Query Processing for Local Search Relevance
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Solution Overview
Problem
Current search engines fail to provide quality results for local queries as they do not effectively process user-entered search terms to distinguish between general and local business information, leading to unsatisfactory results when users enter queries in a non-structured manner or use the general search box.
Innovation Solution
A computer-implemented method and system that processes user queries by parsing and normalizing terms using a probabilistic dictionary and Hidden Markov Model to identify business and location entities, generating an optimized search query that incorporates modifiers and constraints to improve search relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If search engines use general search boxes for all queries, then ease of operation is improved, but search result quality for local queries deteriorates
Solution Approach 1:
The patent segments the search process by introducing specialized search boxes for local business queries alongside the general search box. This segmentation allows users to choose the appropriate search type, while the system automatically detects and routes queries to specialized processing when local business intent is identified, thereby maintaining ease of operation while improving search result quality for local queries.
Solution Approach 2:
The patent introduces an intermediary query processing system that sits between the user input and the search databases. This intermediary layer analyzes user queries, detects local business intent, and automatically routes queries to specialized local business databases or applies appropriate processing, thereby improving search result quality without requiring users to manually select specialized search boxes.
2Manufacturing precision
If search engines require structured input for specialized searches, then search result quality is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the search system to automatically detect query type and structure queries appropriately without requiring user intervention. The system analyzes user input, identifies local business intent, and automatically applies specialized processing or routes to appropriate databases, thereby maintaining ease of operation while improving search result quality.
Solution Approach 2:
The patent applies preliminary action by pre-processing user queries to detect intent and structure data before the actual search execution. The system analyzes queries in advance, identifies local business entities, and prepares optimized search parameters, thereby ensuring high search result quality while keeping the user interface simple and easy to operate.
3Manufacturing precision
If search engines access multiple specialized databases, then search result quality is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by creating a unified search interface and processing system that handles both general and specialized local business queries. The system incorporates multiple database access capabilities within a single search engine architecture, allowing it to serve multiple functions (general search, local business search, hybrid search) without requiring separate systems, thereby improving search result quality while managing device complexity.
4Manufacturing precision
If search engines perform extensive query analysis, then search result quality is improved, but processing time increases
Solution Approach 1:
The patent applies local quality by implementing targeted query analysis that focuses computational resources on specific aspects of the query based on detected intent. When local business intent is identified, the system applies specialized analysis and database access; for general queries, it uses standard processing. This selective approach improves search result quality for local queries while minimizing unnecessary processing time for other query types.
Data Source
AI summary
A computer-implemented method for processing user entered query data to improve results of a search of pages using a database, when searching the internet, is disclosed. The method includes receiving the user entered query data and parsing each word of the query data and segmenting words using probability to determine a likelihood that the word is for a particular name. And, associating the particular names with a name tag to create one or more tagged name terms. Then, normalizing each of the tagged name terms and the normalizing including boosting information if found in the database and determining proximity between selected ones of the tagged name terms. The method then generates an optimized search query that incorporates normalized terms and operators. The optimized search query being applied to the internet to enable search results to be produced and displayed to the user in response to the entered query data.