DETAILED ACTION
A. This action is in response to the following communications: Request for Continued Examination filed 08/14/2026.
B. Claims 1-20 remains pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/14/2026 has been entered.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Parasnis, Abhay et al. (US Pub. 2024/0265274 A1), herein referred to as “Parasnis” in view of Shaviv, Guy et al. (US Pub. 2018/0211444 A1), herein referred to as “Shaviv”.
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)).
Parasnis does not go into specific function about how the presentation data structure stores the composite image data; however in the same field of endeavor Shaviv teaches wherein the presentation data structure (item display engine 270) stores the composite image data, the composite image data comprising a plurality of image elements or text elements of the composite image data and wherein the presentation data structure is associated with presentation logic (overlay engine 250) including instructions for mapping the composite image data to one or more portions of a corresponding item listing interface; based on the composite image data instruction, accessing updated composite image data by using the presentation logic associated with the presentation data structure to retrieve or rotate at least one image element or text element from the plurality of image elements or text elements of the composite image data; and communicating the updated composite image data to cause display of the updated composite image data on the item listing system client (par. 24 the catalog engine stores ID of image information that is passed to item display engine where the image is generated or retrieved from a database in which is then passed to overlay engine where the image is composited to final presentation, wherein the image at this stage can be manipulated (e.g. placement over one or more frames, resizing, rotating). As illustrated, the interactive overlay system 150 comprises a catalog engine 260 and a item display engine 270. The catalog engine 260 is configured to receive requests for an item image from the overlay engine in the interactive overlay application 114. The item can be identified in the request via a unique identifier (e.g., a stock keeping unit (SKU)) that is unique to the item in the network 104. The catalog engine 260 can identify the item using the identifier and transmit the identifier to the item display engine for image generation. The item display engine 270 is configured to generate an item image of the item specified in the request. In some example embodiments, the item image is a 2D image of the item. The item display engine can retrieve the 2D image of the item from the database 126 and transmit it to the overlay engine 250 for further processing (e.g., placement over the one or more frames, resizing of the 2D image).
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Specification figure 1F shows user interface with composite images of listing (item for sale) composited into a room.
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Fig. 5A-B shows similar user interface which displays items for sale fig. 5A and then a composite image of item for sale into composited room image.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Shaviv into Parasnis because Shaviv suggests in paragraph 2, increasingly, users are browsing for items, such as couches, chairs, and tables, on Internet websites. Some Internet websites display pictures of the items with descriptions specifying the items' attributes, such as size and color. While Internet websites can be useful for perusing different items, users are forced to make selections without inspecting the items in person. Some users may avoid purchasing items without inspecting the items in person. Thus, the Internet websites may lose traffic as those users opt for brick-and-mortar stores to make their in-person inspection and item selections.
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). Parasnis does not specifically mention interface-specific mapping; however Shaviv teaches wherein the presentation logic is configured to generate an interface-specific mapping of the composite image data based on item listing interface data associated with the corresponding item listing interface (par. 24 and 35 mapping item listing to composite display of images and text elements along with graphics (e.g. arrows).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Shaviv into Parasnis because Shaviv suggests in paragraph 2, increasingly, users are browsing for items, such as couches, chairs, and tables, on Internet websites. Some Internet websites display pictures of the items with descriptions specifying the items' attributes, such as size and color. While Internet websites can be useful for perusing different items, users are forced to make selections without inspecting the items in person. Some users may avoid purchasing items without inspecting the items in person. Thus, the Internet websites may lose traffic as those users opt for brick-and-mortar stores to make their in-person inspection and item selections.
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).
Shaviv teaches wherein accessing the updated composite image data comprises, based on the composite image data instruction, using the presentation logic associated with the presentation data structure to update the mapping of the composite image data by replacing a first image element or text element mapped to a first portion of the corresponding item listing interface with a second image element or text element from the plurality of image elements or text elements of the composite image data (par. 24, 29 and 35 The item display engine 270 can then identify the item, identify a 3D model of the item, and manipulate (e.g., rotate, resize) the 3D model per the metadata. In some example embodiments, a 2D render of the manipulated 3D model is then sent to the interactive overlay application for positioning over frames from the live video feed. In this way, image data for the item (e.g., high resolution item image data, 3D model data) need not be stored in the package file, thereby making manipulation of the composite image faster and less computationally expensive.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Shaviv into Parasnis because Shaviv suggests in paragraph 2, increasingly, users are browsing for items, such as couches, chairs, and tables, on Internet websites. Some Internet websites display pictures of the items with descriptions specifying the items' attributes, such as size and color. While Internet websites can be useful for perusing different items, users are forced to make selections without inspecting the items in person. Some users may avoid purchasing items without inspecting the items in person. Thus, the Internet websites may lose traffic as those users opt for brick-and-mortar stores to make their in-person inspection and item selections.
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)).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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 September 2, 2026
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