Search Insight Data via Dependency Graphs
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Solution Overview
Problem
Traditional search systems fail to provide contextual analysis of search results, requiring users to manually review and refine queries multiple times to achieve relevant results, leading to inefficient use of time, bandwidth, and processing resources.
Innovation Solution
Generating insight data by processing search results through parse trees and dependency graphs to provide contextual information, allowing for refined search results and reducing the need for multiple queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search is used, then search speed is fast, but contextual understanding and result relevance are poor
Solution Approach 1:
The system performs preliminary contextual analysis by generating parse trees and dependency graphs for search results before the user views them. This advance processing identifies key entities, relationships, and contextual patterns, so when users view results, the contextual understanding is already prepared, eliminating the need for manual review to assess relevance.
Solution Approach 2:
The patent introduces an intermediary contextual analysis layer between the keyword search and the user. This layer includes parse tree generation, dependency graph creation, and insight data computation that mediates between simple keyword matching and complex user understanding needs, providing enriched results without requiring users to perform manual contextual analysis.
2Loss of information
If manual review of search results is performed, then contextual understanding improves, but processing resources and time increase
Solution Approach 1:
The system extracts only the most relevant contextual information from search results using dependency graphs and parse trees. Instead of processing or presenting all possible contextual data, it selectively extracts key entities, relationships, and insights that are most valuable for understanding result relevance, reducing the processing burden while maintaining contextual understanding.
Solution Approach 2:
The patent transforms search results by changing their parameter representation - converting plain text into structured parse trees and dependency graphs with computed insight data. This parameter transformation enables automated contextual analysis without requiring proportional increases in processing resources, as the structured format allows for efficient computation and filtering.
3Measurement precision
If multiple queries are executed to refine results, then search accuracy improves, but bandwidth and processing resources increase
Solution Approach 1:
The system performs preliminary contextual enrichment on the initial search results, incorporating dependency analysis and insight data generation before the user needs to refine queries. This advance preparation provides sufficient contextual understanding in the first query results, reducing the need for multiple iterative queries and the associated bandwidth and processing resource consumption.
Data Source
AI summary
The present disclosure relates to generating additional insight data for search results. Individual search results may be obtained and used to generate a parse tree for the search result data. A dependency graph may then be generated using the parse tree, where the dependency graph includes a root node that corresponds to a query parameter. A scoring or weighting algorithm is applied to the dependency graph to generate and aggregate scores for nodes in the dependency graph relative to the root node. The generated scores or weights are used to generate insight data for the search results.