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 Office Action is in response to the application filed on 08/21/2024.
The IDS filed on 08/26/2025 is considered and entered into the application file.
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)(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.
Claims 1-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cheng et al (US 20250117998 A1).
Cheng et al (“Cheng”) is directed to Personalized Branding With Prompt Adaptation In Large Language Models And Visual Language Models.
As per claim 1, Cheng discloses a method (see a flowchart of Figs. 2A, 2B, 4 and/or 5) implemented by one or more processors, the method comprising:
receiving user input associated with a user of a client device; [0003] An example method implemented in a data processing system includes receiving a natural language prompt input by a user of a design application
processing, using a generative model, a generative model input based upon the user input to generate a first generative model output that comprises a first set of items, wherein each item of the first set of items is associated with a corresponding prompt for subsequent processing by the generative model ([0003] analyzing the natural language prompt using a first language model trained to output a prediction whether the user intended to generate personalized content using a brand kit comprising a set of electronic assets providing example of a visual identity of a brand associated with the user; responsive to the first language model outputting a prediction that the user intended to generate personalized content using a brand kit);
causing the first set of items to be visually rendered at the client device using a first set of GUI elements; [0054] FIG. 3A shows an example of the user interface 305 which includes a query pane 315 and a results pane 335. The query pane 315 includes a prompt field in which the user can enter a natural language prompt describing a design that the user would like to have generated automatically. The prompt can include a detailed description of the colors, fonts, images, content type, and/or other details of the design to be generated.
in response to receiving a user selection of a GUI element corresponding to an item of the first set of items, processing, using the generative model, the prompt associated with the selected item to generate second generative model output that comprises a second set of items; ([0055] FIG. 3B shows an example of the user interface 305 in which the user has added an image to the natural language query. The prompt construction layer 140 can provide these sample images as an input to one or more of the generative models. In non-limiting example, the sample images may be included in the layout of the design generated by the layout generation model 128, and the palette generation model 134 extracts color information to add to the color palette from the sample images in some implementations).
causing the second set of items to be visually rendered at the client device using a second set of GUI elements ([0056] FIG. 3C shows an example of the user interface 305 in which the user has input a query that expressly indicates that the user would like to create a brand kit. However, as discussed in the preceding examples, the prompt construction layer 140 provides the natural language query input by the user to the intent determination model 122 to obtain a prediction whether the user intends to create a new brand kit and/or apply a brand kit to content generated in response to the natural language query); and
determining an update for at least one prompt associated with the first set of items based upon a user interaction with the second set of GUI elements ([0058] FIG. 3G provides an example of the user interface 305 of a design being presented that has been generated in response to the natural language prompt which includes a sample image).
As per claim 2, Cheng further discloses that the method of claim 1, wherein the method further comprises:
determining an updated prompt based upon the determined update ([0057] FIG. 3Dthathe that the user can enter a natural language prompt which describes the brand for which the brand kit is being created. The natural language prompt can describe a preferred style, preferred colors, and/or other features of the brand kit. The brand kit pane 325 shows the color palette associated with the brand kit); and
processing, using the generative model, the updated prompt to generate third generative model output ([0057] FIG. 3E shows additional aspects of the brand kit pane 325 which provides options for selecting fonts associated with the brand kit. The user may select or otherwise activate the save button to cause the brand kit information to be added to the design content datastore 192 for new brand kits or update the brand kit information in the design content datastore 192).
As per claim 3, Cheng further discloses that the method of claim 2, wherein the method further comprises: causing the third generative model output to be visually rendered at the client device using a third set of GUI elements [0054] FIG. 3A shows an example of the user interface 305 which includes a query pane 315 and a results pane 335. …The results pane 335 shows examples of generated content that includes multiple variations of generated content generated by the generative models of the AI services 120. [0058] FIG. 3G provides an example of the user interface 305 of a design being presented that has been generated in response to the natural language prompt which includes a sample image. In this example, the sample image provided by the user has been integrated into the design).
As per claim 4, Cheng further discloses that the method of claim 3, wherein processing, using the generative model, the updated prompt to generate the third generative model output is in response to receiving a user selection of a GUI element associated with the updated prompt. ([0052] The process 270 includes operation 284 of updating the brand kit information in the design content datastore 192. Any modifications made to the brand kit by the user are made to the brand kit information in the design content datastore 192 and the process 270 returns to operation 278 in which the updated brand kit information is stored in the design content datastore 192. [0057] FIG. 3E show additional aspects of the brand kit pane 325 which provides options for selecting fonts associated with the brand kit. The user may select or otherwise activate the save button to cause the brand kit information to be added to the design content datastore 192 for new brand kits or update the brand kit information in the design content datastore 192).
As per claim 5, Cheng further discloses that the method of claim 2, wherein the prompt to be updated is different to the prompt corresponding to the selected GUI element of the first set of GUI elements ([0028] The palette generation model 134 is a generative model trained to generate a color palette comprising a set of colors that may be included in a brand kit. The palette generation model 134 selects colors based on the natural language prompt and additional information associated with the user, as discussed above. The color palette output by the palette generation model 134 can be provided as an input to the image generation model 126 along with an image to be customized according to the color palette, and the image generation model 126 outputs a customized version of the image in which the image has been updated according to the color palette. Alsos see [0032, 0043, 0057]).
As per claim 6, Cheng further discloses that the method of claim 2, wherein the prompt to be updated is the same prompt corresponding to the selected GUI element of the first set of GUI elements ([0028] The color palette output by the palette generation model 134 can be provided as an input to the image generation model 126 along with an image to be customized according to the color palette, and the image generation model 126 outputs a customized version of the image in which the image has been updated according to the color palette).
As per claim 7, Cheng further discloses that the method of claim 1, wherein the method further comprises: determining one or more constraints based upon the user interaction with the second set of GUI elements; and wherein determining an update for at least one prompt is based upon the determined one or more constraints ([0033] The fact checking unit 178 analyzes factual statements made in in the intermediate content and/or personalized design content automatically generated according to the techniques herein. The fact check and safety check unit 178 implements an automated fact check that verifies assertions made in the generated content. If the fact check and safety check unit 178 identifies any assertions that cannot be verified or are predicted to be factually inaccurate, the fact check and safety check unit 178 identifies these assertions and can prompt the user to update the design to remove or correct these assertions).
As per claim 8, Cheng further discloses that the method of claim 1, wherein processing, using the generative model, the prompt associated with selected item to the generate second generative model output (Processor(s) of a system can: receive user input; process, using a generative model (GM), a GM input based upon the user input to generate a first GM output that includes a first set of items associated with a corresponding prompt for subsequent processing by the GM (Abstract); comprises:
invoking an external application based upon processing the prompt using the generative model ([0023] The language model 124 is a machine learning model trained to generate textual content in response to natural language prompts input by a user via the native application 114 or via the browser application 112. [ 0036] the browser application 112 can be used for accessing and viewing web-based content provided by the application services platform 110. The application services platform 110 supports both the native application 114 and a web application 190 in some implementations, and the users may choose which approach best suits their needs),
receiving, from the external application, one or more responses to the invocation ([0090] The prompt construction layer 140 receives natural language prompts input by users of the design application implemented by the native application 114 and/or the web application 190. and
generating, by the generative model, the second generative model output based upon the one or more responses ([0003] responsive to the first language model outputting a prediction that the user intended to generate personalized content using a brand kit, obtaining a brand kit associated with the user).
As per claim 9, Cheng further discloses that the method of claim 1, wherein an item of the first set of items is associated with a plurality of sub-prompts ([0032] The moderation services 168 performs several types of checks on the natural language prompts entered by the user in the native application 114 or the web application 190 and/or content generated by the language model 124 and/or other models of the AI services 120. [0063] The process 400 includes an operation 408 of generating intermediate content based on the natural language prompt by generating a plurality of first model-specific prompts. Each model-specific prompt of the first model-specific prompts is provided as an input to a respective generative model of a plurality of generative models associated with the designer application to cause the respective generative model to generate at least one aspect of the intermediate content); and
wherein determining an update for at least one prompt comprises determining an update for at least one sub-prompt of the plurality of sub-prompts ([0032] The dynamic list check unit 176 provides a dynamic list that can be quickly updated by administrators to add additional prohibited words and/or phrases. The dynamic list may be updated to address problems such as words or phrases becoming offensive that were not previously deemed to be offensive).
As per claim 10, Cheng further discloses that the method of claim 1, wherein each item of the second set of items is associated with a corresponding additional prompt for subsequent processing by the generative model ([0020] The content sources 194 provide sample content that can be included in the designs created using the design application. The sample content can include imagery, illustrations, samples of textual content, samples of layouts of various types of designs, and/or other content that may be included in a design. The sample imagery can also be provided as an input with a natural language prompt to provide additional context to the prompt construction layer 140 for generating content for a design).
As per claim 11, Cheng further discloses that the method of claim 1, wherein the method further comprises: determining an update for at least one GUI element of the first set of GUI elements based upon the user interaction with the second set of GUI elements ([0057] FIG. 3E shows additional aspects of the brand kit pane 325 which provides options for selecting fonts associated with the brand kit. The user may select or otherwise activate the save button to cause the brand kit information to be added to the design content datastore 192 for new brand kits or update the brand kit information in the design content datastore 192. FIG. 3F shows an example of the brand kit pane 325 providing a summary of the brand kit).
As per claim 12, Cheng further discloses that the method of claim 1, wherein at least one GUI element is selected by the generative model ([0027] The font selection model 132 is a language model that provides font recommendations that may be included in a brand kit. The fonts can be selected from among a set of fonts supported by the design application provided by the application services platform 110. The fonts can be selected based on the natural language prompt and additional information associated with the user, as discussed above, also see [0026]).
As per claim 13, Cheng further discloses that the method of claim 1, wherein the first set of GUI elements comprises a selectable tile for each item of the first set of items ([0054] FIG. 3A shows an example of the user interface 305 which includes a query pane 315 and a results pane 335. The query pane 315 includes a prompt field in which the user can enter a natural language prompt describing a design that the user would like to have generated automatically. The prompt can include a detailed description of the colors, fonts, images, content type, and/or other details of the design to be generated. The user can also include one or more samples images that provide context to the generative models that create one or more aspects of the design. The user can click on or otherwise activate the “add image” button to cause the design application to present a file selector interface that enables the user to select one or more preexisting image files).
As per claim 14, Cheng further discloses that the method of claim 13, wherein a selectable tile comprises a thumbnail image representative of the corresponding item ([0015] A personalized brand kit includes preferred fonts, color palettes, logos, images, layout templates, and/or other assets that representative of the visual identity of the brand).
As per claim 15, Cheng further discloses that the method of claim 13, wherein a selectable tile comprises a text caption representative of the corresponding item. ([0057] FIGS. 3D-3F shows an example of a brand kit pane 325 that is used for creating a new brand kit, modifying an existing brand kit, and/or viewing the details of an existing brand kit. FIG. 3E shows additional aspects of the brand kit pane 325 which provides options for selecting fonts associated with the brand kit. See for example text caption in Fig. 3E)
As per claim 16, Cheng further discloses that the method of claim 1, wherein the first set of GUI elements are arranged in a grid layout ([0010] Ideas or GUI elements in FIGS. 3A-3C are arranged in a grid layout).
As per claim 17, Cheng further discloses that the method of claim 1, wherein the generative model is based upon a large language model ([0015] Systems and methods for automatically generating and applying automatic branding are described herein. These techniques utilize prompt adaptation for prompts to large language models (LLM) and visual language models to provide a technical solution to the technical problem of automatically creating content and customizing the content using a personalized brand kit to create personalized content).
As per claim 18, Cheng further discloses a system (e.g., a data processing system of Fig. 1) comprising: one or more processors; and a memory storing computer readable instructions that, when executed by the one or more processors, causes the one or more processors to be operable as recited in claim 1. Thus, claim 18 is rejected under similar citations as given to method claim 1.
As per claim 19, Cheng further discloses a non-transitory computer-readable storage medium ([0014] FIG. 7 is a block diagram showing components of an example machine configured to read instructions from a machine-readable medium and perform any of the features described herein) storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations as recited in claim 1. Thus, claim 19 is rejected under similar citations as given to method claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250245258 A1 discloses presenting a natural language response to a user query for a disputed transaction involves gathering transaction details of the user, sending the transaction details and the query to a generative model, receiving a natural language response from the generative model, and presenting the natural language response to the user in a user interface. The transaction details comprise the transaction and a transaction history of the user. The generative model is trained to present a natural language response to the user about the transaction. The natural language response comprises a datum of the transaction present. Further user interaction involves receiving an additional query about the transaction; and presenting an additional natural language response to the user that contain an additional datum of the transaction. Flagging a user for a disputed transaction involves determining is based on a user's natural language answer, transaction history, and a disputed transaction threshold (Abstract).
US 20240354503 A1 discloses embodiments of the described technologies determine input signals, where the input signals are specific to a user of the user network. The input signals are input to a set of artificial intelligence (AI) models. In response to the input signals, the first set of AI models output a first set of AI-derived signals relating to the input signals. At least one prompt template is applied to the first set of AI-derived signals to create at least one prompt. The at least one prompt is input to at least one generative AI model. In response to the at least one prompt, the at least one generative AI model outputs at least one thought starter machine-generated by the at least one generative AI model. At least one thought starter includes digital content configured to be distributed via the user (Abstract).
5 Any inquiry concerning this communication or earlier communications from the examiner should be directed to TADESSE HAILU whose telephone number is (571)272-4051; and the email address is Tadesse.hailu@USPTO.GOV. The examiner can normally be reached Monday- Friday 9:30-5:30 (Eastern time).
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, Bashore, William L. can be reached (571) 272-4088. 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.
/TADESSE HAILU/ Primary Examiner, Art Unit 2174
Prosecution Insights