Atomic Content Segmentation for Personalized Web Interfaces
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Solution Overview
Problem
Content management systems (CMS) struggle to provide personalized web content for individual users, as they are either limited to generic content or require significant time and resources to adapt to new use cases and content types, and face challenges in identifying and addressing errors in dynamically generated content.
Innovation Solution
A content platform that receives web pages created by a CMS, divides them into atomic elements, and generates personalized content by processing these elements to create a content interface based on user data, allowing for controlled personalization without altering the overall look and feel specified by the content creator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a CMS uses machine learning models to generate personalized content for users, then content personalization is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the web page into atomic elements (text, images, videos, etc.) that can be independently processed and personalized. This segmentation allows the CMS to apply machine learning models selectively to specific content elements rather than generating entire pages from scratch, reducing system complexity while maintaining personalization capabilities.
Solution Approach 2:
The patent implements a pre-compiled template system where the overall page structure and layout are predetermined by content creators. This preliminary action eliminates the need for machine learning models to generate the entire page structure, allowing personalization to focus only on specific content elements within the fixed template framework, thereby reducing computational complexity.
2Adaptability or versatility
If a CMS uses machine learning models to generate personalized content, then content personalization is improved, but the time and resources required to modify content increase
Solution Approach 1:
By dividing content into atomic elements, the system allows content creators to modify individual elements or groups of elements independently. Changes can be made to specific content types (e.g., only text elements or only image elements) without affecting the entire page or requiring retraining of machine learning models, significantly reducing modification time.
Solution Approach 2:
The patent creates a universal template system and standardized atomic element structure that can be applied across multiple web pages and content types. Once a template and element structure are defined, they can be reused and modified systematically, allowing rapid adaptation to new use cases without creating custom machine learning models for each scenario.
3Adaptability or versatility
If a CMS generates content on-the-fly for each user, then content personalization is improved, but error detection and correction become difficult
Solution Approach 1:
The atomic element segmentation allows each content element to be independently validated and error-checked. Since elements are discrete and standardized, validation rules can be applied to each element type separately, making it easier to detect and correct errors in generated content compared to monolithic page-generation approaches.
Solution Approach 2:
The patent uses template copying where standardized atomic element structures are replicated across multiple pages. This copying approach ensures consistency and allows errors to be detected by comparing generated content against the known-good template structure, facilitating easier error identification and correction.
4Ease of operation
If a CMS provides generic content without personalization, then system simplicity is maintained, but user-specific content delivery is insufficient
Solution Approach 1:
The patent segments content into atomic elements that can be independently personalized while maintaining the overall page structure. This allows the system to remain simple in terms of template design and content creation, while adding personalization capability at the element level through machine learning models, thus achieving both simplicity and adaptability.
Solution Approach 2:
The patent applies personalization selectively to specific atomic elements within a page rather than to the entire page structure. This local quality approach allows the CMS to maintain simple, creator-defined templates for the overall layout while applying user-specific personalization only to relevant content elements, balancing system simplicity with personalization capability.
Data Source
AI summary
In various embodiments, a content platform receives, from a user device, a request for a web page. In response to receiving the request for the web page, the content platform retrieves one or more page construction items associated with the web page and one or more page content items associated with the web page, wherein each page construction item included in the one or more page construction items indicates a structure of a different element included in the web page and each page content item included in the one or more page content items indicates a content of a different element included in the web page. The content platform generates a content interface associated with the web page based on the one or more page construction items, the one or more page content items, and user data associated with a user of the user device.