Semantic Content Modification for Search Relevance
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Solution Overview
Problem
Conventional search systems fail to dynamically customize digital content in real-time to match user queries effectively, leading to irrelevant search results and inefficient user experience in electronic environments.
Innovation Solution
The system analyzes search queries to determine semantic relationships between identifiers and terms in a database, modifying retrieved content to include query identifiers, thereby providing customized and relevant results dynamically through machine learning and content modification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search systems are used to retrieve content, then users can access digital content through search engines, but the search results do not closely match user queries and require users to sift through multiple pages
Solution Approach 1:
The system performs preliminary analysis of search queries to determine semantic relationships between identifiers and terms before retrieving content. This pre-processing of query understanding enables more accurate matching without requiring users to manually sift through irrelevant results, directly improving search accuracy and reducing time to find desired content
Solution Approach 2:
The patent replaces traditional mechanical keyword-matching search mechanisms with semantic analysis and machine learning processes. Instead of simple string matching, the system uses semantic relationships and identifiers to dynamically modify and rank content, achieving superior match precision without increasing user effort or time
2Adaptability or versatility
If static content is provided to users, then content delivery is simple, but the content cannot be dynamically customized to match current user interests
Solution Approach 1:
The system transforms static content delivery into a dynamic process by continuously analyzing search queries and modifying content based on real-time semantic relationships. Content is dynamically customized to match current user interests through identifier-based modifications, while the underlying architecture remains manageable through automated machine learning processes
Solution Approach 2:
The system performs self-service by automatically analyzing queries, determining semantic relationships, and modifying content without requiring manual intervention. The machine learning processes autonomously adapt content to user interests, reducing the operational complexity despite the enhanced adaptability
3Loss of substance
If content providers are separated from delivery systems, then content delivery can be tracked and user information collected, but content changes occur after the fact and may not track to current interests
Solution Approach 1:
The system implements feedback loops where user search queries are analyzed in real-time to determine semantic relationships and modify content accordingly. This continuous feedback mechanism ensures content adapts to current user interests rather than relying on post-factum changes, while maintaining the separation between content providers and delivery systems
Data Source
AI summary
Systems and methods are disclosed to modify content in accordance with a query and based at least in part on semantic relationships between terms in the content and query. An initial determination is performed for first identifiers from a query. A further determination is performed for a first semantic relationship that includes a first measure between the first identifiers. A second semantic relationship with second measures of relationships is determined between each of the identifiers and a plurality of terms in a database of terms. A search is performed of a content database using the first identifiers. Retrieved titles associated with a match from the search are subject to modification in their respective titles based at least in part on the first measure and the second measures. Content with the modified title is provided to a client device.