Contextual Query Disambiguation via Rule Applier System
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Solution Overview
Problem
Conventional search engines face difficulties in disambiguating user intent behind queries due to their underspecified or ambiguous nature, leading to irrelevant search results and user dissatisfaction.
Innovation Solution
A computing system that includes a processor and memory with a rule applier system, which assigns context to queries, identifies content retrieval rules, and executes these rules to reformulate queries or retrieve relevant content based on user context, such as recent search history and interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search engines process queries without contextual analysis, then processing speed is maintained, but search result relevance deteriorates
Solution Approach 1:
The system performs preliminary context analysis by examining user history, current page, and search session data before processing the query. This preliminary action assigns contextual metadata to the query, enabling more accurate search results without adding complexity to the core search processing pipeline.
Solution Approach 2:
The patent introduces a context assignment module as an intermediary between query reception and search execution. This mediator analyzes multiple data sources (user profile, session history, current page) and transforms them into contextual representations that guide the search process, isolating the complexity from the core search engine.
2Measurement precision
If contextual information is collected and analyzed, then user intent accuracy is improved, but processing time increases
Solution Approach 1:
Contextual data such as user profiles, search history, and page metadata are collected and pre-processed in advance, before the actual query is submitted. This preliminary preparation ensures that when the query arrives, the contextual analysis can be performed quickly using pre-available information.
Solution Approach 2:
The system applies context assignment selectively based on query characteristics. For ambiguous queries requiring disambiguation, full contextual analysis is performed. For clear, unambiguous queries, minimal or no context assignment is needed, reducing processing overhead while maintaining accuracy where it matters most.
3Reliability
If multiple data sources are integrated for context assignment, then contextual accuracy is improved, but system complexity increases
Solution Approach 1:
The context assignment module serves multiple functions: it analyzes user profiles, examines search session history, evaluates current page content, and synthesizes these diverse data sources into a unified contextual representation. This multi-functional approach consolidates complexity into a single versatile component rather than requiring separate processing systems for each data source.
Data Source
AI summary
Various technologies related to generating and applying content retrieval rules are described herein. A content retrieval rule maps a combination of a query and a context to one of a query reformulation or content. The content retrieval rule is learned from search logs of a search engine, and is applied when the query having the context is received at the search engine.