Mood Score Calculation via Verbal Spatial Task Classification
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Solution Overview
Problem
Current methods for assessing worker mental health, such as mood score calculation and neuroscientific techniques, fail to accurately and efficiently quantify mental health states without burdening the worker, as they do not consider the difference in work content or tool load on the brain's working memory and require extensive questionnaires or cumbersome equipment.
Innovation Solution
A mood score calculation apparatus and method that classify manipulation history into verbal and spatial tasks based on a predetermined algorithm, calculating a mood score from the relative relationship between these tasks, allowing for accurate and efficient mental health state assessment without additional burden on the worker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mood assessment methods (POMS, BDI-II) are used, then measurement precision of mental health state is improved, but device complexity and ease of operation deteriorate due to requiring dozens of questions per test
Solution Approach 1:
The patent extracts the essential information needed for mood assessment from the complex questionnaire process. Instead of requiring workers to answer dozens of questions, the system extracts mood-related information from existing terminal manipulation behaviors (typing patterns, click sequences, task completion times), which naturally reflect cognitive load and emotional state without requiring active worker participation in assessments.
Solution Approach 2:
The system enables self-service mood assessment by utilizing the worker's existing terminal usage patterns. The terminal itself generates the assessment data through its normal operation - the way a worker types, clicks, and interacts with applications automatically produces mood assessment information without requiring the worker to manually complete assessment forms or questionnaires.
2Measurement precision
If neuroscientific measurement devices are used, then measurement precision of mental health state is improved, but device complexity and ease of operation deteriorate due to requiring workers to wear equipment and perform tasks
Solution Approach 1:
The patent introduces the terminal manipulation history as an intermediary between the worker and the assessment system. Instead of directly measuring neural activity with complex devices, the system uses the terminal - already present in the worker's environment - as a mediator that captures behavioral indicators of mental state. The terminal's recording of manipulation patterns serves as an indirect but effective measure of cognitive load and emotional state.
Solution Approach 2:
The patent replaces the mechanical/neuroscientific measurement system (brain measurement devices, physiological sensors) with an information-based system that analyzes digital traces of terminal usage. Instead of measuring physical neural or physiological parameters, the system processes information from manipulation histories, substituting complex hardware-based measurement with software-based analysis of behavioral data.
3Ease of operation
If terminal manipulation history is analyzed without considering task type, then ease of operation is improved, but measurement precision deteriorates due to not reflecting neuroscientific knowledge
Solution Approach 1:
The patent applies local quality by differentiating the analysis approach based on task type. Instead of uniformly analyzing all manipulation histories the same way, the system identifies whether manipulations are related to verbal tasks (text processing, communication) or spatial tasks (visual analysis, graphical work) and applies task-specific analysis methods. This localized approach reflects the different cognitive demands and working memory loads associated with different task types, improving measurement precision while maintaining ease of operation through automated classification.
Data Source
AI summary
A mood score calculation apparatus has a memory device that stores a manipulation history of a user on a predetermined device and a arithmetic device that classifies the manipulation history into any one of a verbal task and a spatial task on the basis of a predetermined algorithm, calculates the user's mood score on the basis of a relative relationship between the manipulation histories for each of the verbal task and the spatial task specified in the classification, and outputs information on the mood score into a predetermined output target.