AI Search System Contextual Document Retrieval
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Solution Overview
Problem
Conventional text-based search methods are inefficient and time-consuming, often producing irrelevant results due to the inability to understand context, leading to redundant data and manual labor in searching through vast amounts of information.
Innovation Solution
A system using machine learning algorithms to generate search strings from reference-text, iteratively refining the search process until a performance score exceeds a threshold, ensuring contextually relevant data retrieval and estimation of document novelty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword-based search methods are used, then the search process is simple and fast, but the search results are irrelevant and redundant due to inability to understand context
Solution Approach 1:
The patent replaces conventional mechanical keyword-matching search systems with an AI-based semantic understanding system. The system uses natural language processing, machine learning models, and contextual analysis to understand the meaning and intent behind search queries, transforming the search mechanism from simple string matching to intelligent semantic interpretation. This substitution enables the system to comprehend context, synonyms, and related concepts, significantly improving search accuracy while filtering out irrelevant results.
2Measurement precision
If manual searching of search results is performed, then relevant documents can be identified, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent implements self-service by enabling the system to automatically evaluate, rank, and present relevant documents without requiring manual review. The AI system autonomously analyzes search results, determines document relevance based on semantic understanding, and prioritizes outputs according to user intent and query context. This automation eliminates the need for users to manually search through irrelevant results, significantly reducing search time while maintaining high precision in identifying relevant documents.
3Quantity of substance
If broad search strings are used, then more documents are retrieved, but the user cannot review relevant documents due to the long list of results
Solution Approach 1:
The patent applies partial action by retrieving a comprehensive set of documents through broad search strings but then selectively presenting only the most relevant subset to the user. The AI system analyzes all retrieved documents, evaluates their relevance based on semantic understanding and contextual analysis, and prioritizes the top results. This approach maintains the benefits of broad search coverage while ensuring that users are presented with a manageable, highly relevant subset of results that are easy to review and evaluate.
4Ease of operation
If narrow search strings are used, then fewer documents are retrieved, but important documents may be excluded from the results
Solution Approach 1:
The patent implements universality by creating a search system that handles both broad and narrow search queries effectively through a unified AI-based approach. The system uses semantic understanding to interpret user intent regardless of query breadth, and employs contextual analysis to expand or refine search parameters dynamically. This multi-functional capability ensures that narrow search strings do not exclude important documents by understanding synonyms, related concepts, and contextual relationships, while broad searches maintain relevance through intelligent filtering and prioritization.
Data Source
AI summary
A method for training system to perform text-based search among plurality of documents. The method including receiving starting document, having at least one reference-identifier associated with the document; selecting reference-text from starting document; generating search-string by using a plurality of keywords using at least one first machine learning algorithm and at least one search-operator using at least one second machine learning algorithm; performing search among plurality of documents using search-string to fetch set of relevant documents; deriving reference-identifier distribution, corresponding to the at least one reference-identifier associated with document, for the set of relevant documents; and corelating at least one reference-identifier associated with starting document with reference-identifier distribution to determine performance-score for system. The steps of method are iteratively performed until performance-score exceeds a predetermined threshold, wherein at each iteration at least one of: different search-string is generated, different reference-text is obtained, different document is received.