Methods and apparatuses involving automated website content generation and structure using large data trained models

Abstract

In certain examples, computer-implemented methods include assembling automatically generated content into a website structure that is optimal to a particular user and tailored to user goals and consistent with attributes of media assets of the user. The method further includes: analyzing, for the particular user (e.g., individual or entity), inputs and media assets; constructing a set of context-aware prompts, wherein each of the context-aware prompts is associated with one or more contexts discerned in response to user inputs and media assets being analyzed; and generating, via large language modeling and based on the constructed context-aware prompts, the website content inclusive of text, code, and media elements. In photography examples, such methods customize content based on responses to constructed context-aware prompts, and use a library of predefined website content (e.g., images) with configurable parameters and customized content created dynamically.

Description

METHODS AND APPARATUSES INVOLVING AUTOMATED WEBSITE CONTENT GENERATION AND STRUCTURE USING LARGE DATA TRAINED MODELSBACKGROUND|0001 ] Whether implemented manually or using high-technology tools, designing a website is often difficult. Becoming familiar with the types of data that typically goes into a data set for generating a website presents a significant challenge that is typically accompanied by a large learning curve. If one is attempting to use high-technology tools for generating aspects of the website, the challenge can be exponentially greater.

[0002] Another burdensome issue with designing a website is knowing how to make the appearance of the website attractive in terms of aesthetics, user comfort for its operations, and organizational efficiency. Further, a website design should be particular to the style of the entity (e.g., company, group or individual) that is to use the website.

[0003] Among many other complications, the website needs to work well regardless of the type of computer-based communication tool which provides the UI (user interface) to access the website. This is true whether the computer-based communication tool is a desktop computer, a smart phone, or another type of user-engagement tool.

[0004] Moreover, as ongoing use of broadband data networks (e.g., the Internet) has continued to grow over the past decades, many decision makers behind website designs are becoming less familiar even with the basic technology specifics often used in generating websites and, at the same time, demand that such websites be designed in a manner that is highly customized based on their particular goals, preferences, needs and related uses.

[0005] Accordingly, there is a need for an automated website builder that overcomes the above, and other, challenges.SUMMARY OF VARIOUS ASPECTS AND EXAMPEES{0006] Various examples / embodiments presented by the present disclosure are directed to issues such as those addressed above and / or others which may become apparent from the following disclosure. For example, some of these disclosed aspects are directed to methods and devices that use or leverage from existing metadata, or metadata provided by the entity seeking to design a website. Other aspects are directed to overcoming previously-used techniques, such as discussed above, by generating an automated website builder that generates the full code, structure, content and / or assets for a complete customized website and users associated blog or microsites aligned to a user's goals and preferences, with minimal or no manual intervention.

[0007] In certain specific examples, computer-implemented methods and circuit-based arrangements are directed to assembling automatically generated content into a website structure that is optimal to a particular user in that the website structure is tailored to goals of the particular user and is consistent with attributes of media assets (e.g., images, video, illustrations, audio and 3D objects) of the user. The method further includes: analyzing, for the particular user (e.g., individual or entity ), inputs and media assets; constructing a set of context-aware prompts, wherein each of the context-aware prompts is associated with one or more contexts discerned in response to the user inputs and media assets being analyzed; and generating, via large language modeling and based on the constructed context-aware prompts, the website content inclusive of text, code, and media elements. In more specific examples (e.g., as might be used by a professional photographer), such methods involve using the LLM (large language modeling) algorithm to create custom content blocks based on responses to constructed context-aware prompts, and using a library of predefined website content blocks (e.g., including images) with configurable parameters and alongside customized content blocks that are created dynamically using the LLM algorithm operating in response to the received responses to the constructed context-aware prompts.

[0008] More specific examples may build on the above examples. In one instance, metadata is included as part of the inputs and media assets, and the method further includes: extracting the goals of the particular user from at least one of: the user inputs and media assets, and one or more responses to the set of context-aware prompts; and discerning that the website structure is optimal for a particular user based at least in part on the goals and mediabased attributes included as part of the metadata.

[0009] Other specific examples which may also build on the above (and / or with each other) include steps such as (and / or related data-processing circuitry involving): discerningthat the website structure is optimal for a particular user based at least in part on a plurality of media-based (e.g., image-based) metadata attributes (e.g., one or more colors, one or more geolocations, one or more categories of a type of or particular object, one or more categories, attributes of a ty pe of or particular individual, one or more camera settings, and one or more color pallets); creating and modifying the customized website content to render the customized website content tailored for at least one of different audiences and different geographies, wherein the creating and modifying involve one or more of the following: translation of written content, translation of audio content and soundtracks, translation of content inside an image, and modification of images to make them particular for a revised characterization of a target audience to which the customized website structure is directed; and modifying, based on a revised or different data set related to the inputs and media assets, the customized website structure for a revised characterization involving a target audience of a target geographical region, wherein the customized website structure is modified to adhere to local site design norms and to allow for differences in linguistic and written communications (e.g., dextrosinistral languages).(00101 Such specific steps (and / or related data-processing circuitry) may further include or involve: receiving, over a network at core-application logic circuitry hosted via a data- communications platform, the inputs and media assets from an application or web browser enabled through a user interface operated at a computing-data-processing node by the user; and via at least one of the core-application logic circuitry' and the LLM algorithm, discerning the one or more contexts of the inputs and media assets.(001 11 In yet further specific examples, such methods (and / or circuitry) may further include: (a) using the core-application logic circuitry to manage manually -edited content for the website and, in response, to create website content that is continuously updated byautomated computer processing that uses the manually-edited content to drive one or more artificial intelligence and / or machine learning algorithms; and / or (b) ongoing automated refinement of the generated website content, including search-engine optimization, validation of the generated website content, and website performance monitoring after launch of the website.10(i 121 Another aspect of the present disclosure is directed to a computer-program product comprising computer-readable instructions stored on a non-transitory computer- readable medium that, when executed by a computing data processor, cause the computing data processor to: analyze, for a particular user (e.g., individual or entity), inputs and media assets; construct a set of context-aware prompts, each of the context-aware prompts beingassociated with one or more contexts discerned in response to the user inputs and media assets being analyzed; generate, via an LLM (large language modeling) algorithm that is computer-executed based on the constructed context-aware prompts, website content that includes text, code, and media elements, and assemble the generated content into a website structure that is optimal to the particular user in that the website structure is tailored to goals of the particular user and is consistent with attributes of the media assets.

[0013] In certain apparatus-type example embodiments, one or more of the above aspects is directed to use of certain modules (e.g., as may be configured as part of programmable logic circuitry or one or more computing data processors). In certain of these example embodiments, the apparatus includes: an analysis module, as part of computer-based circuitry, to analyze, for a particular user, inputs and media assets; a prompt construction module, as part of the computer-based circuitry , to construct a set of context-aware prompts, each of the context-aware prompts being associated with one or more contexts discerned in response to the user inputs and media assets being analyzed; content generation module to generate, via an LLM (large language modeling) algorithm that is computer-executed based on the constructed context-aware prompts, website content that includes text, code, and media elements, and a site assembly module to assemble the generated content into a website structure that is optimal to the particular user in that the website structure is tailored to goals of the particular user and is consistent with attributes of the media assets.

[0014] Related more-specific examples may include one of the above previously - discussed aspects and / or one of the following: the computer-based circuitry being configured to compile the generated content into the website structure; a feedback module enabling iterative refinement of generated content through conversational interaction between the particular user and the computer-based circuitry; an AI / ML module that is to interact with a manual website editing user interface and modify the website structure based on attributes and metadata of the website content, and based on the goals of the particular user and the attributes used in creation of the website; a media analysis module to operate locally on one or more user devices by preprocessing media assets before the media assets are to be uploaded to the computer-based circuitry; an ongoing automation module to continually optimize and monitor the website after the website is launched; and a content block builder including: a block definition loader, a user-context loader, a prompter to build prompts, a post-processing module to unpack and validate one or more responses by the LLM algorithm to the build prompts, and a virtual-content block building module to construct user interface components. Further, a communications module (e.g., circuitry to send and receive packetson a bus or wirelessly) to coordinate communications involving one or more responses by the LLM algorithm and the generated prompts which are to be used by the LLM algorithm.

[0015] In yet a further example (also useful as a building block with one or more of the other example aspects hereinabove), a LLM Model Selection module may be included to formulate one of the constructed ones of the context-aware prompts and to choose an appropriate model based on parameters for each available LLM, including the model name, provider, evaluation score, text throughput, API limitations, token limitations, and privacy considerations.

[0016] In certain of the above examples and in related aspects, approaches in accordance with the present disclosure may be implemented as an end-to-end generation and refinement approach to build full websites tailored to users without needing manual technical expertise. According to these and other specific examples, aspects of the present disclosure facilitate and enable users to create engaging, customized websites in a fast, simple and cost-effective way.

[0017] The above discussion is not intended to describe each aspect, embodiment or every implementation of the present disclosure. The figures and detailed description that follow also exemplify various embodiments.BRIEF DESCRIPTION OF FIGURES

[0018] Various example embodiments, including experimental examples, may be more completely understood in consideration of the following detailed description in connection with the accompanying drawings, each in accordance with the present disclosure, in which:

[0019] FIG. 1 is an example (i.e., non-limiting) block flow diagram showing exemplary data flow, according to the present disclosure;

[0020] FIG. 2 is a specific example (i.e., non-limiting) flow diagram showing different exemplary aspects of one of many systems, according to the present disclosure;

[0021] FIG. 3 is another block diagram, useful for building a virtual block according to another specific example in accordance with the present disclosure;

[0022] FIG. 4 is another block diagram exemplifying a large-language model (LLM) prompt and related LLM response according to another specific example of the present disclosure;

[0023] FIG. 5 is another block diagram exemplifying aspects for a related website generation approach, according to the present disclosure, involving dynamically selecting and invoking Large Language Models (LLMs) to generate content;

[0024] FIG. 6 is another flow diagram exemplifying a multi-step approach with exemplary aspects involving user-input collection for onboarding a user, according to the present disclosure;

[0025] FIG. 7 is another flow diagram exemplifying a multi-step approach with exemplary aspects involving page creation for an existing user, according to the present disclosure;

[0026] FIG. 8 is another diagram with example aspects of a site page, according to the present disclosure;[00271 FIG. 9 is another diagram with exemplary aspects of a site page, according to the present disclosure;

[0028] FIGs. 10A-10C are related diagram showing three exemplary aspects as part of an example flow of a chatbox user interface (UI), as a specific example, media analysis using photo-refine hosted service, according to the present disclosure;

[0029] FIG. 11 is another diagram showing an example flow for implementing, as a specific example, media analysis using photo-refine hosted service, according to the present disclosure;

[0030] FIG. 12 is another diagram showing another example flow for implementing, as a specific example, media analysis using photo-refine hosted service, according to the present disclosure;

[0031] FIG. 13 is another diagram showing an example flow for use in a semantic image search and intelligent content generation system, according to the present disclosure; and

[0032] FIGs. 14A and 14B are example screenshots of websites created according to exemplary computer-implemented methods, consistent with the present disclosure.

[0033] While various embodiments discussed herein are amenable to modifications and alternative forms, aspects thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that the intention is not to limit the disclosure to the particular embodiments described. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure including aspects defined in the claims. In addition, the term “example” as used throughout this application is only by way of illustration, and not limitation.DETAILED DESCRIPTION(0034] Aspects of the present disclosure are believed to be applicable to a variety of different types of apparatuses, systems and methods involving devices characterized at least in part by extraction of user input and resources, for example including media attributes, for driving processes to facilitate and drive large language models (LLMs) for automatically determining one or more optimal sets of content for website pages and / or content blocks tailored to needs of a user (e.g., company, group, individual, etc.). In this context, “optimal” refers to or includes a proposed best set of content given the available user inputs and resources, or an improved version over an earlier version in an iteration. While the present disclosure is not necessarily limited to such aspects, an understanding of specific examples in the following description may be understood from discussion in such specific contexts.10035] Accordingly, in the following description various specific details are set forth to describe specific examples presented herein. It should be apparent to one skilled in the art, however, that one or more other examples and / or variations of these examples may be practiced without all the specific details given below. In other instances, well known features have not been described in detail so as not to obscure the description of the examples herein. For ease of illustration, the same connotation and / or reference numerals may be used in different diagrams to refer to the same elements or additional instances of the same element. Also, although aspects and features may in some cases be described in individual figures, it will be appreciated that features from one figure or embodiment can be combined with features of another figure or embodiment even though the combination is not explicitly shown or explicitly described as a combination.(0036] Exemplary aspects of the present disclosure involve computer-implemented methods and circuit-based arrangements directed to assembling automatically generated content into a website structure that is optimal to a particular user in that the website structure is tailored to goals of the particular user and is consistent with attributes of media assets of the user. The method further includes: analyzing, for the particular user (e.g., individual or entity), inputs and media assets; constructing a set of context-aware prompts, wherein each of the context-aware prompts is associated with one or more contexts discerned in response to the user inputs and media assets being analyzed; and generating, via large language modeling and based on the constructed context-aware prompts, the website content inclusive of text, code, and media elements. In more specific examples (e.g., as might be used by a professional photographer), such methods involve using the LLM algorithm to create custom content blocks based on responses to constructed context-aware prompts, and using a libraryof predefined website content blocks (e.g., including images) with configurable parameters and alongside customized content blocks that are created dynamically using the LLM algorithm operating in response to the received responses to the constructed context-aware prompts.[00371 Consistent with the above aspects, such a manufactured device or method of such manufacture may involve aspects presented and claimed in U.S. Provisional Application Serial No. 63 / 533,537 filed on August 18, 2023, to which priority is claimed. To the extent permitted, such subject matter is incorporated by reference in its entirety generally and to the extent that further aspects and examples (such as experimental and / more-detailed embodiments) may be useful to supplement and / or clarify.| 0038] Consistent with the present disclosure, computing data-processing devices and / or methods may be used for producing website content for user websites (as a website used by one or more “users” referring to entities such as companies, groups and / or individuals) which rely on images such as photography and art studios, and other entities benefited by frequently altering their websites based on attributes of metadata (e g., multimedia metadata such as images, videos, hyperlinked sites, articles, screenshots, etc.).

[0039] According to certain more specific examples (which may also relate to and be used alone or with the above examples), the present disclosure is directed to facilitating and leveraging LLMs to automatically determine optimal pages and content blocks tailored to the user's needs. In one such example, the process constructs new block types on demand and allows editing both manually created and system-generated website pages. In certain other particular example implementations according to the present disclosure, the system also enables ongoing automation of tasks like SEO (search engine optimization), validation, and performance monitoring to iteratively refine customized sites after launch, thereby minimizing the need for manual upkeep. This end-to-end automated generation, customization and maintenance of websites facilitates lessening (and in some cases minimizing) the required technical expertise. In certain of the examples herein, users are allowed to create and manage customized websites in a fast, simple and cost-effective way without manual coding or design.

[0040] Other exemplary aspects of the present disclosure are directed to artificial intelligence (Al), specifically the application of natural language processing and machine learning technologies to automate customized website generation, standalone web pages, blog posts for displaying media and other content on the web, refinement of those sites and pages, and maintenance with minimal human input. Where additional human input is needed for therefinement process, the user interface may be personalized automatically by the Al utilizing aspects derived from the media, other content being utilized, site or page branding information (at least one of color, font, logo), or other information from user prompts and questions. For example, in certain aspects the present disclosure introduces methods to automatically synthesize website code, structure, content and assets tailored to a user's context by leveraging large pre-trained language models.

[0041] Certain related aspects of the present disclosure may also be implemented to enable iterative customization of both manually created and system-generated pages through conversational feedback and other personalized user interface (UI) elements created by the Al. Additionally, certain aspects of the present disclosure facilitate ongoing automation of tasks like SEO optimization, validation, and performance monitoring to keep websites updated after launch. This enables accessible website creation, refinement, and maintenance without manual coding or design expertise. Users can rapidly deploy customized sites which stay up-to-date through automated improvements over time.

[0042] In yet further examples, aspects of the present disclosure are directed to an automated system and method to design, generate, and iteratively refine customized websites for users without needing manual coding or design expertise.

[0043] In certain particular example implementations according to the present disclosure, 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 to include from a library of predefined blocks. The library is extensible, allowing the system to construct new block types on demand.

[0044] In yet further specific examples, text content, metadata, alt-text and captions are automatically generated by large pre-trained language models tuned to the user preferences, page context and / or imagery, and videos and other media assets are automatically incorporated from the user portfolio after analysis.

[0045] Before turning to the drawing to be discussed in detail below, it is noted that each of the above (briefly-described) examples is presented in part to illustrate aspects of the present disclosure, as may be recognized by the foregoing discussion. 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 refernng to artificial intelligence and machine learning, respectively, and appreciatingthat 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. 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.

[0046] Turning now to the drawing, FIG. 1 shows a communications arrangement 100 according to one example consistent with the present disclosure, which is illustrated as allowing users 101 to generate websites through an application or web browser 104 with an intuitive user interface (UI) displayed on a desktop computer 103, a mobile device 102 or other types of input and display devices. As shown in FIG 1, the user interacts with the UI to provide website goals, topics, preferences and initiate website creation. The application sends these requests over the internet, passing through a content delivery network (CDN) 105, such as Cloudflare™ for security , caching and traffic management.

[0047] The requests arrive at the core application logic circuitry (or logic) 114 hosted on a cloud computing platform (e.g., Microsoft Azure, Heroku, Kubemetes, cloud servers, data center servers or embedded devices). The logic 114 leverages various AI / ML (Al and / or ML) services to generate the website. In one specific example, the logic 114 includes a site building services module 106, a media analysis service (optionally with a photorefine module) 107, and a database 108. 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 and, in certain circumstances, provide as feedback to the user. The logic 114 also utilizes computer vision APIs like Amazon Rekognition 113 and Google Vertex Al Vision 112 for intelligent media analysis. In such examples, by having the logic configured to act on behalf of one or multiple different and / or disparately managed LLMS, the logic 114 is effectively converted into an ultra-intelligent user-customized node through which the user may be generate an ideal website for particular needs by providing minimal input and access to the media resources (e.g., images, videos and the like).( 0048 ] The logic 114 compiles and processes the LLM and vision API outputs to assemble a fully-functional website personalized to the user’s needs. The generated website is rendered back to the user’s browser or an application 104 for preview. The user can iteratively improve the site through feedback prompts which trigger new generation cycles. All communications occur securely over TLS connections. In certain particular example implementations according to the present disclosure, the system automatically handles scaling of traffic and cloud resources to maintain performance. 0049] This automated communication flow allows users to get custom tailored websites built specific to their business needs using cutting-edge Al without technical expertise. The intelligent services generate high-quality content, assets and code matching user requirements in a fast, automated manner.

[0050] The overall system architecture for Al-assisted website generation (200) includes several key components, as illustrated in FIG. 2. A user (202) interacts with the system through the Site Editor (203), which serves as the primary interface for inputting website design preferences and content. This information is stored in a sophisticated data storage and retrieval system (208) that comprises both traditional and vector databases. The Site Builder service (207) orchestrates the website construction process by coordinating with various other components. Visual content is processed and analyzed by the Al Vision Factory (204) using a set of vision models (201...N) and the PhotoRefme tool (206) on the client machine. The BlockBuilder service (209) constructs individual website components by referencing the Block Definition File (210), which contains specifications for various website block types. The LLM Prompt Builder formulates prompts based on these block definitions and user inputs, while the LLM Model Factory (212) selects and manages the use of different large language models (205) to generate content for each block. Finally, the Block Builder assembles the Al-generated content and user inputs into the final output, a Page Block (213), representing a constructed webpage, blog post or website section.

[0051] The system comprises multiple components arranged in a sequential process flow, as illustrated in FIG. 3 (300-306). The process begins with a Block Definition Loader (301), which retrieves and processes the specifications for the block type being generated, including associated properties, parameters, schemas, and descriptions. Following the Block Definition Loader, a Pre-processor (302) is implemented. This component performs two primary functions: Loading User Context and Setting up Block Property prompts. The Preprocessor integrates user-specific information to customize the block content generation process. The Build Prompt component (303) follows, encompassing three sub-processes:Ingesting User Context, Serialization, and Setting up Few Shot Learning Examples. This stage prepares the comprehensive prompt that will be sent to the LLM. 0052] An Await LLM Response component (304) is then activated, during which the system transmits the assembled prompt to the selected LLM provider and awaits the generated content. Upon receiving the LLM's output, a Block Post-processor (305) is engaged. In certain examples, this stage involves two operations which may be important: Deserialization of the LLM's response and Validation of the generated content to ensure it meets the required standards and specifications. The final stage involves Building the Virtual Block (306). This component performs two key functions: Assembling LLM properties and Constructing the UI Component. These operations transform the processed LLM output into a format suitable for integration into the user's webpage, blog post or application.10053] FIG. 4 illustrates a schematic diagram depicting an exemplary prompt structure and response format for interacting with a Large Language Model (LLM) in accordance with embodiments of the present disclosure. The figure is divided into two primary sections: an LLM Prompt (400) and an LLM Response (402).

[0054] The LLM Prompt (400) is designed to facilitate the construction of prompts sent to the LLM for generating website content tailored to a user's context. It comprises a System Message section (403) that includes subsections for Rules, Context, and Schema. The Rules subsection defines guidelines for the LLM's behavior, the Context subsection provides relevant background information, and the Schema subsection specifies the expected response format. This structure helps define the LLM's role as a web building expert, distinguishing it from a generic chatbot.

[0055] Below the System Message, a Few-shots Learning Data section (404) is presented. This section contains example user prompts and corresponding assistant responses, which serve as sample prompt-response pairs for few-shot learning. These examples are intended to improve response accuracy and conformance to the expected format.

[0050] The final component of the LLM Prompt is the User Prompt section (405). This section consists of multiple LLM Block Property Prompts, each designed to elicit specific information for website content generation. These prompts are assembled from sub-prompts for each block property that requires content, containing the property description, a question for the LLM, and any expected response schema.

[0057] 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. The number of propertyvalue sections may vary based on the number of prompts, as indicated by the ellipsis and label 'N' (N).

[0058] This structured approach to prompting and response formatting enables the transformation of generic LLM capabilities into an intelligent web content generation system. By strategically organizing the input and output, including relevant user context and preferences, the system can produce website-specific content that can be reliably deserialized into block properties. This phased prompting approach with guiding context and pre-training enables automated website construction tailored to user specifications. The complex prompt structure and modular prompt engineering described in this figure unlock Al capabilities for customized website building, representing a unique aspect of the present disclosure.|0059 ] FIG. 5 illustrates a system (500) for dynamically selecting and invoking Large Language Models (LLMs) to generate content. The system comprises several interconnected components that work together to optimize the selection and execution of LLM tasks.

[0060] The process begins with an Input: LLM Prompt Builder (501) that formulates the initial prompt. This input is directed to an LLM Model Selector (503), which consults a Model Definition registry (502). The Model Definition contains crucial parameters for each available LLM, including the model name, provider, evaluation score, text throughput, API limitations, token limitations, and privacy considerations.

[0061] The LLM Model Selector chooses an appropriate model based on these parameters and forwards the selection to the LLM Invoker (504). 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.

[0062] An Execution Policy (505) governs the LLM Invoker's operation. This policy implements three key mechanisms: a circuit breaker, retry logic, and timeout controls. These mechanisms ensure robust execution by handling potential errors and failures.

[0063] After the LLM Invoker receives a response, the system checks for Model Errors(508). If an error is detected, the process loops back to the Execution Policy for remediation. If no error is found, the response proceeds to a Deserialization step (509).

[0064] The system then checks for Deserialization Errors (512). If such errors occur, the process returns to the Execution Policy for further attempts. If deserialization is successful, the final output is produced as a Deserialized LLM Response (511).

[0065] As indicated above, aspects may be implemented to guide new users through an onboarding process to collect crucial context for generating an initial website tailored to their needs. In particular examples, this occurs using minimal steps. One specific example is illustrated in FIG. 6. The first step, as shown at the left of FIG. 6, involves the user signing up in which the user provides key information including one or more of user-specific identifiers (e.g., first and last name, and business name and location), and involves the user uploading media assets such as photos.

[0066] The second step, as also shown in FIG. 6, is for the user to provide other readily - available general particulars such as the type of data offered that may be pertinent or preferred for the creation of the website. Again using a photography studio as the user, as part of this step the user may indicate the shoot type (e.g., wedding, events, fine arts), website goals and priorities, color preferences. In other specific examples, the second step may involve the user indicating one or more of branding preferences; example websites created in a preferred sty le, other websites the user may have created, etc. In certain examples, such input can provide important context for the system to synthesize appropriate textual content and select fitting media assets and design elements for the user's initial site. In other examples, aspects of the information collected in the second step many be comingled and / or combined altogether with that of the first step.

[0067] A third step, as also depicted in FIG. 6, is the processing of the data input to the logic circuitry (e.g., logic 114 of FIG. 1). In this processing, 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.

[0068] By guiding new users to provide key onboarding inputs, certain aspects of the present disclosure gains the necessary signal to bootstrap automated synthesis of tailored websites that reflect users' personal styles and business objectives. This sets up an intelligent starting point, while allowing iterative refinement over time through user feedback. The strategic onboarding experience enables first-time website creation optimized to the user's needs. 0069] For users who already have websites managed by the system, additional pages can be intelligently generated as shown in FIG. 7, by leveraging their existing data for context and without needing further inputs. This occurs through the following w orkflow : (a)The user accesses the site editor and selects the option to construct a new page. A dialog presents page type options, including "Use Artificial Intelligence Page Creator", (b) Upon selecting the Al page builder, the user specifies the desired type of new page such as "About Us" or "Contact", and any preferences, (c) This triggers the automated page construction process. Since the user already exists in the system, their profile, website data, past page types, and media are retrieved from the servers, (d) 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, (e) For example, the "About Us" page can be synthesized with content reflecting the user's brand story, identity, services, and messaging aligned with their website domain. 0070 ] By relying on the user's rich existing data, certain aspects of the present disclosure streamlines additional page generation to intelligently extend websites without needing repetitive data re-entry.

[0071] The smart automation leverages the user's history and brand essence to continue crafting customized pages that maintain consistency for an existing website. Minimal inputs extract maximum context.

[0072] The diagram of FIG. 8 demonstrates an example webpage automatically constructed by the system's Al-driven platform containing common elements tailored to the user's brand. The example webpage includes a header section, and a hero block tops the page with an emotive banner image paired with a tagline drawing attention to the core offering. An "About Me" block follows with a profile picture, biography text, and services summary -personalized to the user's business nature and goals. A portfolio block highlights recent work samples and can link to a dedicated portfolio page showcasing projects or offerings.

[0073] Social media links are included to connect site visitors with the user's brand presence on external platforms. A footer contains copyright text, contact info, sitemap links, and any additional references requiring to conclude the page.

[0074] The diagram as shown in FIG. 9 exemplifies an intelligent content block synthesized by the system's Al platform to populate personalized pages. The composite block includes the following:• A responsive multi-column layout adapting to different device widths. An image occupies the left column while text content fills the right.• The image is algorithmically selected from the user's media library based on computer vision analysis to find the most relevant asset for the block's context.• For the text, font type, size, color scheme and alignment are programmatically determined to optimize readability, visual hierarchy and brand stylistic coherence.• The color palette for elements like background and text are extracted from analysis of the user's brand images, example web sites, guiding an aesthetically pleasing style.• The text content itself is generated by a Large Language Model trained on the user's business domain and context. The content matches the block's purpose within the broader page structure.• The synthesized content, selected media asset and tailored styling align to implicitly reflect the user's brand essence and offerings.[OQ75] This demonstrates that certain aspects of the present disclosure use a technical approach for automatic generation of intelligent content blocks for websites by coordinating layouts, media, typography and / or text tailored to the user and page context through Al guidance. The adaptive modular blocks enable programmatically constructing customized pages to each user's needs by recombining versatile components populated with personalized content. 0076] Exemplary aspects of the present disclosure are directed to systems / methods involving incorporation and use of a conversational chatbot interface as shown in FIGs. 10A- 10C to simplify website editing and creation on mobile devices. This may be implemented to overcome limitations of complex traditional website builders unusable on mobile screens.The chatbot interaction flows exemplify key scenarios: 0077] In FIG. 10A, this first diagram demonstrates creating a new "Services" page via dialog. The chatbot offers Large Language Model-generated page variations to select from, integrates requested features like appointment booking through Zenfolio’s BookMe service (see https: / / zenfolio.com / ), and handles publishing and social promotion of the finalized page, enabling rapid mobile creation.

[0078] In FIG. 10B, this second diagram shows editing an existing page by allowing the user to request localized changes like modifying block text style and tone or replacing media. The chatbot uses Al to regenerate only the requested portions rather than the full page.

[0079] In FIG. 10C, this third diagram details creating a blog post where the chatbot prompts guide page type and gallery selection. The Al platform automatically constructs a post with fitting layout, media and text content optimized for blogs based on the provided context.

[0080] In all cases, the conversational interface mirrors natural human interaction, collecting key inputs while handling complexity behind the scenes via intelligent automation. Users achieve custom site creation, editing and content generation on mobile devices without needing to navigate complex dashboards or options.10081 ] This exemplifies exemplary approaches, according to certain aspects of the present disclosure, involving infusion of an intuitive chat-based interface powered by Al to liberate website building from desktop-centric constraints. Chatbot conversations drive strategic website updates and generation by channeling natural language instructions into focused under-the-hood optimizations.

[0082] As illustrated in FIG. 11, media analysis using a photorefine hosted service pertains to another aspect of the present disclosure. In certain exemplary aspects, automated analysis of user media assets (like photos for metadata extraction) is performed prior to uploading for website usage. This occurs through the following process: 1) The user loads photos into the PhotoRefine tool from their local device or camera. 2) Computer vision algorithms in PhotoRefine analyze each photo to extract properties, including:• Happiness rating• Image sharpness• Focus score• Detected tags / objects• Faces and focal points• Star rating• Shooting location• Optical character recognition• Object Categorization• Color palette

[0083] The extracted metadata is uploaded to the system's media services alongside the image files when the user uploads their photos. Storing this analysis data on the servers eliminates needing further cloud analysis services for metadata extraction, reducing costs and latency. The metadata also enables the system to automatically select optimal images that match the user's website narrative, emotions and aesthetic when assembling media assets into the generated pages.

[0084] By front-loading automated media analysis directly in the user's upload workflow, certain aspects of the present disclosure gains crucial data for contextual content generation while lowering resource overhead. The multi-faceted metadata enhances website customization capabilities.

[0085] Additionally, PhotoRefme can be used to cull and filter the photos based on quality, relevance and other criteria before uploading to the system. This curation of media assets can improve efficiency and accuracy when generating website content using large language models. By providing a refined subset of highly relevant photos, the models are focused on assets that best match the user's website goals rather than sifting through large noisy datasets. The pre-filtered media streamlines prompt engineering to steer the language models towards useful contextual written content about the photos.

[0086] In other examples as illustrated in FIG. 12, the present disclosure is directed to providing an alternative approach for performing automated media analysis in the cloud rather than locally on the user's device. In this embodiment, users may upload their photos and videos to a cloud computing environment. The media files are input into a PhotoRefme service executing on servers in the cloud. The media analysis models implemented in the PhotoRefme service extract a comprehensive set of metadata properties for each media asset. The generated metadata is then stored in a media database (e.g., 108 of FIG. 1) in the cloud computing environment, linked to each original media file.

[0087] Computer vision algorithms in PhotoRefme analyze each photo to extract properties including:• Happiness rating• Image sharpness• Focus score• Detected tags / objects• Faces and focal points• Star rating• Shooting location• Optical character recognition• Object Categorization• Color palette[00881 The extracted metadata is uploaded to the system's media services alongside the image files when the user uploads their photos.[0089 j The cloud-based approach for automated media analysis also advantageously reduces resource usage on user devices, as the computationally intensive analysis operations are offloaded to servers in the cloud. This enables the implementation of a lightweight app on user devices that primarily handles media upload and presentation of analysis results, thereby reducing app size and battery / CPU / GPU usage on the devices.

[0090] Additionally, performing the automated media analysis in the cloud makes it easy to scale the back-end processing as needed by provisioning more servers, since the cloud infrastructure is designed for automatic scaling. This enables supporting large volumes of media analysis requests without degradation in performance.

[0091] Another aspect of the present disclosure is directed to semantic image search and intelligent content generation, as shown in FIG. 13. FIG. 13 depicts the process flow and key components of the Semantic Image Search and Intelligent Content Generation System for Event-Based Websites. The diagram is organized into three main sections: User Interaction, Key Components, and Content Generation Process.

[0092] The process begins with photo upload and proceeds through several keycomponents, including Image Analysis and Embedding Service, Keyword Extraction Module, Event Moment Detection, and Semantic Search Engine. These components work together to analyze and categonze the uploaded images.

[0993] The Content Generation Process involves selecting images for each detected event moment, generating a content structure, creating text content using an LLM, and assembling the web page or blog post. The final stages include user review and refinement, where a Similarity' Search Module can suggest alternative images if needed. The process concludes with the publication of the finalized content.

[0094] This system demonstrates the ability to process image collections, intelligently organize them based on event moments, and generate relevant textual content, while allowing for user input throughout the process.

[0095] In the discussion immediately following, several specific example embodiments are disclosed to aid in the understanding of how aspects and embodiments may be implemented at a more detailed level and / or in specific example (non-limiting) applications.

[0096] Example Embodiment 1 : User Input Collection

[0097] In order for a user to create a website, users first sign up for a plan which will initiate the build process. The user registers by providing a business name, location, contact information and other identifying details through forms and prompts in the Site Viewer user interface (UI) as shown in FIG. 5.

[0098] Additional embodiments could include:• Social login adds additional context / content to be utilized for site building and creation• Billing and payment collection modules to handle subscriptions• Account management and administrative features• Once registered, the user is guided through a sequenced input flow containing additional questions and parameters (Figure 6). Queries relate to business type, products / services offered, website goals, target audience and design preferences.

[0099] Example questions include:• What type of photography do you offer? (weddings, portraits, events etc.)• What is the purpose of your website? (booking services, sharing portfolio etc.)• Describe your ideal customer.• What colors and styles do you prefer?• As the user provides responses, their media assets like photos or videos can be simultaneously uploaded to the system. The Media Processing Unit analyzes and extracts metadata in the background.

[0100] Additional embodiments for input:• Natural language conversations to gather info through chatbot flows• Intelligent clarifying questions using conditional logic• Recommendation of applicable input parameters based on business type

[0101] The structured input is stored to initialize the website generation cascade for the user's specific needs. In certain particular example implementations according to the present disclosure, the system safely manages registration, account creation, billing and media uploads while collecting necessary context.<)0102 Example Embodiment 2: Page and Section Determination(00103] Based on the user's website goals, target audience, and other provided inputs, the system automatically determines an optimal set and sequence of pages to construct. For a photography portfolio site, this may include pages like 'About', 'Portfolio', 'Contact Us'. For an e-commerce site, pages may include 'Home', 'Shop', 'Product Details', 'Cart', 'Checkout'.

[0104] In certain particular example implementations according to the present disclosure, the system analyzes website purpose, business type, and user preferences to select applicable page types. It also determines the optimal information architecture and flow between pages to match user goals.

[0105] Within each page, the system further breaks down the structure into sections that will comprise the content blocks. This includes headers, footers, product galleries, text sections, media blocks, etc.

[0106] In certain particular example implementations according to the present disclosure, 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. For example, a 'About Us' page may contain blocks like 'Hero Image', 'Team Photo Grid', 'Text Bio Section', 'Testimonials Carousel'. The blocks are assembled into the final page template.[0010? ] This automated page and section modeling based on high-level user inputs enables personalized information architecture construction tailored to the website goals, rather than template selection. In certain particular example implementations according to the present disclosure, the system determines optimal website structure and content sequencing customized for each user.

[0108] Example Embodiment 3 : Media Analysis

[0109] Either during the media upload process (utilizing computational resources on the user’s device or machine) or after the user uploads media assets, like images and videos, the files are sent to the Al Vision Client module for content analysis (FIG. 9). Computer vision algorithms extract a variety of attributes including:• Prominent colors in images for palette generation• Detecting faces, objects and scenes through object recognition• Assessing technical qualities like sharpness, resolution, brightness• Identifying regions of interest or focal points in images

[0110] The Media Processing Unit selects the optimal media assets to include based on analysis results and specified website goals. Only high quality, relevant images are chosen. Focal points are identified by detecting faces, products or other salient regions.[0011 1 ] Additional embodiments could include:• Categorizing images (portraits, products, nature, etc.)• OCR text extraction from images for alt text• Detecting logos, landmarks or custom objects(00112] The extracted visual attributes are structured into metadata that informs prompt construction for the language models. The prompts describe images contextually such as "photo of bride and groom at wedding" or "image of person rock climbing outdoors".

[0113] An alternative embodiment uses the Photorefine Client App that runs locally on the user's device (FIGs. 10A-10C). This allows pre-processing media before uploading to analyze content, quality and focal points. The app reduces cloud costs and time by distributing workload.

[0114] In yet another embodiment, the PhotoRefine service can also be deployed in a self-hosted cloud environment managed by the user. This provides more control over data security and privacy compared to using a third-party public cloud provider. The self-hosted option also allows customization of the media analysis models and pipelines to better fit the user's specific use cases and datasets. Furthermore, running PhotoRefine as a self-hosted cloud service enables easy scaling of the media analysis workload by adding more servers to the back-end processing cluster. This allows the system to handle large volumes of media assets efficiently.[001 15] The multimedia analysis results are used throughout the system to optimize website design and content generation suited to the provided visual assets. Additional user feedback loops can be incorporated to improve extracted image attributes.[001 16] Example Embodiment 4: Content Generation using LLMs

[0117] In certain particular example implementations according to the present disclosure, the system utilizes a library of predefined blocks with parameters to render components like headers, content sections, media blocks etc. For these blocks, the system configures the parameters and generates text, media and metadata to populate the components.[001 18] Additionally, certain aspects of the present disclosure can construct completely new block types tailored to the user's website goals and context. Based on analysis of the userpreferences, target audience and content needs, the system determines optimal parameters for custom blocks:• Number of columns, rows, layout format,• Style including colors, fonts, spacing,• Appropnate media types (image, video, etc ),• Length and topics of auto-generated text content 001 19] These parameters are used to construct a prompt for the large language model (LLM) describing the context and requirements for the custom block. The LLM response populates the component properties to generate a tailored block type for that specific user. The flexibility to construct new blocks optimized for each user, combined with assembling predefined blocks, allows generating highly customized website designs and content flows. In certain particular example implementations according to the present disclosure, the system can build unique sites that match user needs more precisely than relying solely on preexisting templates.

[0120] These system- and / or method-related aspects may be used to mixes and matches auto-generated custom blocks with configurable predefined blocks to balance flexibility, personalization and rapid iteration for users. This example embodiment describes the core capability to automatically construct new components using Al that can extend system block libraries on demand.

[0121] Example Embodiment 5: Website Construction and Rendering

[0122] Once content has been generated by the large language model (LLM) as described above, the system constructs the website UI and renders it for user preview. The LLM produces responses containing content for each defined prompt. These prompts are designed to populate properties of UI components and blocks that will make up the website.

[0123] In certain particular example implementations according to the present disclosure, the system enumerates the block property responses and applies each to its respective UI component definition. This iterates for every block defined for the page.

[0124] With all UI components constructed for each block, the components are assembled into a complete page representation and saved to a database. For user preview, the page is loaded from the database into the website editor interface. Each UI component is converted to underlying HTML and CSS. Standard web rendering converts the HTML and CSS into a visual page for the user to review. This automates construction of a website pagematching the defined schema and personalized for the user based on the LLM-generated content.

[0125] If needed, the user can utilize the editor interface to modify the page before publishing online. The automated content generation and construction minimizes manual layout efforts for creating highly customized, engaging websites. The separation of UI component definition from content generation also provides flexibility. In certain particular example implementations according to the present disclosure, the system can construct web pages for different UI templates using the same LLM content.

[0126] In summary, certain aspects of the present disclosure leverages Al content creation and automated UI construction to streamline personalized website building with minimal human involvement. Users can rapidly deploy customized sites with engaging, relevant content.

[0127] Example Embodiment 6: Interactive Feedback Implementation

[0128] According to the present disclosure, at least one exemplary aspect 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. These options are offered through a conversational chatbot interface. This allows the user to interact in natural language to request changes or provide feedback. Based on the user feedback intent detected via the chatbot, the system performs targeted regeneration of specific sections as needed to match the requested changes.

[0129] The same automated content generation approach is utilized, injecting the user feedback context to tune the output. In addition, the system enables editing of both manually created pages, as well as pages automatically generated by the system. For manual pages, users can directly edit content through the page editor UI. For automated pages, the system interprets the requested changes from the chatbot and reconfigures the underlying page representation accordingly, without needing manual layout alterations. 00130] When a user edits a page created by the automated system, the system remembers the edit made by the user. So when the user requests the automated system to make any further changes, it leaves out the edits previously made by the user without overwriting.

[0131] After performing the requested edits and / or re-generating appropriate sections, the refined page is reassembled and rendered for the user to review the changes. If needed, further iterative feedback can be provided to make additional targeted tweaks. Otherwise the finalized page can be published after meeting the user's quality standards. This interactivefeedback implementation automates the iterative improvement process for both manually built and system-generated website pages

[0132] Example Embodiment 7 : Ongoing Automation

[0133] Once a site has been launched, the system provides ongoing automation to optimize and update the website without requiring manual efforts:• SEO Optimization - In certain particular example implementations according to the present disclosure, the system automatically enhances on-page elements including meta tags, alt text, content structure to improve SEO visibility based on the latest algorithms and best practices.• Mobile Validation - Pages are continually validated for mobile friendliness using Google Mobile Friendly tests and other mobile rendering diagnostics. Issues are automatically flagged.• Accessibility Validation - Color contrast, screen reader capability and other accessibility parameters are validated across pages to identify areas needing improvement.• Performance Monitoring - Page load times, resource usage and other performance metrics are tracked to detect degradations or optimization opportunities.• Promotion Recommendations - In certain particular example implementations according to the present disclosure, the system analyzes site content, user analytics and external events to suggest relevant promotions, notices, or messaging to display on the website.• Blog entries or creation of other time sensitive content is optimized.• Automatic Fixes - Where possible, the system automatically resolves detected issues, such as enhancing alt text, fixing mobile layouts, optimizing images.

[0134] Ongoing automation enables continually enhancing and monitoring websites after launch without ongoing manual oversight. Users receive recommendations for incremental refinements over time to maximize customer engagement.

[0135] Example Embodiment 8 : Automated Website Image Generation using Al

[0136] According to the present disclosure, at least one exemplary aspect provides embodiments for intelligently producing images and media assets to populate website pages by leveraging generative artificial intelligence models. 00137] Various image categories required for websites are synthesized tailored to the user's business and context:• Logos representing the brand identity and business are generated reflecting keywords, descriptions, and sample creatives provided by the user.• "About me" profile pictures are produced based on the user-provided demographic data including age, gender, ethnicity, profession etc.• Images depicting the services and products offered are created from details like business type and descriptions.• Believable profile photos are composed for testimonials and reviews based on reviewer demographics.• Watermarks embedding brand names, slogans or icons are algorithmically composed and overlaid on photographs.• Page background textures and illustrations are synthesized representing the brand sty le.

[0138] In certain particular example implementations according to the present disclosure, the system invokes generative Al models like DALL-E, MidJoumey and Google Vertex Al at appropriate stages to produce relevant images. The models transform the provided text and context into corresponding photorealistic or artistic renderings.

[0139] This automated image synthesis reduces manual overhead in sourcing visual assets during site creation. Websites populated with tailored images increase perceived trust and engagement. According to the present disclosure, at least one exemplary aspect democratizes access to brand-coherent media.

[0140] Example Embodiment 9: Expanded Automated Website Media Generation using Al

[0141] This embodiment builds upon Embodiment 8 by leveraging generative Al to produce videos, 3D objects, and audio in addition to images. Various media assets required for websites are synthesized tailored to the user's business or needs, as discussed below.

[0142] Promotional videos introducing the brand and offerings are composed from provided business descriptions and keywords. Al video generation models like RunwayML are utilized.

[0143] 3D models and animations of the products and services are rendered based on details like product sketches and usage contexts. Platforms like MetaHuman can create photorealistic 3D avatars.

[0144] Voiceovers explaining the business proposition and testimonials are synthesized using text-to-speech models, matching accents to provide speaker demographics.00145 Background music and sound effects representing the brand style are algorithmically generated based on emotional keywords and genre specifications. Magenta Al by Google can create original music compositions. 00146] Dynamic visual effects and transitions between media assets are added using video editing intelligence.

[0147] The generated videos, 3D objects and audio clips integrate cohesively with the website interface and theme. The expanded automated media synthesis further reduces manual efforts in sourcing assets and creates more engaging websites. Users without professional media creation skills can obtain production-quality assets tailored to their needs.

[0148] Example Embodiment 10: Expanded Automated Website Galleries using Al

[0149] This embodiment enhances website media galleries by leveraging Al to automatically generate compelling presentations of images and videos tailored to each user's unique needs. In certain particular example implementations according to the present disclosure, the system first analyzes metadata associated with the user's media assets as extracted by the PhotoRefine tool or other tagging or media analysis processes described in Example Embodiment 8. Using this metadata analysis, the system determines optimal gallery sty ling, color schemes, layouts, groupings, sequencing and narrative flows to create galleries aligned with the user's brand identity and website goals.

[0150] The large language model (LLM) generates textual narratives and captions to accompany each gallery. The text provides engaging descriptions and storytelling tailored to the photos or videos being showcased.

[0151] Advanced features are incorporated into the gallery using Al :• Animated slideshows are produced using pan / zoom effects, transitions, text / caption overlays matched to the visual sequencing.• Stylized videos are algorithmically generated from source images / footage using ML techniques like style transfer and motion graphics. This brings motion and dynamism to static galleries.• On-demand image / video synthesis creates additional media variations using generative Al, enabling unlimited fresh galleries.• Curated image collages and mosaics are composed automatically by sampling and blending gallery assets based on their visual properties.• Shoppable galleries enable visitors to directly purchase featured products through integrated e-commerce flows.

[0152] Accordingly, in some of the discussed aspects, the present disclosure is directed to using an Al algorithm (e.g., as executed by a data-processing computer circuit) to transform standard galleries into dynamically generated multimedia experiences that engage and convert visitors. Automation makes professional media curation capabilities accessible to everyday users. 00153] Example Embodiment 11: Automated Localization and Translation of Websites using Al

[0154] Embodiment 11 builds upon Embodiments 4, 5, 6 and 7 where 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 .

[0155] In certain particular example implementations according to the present disclosure, the system first prompts the user to specify the target language and country for generating a localized site variation.

[0156] The original text content is machine translated using advanced neural translation models like Google Translator or Meta Al. The translated text maintains contextual accuracy and content flow.

[0157] Site architecture is adapted by the system to match local conventions. This includes changes like:• Adapting date / number formats• Adjustment of layout and coded support for complex regional font’s• Adjusting directionality for right-to-left languages• Adding culturally appropriate imagery and icons• Following regional web design patterns• Mirroring regional information architecture norms• Ensuring compliance with geo-specific regulations• Allowing for rapid and broad based dynamic language switching for the website

[0158] User-provided media is analyzed using Al to detect text, signs, and objects that require localization. Detected text is translated or replaced. Images are filtered to match target cultural sensitivities.[00159| Prompts are constructed to invoke generative Al services like DALL-E to produce additional region-specific media like logos, illustrations, and photographs tailored to the locale. 00160] The translated content, adapted information architecture, and synthesized media are compiled into localized site variations optimized for the specified target geography and languages.

[0161] This embodiment enables global expansion and adaptation of websites through automated translation and localization. Al powered automation reduces the costs, resources and manual efforts needed for traditional multilingual web publishing.

[0162] Based upon the above discussion and illustrations throughout the present disclosure, those skilled in the art will readily recognize that various modifications and changes may be made to the various embodiments without strictly following the exemplary embodiments and applications illustrated and described herein. For example, although aspects and features may in some cases be described in individual figures, it will be appreciated that features from one figure can be combined with features of another figure even though the combination is not explicitly shown or explicitly described as a combination. Such modifications do not depart from the true spirit and scope of various aspects of the disclosure, including aspects set forth herein.

[0013] Similarly, 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.). Such devices may be configured to provide services to other circuit-based devices. Moreover, various other circuit-related terminology is used in a similar context as apparent to the skilled artisan, as is the case with each such apparatus which refers to or includes otherwise known circuit-based structures. As one specific example, a personal assistant device may be implemented a user endpoint device including a camera and / or graphic user interface (integrated directly with the user endpoint device or as a separate tool communicatively coupled (e.g., via a wired connection or a wireless connection) to the personal assistant device. Each such device (e.g., personal assistant device, endpoint device or other device) includes a communication circuit and / or data-processing computer circuits and those with a communication circuit are configurable to establish a network connection (e.g., communication sessions with other suchdevices over the Internet or other network). Such networks include but are not limited to a broadband network (such as the Internet or a cellular communications network), local area networks, device-to-device connections (e.g., Bluetooth).

[0164] In certain embodiments, such devices (e.g., one with a processing circuit and communications circuitry), are configured to execute (e.g., after downloading over a network) a set (or sets) of instructions (and / or configuration data) which can be in the form of software stored in and accessible from a memory circuit, and where such circuits are directly associated with one or more algorithms (or processes), the activities pertaining to such algorithms are not necessarily limited to the specific flows such as shown in the flow charts illustrated in the figures (e.g., where a circuit is programmed to perform the related steps, functions, operations, activities, etc., the flow charts are merely specific detailed examples). The skilled artisan would also appreciate that different (e.g., first and second) modules can include a combination of a central processing unit (CPU) hardware-based circuitry and a set of computer-executable instructions, in which the first module includes a first CPU hardware circuit with one set of instructions and the second module includes a second CPU hardware circuit with another set of instructions.

[0165] Certain embodiments are directed to a computer program product (e.g., nonvolatile memory device), which includes a machine or computer-readable medium having stored thereon, instructions which may be executed by a computer (or other electronic device) that includes a computer processor circuit to perform one or more of the operations / activities disclosed herein (e.g., as in the various example embodiments, specific execution of an Al algorithm, using or generating a model, etc.). In certain example environments, these instructions may reflect activities or data flows as may be exemplified by way of figures, flow charts, and the detailed description.

[0166] Example Embodiment 12: RAG-Enhanced Content Relevance and Accuracy

[0167] This embodiment enhances the automated website generation system described in previous embodiments by incorporating Retrieval-Augmented Generation (RAG) to improve content relevance and accuracy. The RAG system leverages existing user data, previously generated pages, and external knowledge bases to create more contextually appropriate and informative website content.

[0168] RAG Integration Process:• Knowledge Base Construction: The system creates and maintains a comprehensive knowledge base comprising: a. Existing user data (business information, preferences, target audience)b. Previously generated website pages and content blocks c. User-provided media assets and their associated metadata d. Industry-specific information and trends• Content Retrieval: a. When a new page or content block is requested, the system uses the user's input and context to query the knowledge base. b. The retrieval algorithm employs advanced semantic search techniques to find the most relevant information.• Relevance Scoring: a. Retrieved content is scored based on relevance to the user's specific needs, industry, and target audience. b. Factors considered include content freshness, source reliability, and alignment with user preferences.• Prompt Enhancement: a. 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.• 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.• Dynamic Content Updates: a. The system periodically re-queries the knowledge base to identify new relevant information. b. Website content is automatically updated to reflect industry' changes, emerging trends, or new user data.• Performance Feedback Loop: a. User engagement metrics and feedback on the generated content are collected and fed back into the knowledge base. b. This continually improves the relevance and effectiveness of future content generation.00169'| Implementation Details :• The RAG system utilizes a vector database for efficient similarity search of content.• Natural Language Processing (NLP) techniques are employed to understand the context and intent behind user inputs and retrieved information.• A custom ranking algorithm weighs various factors to determine the most relevant retrieved content for each specific use case.• The system employs techniques to maintain a diverse set of sources and viewpoints, avoiding bias in the generated content.[00T70] Benefits:1. Improved Relevance: Websites are populated with highly relevant, industryspecific content.2. Enhanced Accuracy: Fact-checking against the knowledge base reduces errors and misinformation.3. Up-to-Date Content: Regular updates ensure the website remains current with industiy trends.4. Personalization: Content is tailored based on user data and preferences, creating a more engaging user experience.5. Efficiency: The RAG system reduces the need for manual research and content curation.

[0171] This RAG-enhanced embodiment significantly improves the automated website generation process by creating more relevant, accurate, and up-to-date content, thereby increasing the value and effectiveness of the generated websites for users across various industries.

[0172] Example Embodiment 13: AI-Driven UI Modification for Enhanced Website Editing

[0173] This embodiment extends the capabilities of the automated website generation system by incorporating Al-driven modifications to the manual editing user interface (UI). The system leverages context and information about the website, web pages, or media being hosted to dynamically adjust the UI, providing users with more intuitive and efficient tools for making changes to their sites or pages.100174] Key F eatures :1. Context- Aware UI Adaptation: o The system analyzes the current website content, user preferences, expressed intent, and editing history to tailor the UI components. o UI elements such as color pickers, font selectors, and layout options are dynamically adjusted based on the website's existing design and branding.2. AI-Generated Custom Block Suggestions: o Building upon the media analysis described in Embodiment 3, the system presents a personalized set of custom-designed blocks in the UI. o These blocks are tailored to match the user's content, style, and objectives, facilitating rapid integration of cohesive design elements.3. Intelligent Asset Generation: o The Al generates and suggests graphical elements including logos, icons, illustrations, and animations specifically designed for the user's web pages or sites. o These assets are created to complement the existing design and enhance the overall visual appeal of the website.4. Conversational Editing Interface: o Incorporating the chatbot interface described in Embodiment 6, the system provides a natural language interaction method for making edits and changes. o Users can describe desired modifications in plain language, and the Al interprets these requests to apply appropriate changes to the website.5. Contextual Editing Prompts: o Based on analysis of the current page content and user behavior, the system suggests potential improvements or additions. o These prompts appear as non-intrusive notifications within the editing interface, guiding users towards optimizing their website's effectiveness.

[0175] Implementation Details :• The system employs machine learning algorithms and other Al mechanisms to continuously analyze user interactions with the editing interface, refining suggestions and UI modifications over time.• Natural Language Processing (NLP) techniques are used to interpret user inputs in the conversational interface and translate them into specific editing actions.• Computer vision algorithms analyze the visual elements of the website to ensure that Al-generated assets and suggestions maintain visual coherence with the existing design.

[0176] Benefits:1. Enhanced User Experience: The adaptable UI reduces the learning curve for website editing, making it more accessible to users with varying levels of technical expertise.2. Increased Efficiency: By presenting context-aware tools and suggestions, the system streamlines the editing process, allowing users to make changes more quickly and effectively.3. Improved Design Consistency: Al-generated assets and block suggestions help maintain the end user’s brand, a cohesive look and feel across the website, even as users make manual edits.4. Personalized Guidance: The system provides tailored advice and prompts, helping users optimize their websites without requiring extensive design or marketing knowledge.5. Reduced Cognitive Load: By simplifying complex editing tasks through intelligent UI adaptation, users can focus on content and creativity rather than technical details.

[0177] This Al-driven UI modification embodiment significantly enhances the usability of the website creation and editing process. By dynamically adapting the interface to each user's specific context and needs, the system bridges the gap between automated website generation and manual customization, enabling users to create and maintain professionalquality websites with greater ease and efficiency.

[0178] Example Embodiment 14: Semantic Image Search and Intelligent Content Generation for Event-Based Websites

[0179] This embodiment enhances the automated website generation system by incorporating advanced image analysis and semantic search capabilities. The system leverages multimodal embedding techniques and vector search to intelligently select and organize images, and then uses this information to guide the generation of contextually relevant text content using Large Language Models (LLMs).|00180] Key Components:1. Image Analysis and Embedding Service: o Calculates embeddings for uploaded images using multimodal embedding techniques. o Stores image vectors in a PostgreSQL database with the pgvector extension for efficient similarity search.2 Keyword Extraction Module: o Uses pre-calculated embeddings for a set of keywords covering objects, persons, occasions, photography styles, emotions, lighting conditions, etc. o Compares image embeddings with keyword embeddings to extract relevant metadata.3. Semantic Search Engine: o Enables search queries based on natural language descriptions or concepts. o Translates search queries into vector representations for comparison with image embeddings.4. Similarity Search Module: o Allows users to find visually similar images based on a reference image.5. Event Moment Detection: o Analyzes image metadata and embeddings to identify distinct moments or phases within an event (e.g., different stages of a wedding).6. Intelligent Content Generation: o Uses extracted image metadata and moment detection results to guide LEM-based text generation for web pages and blog posts.

[0181] Process Flow:1. Image Import and Analysis : o User uploads a collection of event photos (e.g., a wedding photoshoot). o The system calculates embeddings for each image and stores them in the PostgreSQL database. o The Keyword Extraction Module processes each image, tagging it with relevant metadata.2. Event Moment Detection: o The system analyzes the collection of images to identify distinct moments or phases of the event (e.g., detail shots, getting ready, ceremony, reception).o It clusters images based on their embeddings and extracted keywords to group them into these moments.3. Image Selection for Each Moment: o For each identified moment, the system uses semantic search to find the most representative and high-quality images. o It considers factors such as image quality, emotional impact, and diversity of shots within each moment.4. Content Structure Generation: o Based on the identified moments and selected images, the system creates a structure for the web page or blog post. o It determines the optimal sequence of moments to tell a cohesive story of the event.5. Text Content Generation: o For each moment, the system constructs a prompt for the LLM, incorporating:■ The moment type (e.g., "getting ready")■ Extracted keywords from the selected images (e.g., "bride", "excitement", "natural light")■ Any specific style or tone preferences set by the user o The LLM generates descriptive and engaging text content for each moment, tailored to the specific images and event context.6. Web Page or Blog Post Assembly: o The system combines the selected images and generated text content into a cohesive layout. o It applies appropriate styling and formatting based on the website's overall design theme.7. User Review and Refinement: o The generated content is presented to the user for review. o The user can request refinements, such as regenerating text for specific sections or selecting different images. o The system uses the similarity search function to offer alternative images if the user is unsatisfied with any selections.

[0182] Example Use Case: Wedding Photography Website1. A wedding photographer uploads a collection of 500 photos from a recent wedding.2. The system processes the images, calculating embeddings and extracting keywords.3. The Event Moment Detection module identifies key moments: detail shots, bride getting ready, groom getting ready, ceremony, cocktail hour, reception, and first dance.4. For each moment, the system selects the top 3-5 images using semantic search, considering factors like image quality and emotional impact.5. The system generates a blog post structure, deciding to present the moments chronologically.6. For each moment, it generates descriptive text. For example, for the "bride getting ready" moment: Prompt to LLM: "Describe the 'bride getting ready' moment of a wedding. Use these keywords from the selected images: 'excitement', 'laughter', 'sunlit room', 'white robe', 'bridesmaids'. Maintain a warm and joyful tone." Generated text: "As the morning sun streamed through the windows, the bride's suite was alive with excitement and laughter. Surrounded by her closest friends, the bride, wrapped in a soft white robe, savored these precious moments before the ceremony. The air buzzed with anticipation as the bridesmaids helped with final touches, each shared glance and smile capturing the joy of this special day."7. The system assembles the blog post, alternating between the selected images and generated text for each moment.8. The photographer reviews the generated content, requesting a different image for the ceremony section. The system uses similarity search to suggest alternatives, and the photographer selects a preferred option.9. After final adjustments, the system publishes the polished blog post to the photographer's website, showcasing the wedding story through a perfect blend of carefully selected images and engaging, context-aware text.

[0183] Screenshots of example websites, generated according to certain specific implementations of the present disclosure, are exemplified below.

[0184] The exemplary more-detailed system diagram (e.g., a system for experimental purposes) demonstrates generating a new webpage for an existing user. The following screenshot shows the user interface allowing the user to initiate creation of a new page. Theuser can select "Al Page" to leverage the automated system. In one example screenshot (not shown in the drawing), the user is prompted to provide the type of page to generate, such as “Home”, "About Us" or "Contact". This input sends a request to the system backend. Since the user already exists in the platform, the system retrieves the user's profde, website data, and other context from the database to inform the system to start generating the web page. Such a screen may prompt the user to input information with a query such as: Tell us what page you want to build? , followed by asking the user for the page name: , and then a “Generate” tab which the user may select to cause generation, by the Al Assist Engine, of an initial version of the website. (Note: blanks intentional to depict example).[00185 } In another screenshot (also not in the drawing), a portion of a page generated by the system is shown with the header and hero block. For the header, the visual design includes colors, fonts and styling that is synthesized based on analysis of the user's data. The hero image is selected by the system from the user's media gallery through computer vision analysis to identify the most relevant, high-quality image. The image is then processed to center the main subject's face for optimal composition and impact. Finally, the hero title and tagline text are constructed by a language model tailored to the user's business context and website goals. The screen may also include a user-selectable tab (“View Portfolio”) that permits the user to see a portfolio of images exemplifying the user’s work (e.g., with the user being a photographer or photography studio).

[0186] In other examples, screenshots exhibit the services section created automatically by the system. It contains three service offerings personalized to the user's photography specialty. The service descriptions and details are synthesized by the large language model. The model generates customized text explaining each service, options included. Additionally, the prices align with what customers would expect to pay for each of the services

[0187] FIG. 14A shows a screenshot that displays two additional auto generated sections - an image carousel and testimonials - for the purpose of showcasing the user's work The image carousel surfaces the best photographs from the user's portfolio, selected by visually analyzing the gallery to identify' high-quality, relevant images. The testimonials showcase positive customer feedback.

[0188] In certain applications of embodiments of the present disclosure, the language model can be used to generate reviews reflecting business types, along with reviewer names synthesized by a generative model (e g., as may generated via a third party that is paid to provide such reviews, fictional reviews, formatting a collection of cunent and / or historicalreviews related to a predecessor business, etc.). This provides initial persuasive testimonial content that the user can subsequently update with more recent customer data.

[0189] FIG. 14B shows a screenshot that exhibits the contact and footer blocks automatically created by the system. The contact section contains a form allowing prospective clients to get in touch with the user by providing their name, email, phone number and message. This enables lead generation. The footer section includes standard elements generated automatically based on conventions, including copyright text, links to privacy policy and cookie consent notices. Together, these synthesized sections provide a turnkey contact method for the user to receive inquiries, along with a compliant footer to conclude the page. The system automatically constructs these standardized components to minimize manual effort for a production-ready site.100190] In another example (also according to the present disclosure but not in the drawing), a screenshot showcases a beautifully designed preproduction web page created using the Al-driven UI modification embodiment for a wedding website. 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. A chosen color scheme (e.g., dominated by a thematic color or colors such as warm orange hues of the bridesmaids' dresses) can be balanced with another chosen color (e.g., the white of a bride's gown and natural green elements in the background). The system can also generate appropriate text content, such as describing the bridesmaids' presence and impact on the wedding day, which is perfectly aligned with the chosen imagery and color theme. The fonts for the title and body text can be chosen to enhance the romantic and joyful atmosphere of the page.

[0011] The above disclosure involving photography-specific screenshots are just a few of many examples of outputs of various example embodiments according to implementations of the present disclosure. In different example implementations, different outputs of the process include different types of websites that have significantly favorable characteristics which are generated automatically, and which can be modified in an iterative process by leveraging the prior version of the website. If there is no prior website and / or additional contextual data, the iterative process can take advantage of LLMs and other Al technology to output a website, web pages or modify the content therein to provide an end user experience which has a more favorable set of characteristics. Those characteristics include but are not limited to: website branding including color palette, logo, font and other branding elements;website look and feel including controls and positioning of control elements; information architecture of the website including site map; creation of descriptions for media and other content on the site; presentation of the content and media on the page (transforming the media into slideshows, video clips, audio clips, playlists and image transitions), and SEO (Search Engine Optimized) metadata for the website.

[0012] In the context of AI / ML, supervised and unsupervised learning may be applied. Further, a component for implementation of trained AI / ML computer processing may be configured to apply a ranker to generate relevance scoring (or weighting) to assist with any processing determinations with respect to any relevance analysis via individual relevance scoring metrics and / or grouped scoring. In some examples where weighting is used, weighting may be applied in a manner that prioritizes one relevance scoring metric over another depending on factors such as outcomes of search engine relevance scores and / or user settings to bias certain contexts. Such approaches may include comparing to preset thresholds as part of the analysis step(s). Further and unless specified otherwise (such as by functionality or “plurality of . . . ”), the use of certain terms (e.g., circuit, LLM and algorithm) in the singular may be interchangeable with the plural use of the same (e.g., circuits or circuitry, LLMs and algorithms).

[0193] Also, unless indicated otherwise, the term "website" is to be interpreted to encompass one or more of: multi-page websites; individual web pages; blogging sites; blog posts; landing pages; single-page applications (SPAs); web-based user interfaces; photography, videography and other digital media galleries; and other related forms of content presented through a web browser or similar internet-connected application.

[0194] It is recognized and appreciated that as specific examples, the abovecharacterized figures and discussion are provided to help illustrate certain aspects (and advantages in some instances) which may be used in the manufacture of such structures and devices. These structures and devices include the exemplary structures and devices described in connection with each of the figures as well as other devices, as each such described embodiment has one or more related aspects which may be modified and / or combined with the other such devices and examples as described hereinabove.

[0015] The skilled artisan would also recognize various terminology as used in the present disclosure by way of their plain meaning. As examples, the Specification may describe and / or illustrates aspects useful for implementing the examples by way of various semiconductor materials / circuits which may be illustrated as or using terms such as layers, blocks, modules, device, system, unit, controller, and / or other circuit-type depictions. Suchfeatures (e.g., structure and their associated functionalities) and whether semiconductor, circuit elements, circuits and / or related circuitry may be used together (sometimes in cooperative combination) with other aspects to exemplify how certain examples may be carried out in the form or structures, steps, functions, operations, activities, etc. It would also be appreciated that terms to exemplify orientation, such as upper / lower, left / right, top / bottom and above / below, may be used herein to refer to relative positions of elements as shown in the figures. It should be understood that the terminology is used for notational convenience only and that in actual use the disclosed structures may be oriented different from the orientation shown in the figures. Thus, the terms should not be construed in a limiting manner.

[0196] Based upon the above discussion and illustrations, those skilled in the art will readily recognize that various modifications and changes may be made to the various embodiments without strictly following the exemplary embodiments and applications illustrated and described herein. For example, methods as exemplified in the Figures may involve steps carried out in various orders, with one or more aspects of the embodiments herein retained, or may involve fewer or more steps. Such modifications do not depart from the true spirit and scope of various aspects of the disclosure, including aspects set forth in the claims.

Claims

What is Claimed:

1. A computer-implemented method comprising: analyzing, for a particular user (e.g., individual or entity), inputs and media assets; constructing a set of context-aware prompts, each of the context-aware prompts being associated with one or more contexts discerned in response to the user inputs and media assets being analyzed; generating, via an LLM (large language modeling) algorithm that is computer- executed based on the constructed context-aware prompts, website content that includes text, code, and media elements; and assembling the generated content into a website structure that is optimal to the particular user in that the website structure is tailored to goals of the particular user and is consistent with attributes of the media assets.

2. The computer-implemented method of claim 1, wherein metadata is included as part of the inputs and media assets, the method further including: extracting the goals of the particular user from at least one of: the user inputs and media assets, and one or more responses to the set of context-aware prompts; and discerning that the website structure is optimal for a particular user based at least in part on the goals and media-based attributes included as part of the metadata.

3. The computer-implemented method of claim 1, further including the step of discerning that the website structure is optimal for a particular user based at least in part on a plurality of media-based attributes of the metadata from among: one or more colors, one or more geolocations, one or more categories of a type of or particular object, one or more categories or attributes of a type of or particular individual, one or more camera settings, and one or more color pallets.

4. The computer-implemented method of claim 1, further including creating and modifying the customized website content to render the customized website content tailored for at least one of different audiences and different geographies, wherein the creating and modifying involves one or more of the following: translation of written content, translation of audio content and soundtracks, translation of content inside an image, and modification ofimages to make them particular for a revised characterization of a target audience to which the customized website structure is directed.

5. The computer-implemented method of claim 1, further including modifying, based on a revised or different data set related to the inputs and media assets, the customized website structure and / or branding for a revised characterization involving a target audience of a target geographical region, wherein the customized website structure is modified to adhere to one or more of the following: local site design norms and to allow for differences in linguistic and written communications, and changes in branding associated with the user, wherein the branding includes at least one of color, font, logo.

6. The computer-implemented method of claim 1, further including: receiving, over a network at core-application logic circuitry hosted via a data- communications platform, the inputs and media assets from an application or web browser enabled through a user interface operated at a computing-data-processing node by the user; and via at least one of the core-application logic circuitry and the LLM algorithm, discerning the one or more contexts of the inputs and media assets.

7. The computer-implemented method of claim 1, further including receiving responses to the constructed context-aware prompts; using the LLM algorithm to create custom content blocks based on the responses to the constructed context-aware prompts; and using a library of predefined website content blocks with configurable parameters and alongside customized content blocks that are created dynamically using the LLM algorithm operating in response to the received responses to the constructed context-aware prompts.

8. The computer-implemented method of claim 6, further including using the coreapplication logic circuitry to manage manually -edited content for the website and, in response, to create website content that is continuously updated by automated computer processing that uses the manually-edited content to drive one or more artificial intelligence and / or machine learning algorithms.

9. The method of claim 1, further comprising ongoing automated refinement of the generated website content, including search-engine optimization, validation of the generated website content, and website performance monitonng after launch of the website.

10. A computer program product comprising computer-readable instructions stored on a non-transitory computer-readable medium that, when executed by a computing data processor, cause the computing data processor to: analyze, for a particular user (e.g., individual or entity), inputs and media assets; construct a set of context-aware prompts, each of the context-aware prompts being associated with one or more contexts discerned in response to the user inputs and media assets being analyzed; generate, via an LLM (large language modeling) algorithm that is computer-executed based on the constructed context-aware prompts, website content that includes text, code, and media elements; and assemble the generated content into a website structure that is optimal to the particular user in that the website structure is tailored to goals of the particular user and is consistent with attributes of the media assets.

11. An apparatus comprising: an analysis module, as part of computer-based circuitry, to analyze, for a particular user, inputs and media assets; a prompt construction module, as part of the computer-based circuitry, to construct a set of context-aware prompts, each of the context-aware prompts being associated with one or more contexts discerned in response to the user inputs and media assets being analyzed; content generation module to generate, via an LLM (large language modeling) algorithm that is computer-executed based on the constructed context-aware prompts, website content that includes text, code, and media elements; and a site assembly module to assemble the generated content into a website structure that is optimal to the particular user in that the website structure is tailored to goals of the particular user and is consistent with attributes of the media assets.

12. The apparatus of claim 11, wherein the computer-based circuitry is to compile the generated content into the website structure.

13. The apparatus of claim 11, wherein the LLM algorithm, when executed, is to process the one or more discerned contexts and / or the user inputs and media assets via a plurality of disparate LLMs, wherein in response to each of the disparate LLMs providing feedback, the computer-based circuitry assesses and compares the feedback to the user’s input and mediabased assets.

14. The apparatus of claim 11, further comprising a feedback module enabling iterative refinement of generated content through conversational interaction between the particular user and the computer-based circuitry'.

15. The apparatus of claim 11, further comprising an AI / ML module that is to interact with a manual website editing user interface and modify the website structure based on attributes and metadata of the website content, on the goals of the particular user and the attributes used in creation of the website.

16. The apparatus of claim 11, further comprising a media analysis module to operate locally on one or more user devices by preprocessing media assets before the media assets are to be uploaded to the computer-based circuitry.

17. The apparatus of claim 11, further comprising an ongoing automation module to continually optimize and monitor the website after the website is launched.

18. The apparatus of claim 11, further comprising a content block builder including: a block definition loader, a user-context loader, a prompter to build prompts, a post-processing module to unpack and validate one or more responses by the LLM algorithm to the built prompts, and a virtual-content block building module to construct user interface components.

19. The apparatus of claim 11, further comprising a communications module to coordinate communications involving one or more responses by the LLM algorithm and the generated prompts which are to be used by the LLM algorithm.

20. The apparatus of claim 11, further comprising a LLM Model Selection module to formulate one of the constructed one of the context-aware prompts and to choose an appropriate model based on parameters for each available LLM, including the model name, provider, evaluation score, text throughput, API limitations, token limitations, and privacy considerations.

Images