Parasnis teaches. The system of claim 1, wherein the composite image data comprises two or more of the following: a non-generative AI data element, a generative AI data element, and a generative AI item listing interface element (par. 88 and fig. 7; generated text and images can be used within a project and stored in a layout defined by a template as discussed above).
As for claim 8, Parasnis teaches. The system of claim 1, wherein the updated composite image data is accessed via the presentation data structure that stores a plurality of images or text associated with the composite image data (fig. 7 is a user interface for projects which relates to already made templates comprised of images and text generatively created with the trained models).
As for claim 9, Parasnis teaches. The system of claim 1, the operations further comprising: communicating a request instance associated with image data of the item listing system; based on communicating the request instance, accessing an instance of composite image data, the instance of composite image data is associated with a seller interface, a buyer interface, or an image composition interface (fig. 5 and 7 shows different interface one of which is a selling catalog created with text to image prompts and text generation as shown in fig. 7; fig. 12a shows the combination of generated images and text to be generated followed in a template layout);
causing display of the instance of composite image data (fig. 7 user interface for displaying generated content) ; accessing an instance of a composite image data instruction; based on the instance of the composite image data instruction, accessing an instance of updated composite image data; and causing display of the instance of updated composite image data (par. 88 and fig. 7 project user interface allows the user to select a saved project to open up a template that has saved generative content stored therein to fine tune the edits as discussed in figs. 17-23).
As for claim 10, Parasnis teaches. The system of claim 1, the operations further comprising:
accessing a training dataset associated with training an instance of a generative AI model (par. 86 creating custom training models); executing the presentation training operations on the training dataset to generate the instance of the generative AI model (par. 86 – 87 based upon training the correct image is generated based upon contextual information); and
deploying the instance of the generative AI model to support generating composite image data interface in the item listing system (par.87-88 correct images created based upon training, saved as projects and displayed in fig.7 for user selection to publish or edit further).
(Note :) It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Document ID
US 20250068893 A1
Date Published
2025-02-27
Abstract
Techniques for generating personalized content using generative artificial intelligence (AI) are provided. In an example method, a processing device including a personalization module receives an indication that a user interacted with content displayed on a web page. The personalization module receives a set of attributes, comprising information about the user and information about the content, and information about a segment to which the user belongs. The processing module then determines a tuning parameter, wherein the tuning parameter controls the randomness of the output of the generative AI model. The personalization module next inputs to the generative AI model the tuning parameter and a prompt comprising the set of attributes and the information about the segment and subsequently receives personalized content responsive to the tuning parameter and the prompt. The personalization module can then display the personalized content in a dynamic content field associated with the content.
Document ID
US 20250078323 A1
Date Published
2025-03-06
Abstract
Systems and methods for generating geolocation-based images for a target object are provided. A geolocation module receives a set of geolocations associated with a geographic region of interest. Each geolocation of the set of geolocations is mapped to context data associated with the geolocation. A prompt generation module generates multiple prompts based on the set of geolocations and the context data. The prompt generation module comprises a first generative artificial intelligence (AI) model. An image generation module generates multiple synthetic images based on the multiple prompts. The image generation module comprises a second generative AI model. Each synthetic image depicts the target object in a background generated based on a prompt.
Inquires
Any inquiry concerning this communication should be directed to NICHOLAS AUGUSTINE at telephone number (571)270-1056.
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.
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/NICHOLAS AUGUSTINE/Primary Examiner, Art Unit 2178 November 14, 2025