Approximation Query Matrix for Remote Document Search
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Solution Overview
Problem
Existing document search systems are limited by the performance of database management systems and storage devices, and are not compatible with remote searches, leading to inefficiencies in categorizing and retrieving documents based on labels.
Innovation Solution
The system encodes document label information as topics and differentials within a matrix structure, allowing for efficient searches within an encoded search space without directly involving the document repository, using sparse matrices to represent document-topic, topic-label, and document-topic differential relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database management systems and storage devices are used for document search, then document categorization and retrieval can be performed, but search performance is bound by the performance of these systems and remote searches are not compatible
Solution Approach 1:
The patent creates a copied representation of the document repository in the form of a document-topic matrix encoded in a vector space. This matrix copy contains topic assignments for documents, allowing search operations to be performed on the copied data structure rather than directly on the remote repository, enabling remote searches while maintaining search performance.
Solution Approach 2:
The patent introduces an intermediary representation layer between the client and the document repository. The document-topic matrix serves as this intermediary, translating search queries into topic-based operations that can be executed remotely without direct database access, thus resolving the contradiction between search performance and remote compatibility.
2Productivity
If documents are stored and searched using traditional database tables and indexes, then document retrieval based on labels is possible, but the system complexity increases and performance is limited by database systems
Solution Approach 1:
The patent replaces the mechanical database management system with a mathematical vector space representation. Documents and topics are represented as vectors in a continuous space, allowing for efficient similarity search through geometric operations rather than traditional database queries, thereby increasing search speed while reducing system complexity.
Solution Approach 2:
The patent transforms discrete label-based document representation into continuous vector representations. By changing the parameter space from categorical labels to continuous vector values, the system enables faster search operations through vector similarity metrics while eliminating the need for complex database indexing structures.
Data Source
AI summary
Documents may be maintained in a repository and retrieved based on searches that specify labels as criteria. Documents may be associated with groups of labels identified as topics. Searches may be performed using binary-encoded matrices specifying relationships between documents and topics, topics and labels and differential information indicating differences between topics and labels associated with documents. An initial result estimate may be based on forming a product of a documents-topics matrix and a topics-labels matrix. The initial estimate may be corrected by applying the differential information.