Query Disambiguation Framework Using Contextual Data
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Solution Overview
Problem
Existing search tools often fail to provide accurate results when users submit ambiguous or imprecise queries, leading to either too many irrelevant results or insufficient relevant information, due to the requirement for precise query formulation.
Innovation Solution
A computer-implemented method that receives a user query, calculates a confidence metric, and if below a threshold, supplements the query with contextual data, applying disparate matching techniques to enhance search results, including initial and further sets of matched responses, to improve the quality of matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the search tool requires precise query formulation, then the accuracy of search results is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary disambiguation framework that sits between the user's ambiguous query and the search results. This framework includes components such as a query analyzer, context extractor, and result disambiguator that automatically interpret the user's intent without requiring the user to formulate precise queries. The system acts as a mediator that translates imprecise user input into accurate search results.
Solution Approach 2:
The system performs self-service by automatically detecting ambiguity in queries, extracting relevant context, and disambiguating results without requiring user intervention. The framework includes automated mechanisms for identifying ambiguous queries, gathering contextual information from multiple sources, and selecting the most relevant results based on inferred user intent, all without requiring the user to reformulate their query.
2Quantity of substance
If the search tool provides multiple search results for ambiguous queries, then the completeness of information is improved, but the loss of information increases due to irrelevant results
Solution Approach 1:
The patent applies local quality by treating different search results differently based on their relevance to the inferred user intent. Instead of uniformly presenting all possible results, the system evaluates each result individually using relevance criteria derived from contextual analysis. Highly relevant results are prioritized and presented prominently, while less relevant results are either downgraded or excluded, ensuring that the information presented is both complete and relevant.
Solution Approach 2:
The system changes parameters such as relevance thresholds, context weights, and result ranking criteria dynamically based on the analyzed query context. By adjusting these parameters according to the specific ambiguous query and extracted context, the system optimizes the balance between providing complete information and filtering out irrelevant results for each specific search scenario.
3Device complexity
If the search tool uses a single matching technique, then the device complexity is reduced, but the reliability of search results deteriorates when queries are ambiguous
Solution Approach 1:
The patent segments the search process into distinct modular components: initial query matching, ambiguity detection, context extraction, and result disambiguation. Each component performs a specific function using appropriate techniques for that stage. This segmentation allows the system to use simple matching for initial retrieval while applying more complex analysis only when needed, maintaining reliability for ambiguous queries without unnecessarily complicating the entire system.
Solution Approach 2:
The system dynamically adjusts the complexity of matching techniques based on the detected ambiguity of the query. For clear, unambiguous queries, simple matching techniques are used. For ambiguous queries, the system activates additional context extraction and disambiguation mechanisms. This dynamic adaptation allows the system to maintain simplicity for common cases while ensuring reliability for challenging ambiguous queries.
Data Source
AI summary
Methods, software and devices for responding to a user query are disclosed. A user query received from a user is matched to stored responses to obtain initial matched responses. A confidence metric indicating the likelihood of the initial matched responses satisfying the user query is calculated. Upon determining the confidence metric to be below a specified threshold, the user query is supplemented with contextual data. The user query, supplemented by contextual data, is further matched to stored responses by applying disparate matching techniques to obtain further matched responses, where at least one of the disparate matching techniques differs from techniques used to obtain the initial matched responses. Degree-of-match metrics are calculated taking into account the contextual data, where each degree-of-match metric indicates a quality of match between the user query and a matched response. At least one of the matched responses is presented to the user.