DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. 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.
Withdrawal from Issue
3. A Withdrawal from Issue for the present Application was mailed 9 September 2026, and prosecution was reopened.
Information Disclosure Statement
4. The Information Disclosure Statement filed 15 July 2026 has been fully considered by Examiner. An annotated copy is included with the present Office Action.
Claim Rejections - 35 USC § 102
5. 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.
6. Claims 1, 11 and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Banerjee (WO-2025/207352-A1).
Regarding claim 1: Banerjee discloses a data processing system (fig 1 and fig 4 of Banerjee) comprising: a processor (fig 4(404) and paragraph [102] of Banerjee); and a memory storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations (fig 4(402), paragraph [102], and paragraph [125] of Banerjee, ‘any logic or application described herein that comprises software or code can be embodied in any non-transitory computer-readable medium or memory for use by or in connection with an instruction execution system such as a processing component in a computer system’) of: providing an image generation system configured to operate in a first generation mode and a second generation mode, the first generation mode providing requested image contents based on prestored image assets from an image asset repository that organizes and stores image assets (fig 1(132), image retrieval [first generation mode]; and paragraph [117] of Banerjee, retrieve an image from image index 140.; Also note fig. 1 database 142 for in image retrieval), and the second generation mode (fig 1(134) of Banerjee, image generation) generating the requested image content by an artificial intelligence model (paragraph [27] of Banerjee, ‘In various examples, latent diffusion models and/or other image generation models may be used to generate images in response to natural language input data, as described herein.’); receiving a first textual prompt from a client device requesting a first image content (paragraph [115] of Banerjee, Process 600 may begin at action 602, at which first natural language input data may be received. For example, an input query may be received, which may be a user request and/or a natural language processing system response to the user request; paragraph [32] of Banerjee, As used in, natural language input data may comprise audio data (e.g., representing a user request or command), text data [first textual prompt], and/or other representation data representing natural language for input into a natural language processing system; paragraph [14] of Banerjee, The query understanding an enrichment component may use machine learning-based models and/or techniques to evaluate natural language input data in order to select between image retrieval processing, image generation processing, neither, or both.); analyzing the first textual prompt to determine whether the image generation system includes prestored image in the image asset repository that satisfies a threshold condition for providing the first image content corresponding to the first textual prompt by returning a prestored image asset or a modified prestored image asset from the image asset repository (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132. Similarly, the generation classifier 124 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are not appropriate for image generation 134.; see paragraph [55] of Banerjee, Input queries 110 that include named entities may be labeled as including an entity mention (e.g., with entity indicator data) and may be subjected to an entity image retrieval workflow that is part of image retrieval 132. Reviewer notes: analyzing is interpreted as deciding if the textual prompt has an entity mentioned, determining whether the image generation system includes prestored image in the image asset repository that satisfies a threshold condition is interpreted as a name entity being subject to retrieval since non-named entity [doesn’t satisfy the threshold] is not subject to retrieval); depending on the result of the analyzing, selectively control the image generation system to operate in one of the first generation mode and the second generation mode (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132. Similarly, the generation classifier 124 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are not appropriate for image generation 134.; paragraph [63] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), retrieval-suitable (by retrieval classifier 118) may be sent to query encoder 130.; paragraph [66] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), and generation-suitable (by generation classifier 124) may be sent to image generation 134); operating the image generating system in the first generation mode to provide the first image content based on the first textual prompt by returning a prestored image asset or a modified prestored image asset corresponding to the first textual prompt from the image asset repository, in response to determining that the image generating system includes prestored image content in the image asset repository that satisfies the threshold condition for providing the first image content corresponding to the first textual prompt (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132; paragraph [63] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), retrieval-suitable (by retrieval classifier 118) may be sent to query encoder 130.; paragraph [117] of Banerjee, In the example of FIG.6, image retrieval 132 may be selected. This may be because there is an entity mentioned in the first natural language input data and/or because the first natural language input data is classified as non-creative (and/or appropriate for retrieval). In various examples, if an entity is detected in the first natural language input data by entity classifier 120, the query understanding and enrichment component 112 may use the entity-based retrieval workflow to retrieve an image related to the entity from graph/entity image index 142.); operating the image generation system in the second generation mode to generate the first content corresponding to the first textual prompt by the artificial intelligence model, in response to determining that the image generation system does not include prestored image content in the image asset repository that satisfies the threshold condition for providing the first image content corresponding to the first textual prompt (paragraph [66] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), and generation-suitable (by generation classifier 124) may be sent to image generation 134. In various examples, image generation 134 may be a generative model trained to generate image data from text prompts; paragraph [117] of Banerjee, It should be noted that for other natural language inputs, the image generation 134 task may be selected for image generation (either with image retrieval or alone).); and provide the first image content to the client device (paragraph [68] of Banerjee, The top K images 150 that are ranked and deemed permissible for output may be sent to device 102 (and/or any number of other devices) for display. In various examples, depending on the particular user experience desired, a user may have the option of having either retrieved images, generated images, or both displayed for a given input query 110).
Regarding claim 11: Banerjee discloses a method implemented in a data processing system for operating an image generation system (fig 1, fig 4, and paragraphs [102]-[103] of Banerjee), the method comprising: providing an image generation system configured to operate in a first generation mode and a second generation mode, the first generation mode providing requested image contents based on prestored image assets from an image asset repository that organizes and stores image assets (fig 1(132), image retrieval [first generation mode]; and paragraph [117] of Banerjee, retrieve an image from image index 140.; Also note fig. 1 database 142 for in image retrieval), and the second generation mode (fig 1(134) of Banerjee, image generation) generating the requested image contents by an artificial intelligence model (paragraph [27] of Banerjee, ‘In various examples, latent diffusion models and/or other image generation models may be used to generate images in response to natural language input data, as described herein.’); receiving a first textual prompt from a client device requesting first image content (paragraph [115] of Banerjee, Process 600 may begin at action 602, at which first natural language input data may be received. For example, an input query may be received, which may be a user request and/or a natural language processing system response to the user request; paragraph [32] of Banerjee, As used in, natural language input data may comprise audio data (e.g., representing a user request or command), text data [first textual prompt], and/or other representation data representing natural language for input into a natural language processing system; paragraph [14] of Banerjee, The query understanding an enrichment component may use machine learning-based models and/or techniques to evaluate natural language input data in order to select between image retrieval processing, image generation processing, neither, or both.); analyzing the first textual prompt to determine whether the image generation system includes prestored image content in the image asset repository that satisfies a threshold condition for providing the first image content corresponding to the first textual prompt by returning a prestored image asset or a modified prestored image asset from the image asset repository (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132. Similarly, the generation classifier 124 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are not appropriate for image generation 134.; see paragraph [55] of Banerjee, Input queries 110 that include named entities may be labeled as including an entity mention (e.g., with entity indicator data) and may be subjected to an entity image retrieval workflow that is part of image retrieval 132. Reviewer notes: analyzing is interpreted as deciding if the textual prompt has an entity mentioned, determining whether the image generation system includes prestored image in the image asset repository that satisfies a threshold condition is interpreted as a name entity being subject to retrieval since non-named entity [doesn’t satisfy the threshold] is not subject to retrieval); depending on a result of the analyzing, selectively controlling the image generation system to operate in one of the first generation mode and the second generation mode (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132. Similarly, the generation classifier 124 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are not appropriate for image generation 134.; paragraph [63] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), retrieval-suitable (by retrieval classifier 118) may be sent to query encoder 130.; paragraph [66] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), and generation-suitable (by generation classifier 124) may be sent to image generation 134); operating the image generation system in the first generation mode to provide the first image content based on the first textual prompt by returning a prestored image asset or a modified prestored image asset corresponding to the first textual prompt from the image asset repository, in response to determining that the image generation system includes prestored image content in the image asset repository that satisfies the threshold condition for providing the first image content corresponding to the first textual prompt (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132; paragraph [63] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), retrieval-suitable (by retrieval classifier 118) may be sent to query encoder 130.; paragraph [117] of Banerjee, In the example of FIG.6, image retrieval 132 may be selected. This may be because there is an entity mentioned in the first natural language input data and/or because the first natural language input data is classified as non-creative (and/or appropriate for retrieval). In various examples, if an entity is detected in the first natural language input data by entity classifier 120, the query understanding and enrichment component 112 may use the entity-based retrieval workflow to retrieve an image related to the entity from graph/entity image index 142.); and operating the image generation system in the second generation mode to generate the first image content corresponding to the first textual prompt by the artificial intelligence model, in response to determining that the image generation system does not include prestored image content in the image asset repository that satisfies the threshold condition for providing the first image content corresponding to the first textual prompt (paragraph [66] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), and generation-suitable (by generation classifier 124) may be sent to image generation 134. In various examples, image generation 134 may be a generative model trained to generate image data from text prompts; paragraph [117] of Banerjee, It should be noted that for other natural language inputs, the image generation 134 task may be selected for image generation (either with image retrieval or alone).); and providing the first image content to the client device (paragraph [68] of Banerjee, The top K images 150 that are ranked and deemed permissible for output may be sent to device 102 (and/or any number of other devices) for display. In various examples, depending on the particular user experience desired, a user may have the option of having either retrieved images, generated images, or both displayed for a given input query 110).
Regarding claim 18: Banerjee discloses a data processing system (fig 1 and fig 4 of Banerjee) comprising: a processor (fig 4(404) and paragraph [102] of Banerjee); and a memory storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations (fig 4 (402), paragraph [102], and paragraph [125] of Banerjee, ‘any logic or application described herein that comprises software or code can be embodied in any non-transitory computer-readable medium or memory for use by or in connection with an instruction execution system such as a processing component in a computer system’) of: providing an image generation system comprising an image asset repository that stores and organizes prestored image assets, the image generation system being configured to provide requested image assets in response to prompts for the requested image assets, the image generation system being configured to operate in a first generation mode and a second generation mode, the first generation mode providing requested image assets based on the prestored image assets from the image asset repository without using an artificial intelligence model to generate the requested image assets (fig 1(132), image retrieval [first generation mode]; and paragraph [117] of Banerjee, retrieve an image from image index 140.; Also note fig. 1 database 142 for in image retrieval), and the second generation mode (fig 1(134) of Banerjee, image generation) generating the requested image assets using the artificial intelligence model (paragraph [27] of Banerjee, ‘In various examples, latent diffusion models and/or other image generation models may be used to generate images in response to natural language input data, as described herein.’); receiving a first textual prompt from a client device requesting first image content from the image generation system (paragraph [115] of Banerjee, Process 600 may begin at action 602, at which first natural language input data may be received. For example, an input query may be received, which may be a user request and/or a natural language processing system response to the user request; paragraph [32] of Banerjee, As used in, natural language input data may comprise audio data (e.g., representing a user request or command), text data [first textual prompt], and/or other representation data representing natural language for input into a natural language processing system; paragraph [14] of Banerjee, The query understanding an enrichment component may use machine learning-based models and/or techniques to evaluate natural language input data in order to select between image retrieval processing, image generation processing, neither, or both.); analyzing the first textual prompt to determine whether the image generation system includes prestored image assets in the image asset repository that satisfies the first textual prompt (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132. Similarly, the generation classifier 124 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are not appropriate for image generation 134.; see paragraph [55] of Banerjee, Input queries 110 that include named entities may be labeled as including an entity mention (e.g., with entity indicator data) and may be subjected to an entity image retrieval workflow that is part of image retrieval 132. Reviewer notes: analyzing is interpreted as deciding if the textual prompt has an entity mentioned, determining whether the image generation system includes prestored image in the image asset repository that satisfies a threshold condition is interpreted as a name entity being subject to retrieval since non-named entity [doesn’t satisfy the threshold] is not subject to retrieval); selectively controlling the image generation system to operate in one of the first generation mode and the second generation mode depending on a result of analyzing the first textual prompt (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132. Similarly, the generation classifier 124 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are not appropriate for image generation 134.; paragraph [63] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), retrieval-suitable (by retrieval classifier 118) may be sent to query encoder 130.; paragraph [66] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), and generation-suitable (by generation classifier 124) may be sent to image generation 134) by: operating the image generation system in the first generation mode to provide the first image content based on the first textual prompt based on the prestored image assets stored in the image asset repository in response to determining that the image generation system includes prestored image assets in the image asset repository that satisfy the first textual prompt (paragraph [60] of Banerjee, Accordingly, the retrieval classifier 118 may learn, during training, that input queries 110 that are labeled as including entities (by entity classifier 120) are appropriate for image retrieval 132; paragraph [63] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), retrieval-suitable (by retrieval classifier 118) may be sent to query encoder 130.; paragraph [117] of Banerjee, In the example of FIG.6, image retrieval 132 may be selected. This may be because there is an entity mentioned in the first natural language input data and/or because the first natural language input data is classified as non-creative (and/or appropriate for retrieval). In various examples, if an entity is detected in the first natural language input data by entity classifier 120, the query understanding and enrichment component 112 may use the entity-based retrieval workflow to retrieve an image related to the entity from graph/entity image index 142.); and operating the image generation system in the second generation mode to generate the first image content using the artificial intelligence model in response to determining that the image generation system does not include prestored image assets in the image asset repository that satisfy the first textual prompt (paragraph [66] of Banerjee, As shown in FIG.1, input queries 110 that are labeled as non-discard (by discard classifier 114), permissible (by impermissible classifier 122), and generation-suitable (by generation classifier 124) may be sent to image generation 134. In various examples, image generation 134 may be a generative model trained to generate image data from text prompts; paragraph [117] of Banerjee, It should be noted that for other natural language inputs, the image generation 134 task may be selected for image generation (either with image retrieval or alone).); and providing the first image content to the client device (paragraph [68] of Banerjee, The top K images 150 that are ranked and deemed permissible for output may be sent to device 102 (and/or any number of other devices) for display. In various examples, depending on the particular user experience desired, a user may have the option of having either retrieved images, generated images, or both displayed for a given input query 110).
Allowable Subject Matter
7. Claims 2-10, 12-17, 19 and 20 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James A Thompson whose telephone number is (571)272-7441. The examiner can normally be reached M-F 8am-6pm.
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/JAMES A THOMPSON/Primary Examiner, Art Unit 2615
