Mental State Assessment via Drawing Pattern Analysis
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Solution Overview
Problem
Current methods lack an effective way to automatically assess a user's mental state from drawings, which is crucial for understanding personality characteristics and detecting mental issues such as anger, anxiety, and depression, especially in children and elderly individuals, to determine appropriate therapeutic actions.
Innovation Solution
A computer-implemented method and system that digitizes drawings, analyzes characteristics like color, objects, location, and time, and uses machine learning to interpret these features, combined with video analysis and user interactions, to determine the mental state and select appropriate user actions, including therapy recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of drawings is performed, then accuracy of mental state assessment is improved, but time consumption and labor requirements increase
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer system that uses image processing algorithms to detect drawing characteristics such as color, shape, size, and spatial arrangement. The system automatically extracts features and feeds them to machine learning models to determine mental state, eliminating the need for human analysts to manually examine each drawing while maintaining assessment accuracy.
Solution Approach 2:
The system performs self-service by automatically analyzing drawings without requiring external human intervention. The machine learning model processes drawing data independently, making decisions about mental state assessment autonomously based on the extracted features and trained patterns, thereby reducing time consumption while maintaining precision.
2Measurement precision
If comprehensive analysis of multiple drawing characteristics is performed, then accuracy of mental state assessment is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct modules: image processing module for extracting visual features, feature extraction module for identifying specific characteristics (color, shape, size, position), and machine learning classification module for determining mental state. This segmentation allows each component to handle a specific aspect of analysis independently, making the overall system more manageable while maintaining comprehensive assessment capability.
Solution Approach 2:
The system employs a universal machine learning framework that can process multiple drawing characteristics simultaneously and adapt to different mental state assessments. The same core system architecture handles various analysis tasks (color analysis, spatial arrangement, shape recognition) through a single integrated platform, reducing the need for separate specialized systems for each analysis type.
3Productivity
If automated analysis system is implemented, then productivity is improved, but reliability of assessment may worsen due to lack of human judgment
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from new data and refines its assessments. The extracted drawing characteristics are fed back into the model to improve future predictions, and the system can adjust its analysis based on patterns it detects, thereby maintaining high reliability while achieving automated high-speed processing.
Solution Approach 2:
The system performs preliminary training using extensive datasets of drawings with known mental states before actual assessment. This preliminary learning phase enables the model to make reliable assessments automatically during operation, as it has already internalized the relationships between drawing characteristics and mental states through pre-training on diverse examples.
Data Source
AI summary
A method and system for automatically assessing the mental state of a user from a drawing made by the user. The mental state of the user is automatically assessed by digitizing a drawing and determining and analyzing drawing characteristics, including color, objects, and location. Video analytics are used to determine and analyze user time characteristics and mood characteristics. The mental state of the user is automatically determined by interpreting the color, object, location, time and mood characteristics and to automatically select a user action. A machine learning algorithm can learn developmental patterns of the user from historical data about a plurality of the user's drawings and the characteristics of the drawings to determine the mental state of the user. The machine learning algorithm can be used in selecting the user action.