Document Similarity Vector Analysis for Faster Relevant Search
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Solution Overview
Problem
The challenge of efficiently locating relevant documents within large document repositories is significant due to the vast number of stored documents, making it difficult to identify documents with similar content to a given reference document.
Innovation Solution
A system and method for document similarity assessment involving a user interface, an archived document repository, and a document quantification and similarity analysis engine, utilizing natural language processing to extract, tokenize, and vectorize text and metadata, and perform similarity analysis using n-gram and word-path models to identify documents with similar content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of documents is performed, then accuracy of locating relevant documents is improved, but productivity and time consumption deteriorate
Solution Approach 1:
The patent introduces an automated document similarity assessment system as an intermediary between the document repository and the user. The system uses natural language processing, tokenization, and vectorization techniques to automatically compare documents and identify similar ones, replacing manual review while maintaining accuracy through sophisticated algorithms including n-gram and word-path models.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. The system transforms documents into vector representations and uses mathematical models to calculate similarity, substituting human cognitive processing with algorithmic processing that achieves both high accuracy and high productivity.
2Productivity
If automated document search is implemented, then productivity is improved, but measurement precision and similarity assessment accuracy deteriorate
Solution Approach 1:
The patent transforms documents from their original text form into vector representations with multiple parameters (n-gram vectors, word-path vectors). This parameter transformation enables automated computation while preserving semantic information. The system adjusts various parameters including n-gram size, vector dimensions, and similarity thresholds to optimize both speed and accuracy.
Solution Approach 2:
The patent moves document comparison from textual space to vector space, adding mathematical dimensions for similarity calculation. By representing documents as vectors in multi-dimensional space and using geometric relationships (cosine similarity, Euclidean distance), the system achieves accurate automated comparison that maintains precision while enabling high-speed processing.
Data Source
AI summary
Systems and methods for assessing similarity of documents are provided. Embodiments of the systems and methods include extracting a reference document text from a reference document, extracting an archived document text from an archived document, and quantifying the reference document and the archived document. The systems and methods may also include determining a document similarity value of the quantified reference document and the archived document. Determining the document similarity value includes calculating a set of vector similarity values for a set of combinations of a reference document text vector and an archived document text vector, and calculating the document similarity value, including a sum of the plurality of vector similarity values.