Query Pattern Weighting for Search Accuracy
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Solution Overview
Problem
Users face challenges in obtaining relevant search results due to variations in how they construct search queries, leading to the need for repeated alterations, which is time-consuming and often ineffective, as different wordings, orders, and combinations of words can produce different results.
Innovation Solution
A computer-implemented method that selects multiple search query patterns, assigns weights to each, generates weighted keyword queries, and iteratively adjusts these weights using an algorithm to optimize the number of relevant search results returned, allowing for automatic refinement of search queries without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually alter search queries multiple times to obtain relevant results, then search accuracy may improve, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating multiple keyword query variations from the user's natural language query before the user has to manually search. The query pattern matching system pre-processes the query into multiple potential search forms, and the automated execution of these queries eliminates the need for users to manually retry different search formulations, thus saving time while maintaining search accuracy.
Solution Approach 2:
The system enables self-service by automatically optimizing and executing multiple search queries without requiring user intervention. The automated query optimization system analyzes the user's intent, generates improved query variations, executes them, and presents the best results automatically. This self-service mechanism allows the system to improve search accuracy without increasing user time investment, as the optimization process occurs autonomously in the background.
2Reliability
If multiple search query patterns are used to improve result coverage, then search completeness improves, but system complexity increases
Solution Approach 1:
The system applies segmentation by breaking down the complex task of comprehensive search into manageable components: multiple distinct query patterns are defined separately (e.g., different grammatical structures, different keyword arrangements), each pattern is processed independently through its own matching and weighting logic, and results from each segment are aggregated. This modular segmentation allows the system to achieve search completeness while keeping individual components simple and maintainable.
Solution Approach 2:
The query pattern matching system implements universality by designing a multi-functional framework that handles various types of search queries through a single unified mechanism. The same pattern matching engine processes different query structures (questions, statements, commands), different domains (weather, news, information), and different user intents. This universal approach allows comprehensive search coverage without proportionally increasing system complexity, as one system serves multiple functions.
3Ease of operation
If automated query optimization is implemented to reduce user effort, then ease of operation improves, but computational requirements increase
Solution Approach 1:
The system applies partial action by selectively optimizing only the most promising query variations rather than exhaustively processing all possible query forms. The pattern matching system identifies and prioritizes high-value query patterns based on predefined criteria and historical performance, executing only those that are most likely to yield relevant results. This partial optimization approach reduces computational requirements compared to exhaustive search while still significantly improving ease of operation by eliminating the need for users to manually explore multiple query variations.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting query parameters (such as keyword selection, query structure, weighting factors) based on the specific input query and contextual information. Rather than using a fixed, computationally intensive optimization algorithm, the system modifies query parameters selectively to improve effectiveness. This approach reduces computational requirements while maintaining ease of operation, as the parameter adjustments are made efficiently based on pattern matching and predefined optimization rules rather than exhaustive computation.
Data Source
AI summary
A computer-implemented method for optimizing search query results is disclosed. The computer-implemented method includes receiving a first natural language query, selecting a plurality of search query patterns based on the first natural language query, assigning a weight to each search query pattern in the plurality of search query patterns, generating one or more weighted keyword queries from each search query pattern based on the weight assigned to each search query pattern and one or more keywords from the first natural language query, evaluating a relevancy of one or more search results returned by each of the one or more weighted keyword queries for the first natural language query, and iteratively altering the weight assigned to a search query pattern until an optimal number of relevant search results are returned from the search query pattern for the first natural language query.