Context-Sensitive Search Query Generation for Multi-Engine Result Ranking
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Solution Overview
Problem
Existing search engines rely solely on query terms to assess and rank search results, which is inadequate for representing a user's complex work context, and they struggle to combine results from multiple sources due to differing scoring algorithms and presentation formats.
Innovation Solution
A system that automatically generates search queries based on a rich model of the user's current work context, including text from documents and other contextual factors, to assess, rank, and organize search results, allowing for the integration of results from multiple search engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines use query terms alone to assess and rank search results, then the search process is simple and fast, but the relevance accuracy deteriorates because the query cannot represent complex user work context
Solution Approach 1:
The patent introduces a context model as an intermediary representation that captures user work context without requiring direct complex queries to search engines. The context model serves as a mediator between the complex user context and the search engine, allowing accurate relevance assessment while maintaining search engine simplicity. The context model includes user profile, document context, and task context components that collectively represent the user's work situation.
Solution Approach 2:
The patent segments the user context into multiple independent components: user profile (role, organization), document context (current document properties), and task context (task type, stage). This segmentation allows each component to be processed and combined systematically to form a comprehensive context model, improving relevance accuracy while managing complexity through modular organization.
2Adaptability or versatility
If search engines limit query length and format, then the search engine operation is simplified, but the ability to represent complex user context deteriorates
Solution Approach 1:
The context model acts as an intermediary that translates complex user context into a structured representation compatible with search engine constraints. It captures nuanced context information (user role, organization, task stage) and transforms it into a format that search engines can process, thereby maintaining versatility while respecting search engine simplicity constraints.
Solution Approach 2:
The patent changes the parameters of context representation from raw query text to a structured context model with specific fields (user profile, document context, task context). This parameter transformation allows complex context to be represented within search engine limitations while preserving full contextual information for accurate search result assessment.
3Adaptability or versatility
If search engines use different scoring algorithms for different sources, then each source can be optimized, but the ability to combine results from multiple sources deteriorates
Solution Approach 1:
The patent applies equipotentiality by creating a unified context model that serves as a common reference frame for assessing search results from multiple sources. Instead of allowing each source to use its own scoring algorithm independently, the context model provides a standardized basis for evaluating all sources, enabling consistent ranking and reliable combination of results while preserving source-specific optimizations.
Data Source
AI summary
Methods and apparatus assessing, ranking, organizing, and presenting search results associated with a user's current work context are disclosed. The system disclosed assesses, ranks, organizes and presents search results against a user's current work context by comparing statistical and heuristic models of the search results to a statistical and heuristic model of the user's current work context. In this manner, search results are assessed, ranked, organized, and/or presented with the benefit of attributes of the user's current work context that are predictive of relevance, such as words in a user's document (e.g., web page or word processing document) that may not have been included in the search query. In addition, search results from multiple search engines are combined into an organization scheme that best reflects the user's current task. As a result, lists of search results from different search engines can be more usefully presented to the user.