Document Clustering via Semantic Affinity Modeling
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Solution Overview
Problem
Document collaboration systems face challenges in helping new users find related documents within a collaborative environment, as existing techniques lack a relational framework to determine document affinities, leading to difficulties in finding and working on relevant documents efficiently.
Innovation Solution
A method utilizing topic modeling and distance analysis to derive a collaborative document relational model, which groups content through document clustering and displays document clusters in a graphical user interface, enabling users to identify and work on related documents effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If document collaboration systems use traditional search functions to help users find related documents, then users can search for documents using keywords, but the systems lack a relational framework to determine document affinities, making it difficult to find relevant documents efficiently
Solution Approach 1:
The system pre-computes document embeddings and affinity relationships before users need to search. By analyzing document content, metadata, and collaboration patterns in advance, the system builds a relational framework that enables rapid retrieval of related documents without requiring users to spend time formulating search queries or waiting for results
Solution Approach 2:
The patent replaces traditional keyword-based mechanical search with a semantic similarity system using machine learning models. Document embeddings capture semantic meaning and relationships, allowing the system to identify related documents through mathematical similarity computations rather than manual keyword matching, significantly improving both ease of use and speed
2Adaptability or versatility
If document collaboration systems provide comprehensive search functions with multiple filters and options, then users can refine their searches, but the system complexity increases, making the interface more difficult to use
Solution Approach 1:
The system automatically computes document affinities and organizes results based on semantic relationships without requiring users to manually apply filters or adjust search parameters. The AI model self-adapts to user needs by learning from collaboration patterns and document metadata, providing relevant results automatically while keeping the interface simple
Solution Approach 2:
The patent transforms the search problem from a complex multi-parameter filtering task into a simpler semantic similarity computation. By changing the search parameter from keywords to document embeddings, the system achieves comprehensive adaptability through a unified mathematical framework that naturally handles various search scenarios without requiring complex interface controls
Data Source
AI summary
A method, computer system, and computer program product for collaborative document relations modeling are provided. The embodiment may include parsing, by a processor, a document corpus utilizing topic modeling and distance analysis techniques. The embodiment may also include deriving a collaborative document relational model to combine the results of the parsing into a matrix. The embodiment may further include grouping content of the parsed document corpus through document clustering utilizing the generated collaborative document relational model. The embodiment may also include displaying the grouped content as document clusters in a graphical user interface of a document management application.