Dynamic Site Compilation With AI Tagging for Faster Content Delivery
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Solution Overview
Problem
Website development systems face challenges in optimizing a mix of static and dynamic content delivery for non-technical users, leading to performance issues like slow loading times and increased server load due to repetitive content queries.
Innovation Solution
A system and method for dynamic site compilation in a cloud-based content hub environment that allows users to mark components as static or dynamic, using an AI/ML engine for analytics-driven tagging, reducing CMS queries by compiling static content during site creation, and offloading repetitive calls to the CMS server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic content is used to keep website updated, then content freshness is improved, but loading speed deteriorates
Solution Approach 1:
The patent segments website content into static and dynamic components, allowing each to be handled differently. Static content is compiled and cached for fast loading, while dynamic content is fetched on-demand from the CMS. This segmentation resolves the contradiction by enabling both fast loading (for static parts) and content freshness (for dynamic parts) simultaneously.
Solution Approach 2:
The patent implements preliminary compilation of static content during the website building phase. By pre-compiling and caching static content before the user visits the site, the system eliminates the need to query the CMS for static content on each request, thereby improving loading speed while maintaining content freshness through selective dynamic fetching.
2Speed
If static content is cached locally, then loading speed is improved, but content updates deteriorate
Solution Approach 1:
The patent introduces dynamic marking on content items, allowing the system to automatically determine whether content should be treated as static (compiled and cached) or dynamic (fetched on-demand). This dynamic classification mechanism resolves the contradiction by enabling fast loading for appropriately marked static content while maintaining the ability to update content when marked as dynamic.
Solution Approach 2:
The patent implements a feedback mechanism where content creators can mark content items as static or dynamic, and the system automatically adjusts the rendering behavior accordingly. This feedback loop ensures that content is compiled and cached when appropriate for performance, while still allowing updates when content is marked as dynamic, thus resolving the contradiction between loading speed and content updates.
3Adaptability or versatility
If CMS queries are executed on every page load, then content freshness is maintained, but server load increases
Solution Approach 1:
The patent extracts static content queries from the runtime page load process and moves them to the compilation phase. By pre-fetching and compiling static content during website building, the system removes repetitive CMS queries from each page load, significantly reducing server load while maintaining content freshness through selective dynamic fetching only when necessary.
Solution Approach 2:
The patent performs preliminary CMS queries during the website compilation phase rather than during runtime page loads. By executing queries upfront to fetch and compile static content, the system reduces the number of repetitive queries to the CMS server during normal operation, thereby reducing server load while maintaining content freshness for dynamically marked content.
4Adaptability or versatility
If complex content management queries are used, then content flexibility is improved, but query performance deteriorates
Solution Approach 1:
The patent executes complex CMS queries during the website compilation phase as a preliminary action, rather than during runtime page loads. By performing these queries upfront to fetch and compile static content, the system batches the complexity of content management queries into a single compilation process, significantly improving query performance while maintaining content flexibility through the ability to mark individual content items as dynamic when needed.
Data Source
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AI summary
Described herein are systems and methods automatic selection and suggestion for dynamic site compilation in a cloud-based content hub environment. In accordance with an embodiment, embodiments provide an artificial intelligence/machine learning (AI/ML) engine that monitors and collects both content and consumption analytics associated with content items on a webpage. Based upon an analysis of such metrics, automatic suggestions can be generated for content to be tagged such that the content item can be statically compiled with the website (optimized for viewing speed and user experience), or dynamically fetched/loaded on a load or refresh of the website.