Enterprise Search Service Semantic Indexing and Confidence Emphasis
Solution Overview
Problem
Traditional search solutions rely on keyword-based document analysis, which is limited in understanding content at a granular level, making it difficult to find relevant data in large datasets and failing to provide context and accurate answers to user queries.
Innovation Solution
An enterprise search service using machine learning to index and search text-based documents, enabling natural language queries, context understanding, and personalized answers by leveraging question-based queries, reserved fields for uniform context, and confidence-based result emphasis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based document analysis is used, then search implementation is simple, but content understanding at granular level is limited
Solution Approach 1:
The patent replaces traditional keyword-based mechanical search with machine learning-based semantic understanding. The system uses ML models to analyze document content at a granular level, enabling natural language queries and contextual understanding while maintaining ease of use through automated processing.
Solution Approach 2:
The patent changes the fundamental parameters of search from keyword matching to semantic similarity measurement. By transforming search queries and documents into vector representations and using ML models to compare them, the system achieves both simplicity in operation and precision in content understanding.
2Device complexity
If traditional keyword search is used, then system complexity is low, but relevance of search results is insufficient
Solution Approach 1:
The patent substitutes simple keyword matching mechanisms with complex machine learning systems that can understand semantic relationships, context, and nuances. This replacement increases system complexity but significantly improves search result relevance through sophisticated content analysis and ranking algorithms.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between user queries and search results. These intermediaries process and analyze content at multiple levels, filtering and ranking results based on semantic relevance rather than simple keyword matching, thereby improving reliability.
3Measurement precision
If machine learning-based search is implemented, then content understanding and result accuracy improve, but implementation complexity increases
Solution Approach 1:
The patent segments the search system into distinct components: data ingestion pipeline, machine learning models for content analysis, ranking algorithms, and result presentation. This segmentation allows each component to be developed, tested, and optimized independently, managing overall implementation complexity while maintaining high accuracy.
Solution Approach 2:
The patent employs universal machine learning models that can handle multiple search tasks and document types simultaneously. By using multi-functional models for various analysis operations, the system reduces the need for separate specialized components, thereby managing implementation complexity while achieving high search accuracy.
4Measurement precision
If granular content analysis is performed, then answer accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary content analysis and indexing of documents before actual search queries are executed. By pre-processing and storing document information in optimized formats, the system can quickly retrieve and analyze only the necessary portions during search operations, maintaining high answer accuracy while reducing processing time for individual queries.
Solution Approach 2:
The patent applies partial content analysis by focusing ML processing on the most relevant portions of documents identified through initial filtering. Rather than analyzing every word in detail, the system uses efficient algorithms to identify and process only the critical segments, achieving accurate answers with reduced processing time.
Data Source
AI summary
Techniques for displaying a search are described. An exemplary method includes receiving a search query, performing the search query on a plurality of documents, the documents including text passages, to generate a search query result, determining an aspect of the search query result that has a confidence value that exceeds a first confidence threshold with respect to its relevance to the search query; and, displaying the search result including an emphasis on the aspect of the result exceeds the first confidence threshold.