Relationship Network Generation via Vector Semantics
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Solution Overview
Problem
Existing search methods, such as keyword and phrase-based searching, fail to provide results relevant to the user's intent due to their inflexibility and inability to simulate human-like analysis of relationships between objects, lacking interactive control over context and relationship quality.
Innovation Solution
A system and method for generating and visualizing relationship networks by creating vectors from an information database, which allows for interactive analysis and visualization of relationships between objects, using distance metrics and filters to control the context and quality of relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If keyword or phrase-based searching is used, then search speed and simplicity are improved, but result relevance and relationship accuracy deteriorate
Solution Approach 1:
The patent replaces traditional keyword-matching mechanical search methods with a vector-based semantic representation system. Objects are transformed into vectors that capture their meaning and relationships, allowing the system to compute semantic similarity and relevance through vector operations rather than simple string matching, thereby improving result relevance while maintaining search efficiency
Solution Approach 2:
The system changes the fundamental parameter of representation from discrete keywords to continuous vector spaces. By representing objects as vectors with multiple dimensions capturing various aspects of meaning, the system can measure relationships through distance metrics and similarity calculations, enabling more precise relevance assessment compared to binary keyword matching
2Measurement precision
If relationship-based search methods are used, then result relevance is improved, but flexibility and interactivity deteriorate
Solution Approach 1:
The patent implements a dynamic relationship network where users can interactively explore and modify the context of relationships. The system allows users to navigate through relationship networks, adjust context parameters, and dynamically control which relationships are displayed and how they are visualized, transforming static relationship searches into interactive explorations
Solution Approach 2:
The system provides feedback mechanisms that allow users to refine their searches by observing relationship patterns and adjusting their queries accordingly. The visual representation of relationship networks gives users feedback about the structure and quality of relationships, enabling them to iteratively improve their search context and focus on relevant areas
3Measurement precision
If comprehensive relationship analysis is performed, then information quality is improved, but computational complexity and processing time deteriorate
Solution Approach 1:
The patent segments the complex task of relationship analysis into manageable components: vector generation for each object, relationship computation between pairs of objects, and network construction from individual relationships. This segmentation allows the system to process complex analyses in modular steps, reducing overall computational complexity while maintaining comprehensive relationship analysis
Solution Approach 2:
The system performs preliminary actions by pre-computing vectors for all objects in the database and storing them for reuse. This preliminary vector generation eliminates the need to recompute representations during each search operation, significantly reducing processing time for subsequent relationship analyses while maintaining high information quality
Data Source
AI summary
A computer-implemented system and process for generating a relationship network is disclosed. The system provides a set of data items to be related and generates variable length data vectors to represent the relationships between the terms within each data item. The system can be used to generate a relationship network for documents, images, or any other type of file. This relationship network can then be queried to discover the relationships between terms within the set of data items.