H10762
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 .
DETAILED ACTION
The Action is responsive to the Amendments and Remarks filed on 2/4/2026. Claims 1-23 are pending claims. Claims 1, 12, and 23 are written in independent form.
Claim Objections
Claims 1, 4, 8, 11, 12, 19, 22, and 23 are objected to because of the following informalities:
Claims 1 and 12 appear to recite a typographical error by reciting “…or seeking images,;” because it ends with a comma and a semi-colon. The limitation is understood as intending to only end with a semi-colon by reciting “…or seeking images; [[images,;]]”
Claims 1 and 12 appear to recite a typographical error by reciting “…to generate a search result page.;” because it ends with a period and a semi-colon. The limitation is understood as intending to only end with a semi-colon by reciting “…to generate a search result page; [[page.;]]”.
Claims 4, 8, 11, 19 and 22 appear to recite a typographical errors by reciting previously presented limitations that still contain minor edit indicators such as underlining previously presented language and crossing through previously deleted language.
Claim 23 appears to recite a typographical error by reciting “dynamically generating…at least one webpage for each webpage generation strategy…and causing the at least one webpage corresponding to two or more webpage generation strategies to be concurrently viewed on the remote computing device” because “at least one webpage corresponding to two or more webpage generation strategies” was not previously recited. The limitation is understood as being intended to recite “causing [[the]] at least one webpage corresponding to two or more webpage generation strategies to be concurrently viewed on the remote computing device”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Independent Claims 1 and 12 contain the subject matter “the Internet search comprises, in parallel, (i) searching…and (ii) generating a new webpage…” which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.It is not clearly stated in the written description any Internet Search comprising, in parallel, searching existing webpages and generating a new webpage viewing of webpages, let alone concurrently viewing webpages corresponding to two or more webpage generation strategies, on the remote computing device.It is noted that while Applicant states in the Remarks dated 2/4/2026 that the “amendments find support, inter alia, in pars. 3-5 and 32-35 of the specification, the cited paragraphs merely recite that “The search result page can be used, in parallel, to dynamically generate, at 220, a new webpage responsive to the query 205 (using, for example, the workflow in diagram 300 of FIG. 3). This new webpage comprises a new form of webpage feature in a built-in AI copilot in which users can free chat and ask questions related to the content on the webpage. In addition, existing webpages can be searched to find content matching the query 205. Thereafter, the new webpage and the matching existing webpages can, at 225, be merged from which, at 230, a search result page can be generated” (Specification Para. [0032] & Fig. 2). Further, the specification recites “The query is modified, at 1530, by the LLM to reflect the determined intent which then results in a contextualized query. This contextualized query used to perform, at 1540, an Internet search (e.g., poll search engines, crawl the web, etc.) to receive content responsive to the contextualized query. The LLM using the received content responsive to the contextualized query dynamically generates, at 1550, at least one webpage responsive to the user-generated query.” (Specification Para. [0038] & Fig. 15)For purposes of compact prosecution, the claim limitation is being interpreted as “performing, using the contextualized query, an Internet search and receiving content responsive to the contextualized query the Internet search comprisesin parallel with searching existing webpages, generating a new webpage responsive to the query, and merging results from the searching existing webpages and the generating a new webpage to generate a search result page;” in light of Paragraph [0032] of the Specification and Figure 2.
Independent Claim 23 contains the subject matter “causing at least one webpage corresponding to two or more webpage generation strategies to be concurrently viewed on the remote computing device” which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.It is not clearly stated in the written description any concurrent viewing of webpages, let alone concurrently viewing webpages corresponding to two or more webpage generation strategies, on the remote computing device.It is noted that while Applicant states in the Remarks dated 2/4/2026 that the “amendments find support, inter alia, in pars. 3-5 and 32-35 of the specification, the cited paragraphs merely recite that “in some variations, multiple different page strategies can be used to populate a single webpage” (Specification Para. [0004]) and “With reference to diagrams 600, 700 of FIGs. 6 and 7, a first strategy can result in content such as illustrated in the user interface view 610 in FIG. 6, a second strategy can result in content as illustrated in the user interface view 710 in FIG. 7, and a third strategy can result in content as illustrated in the user interface view 720 in FIG. 7.” (Specification Para. [0033]) where “FIG. 6 is a first user interface view illustrating aspects of a webpage;” (Specification Para. [0019]) and “FIG. 7 is a second user interface view illustrating aspects of a webpage;” (Specification Para. [0020]).For purposes of compact prosecution, the claim limitation is being interpreted as “causing at least one webpage corresponding to two or more webpage generation strategies to be [[concurrently]] viewed on the remote computing device” in light of Paragraphs [0004], [0019]-[0020], and [0033] of the Specification.
Dependent Claims 2-11 and 13-22 inherit the deficiencies of their parent claims and are therefore being rejected based upon the same reason(s) stated for their parent claims.
Claim Rejections - 35 USC § 103
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) 1-5, 7-16, and 18-23 are rejected under 35 U.S.C. 103 as being unpatentable over Chrysanthou (U.S. Pre-Grant Publication No. 2024/0176839) and further in view of International Publication Number WO 2025/042852A1, hereinafter referred to as Barraclough and Ciminelli et al. (U.S. Pre-Grant Publication No. 2023/0409298, hereinafter referred to as Ciminelli).
Regarding Claim 1:
Chrysanthou teaches a computer-implemented method comprising:
Receiving a user-generated query comprising a natural language prompt;
Chrysanthou teaches “the user inputs a description using their user computing device 104, as described herein” (Para. [0112]) where the user input description is “of a business or organization” where the user input description is a query/request to dynamically generate a website (Paras. [0042]-[0043]).Chrysanthou further teaches “At step 300, the user inputs a description using their user computing device 104, as described herein. At step 302, the NLP engine 206 processes the description text string to extract key terms, as described herein.” (Para. [0112]) thereby teaching the user-generated query comprising a natural language prompt requiring natural language processing by NLP engine 206.
Determining an intent of the query,
Chrysanthou teaches determining “suggested key term(s)” related to the user input description (Para. [0113]) thereby teaching determining an intent of the query.
Modifying the query based on the determined intent to result in a contextualized query,
Chrysanthou teaches creating a contextualized query in the form of an “approved key term list” by modifying the user inputs description 300 to only include key terms (Fig. 3 & Paras. [0112]-[0114])
the contextualized query specifying (i) one or more data sources to be searched and (ii) one or more content types to be obtained;
Performing, using the contextualized query, an Internet search and receiving content responsive to the contextualized query,
Crysanthou teaches “scanning the Internet using an approved key term list” (Fig. 4 & Para. [0115]) thereby teaching Performing an Internet search using the contextualized query. Crysanthou further teaches “the Internet scanning engine 208 performs keyword-based searching against text, copy, content, headers, descriptors, and tags of third-party websites to identify websites relevant to the user's business or organization” (Para. [0115]) thereby teaching receiving content responsive to the key term list (contextualized query).
Crysanthou further teaches “the content generation engine 214 searches the Internet to identify multimedia content and text content which is similar to, but not identical to, the multimedia content and text content found on the relevant third-party websites identified by the Internet scanning engine 208, as described herein.” (Para. [0123]).
the Internet search comprises searching existing webpages for matching content,
Chrysanthou teaches “the Internet scanning engine 208 performs keyword-based searching against text, copy, content, headers, descriptors, and tags of third-party websites to identify websites relevant to the user's business or organization. In addition, the Internet scanning engine 208 searches source code, such as HTML code and metadata, of third-party websites to identify relevant websites matching the approved key term list, or including key terms in the approved key term list, as described herein.” (Para. [0115]).
generating a new webpage responsive to the query, and
Chrysanthou teaches “the template generation engine 210 can generate a website template based on the aggregate weighted template information from the relevant third-party websites” (Para. [0086]) and “The template generation engine 210 can select a pre-defined template from the template database 228 which most closely matches the aggregate weighted template information. In this embodiment, the template generation engine 210 can edit or modify the template to better match the aggregate weighted template information more closely” (Para. [0087]).
merging results from the searching existing webpages and the generating a new webpage to generate a search result page; and
Chrysanthou teaches “the template generation engine 210 can generate multiple website templates, and provide each of these in a side-by-side on the user computing device 104 for the user to review and select” (Para. [0094]) thereby teaching merging the combined results from the template generation engine from the third-part websites and the pre-defined template database to generate a search results page.
Dynamically generating, derived from the received content responsive to the contextualized query, at least one webpage responsive to the user-generated query,
Chrysanthou teaches “the content generation engine 214 creates a website using the identified multimedia content, text content, and/or social media content” (Para. [0125]) where at least the multimedia content and text content are derived based on a being relevant to “content found on the relevant third-part websites identified by the Internet scanning engine” (Paras. [0121]-[0123]).
The LLM generating a page outline defining content sections and a writing plan, and
Chrysanthou teaches “a template generation engine 210 analyzes each relevant third-party website for various template information” (Para. [0076]) where “The structure and layout of elements on the relevant third-party website, such as, for example, the location of the fold, use of frames, header and footer locations, menu and menu bar locations, social media link locations, widget locations, and the like.” (Para. [0083]) and “The template generation engine 210 creates a website template taking into account at least one of: the types of pages contained on the identified websites, the types of content included on the identified websites, the context of the content on the identified websites, what the content on the identified websites depict, the color schemes of the identified websites, the font and font sizes utilized on the identified websites, the structure and layout of elements on the identified websites, and the social media information utilized on the identified websites.” (Para. [0086]).Therefore, Chyrsanthou teaches generating a page outlining content sections and a writing plan via structure and layout of elements, types of content, context, what the content depicts, font and font size, etc.
generating content on a section-by-section basis according to the writing plan.
Chrysanthou teaches “the approved website template is processed by a content generation engine 214 to create a website. In an embodiment, the content generation engine 214 can generate various content for the website, such as, for example, images, videos, text, and copy.”
Crysanthou explicitly teaches all of the elements of the claimed invention as recited above except:
Determining, using a large language model (LLM), an intent of the query;
The prompt being classified by the LLM into one of a plurality of intent categories comprising seeking information, seeking products, seeking a website, or seeking images;
Modifying, by the LLM, the query;
Performing user interface generation steps in parallel.
Dynamically generating, by the LLM, at least one webpage;
However, in the related field of endeavor of automated website content generation, Barraclough in combination with Chrysanthou teaches:
Determining, using a large language model (LLM), an intent of the query;
Barraclough teaches “Natural Language Processing (NLP) techniques are employed to understand the context and intent behind user inputs and retrieved information.” (Para. [00168]) where LLMs are known NLP techniques.
The prompt being classified by the LLM into one of a plurality of intent categories comprising seeking information, seeking products, seeking a website, or seeking images;
Barraclough teaches “techniques are employed to understand the context and intent behind user inputs and retrieved information.” (Para. [00169]) and “The logic sends user context to pre-trained large language models (LLMs) such as OpenAI's GPT-3.5 or GPT4 111, Anthropic's Claude 109, Azure Al 110 and Google Vertex Al 112 for generating text, code and assets. One or more such LLMs can be used for different purposes and/or disparately managed (e.g., different ones may produce better results in terms of generating certain types of texts, video, and/or graphics) and in some cases the same purposes with different results useful for the logic 114 to assess and compare to the user’s input and media-based assets” (Para. [0047]).Barraclough further teaches “the collected data is injected into prompts for Large Language Models to steer them into producing website content, page layouts, and component configurations matching the user's identity and business niche. For example, a wedding photography business has different site needs compared to a sports photography brand. The onboarding context focuses the generated output to align with the user's specific goals.” (Para. [0067]) and “The existing context is sufficient for the Large Language Models to generate text, components, and layouts tailored to the user's needs for the specified page type” (Para. [0068]).
Modifying, by the LLM, the query;
Barraclough teaches “The most relevant retrieved information is used to enhance the prompts sent to the large language models (LLMs) described in Example Embodiment 4. b. This creates more context-rich, tailored prompts that guide the LLMs to generate highly relevant content.” (Para. [00168]) thereby teaching as part of the LLM system, modifying the query to enhance prompts.
Dynamically generating, by the LLM, at least one webpage;
Barraclough teaches “dynamically selecting and invoking Large Language Models (LLMs) to generate content” for webpages (Para. [0059]).
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Barraclough and Chrysanthou at the time that the claimed invention was effectively filed, to have combined the use of one or more LLMs in generating a website, as taught by Barraclough, with the systems and methods for automatically generating websites using artificial intelligence, as taught by Crysanthou.
One would have been motivated to make such combination because Barraclough teaches the benefits of an LLM as “An LLM is trained on large amounts of data (e.g., millions of gigabytes of text and/or metadata) from the Internet or from other large data sources, to recognize and generate phrases, interpret languages, discern contexts being used in communications, and understand how words, sentences, intonations and characters work together.” (Para. [0045]) where “one or more such LLMs can be used for different purposes and/or disparately managed (e.g., different ones may produce better results in terms of generating certain types of texts, video, and/or graphics) and in some cases the same purposes with different results useful for the logic 114 to assess and compare to the user’s input and media-based assets and, in certain circumstances, provide as feedback to the user.” (Para. [0047]). It would have been obvious to a person having ordinary skill in the art that using different LLMs to provide different results would create a more versatile system and increase the likelihood of producing user-satisfactory results when generating the website.It is further noted that Crysanthou explicitly recites “using artificial intelligence” without providing further clarity on the types of AI systems being used and Barraclough teaches LLMs as a type of AI program: “As noted with the above discussion, and in connection with further more specific examples described below and in connection with the figures, such aspects include reference to Al and/or ML with Al and ML referring to artificial intelligence and machine learning, respectively, and appreciating that Al may be used interchangeably with ML, and to LLMs (Large language models) which are Al programs that use deep learning to analyze and understand text” (Para. [0046]).
Barraclough and Crysanthou explicitly teach all of the elements of the claimed invention as recited above except:
Performing user interface generation steps in parallel.
However, in the related field of endeavor of generating user interfaces from free text, Ciminelli in combination with Barraclough and Crysanthou teaches:
Performing user interface generation steps in parallel.
Ciminelli teaches “In embodiments of the presently disclosed subject matter, one or more stages illustrated in the figures may be executed in a different order and/or one or more groups of stages may be executed simultaneously.” (Para. [0032])
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Ciminelli, Barraclough, and Chrysanthou at the time that the claimed invention was effectively filed, to have combined the use of target audience preferences/susceptibilities, as taught by Ciminelli, with the use of one or more LLMs in generating a website, as taught by Barraclough, and the systems and methods for automatically generating websites using artificial intelligence, as taught by Crysanthou.
One would have been motivated to make such combination because while Chrysanthou teaches
Scanning third-party websites for various template information, including “color scheme and/or palette of the relevant third-party website, such as, for example, the background color(s), header color(s), font color(s), hyperlink and mouseover color(s), and the like. f. The font(s) and font size(s) used on the relevant third-party website, such as, for example, various heading fonts, copy fonts, footer fonts, hyperlink fonts, and the like.” (Paras. [0081]-[0082]), Ciminelli teaches using known characteristics of the target audience such as “susceptibility to styles and/or designs and/or layouts and/or design elements and/or textual messages and/or color schemes and/or fonts, demographic characteristics (such as age group, gender, ethnicity, religion, income level, education level, etc.) associated with the target audience, inclination to taking specific actions, behavior patterns, languages, and so forth.” (Para. [0038]) and it would have been obvious to a person having ordinary skill in the art that utilizing known preferences of a target audience to personalize the generated websites to the target audience would increase the approval by the audience when viewing the generated websites.
Regarding Claim 2:
Ciminelli, Baraclough, and Chrysanthou further teach:
Wherein there are a plurality of different webpages generated which are responsive to the user-generated query.
Chrysanthou teaches “the content generation engine 214 can generate multiple websites, and provide each of these websites in a side-by-side fashion on the user computing device 104 for the user to review and select.” (Para. [0110]).
Barraclough further teaches “the system prompts users for initial website goals, topics, target audience and branding preferences. The system then automatically determines an optimal set and sequence of pages and sections to build based on the website goals, wherein for each section, the system selects appropriate UI elements, components and styles” (Para. [0043]).
Regarding Claim 3:
Ciminelli, Baraclough, and Chrysanthou further teach:
Wherein each different webpage is generated by the LLM using a different page generation strategy.
Barraclough teaches “the system prompts users for initial website goals, topics, target audience and branding preferences. The system then automatically determines an optimal set and sequence of pages and sections to build based on the website goals, wherein for each section, the system selects appropriate UI elements, components and styles” (Para. [0043]) thereby teaching generating a plurality of different webpages responsive to the query/user input.Barraclough further teaches “the existing context is sufficient for the Large Language Models to generate text, components, and layouts tailored to the user's needs for the specified page type” (Para. [0068]) thereby teaching using different page generation strategies to tailor layouts based at least in part on a specified page type of the “optimal set and sequence of pages and sections to build” (Para. [0043]).
Regarding Claim 4:
Ciminelli, Baraclough, and Chrysanthou further teach:
Inputting the contextualized query into the LLM and obtaining each of the different page generation strategies;
Barraclough teaches “the system dynamically selects an appropriate set of content blocks to populate each page section. The blocks are chosen from the component library based on properties like layout format, text length, media types to match the content needs for that specific page section.” (Para. [00106])
Wherein at least a portion of the dynamically generated at least one webpage comprises sections derived from different page generation strategies.
Barraclough teaches “the page, titled "Blush and Brilliance," features a cohesive and visually appealing layout that highlights the bridesmaids' role in the wedding celebration. As indicated in such a screenshot, the Al-driven system can intelligently curate a collection of wedding photos, arranging them in an elegant grid layout that complements the overall design” (Para. [00190]), thereby teaching multiple sections derived from different page generation strategies including layout.
Barraclough also teaches “the system dynamically selects an appropriate set of content blocks to populate each page section. The blocks are chosen from the component library based on properties like layout format, text length, media types to match the content needs for that specific page section.” (Para. [00106]) thereby teaching multiple content blocks, each chosen based on at least a layout property, where “each page section” can comprise multiple content blocks.
Regarding Claim 5:
Ciminelli, Baraclough, and Chrysanthou further teach:
Wherein the page generation strategies specify content types and layout for the corresponding webpage.
Barraclough teaches “the system dynamically selects an appropriate set of content blocks to populate each page section. The blocks are chosen from the component library based on properties like layout format, text length, media types to match the content needs for that specific page section.” (Para. [00106])
Regarding Claim 7:
Ciminelli, Baraclough, and Chrysanthou further teach:
Wherein the at least one webpage comprises different sections generated by the LLM using different page generation strategies.
Barraclough teaches “the page, titled "Blush and Brilliance," features a cohesive and visually appealing layout that highlights the bridesmaids' role in the wedding celebration. As indicated in such a screenshot, the Al-driven system can intelligently curate a collection of wedding photos, arranging them in an elegant grid layout that complements the overall design” (Para. [00190]), thereby teaching multiple sections derived from different page generation strategies including layout.
Barraclough also teaches “the system dynamically selects an appropriate set of content blocks to populate each page section. The blocks are chosen from the component library based on properties like layout format, text length, media types to match the content needs for that specific page section.” (Para. [00106]) thereby teaching multiple content blocks, each chosen based on at least a layout property, where “each page section” can comprise multiple content blocks.
Regarding Claim 8:
Ciminelli, Baraclough, and Chrysanthou further teach:
Inputting the received content responsive to the contextualized query into the LLM and receiving an output of the LLM;
Barraclough teaches “The most relevant retrieved information is used to enhance the prompts sent to the large language models (LLMs) described in Example Embodiment 4. b. This creates more context-rich, tailored prompts that guide the LLMs to generate highly relevant content” and “the LLM generates initial content based on the enhanced prompts” (Para. [00168])
Wherein the dynamically generated at least one webpage is derived from the output of the LLM.
Barraclough teaches “Content Generation and Augmentation: a. The LLM generates initial content based on the enhanced prompts. b. The system then augments this content by intelligently incorporating key information from the retrieved data.” (Para. [00168])
Regarding Claim 9:
Ciminelli, Baraclough, and Chrysanthou further teach:
Searching pre-existing webpages for content responsive to the contextualized query and obtaining the content responsive to the contextualized query;
Chrysanthou teaches “the Internet scanning engine 208 performs keyword-based searching against text, copy, content, headers, descriptors, and tags of third-party websites to identify websites relevant to the user's business or organization” (Para. [0115]) thereby teaching searching pre-existing third-party websites for content relevant to the contextualized key term list/query, and obtaining content responsive to the searching.
Wherein the dynamically generated at least one webpage is derived from at least one pre-existing webpage having matching content responsive to the contextualized query.
Chrysanthou teaches “the template generation engine 210 analyzes each relevant third-party website for various template information, such as, but not limited to, the types of pages, the type of content on each page, the context of the content, what the content depicts, the color scheme and/or palette, the font(s) and font size(s) used, the structure and layout of elements, and/or the presence or absence of social media content, as described herein.” (Para. [0117]), “generates a website template based on the template information determined the relevant third-party websites,” (Para. [0118]), and “generating a website using the website template” (Para. [0121]).
Regarding Claim 10:
Ciminelli, Baraclough, and Chrysanthou further teach:
Determining, by the LLM, follow up questions to content in the at least one webpage;
Chrysanthou teaches follow up questions after generating a website by teaching “the publishing engine 210 can prompt a number of questions, such as, for example, “Do you like the color scheme?”, “Do you like the font?”, “Do you like the layout?”, and the like, and the pre-defined answers can be, for example, “Yes”, “No”, “Somewhat”, and the like. In another embodiment, the user can be prompted to provide feedback based on a scale of one to ten for various aspects of the website.” (Para. [0105]).
Generating, by the LLM, additional content based on the determined follow up questions; and
Chrysanthou teaches “based on the user feedback, the template generation engine 210 and/or the content generation engine 214 can generate another website template or another website by considering the user feedback.” (Para. [0106]). Where
Enriching the at least one webpage with at least a portion of the generated additional content.
Chrysanthou teaches “based on the user feedback, the template generation engine 210 and/or the content generation engine 214 can generate another website template or another website by considering the user feedback.” (Para. [0106]).
Regarding Claim 11:
Ciminelli, Baraclough, and Chrysanthou further teach:
Determining, by the LLM, that an Internet search for content responsive to the follow up questions is required;
Barraclough teaches “The user can iteratively improve the site through feedback prompts which trigger new generation cycles” (Para. [0047]) and “allows users to iteratively modify and refine generated website content through interactive feedback. For an existing page built using automated Al generation or manual user creation, the system presents options to edit styles, text, media elements, and to rebuild blocks” (Para. [00128]) thereby determining that a new generation cycle, and thus an internet search for content responsive to follow up feedback questions, is required.
Generating, by the LLM, one or more follow up question queries;
Barraclough teaches “allows users to iteratively modify and refine generated website content through interactive feedback. For an existing page built using automated Al generation or manual user creation, the system presents options to edit styles, text, media elements, and to rebuild blocks” (Para. [00128]) thereby teaching generating follow up question queries to provide content when rebuilding blocks for the next iteration of generation cycles.
Performing a second Internet search and receiving content responsive to the one or more follow up question queries; and
Chrysanthou teaches “the content generation engine 214 searches the Internet to identify multimedia content and text content” (Para. [0123]) as part of the website generation cycle. Therefore, Barraclough in combination with Chrysanthou teaches performing a second internet search, based on an interactive feedback system, and receiving content when rebuilding blocks for the next iteration of generation cycles.
Enriching the at least one webpage with content generated by the LLM based on the second Internet search.
Chrysanthou teaches “based on the user feedback, the template generation engine 210 and/or the content generation engine 214 can generate another website template or another website by considering the user feedback.” (Para. [0106]) thereby teaching enriching the webpage by considering the user feedback in the next iteration of generation cycles taught by Barraclough (Paras. [0047] & [00128]), the next iteration of generation cycles including the internet search taught by Chrysanthou (Para. [0123]).
Regarding Claim 12:
Some of the limitations herein are similar to some or all of the limitations of Claim 1.
Ciminelli, Baraclough, and Chrysanthou further teach a system comprising:
At least one data processor (Chrysanthou – Para. [0007]); and
Memory storing instructions which, when executed by the at least one data processor, result in operations (Chrysanthou – Para. [0007]).
Regarding Claim 13:
All of the limitations herein are similar to some or all of the limitations of Claim 2.
Regarding Claim 14:
All of the limitations herein are similar to some or all of the limitations of Claim 3.
Regarding Claim 15:
All of the limitations herein are similar to some or all of the limitations of Claim 4.
Regarding Claim 16:
All of the limitations herein are similar to some or all of the limitations of Claim 5.
Regarding Claim 18:
All of the limitations herein are similar to some or all of the limitations of Claim 7.
Regarding Claim 19:
All of the limitations herein are similar to some or all of the limitations of Claim 8.
Regarding Claim 20:
All of the limitations herein are similar to some or all of the limitations of Claim 9.
Regarding Claim 21:
All of the limitations herein are similar to some or all of the limitations of Claim 10.
Regarding Claim 22:
All of the limitations herein are similar to some or all of the limitations of Claim 11.
Regarding Claim 23:
Chrysanthou teaches a computer-implemented method comprising:
Receiving, over a network from a remote computing device, a user-generated query;
Chrysanthou teaches “the user inputs a description using their user computing device 104, as described herein” (Para. [0112]) where the user input description is “of a business or organization” where the user input description is a query/request to dynamically generate a website (Paras. [0042]-[0043]).Chrysanthou further teaches “The network architecture includes a server 100, a website generator platform 102 coupled to the server 100 via a network connection, at least one user computing device 104 coupled to the server 100 via a network connection, and third-party services 106 (individually, services 106-1 and 106-2) coupled to the server 100 via a network connection.” (Para.[0027]) and “the intake engine 204 displays a text input field on the user computing device 104 that allows the user to input the description” (Para. [0044]).
Determining an intent of the query;
Chrysanthou teaches determining “suggested key term(s)” related to the user input description (Para. [0113]).
Dynamically generating, derived from the received content responsive to the corresponding contextualized query, at least one webpage.
Chrysanthou teaches “the content generation engine 214 creates a website using the identified multimedia content, text content, and/or social media content” (Para. [0125]) where at least the multimedia content and text content are derived based on a being relevant to “content found on the relevant third-part websites identified by the Internet scanning engine” (Paras. [0121]-[0123]).
Crysanthou explicitly teaches all of the elements of the claimed invention as recited above except:
Determining, using a large language model (LLM) being executed on a server, an intent of the query;
Modifying, by the LLM, the query based on the determined intent to result in a plurality of contextualized queries, each of the contextualized queries corresponding to a different webpage generation strategy,
Each webpage generation strategy comprising instructions to generate a webpage including how content is obtained over the Internet,
Each webpage generation strategy comprising specifications for a user interface for conveying information,
At least two of the webpage generation strategies specifying different workflows for obtaining content;
Performing, for each webpage generation strategy, an Internet search according to the corresponding workflow for the webpage generation strategy and receiving content responsive to the corresponding contextualized query; and
Dynamically generating, by the LLM and derived from the received content responsive to the corresponding contextualized query, at least one webpage for each webpage generation strategy responsive to the user-generated query and causing at least one webpage corresponding to two or more webpage generation strategies to be viewed on the remote computing device.
However, in the related field of endeavor of automated website content generation, Barraclough in combination with Chrysanthou teaches:
Determining, using a large language model (LLM) being executed on a server, an intent of the query;
Chrysanthou teaches determining “suggested key term(s)” related to the user input description (Para. [0113]) and Barraclough teaches “Natural Language Processing (NLP) techniques are employed to understand the context and intent behind user inputs and retrieved information.” (Para. [00168]) where LLMs are known NLP techniques.Barraclough further teaches “it will be apparent that various known devices may be used with the aspects and features described herein for example embodiments. As non-limiting examples, such devices may include one or more in combination of the following: devices including communications circuits such as servers, user-operable (e.g., network-enabled) devices such as computer processing circuits (e.g., smart phones and other personal assistant devices (aka user endpoint devices) with user interfaces, laptops, desk-based computer etc.).” (Para. [00163]).
Therefore, Barraclough in combination with Chrysanthou teaches determining, using an LLM being executed on a server, an intent of the query.
Modifying, by the LLM, the query based on the determined intent to result in a plurality of contextualized queries, each of the contextualized queries corresponding to a different webpage generation strategy,
Chrysanthou teaches creating a contextualized query in the form of an “approved key term list” by modifying the user inputs description 300 to only include key terms (Fig. 3 & Paras. [0112]-[0114]) and Barraclough teaches “The most relevant retrieved information is used to enhance the prompts sent to the large language models (LLMs) described in Example Embodiment 4. b. This creates more context-rich, tailored prompts that guide the LLMs to generate highly relevant content.” (Para. [00168]). Barraclough further teaches “the LLM Response (402) is structured to receive and organize the output generated by the LLM. It consists of multiple LLM Block Property Value sections (406), each corresponding to a specific prompt in the User Prompt section.” (Para. [0057]).
Therefore, Barraclough in combination with Chrysanthou teaches modifying, by the LLM system, the query to enhance prompts based on different LLMs/webpage generation strategies corresponding to different prompts in the user prompt section and the determined intent to result in a contextualized query for each of the different LLMs.
Barraclough further teaches “variants of the website can be built and maintained for localized markets or alternate audiences. This embodiment enables automatically adapting an existing website to new languages and geographies by leveraging Al translation, localization and content generation capabilities. Variants can also be generated for different demographic groups/audiences that are not necessarily defined by geography, but by other attributes where a site that provides targeted content and SEO would be advantageous” (Para. [00154]) thereby teaching different webpage generation strategies for specifying different workflows for obtaining content suited for the different demographic groups/audiences to provide targeted content.
Each webpage generation strategy comprising instructions to generate a webpage including how content is obtained over the Internet,
Barraclough teaches “The most relevant retrieved information is used to enhance the prompts sent to the large language models (LLMs) described in Example Embodiment 4. b. This creates more context-rich, tailored prompts that guide the LLMs to generate highly relevant content.” (Para. [00168]).
At least two of the webpage generation strategies specifying different workflows for obtaining content;
Barraclough teaches the webpage generation strategies specifies different workflows for obtaining content by teaching “The LLM Invoker is the central component that interfaces with various LLM providers through their respective Software Development Kits (SDKs) as depicted in the (506 through N). Each SDK connects to its corresponding LLM service, such as Open Al , Google Vertex, Claude, and Azure (e.g., as in FIG. 5). A Self-hosted LLM option (507) is used to locally run custom fine-tuned models within the infrastructure instead of using cloud providers.” (Para. [0061]).Barraclough further teaches “variants of the website can be built and maintained for localized markets or alternate audiences. This embodiment enables automatically adapting an existing website to new languages and geographies by leveraging Al translation, localization and content generation capabilities. Variants can also be generated for different demographic groups/audiences that are not necessarily defined by geography, but by other attributes where a site that provides targeted content and SEO would be advantageous” (Para. [00154]) thereby teaching different webpage generation strategies for specifying different workflows for obtaining content suited for the different demographic groups/audiences to provide targeted content via different website variants.
Performing, for each webpage generation strategy, an Internet search according to the corresponding workflow for the webpage generation strategy and receiving content responsive to the corresponding contextualized query; and
Crysanthou teaches “scanning the Internet using an approved key term list” (Fig. 4 & Para. [0115]) thereby teaching Performing an Internet search using the contextualized query. Crysanthou further teaches “the Internet scanning engine 208 performs keyword-based searching against text, copy, content, headers, descriptors, and tags of third-party websites to identify websites relevant to the user's business or organization” (Para. [0115]) thereby teaching receiving content responsive to the key term list (contextualized query). Crysanthou further teaches “the content generation engine 214 searches the Internet to identify multimedia content and text content which is similar to, but not identical to, the multimedia content and text content found on the relevant third-party websites identified by the Internet scanning engine 208, as described herein.” (Para. [0123]).
Barraclough further teaches “variants of the website can be built for different demographic groups/audiences that are not necessarily defined by geography, but by other attributes where a site that provides targeted content and SEO would be advantageous” (Para. [00154]) and “The most relevant retrieved information is used to enhance the prompts sent to the large language models (LLMs) described in Example Embodiment 4. b. This creates more context-rich, tailored prompts that guide the LLMs to generate highly relevant content.” (Para. [00168]) thereby teaching different webpage generation strategies for specifying different workflows for obtaining content suited for the different demographic groups/audiences to provide targeted content via different website variants.
Dynamically generating, by the LLM and derived from the received content responsive to the corresponding contextualized query, at least one webpage for each webpage generation strategy responsive to the user-generated query and causing at least one webpage corresponding to two or more webpage generation strategies to be viewed on the remote computing device.
Chrysanthou teaches “the content generation engine 214 creates a website using the identified multimedia content, text content, and/or social media content” (Para. [0125]) where at least the multimedia content and text content are derived based on a being relevant to “content found on the relevant third-part websites identified by the Internet scanning engine” (Paras. [0121]-[0123]).
Barraclough further teaches “variants of the website can be built for different demographic groups/audiences that are not necessarily defined by geography, but by other attributes where a site that provides targeted content and SEO would be advantageous” (Para. [00154]) and “The most relevant retrieved information is used to enhance the prompts sent to the large language models (LLMs) described in Example Embodiment 4. b. This creates more context-rich, tailored prompts that guide the LLMs to generate highly relevant content.” (Para. [00168]) where “the LLM Response (402) is structured to receive and organize the output generated by the LLM. It consists of multiple LLM Block Property Value sections (406), each corresponding to a specific prompt in the User Prompt section.” and “the system can produce website-specific content that can be reliably deserialized into block properties” (Paras. [0057]-[0058]) thereby teaching a plurality of webpage generation strategies in a single webpage/output corresponding in part to the specific prompt in the user prompt section for specifying different workflows for obtaining content suited for the different demographic groups/audiences to provide targeted content via different website variants.
Barraclough and Crysanthou explicitly teach all of the elements of the claimed invention as recited above except:
Each webpage generation strategy comprising specifications for a user interface for conveying information,
However, in the related field of endeavor of dynamic websites and searching content, Ciminelli teaches:
Each webpage generation strategy comprising specifications for a user interface for conveying information,
Ciminelli teaches “the information related to the target audience and/or historic activities of individuals associated with the target audience may be analyzed to determine a characteristic of the target audience, for example using a rule based analysis. Some non-limiting examples of such characteristics of the target audience may include susceptibility to styles and/or designs and/or layouts and/or design elements and/or textual messages and/or color schemes and/or fonts, demographic characteristics (such as age group, gender, ethnicity, religion, income level, education level, etc.) associated with the target audience, inclination to taking specific actions, behavior patterns, languages, and so forth.” (Para. [0038]).
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Ciminelli, Barraclough, and Chrysanthou at the time that the claimed invention was effectively filed, to have combined the use of target audience preferences/susceptibilities, as taught by Ciminelli, with the use of one or more LLMs in generating a website, as taught by Barraclough, and the systems and methods for automatically generating websites using artificial intelligence, as taught by Crysanthou.
One would have been motivated to make such combination because while Chrysanthou teaches
Scanning third-party websites for various template information, including “color scheme and/or palette of the relevant third-party website, such as, for example, the background color(s), header color(s), font color(s), hyperlink and mouseover color(s), and the like. f. The font(s) and font size(s) used on the relevant third-party website, such as, for example, various heading fonts, copy fonts, footer fonts, hyperlink fonts, and the like.” (Paras. [0081]-[0082]), Ciminelli teaches using known characteristics of the target audience such as “susceptibility to styles and/or designs and/or layouts and/or design elements and/or textual messages and/or color schemes and/or fonts, demographic characteristics (such as age group, gender, ethnicity, religion, income level, education level, etc.) associated with the target audience, inclination to taking specific actions, behavior patterns, languages, and so forth.” (Para. [0038]) and it would have been obvious to a person having ordinary skill in the art that utilizing known preferences of a target audience to personalize the generated websites to the target audience would increase the approval by the audience when viewing the generated websites.
Claim(s) 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ciminelli, Barraclough, and Chrysanthou, and further in view of Akkiraju Venkata et al. (U.S. Pre-Grant Publication No. 2025/0005081, hereinafter referred to as Akkiraju).
Regarding Claim 6:
Ciminelli, Barraclough, and Crysanthou explicitly teach all of the elements of the claimed invention as recited above except:
Wherein the page generation strategies specify sources to search to populate content in the corresponding webpage.
However, in the related field of endeavor of dynamic websites and searching content, Akkiraju teaches:
Wherein the page generation strategies specify sources to search to populate content in the corresponding webpage.
Akkiraju teaches page generation strategies including “connector intent…to select at least one connector for connecting with corresponding at least one data store” where “the NL processor 175 generates consolidated search results including matching website content from two or more different data sources, via corresponding two or more connectors” (Para. [0021])
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Akkiraju, Ciminelli, Barraclough, and Chrysanthou at the time that the claimed invention was effectively filed, to have combined the determination of whether a search query has connector intent for connecting with a particular data store, as taught by Akkiraju, with the use of target audience preferences/susceptibilities, as taught by Ciminelli, the use of one or more LLMs in generating a website, as taught by Barraclough, and the systems and methods for automatically generating websites using artificial intelligence, as taught by Crysanthou.
One would have been motivated to make such combination because Akkiraju teaches “universal search indexer 165 is further configured to determine, using NL processor 175, whether the search query has connector intent (i.e., a search query including an intent to select at least one connector for connecting with corresponding at least one data store). Based on a determination that the search query has connector intent, the NL processor 175 generates consolidated search results including matching website content from two or more different data sources via corresponding two or more connectors.” (Para. [0021]) and it would have been obvious to a person having ordinary skill in the art that allowing a user to provide the intent in their request/query to search a particular one or more data stores would result in improved user-satisfaction in the content results from the search.
Regarding Claim 17:
All of the limitations herein are similar to some or all of the limitations of Claim 6.
Response to Amendment
Applicant’s Amendments, filed on 2/4/2026, are acknowledged and accepted.
Response to Arguments
In light of the Amendments and Remarks filed on 2/4/2026 and further review of the Application’s specification, the 101 rejection of claims 1-22 for being directed to an abstract idea without significantly more has been withdrawn. In particular, Applicant convincingly argues on Pages 11-16 of the Remarks that “Claims 1 and 12 require: (i) LLM-based classification of a natural language prompt into specific intent categories, and LLM construction of a contextualized query that expressly specifies data sources and content types; (ii) execution of a parallel Internet-search pipeline that simultaneously searches existing webpages and generates a new webpage responsive to the same query, followed by merging the heterogeneous outputs into a single search result page; and (iii) dynamic webpage generation by producing a page outline with content sections and a writing plan and then generating content on a section-by-section basis... according to that plan.” (Remarks Pages 11-12) where “the claims solve a computer- and network-centric problem assembling and presenting relevant, intent-aligned content in response to natural language prompts-using a specific architecture that changes how the computer operates: per-branch concurrent retrieval and generation; explicit merging and ranking to complete a single SRP; and section-wise generation constrained by an LLM-authored outline and writing plan. The additional elements are not generic invocations; they are structural constraints that meaningfully limit the claim to a computer-implemented solution. Similar to Core Wireless and Data Engine Technologies, the claimed user-facing behavior is tied to a specific manner of structuring and presenting information that improves how the computer system provides access to data, rather than claiming an abstract presentation of information.” (Remarks Page 13) and
In light of the Amendments and Remarks filed on 2/4/2026 and further review of the Application’s specification, the 101 rejection of claim 23 for being directed to an abstract idea without significantly more has been withdrawn. In particular, Applicant convincingly argues on Pages 11-16 of the Remarks that “Claim 23 further requires generating a plurality of contextualized queries, each mapped to a different webpage-generation strategy with at least two strategies specifying different content-obtaining workflows; performing per-strategy searches according to the respective workflows; and concurrently viewing multiple strategy outputs on the remote device.” (Remarks Pages 11-12) where “Claim 23 adds further integration: a plurality of contextualized queries, each corresponding to a distinct webpage-generation strategy, at least two of which specify different content-obtaining workflows; per-strategy Internet searches according to those workflows; and concurrent viewing of multiple strategy outputs. These constraints effect a particular machine- implemented orchestration of inputs, retrieval workflows, content assembly, and UI behavior- precisely the sort of integration into a practical application endorsed in cases such as DDR Holdings and Trading Technologies.” (Remarks Page 14).
On page 17 of the Remarks filed on 2/4/2026, Applicant argues that “the art of record fails to teach explicit prompt classification into defined intent categories and the generation of a contextualized query that specifies both data sources to search and content types to obtain. While Chrysanthou extracts keywords and scans the Internet based on a key-term list, it does not teach LLM-based intent classification into defined categories nor a contextualized query that expressly specifies both data sources and content types.”.Applicant’s argument is convincing that Chrysanthou does not teach the amended limitation. However, upon further consideration of all of the previously cited prior art, Barraclough was found to teach the scope of the argued/amended limitation as is further addressed in the rejection above.It is further noted that the claim limitation recites classifying the prompt, but not using the classification result anywhere else in the claims in a meaningful manner.
On page 17 of the Remarks filed on 2/4/2026, Applicant argues that “the art of record fails to teach a parallel Internet-search architecture that simultaneously (i) searches existing webpages for matching content and (ii) generates a new webpage responsive to the query, followed by merging results from both branches to generate a search result page. Chrysanthou's workflow scans existing sites and generates templates/websites but does not teach merging results from an "existing webpage search" with simultaneously generated on-the-fly pages.”Upon further consideration of all of the previously cited prior art, while the Crysanthou and Barraclough references were not found to teach all of the supported features (in the scope following the 112(b) rejection stated above) being argued, Applicant’s argument is not convincing that “the art of record fails to teach” the argued limitation because Ciminelli in combination with Barraclough and Crysanthou was found to teach the supported features of the argued limitation (in the scope following the 112(b) rejection stated above).
It is further noted that the claim limitation recites generating a search result page, but not using or displaying the search result page anywhere else in the claims in a meaningful manner.
On page 17 of the Remarks filed on 2/4/2026, Applicant argues that “the art of record fails to teach dynamic generation by first producing a page outline defining content sections and a writing plan, and then generating content on a section-by-section basis according to the plan. Barraclough describes LLM-driven content for website construction and model selection, but it does not disclose the claimed parallel search-plus-page-generation merge to form a search result page, nor the outline-and-writing-plan, section-by-section generation flow recited in claims 1 and 12.Applicant’s argument is convincing that Baraclough alone does not teach the amended limitations being argued. However, upon further consideration of all of the previously cited prior art, Barraclough in combination with Chrysanthou were found to teach the scope of the argued/amended limitations as is further addressed in the rejection above.It is further noted that the claim limitations being argued recite disjointed/unrelated features such as generating a search result page, then dynamically generating at least one webpage unrelated to the search result page, and generating a page outline defining content sections and a writing plan and generating content according to the writing plan, unrelated to the search result page or the generated at least one webpage.
On page 17 of the Remarks filed on 2/4/2026, Applicant argues, with respect to amended independent claim 23, that “Chrysanthou describes scanning and template-based generation and Barraclough describes LLM content generation and model selection, however, neither reference teaches the claimed plurality of contextualized queries mapped to different strategies, each with a different content-obtaining workflow, with per-strategy retrieval and concurrent viewing. Ciminelli's UI personalization does not supply these multi-strategy, per-workflow retrieval mechanics. The Examiner's mapping does not identify a teaching or suggestion of concurrently viewing webpages corresponding to multiple strategy results as claimed.”Applicant’s argument is moot in light of the 112(a) rejection, further explained above, related to the argued limitation because support for “concurrently viewing webpages” was not found in the specification.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bradea et al. (U.S. Pre-Grant Publication No. 2025/0068893) teaches 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.
Khorashadi et al. (U.S. Pre-Grant Publication No. 2014/0149850) teaches at least two web browsers operable on at least two different computing devices. A server processes requested code to return binary code as metadata to assist a computing device render a webpage. The server transmits the generated metadata to at least one computing device. The computing device renders a webpage using at least a portion of the provided metadata. The metadata may identify portions of JavaScript that can be processed in parallel. The metadata may identify a library portion that does not have to be loaded. The metadata may identify a portion of the webpage that may be rendered first before a second portion of the webpage. Returning metadata to the computing device can assist the computing device in parsing, analyzing or executing the request for the webpage.
Saxena (U.S. Pre-Grant Publication No. 2024/0330579) teaches a website development system automatically generates text for a webpage. The system obtains a prompt template associated with a section of the webpage, where the prompt template includes one or more parameters. Based on the webpage, the prompt template determines a first value for a first one of the one or more parameters. A request to provide input for a second value of a second parameter is sent for display to a user. Using the prompt template, the first value, and the second value, the system generates a prompt to a large language model to generate text for the section of the webpage.
Murali et al. (U.S. Patent No. 11,042,600) teaches techniques for enabling webpages to be generated and customized based on rules created by a user. A user accessing a user interface may select a set of webpage features, the dimensions or other characteristics of the features, and a corresponding set of rule conditions, such as characteristics of a user device, user account, connection, or webpage. When a request to access a webpage is received, an existing body of rules is queried, and the particular rule that is satisfied by the request is determined. A webpage that includes the corresponding features and feature characteristics of the satisfied rule is generated and provided to the requesting device.
Jain (U.S. Patent No. 12,072,950) teaches a dynamic unified object generation platform, including a dynamic unified object generation computer server coupled to at least a first partner and a second partner and configured to electronically receive and store in a server database at least a first set of rules from the first partner and a second set of rules from the second partner for a publisher website, a server processor coupled to the server database and configured to automatically and dynamically create a comparison between a unified set of rules and the first set of rules and the second set of rules, a pre-deployment processor coupled to the server processor and configured to dynamically convert the comparison into a single unified code structure based on the unified set of rules, a post-deployment processor coupled to the pre-deployment processor and configured to dynamically integrate the single unified code structure into the publisher website as a unified rule set to control at least two predetermined operational aspects of the publisher website based on the first and second set of rules and at least one graphical user interface configured to display to a publisher user of the publisher website at least one operational aspect controlled by the unified rule set.
Rehn (U.S. Pre-Grant Publication No. 2021/0209181) teaches generating dynamic websites that includes a memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations include receiving an order from a customer device, determining whether the destination address is eligible for delivery by a first time, and based on determining the destination address is eligible, searching a first database to retrieve information of the at least one product. The operations may also include generating a user interface element indicating whether delivery by the first time is possible (the user interface element being configured to modify a website displayed in the customer device) and sending the user interface element to the customer device. Further, the operations may include receiving a response from the customer device and modifying an entry in a second database to indicate the promised delivery date for the product is the first time.
Perez et al. (U.S. Pre-Grant Publication No. 2024/0386197) teaches integrating enhanced model interaction within a website building system. Models leveraged according to embodiments may include trained generative artificial intelligence models that are leveraged to customize structure and content within a website building system. Improved generation of composite prompts leads to improved generation of customized structure and content within the website building system.The reference further teaches “Another quality assurance operation may include reattempting a request (e.g., resubmitting generated prompts and some or all of the data included above as inputs to the AI model) or part of a request. In some embodiments, a request may be modified (e.g., prompts, descriptions, etc.) based on a review of the output map data structure and content, or may include instructions directing the AI model to generate response which is different than the previous response (e.g., in its entirety, or for specific areas in the response).” (Para. [0042]).
Fei et al. (U.S. Pre-Grant Publication No. 2025/0307528) teaches a web page generation method and a device, and relates to the field of front-end development technologies. The method includes: obtaining requirement description information of a web page; inputting the requirement description information into an outline generation model to obtain web page outline information for describing web page logic; rendering the web page based on the web page outline information and a preset web page template; and generating the web page based on the web page outline information and the preset web page template in response to the web page being successfully rendered.
Bista et al. (U.S. Pre-Grant Publication No. 2025/0094455) teaches contextual query rewriting. The techniques include inputting a first user utterance and a conversation history to a first language model. The first language model identifies an ambiguity in the first user utterance and one or more terms in the conversation history to resolve the ambiguity, modifies the first user utterance to include the one or more terms identified to resolve the ambiguity to generate a modified utterance, and outputs the modified utterance. The computing system provides the modified utterance as input to a second language model. The second language model performs a natural language processing task based on the input modified utterance and outputs a result. The computing system outputs a response to the first user utterance based on the result.
Chamberlain (U.S. Pre-Grant Publication NO. 2003/0208369) teaches a user to request information over a network (702) and receive the requested information (708) through one or more information channels. A user, through a client device, may access, through a network, a web page that is hosted on a server. The server, while providing primary information, may further provide an opportunity for the user to request secondary information (710). The user may request access to the secondary information, while maintaining access to the primary information. Included in the request may be a channel selection (704), and associated channel selection information, through which the user wishes to receive the information.
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/ROBERT F MAY/Examiner, Art Unit 2154 3/20/2026
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154