String Match Matrix Using Embeddings for Document Deduplication
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Solution Overview
Problem
Traditional data analysis techniques struggle with comparing and matching content across different formats, structures, and access privileges, leading to inefficiencies in managing large-scale, heterogeneous document repositories, increased storage requirements, and potential propagation of errors or inconsistencies.
Innovation Solution
A method involving string generation, vectorization, and embedding comparison using a multimodal generative AI model to create a string match matrix data structure for identifying and remediating duplication errors across structured and unstructured documents, ensuring proper access controls and data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis techniques are used to compare and match content across different formats and structures, then the system can handle simple document comparisons, but it fails to accurately compare and match content across heterogeneous document repositories with varying formats, structures, and access privileges
Solution Approach 1:
The patent introduces an intermediary processing layer that converts diverse document formats (structured and unstructured text, images) into a unified representation. This intermediary step involves extracting content from various formats, generating embeddings, and creating a common vector space where all document types can be compared equally, thus resolving the contradiction between maintaining precision across different formats
Solution Approach 2:
The system transforms documents from their original format parameters into a standardized embedding parameter space. By converting all document types into numerical vectors with consistent dimensions, the system enables accurate comparison across heterogeneous formats while maintaining the ability to handle diverse input types
2Reliability
If comprehensive document comparison is performed across large-scale repositories, then duplication errors can be detected, but the storage requirements increase and access times slow down
Solution Approach 1:
The patent extracts only the essential content features from documents and stores them as compact embeddings rather than storing and comparing entire documents. This extraction process creates a condensed representation that maintains duplication detection capability while significantly reducing storage requirements and accelerating access times
Solution Approach 2:
The system creates simplified copies of documents in the form of embedding vectors that capture the essential semantic information. These embedding copies enable rapid comparison operations without requiring access to the full original documents, thus reducing access time while maintaining detection reliability
3Productivity
If AI-generated content is rapidly added to repositories, then content volume increases, but storage requirements increase, access times slow down, and error propagation risks increase
Solution Approach 1:
The patent performs preliminary validation by generating embeddings for newly added AI-generated content and immediately comparing these embeddings against existing repository content. This preliminary action detects potential duplications before they propagate through the system, maintaining data integrity despite rapid content addition
Solution Approach 2:
The system implements feedback mechanisms where the results of embedding comparisons are used to control the addition of new content. When duplications are detected through embedding similarity checks, the system provides feedback to prevent or flag the addition of redundant AI-generated content, thus maintaining reliability while allowing high productivity
Data Source
AI summary
A method for remediating duplication errors associated with first and second documents includes obtaining a first document and a second document, generating a corresponding first document string set and a second document string set, generating a second document string subset that is a proper subset of the second document string set, dividing the second document string subset into second document substring, transforming the second document substrings into a synthetic substring set, converting the first document string set into first document embeddings in vector space, converting the synthetic substrings into synthetic embeddings, generate a closest embedding set of one or more first document embeddings, generating, using a respective one of the closest embedding sets and a linking string generator, a linking string defining an association between the respective substring and one or more respective first document portions, and generating a string match matrix data.