AI Document Analysis for Emotion-Based Group Relationship Mapping
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Solution Overview
Problem
Existing methods for assessing mental health and interpersonal relationships among group members rely heavily on explicit psychological diagnostic tests, which are limited in naturally capturing day-to-day interactions and interactions, lacking comprehensive and accurate analysis of emotions and relationships.
Innovation Solution
A document analysis system using artificial intelligence to analyze naturally occurring documents, such as shared diaries, to extract key emotions and keywords, determining relationships among group members by analyzing keyword and emotional similarities, and identifying isolated members through AI-based emotion and keyword extraction units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit psychological diagnostic tests are used to assess mental health and interpersonal relationships, then diagnostic accuracy can be improved, but the ability to naturally capture day-to-day interactions and emotions deteriorates
Solution Approach 1:
The system segments the assessment into multiple dimensions: emotion extraction from text, keyword analysis, relationship mapping, and isolation detection. Each segment processes specific aspects of mental health and interpersonal dynamics independently, then integrates results for comprehensive diagnosis.
Solution Approach 2:
The patent introduces an AI-based natural language processing intermediary that mediates between raw document text and psychological assessment. This intermediary extracts emotions and keywords automatically, bridging the gap between natural language expressions and diagnostic criteria without requiring explicit test questions.
2Productivity
If AI-based emotion extraction from documents is used, then comprehensive analysis of emotions and relationships can be improved, but system complexity increases
Solution Approach 1:
The AI processing unit performs multiple functions: emotion extraction, keyword identification, relationship analysis, and isolation detection. This multi-functional approach consolidates what would otherwise require separate systems into a single unified platform, managing complexity through functional integration.
Solution Approach 2:
The system uses the documents already being created by group members for their intended purposes (communication, record-keeping) and simultaneously leverages them for psychological assessment without requiring additional input from members. The existing documents serve dual purposes, reducing the burden on users while enabling comprehensive analysis.
3Ease of operation
If natural language documents are analyzed to detect emotions and relationships, then ease of operation can be improved, but measurement precision of psychological states may deteriorate
Solution Approach 1:
The system continuously analyzes documents and provides feedback through relationship maps and isolation alerts. This feedback loop allows for ongoing adjustment and refinement of assessments, improving measurement precision over time while maintaining ease of operation through automated processing.
Solution Approach 2:
The patent combines multiple analysis methods (emotion extraction, keyword analysis, relationship mapping) into a composite assessment approach. By integrating multiple indicators rather than relying on a single measure, the system achieves higher measurement precision while keeping the user interface simple and easy to operate.
Data Source
AI summary
The present invention relates to a document analysis system that utilizes artificial intelligence to analyze documents written by group members in their daily lives, extract the key emotions and key keywords of the document writer, and determine relationships among group members. By utilizing the extracted key emotions and key keywords, the system analyzes and intuitively presents whether certain members are connected as a group or close companions, or whether certain members are isolated and not interacting with others.