Search Query Deconstruction and Source Allocation
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Solution Overview
Problem
Current search engines lack the ability to efficiently deconstruct and reconstruct natural language queries into specific sub-queries that can be directed to multiple data sources, leading to suboptimal search results and increased computational resources.
Innovation Solution
A system and method that deconstructs a search query into n-grams, reconstructs them into sub-phrases, and allocates each sub-phrase to the most appropriate data source for processing, using noun-phrase recognition and n-gram analysis, allowing for targeted searches across multiple databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search engine processes natural language queries as single unified searches, then it maintains simplicity in processing, but it achieves lower search precision and returns less relevant results
Solution Approach 1:
The patent segments a natural language query into multiple sub-queries by identifying different entities and their relationships. For example, a query about a movie is broken down into sub-queries for the movie itself, the director, the cast, and related works. Each sub-query is then routed to the most appropriate data source, improving precision while managing complexity through systematic decomposition.
Solution Approach 2:
The patent introduces an intermediary component that acts as a router or dispatcher between the received query and multiple specialized data sources. This intermediary analyzes the query, determines the appropriate data sources for each sub-query, and routes them accordingly. This mediator layer enables precise searching across multiple sources without requiring the entire system to become unnecessarily complex.
2Productivity
If a search engine queries multiple data sources with the entire search query, then it ensures comprehensive coverage, but it increases computational resources and processing time
Solution Approach 1:
By segmenting the original query into multiple targeted sub-queries, the system sends only relevant portions to specific data sources. This segmentation avoids the computational waste of sending entire queries to all possible data sources, thereby improving search efficiency while reducing the energy and computational resources consumed.
Solution Approach 2:
The patent applies local quality by matching specific sub-queries to the most appropriate data sources based on their specialized content. Each data source receives only the queries for which it is best suited, rather than receiving all queries. This localized matching optimizes resource utilization and improves overall search productivity by directing computational efforts where they are most effective.
3Adaptability or versatility
If a search engine uses a single data source for all queries, then it reduces system complexity, but it limits adaptability to different types of search requests
Solution Approach 1:
The intermediary component provides a unified interface for receiving queries while internally routing them to multiple specialized data sources. This mediator absorbs the complexity of managing multiple data sources, presenting a simple interface to users while enabling adaptive routing based on query type. The result is high adaptability without proportionally increasing user-facing system complexity.
Solution Approach 2:
The patent creates a universal search system that can handle diverse query types by integrating multiple data sources through a common framework. The system performs multiple functions (routing, analysis, query generation, result aggregation) within a unified architecture, enabling it to adapt to different search requests while maintaining manageable overall system complexity through standardized processes.
Data Source
AI summary
A machine is configured to access a search phrase or other communicated phrase and deconstruct the accessed phrase into multiple sub-phrases. The machine performs an analysis of n-grams that occur within the sub-phrases, generates a set of potentially different sub-phrases from the n-grams, and selects which data source among multiple available video sources should be accessed for each generated sub-phrase in the generated set. For example, the machine may allocate each generated sub-phrase to a corresponding data source and cause the corresponding data source to execute a query based on its corresponding sub-phrase. Thus, the machine chooses from which data source to obtain partial search results that correspond to each sub-phrase generated based on the analyzed n-grams in the accessed phrase.