Semantic Search Query Refinement for Mobile Interfaces
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Solution Overview
Problem
Current search engines struggle to provide relevant expanded search results by relying on historical data, leading to user frustration, especially on mobile devices where inputting and editing search queries is cumbersome due to limited display space and the need for typed input.
Innovation Solution
A search management system that utilizes query semantics to identify key terms and provide suggested replacement terms, allowing users to edit and manipulate search queries through graphical user interfaces and touch gestures, enabling more precise search queries on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines use historical data to identify expanded search results, then search result volume increases, but search result relevance deteriorates
Solution Approach 1:
The patent replaces the traditional mechanical approach of matching search queries to historical data with a semantic analysis system that understands the meaning and context of search terms. The semantic parser analyzes the intent behind search queries and identifies relevant entities, concepts, and relationships, substituting simple keyword matching with intelligent semantic understanding to improve result relevance while maintaining volume.
Solution Approach 2:
The system changes the parameters used for search expansion from simple historical frequency matching to multi-dimensional semantic analysis including entity recognition, concept relationships, contextual understanding, and user intent analysis. This parameter transformation allows the system to generate relevant expanded results based on semantic meaning rather than just historical occurrence patterns.
2Measurement precision
If users manually edit search queries on mobile devices, then search query precision improves, but user effort and time increase
Solution Approach 1:
The system enables search queries to self-optimize by automatically analyzing the user's input, identifying potential improvements, and presenting suggested refinements. The semantic parser detects ambiguities, missing information, and optimization opportunities, then the system autonomously generates corrected or enhanced query versions, allowing the search system to serve itself rather than requiring constant manual user intervention.
Solution Approach 2:
The system implements a feedback loop where search results and user interactions are analyzed to provide suggestions for query improvement. The system monitors search patterns, identifies common errors or suboptimal queries, and feeds this information back to users as actionable suggestions, enabling continuous query refinement without requiring users to manually edit every query from scratch.
3Adaptability or versatility
If search engines return expanded search results based on historical data, then search coverage increases, but user interest alignment deteriorates
Solution Approach 1:
The system performs preliminary semantic analysis of search queries before generating expanded results, pre-identifying user intent, relevant entities, and contextual constraints. By analyzing the semantic structure and user goals in advance, the system can pre-filter and pre-rank potential expanded results to ensure they align with user interest before presentation, rather than generating all possible expansions and filtering afterward.
Solution Approach 2:
The system segments the search result expansion process into distinct semantic components: entity recognition, concept identification, relationship analysis, and context evaluation. Each segment handles a specific aspect of semantic understanding, allowing the system to maintain comprehensive search coverage while ensuring each segment contributes to maintaining user interest alignment through targeted semantic analysis.
Data Source
AI summary
The present disclosure is directed toward systems and methods for utilizing semantic information in association with a search query. For example, one or more embodiments described herein identify key terms within a search query and utilize semantic information associated with the identified key terms to provide suggested replacement terms. A user can select one or more suggested replacement terms to broaden or refine a search query so as to add more meaning and specificity to the search query. Furthermore, one or more embodiments provide unique and interactive user interfaces to allow users to efficiently refine and improve search queries when using mobile devices with smaller or more limited display and input capabilities.