Questionnaire-Based Happiness Model Generation for Factor Identification
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Solution Overview
Problem
Existing technologies struggle to identify factors affecting happiness, limiting the accuracy of happiness deduction.
Innovation Solution
A happiness model generation apparatus that constructs a happiness model based on a questionnaire survey, incorporating factors related to work, first private life, and second private life, using covariance structure analysis to decompose happiness into three factors: private, working, and community, with each factor having a contribution rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If happiness is deduced based on activity amount distribution using wearable devices, then happiness can be measured, but the factors affecting happiness cannot be identified
Solution Approach 1:
The patent segments happiness into three distinct factors: work-related happiness, first private life happiness, and second private life happiness. This segmentation allows the system to measure happiness comprehensively while identifying specific factors that influence it, resolving the contradiction between measurement precision and factor identification.
Solution Approach 2:
The patent introduces a multi-dimensional approach by adding private life dimensions to the traditional work-based happiness measurement. This dimensional expansion enables the system to capture both the overall happiness state and the specific factors contributing to it, thereby identifying factors affecting happiness without losing measurement precision.
2Loss of information
If a comprehensive happiness model with multiple factors is constructed, then factor identification is improved, but the complexity of the model increases
Solution Approach 1:
The patent divides the complex happiness model into three manageable segments or dimensions: work-related, first private life, and second private life. This segmentation reduces model complexity by organizing factors into distinct categories that can be measured and analyzed independently, while still providing comprehensive factor identification.
Solution Approach 2:
The patent uses questionnaire items that may include more detailed information than strictly necessary (excessive action), but this allows the system to identify factors affecting happiness more accurately. The excess information in the questionnaire can be processed to extract the essential factors, achieving both comprehensive factor identification and manageable model complexity.
Data Source
AI summary
A happiness model generation apparatus (100) includes a questionnaire content construction unit (110) and a happiness model construction unit (140). The questionnaire content construction unit (110) constructs as a questionnaire for model construction, a questionnaire for constructing a happiness model that deduces based on a result of administering a happiness survey questionnaire to a target person, happiness of the target person, and that describes the happiness based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor. The happiness model construction unit (140) constructs the happiness model based on, among questionnaire items of the questionnaire for model construction, a questionnaire item to understand happiness and a questionnaire item that correlates with the happiness.