Relationship Tree Search for Biological Entity Discovery
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Solution Overview
Problem
Search engines generate extensive lists of search results, making it difficult for users to efficiently find and identify patterns or trends, particularly for knowledge discoverers seeking previously unknown information, as relevant but lesser-known associations are often buried deep within the results.
Innovation Solution
A computer-implemented system and method using a relationship tree to determine biological entities of interest by identifying non-results with known associations within a defined boundary, calculating scores based on the number and proximity of associated results, and filtering entities based on user-defined thresholds and evidence types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If search engines present results in ordered list format, then information retrieval efficiency is improved for established facts, but knowledge discovery of patterns and trends becomes difficult
Solution Approach 1:
The patent transforms the traditional one-dimensional ordered list into a two-dimensional hierarchical visualization using relationship trees. This dimensional change allows users to simultaneously view multiple results at different hierarchical levels, revealing patterns and relationships that are invisible in linear formats. The tree structure organizes results by semantic relationships (parent-child, sibling connections) rather than mere relevance scores, enabling knowledge discovery while maintaining retrieval efficiency.
2Measurement precision
If search results are ordered by relevance algorithm, then most relevant information appears at the top, but lesser-known but potentially significant associations are buried deep in results
Solution Approach 1:
The patent segments the flat list of search results into hierarchical groups based on semantic relationships. Instead of presenting all results in a single relevance-ordered sequence, the system divides results into parent categories and child items, allowing users to efficiently navigate through organized groups. This segmentation enables users to quickly identify significant associations within specific hierarchical branches without scanning through hundreds of results ordered purely by algorithmic relevance.
3Quantity of substance
If extensive search results are generated, then comprehensive information is provided, but user ability to sift through and filter results efficiently deteriorates
Solution Approach 1:
The relationship tree structure serves multiple functions simultaneously: it organizes results hierarchically, reveals patterns and relationships, enables efficient navigation, and provides implicit filtering through hierarchical grouping. This multi-functional approach eliminates the need for separate filtering operations that would be required in traditional list formats, as the tree structure inherently groups related results together while maintaining comprehensive information coverage.
4Quantity of substance
If results are spread across multiple pages, then comprehensive coverage is achieved, but detecting relationships between results on different pages becomes difficult
Solution Approach 1:
The patent merges results that would traditionally be分散 across multiple pages into a single unified hierarchical view. The relationship tree consolidates all search results while organizing them by semantic relationships, allowing users to see connections between results regardless of their original position in the search index. This merging preserves comprehensive coverage while making relationship detection trivial, as related results are visually connected through parent-child and sibling relationships in the tree structure.
Data Source
AI summary
A system for determining biological entities of interest is described. The system comprises a user input module configured to receive a search term comprising a representation of a biological entity; a search module configured to determine which biological entities of a set have a known association with the biological entity of the search term, those having a known association being results and those not having a known association being non-results, wherein biological entities of the set are related to each other by parent-child relationships in a relationship tree; and an analysis module configured to determine biological entities of interest by identifying non-results that have one or more results within a boundary in the relationship tree.