Semantic Relevancy Scoring for Content Editing
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Solution Overview
Problem
Existing web-based editing applications lack effective methods to enhance content production by aligning user-generated content with the semantic depth and breadth of previously indexed content by search engines, resulting in suboptimal search engine rankings.
Innovation Solution
An AI-based content editing system that generates a content model for user input, compares it with a target content derived model using vector operations, and provides real-time feedback to users through recommendations, ensuring semantic relevancy and increasing the likelihood of higher search engine rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional grammar and spelling checkers are used, then basic writing errors are detected, but semantic depth and search engine optimization are not improved
Solution Approach 1:
The patent introduces content models as intermediary representations that bridge user content and search engine requirements. These models include semantic concepts, entities, and relationships, allowing the system to analyze semantic depth without directly complex interactions between the editor and search engine algorithms.
Solution Approach 2:
The system implements feedback loops where content models are continuously refined based on comparison with target content models. Relevancy scores provide feedback to users about semantic completeness, enabling iterative improvement of content quality and search engine optimization.
2Productivity
If real-time content analysis is performed, then immediate feedback is provided, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary analysis by generating content models incrementally as users type, rather than analyzing complete content. Target content models are pre-established from high-ranking search results, enabling faster comparison and feedback during the writing process.
Solution Approach 2:
The content analysis is segmented into manageable components: individual content models for different semantic concepts, entities, and relationships. This segmentation allows parallel processing and reduces the computational burden of analyzing entire documents at once.
3Reliability
If comprehensive semantic analysis is implemented, then content quality and search rankings improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The system creates simplified copies of search engine requirements through target content models, which replicate the essential semantic structure of high-ranking content. These models serve as templates that guide content creation without requiring direct integration with complex search engine algorithms.
Solution Approach 2:
The content modeling system serves multiple functions: it analyzes semantic depth, compares with target content, generates relevancy scores, and provides editing recommendations. This multi-functionality reduces the need for separate specialized systems while achieving comprehensive content optimization.
Data Source
AI summary
A method of content production (e.g., content editing) using content modeling to facilitate content production. In one embodiment, an automated process is configured to render content. For a given content portion, and as the given portion is being rendered, the portion is processed to generate a content model. With respect to a concept expressed in or otherwise associated with the content, the system compares the content model with a target content derived model to generate a relevancy score. The target content derived model is generated by (a) identifying a set of target content portions in which the concept is expressed, (b) generating from each content portion an associated target content model; and (c) performing a vector operation on the associated target content models. Preferably, each associated target content model is built using an Artificial Intelligence (AI)-based content analysis. The relevancy score is used to generate a content production recommendation.