Emotion Estimation Using Subjective Affect Grid Parameters
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Solution Overview
Problem
Existing emotion estimation methods require biometric data and numerous user inquiries, making them complex and inefficient for estimating user emotions based on subjective mood, excitement, and relaxation degrees.
Innovation Solution
An emotion estimation method that obtains subjective mood, excitement, and relaxation parameters from users and estimates an emotion parameter using a simplified affect grid system, eliminating the need for biometric data and reducing user input, with a content determination system that provides tailored content to induce desired emotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biometric data and numerous user inquiries are used for emotion estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential emotional dimensions (arousal and valence) from complex biometric data, using a simplified affect grid model that requires minimal user input while maintaining estimation accuracy. This reduces system complexity by eliminating unnecessary biometric sensors and processing components.
Solution Approach 2:
The patent transforms complex biometric parameters into simplified emotional parameters using established psychological models (affect grid). By changing the parameter representation from physiological measurements to psychological constructs, the system achieves accurate emotion estimation with reduced complexity.
2Measurement precision
If biometric data collection is implemented, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent extracts only the minimal necessary user inputs (arousal and valence ratings) required for emotion estimation, eliminating the need for extensive biometric data collection and numerous user inquiries. This significantly reduces user input burden while maintaining measurement precision through the use of established affect grid methodology.
3Ease of operation
If simplified parameter collection is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent changes the parameter collection approach from complex biometric measurements to simplified psychological ratings based on the affect grid model. This transformation maintains measurement precision by using validated psychological constructs while dramatically improving ease of operation through minimal user input requirements.
Solution Approach 2:
The patent introduces the affect grid model as an intermediary framework that translates simple user ratings into accurate emotion estimates. This mediator enables precise measurement without requiring direct complex biometric data collection, thus maintaining accuracy while improving ease of operation.
Data Source
AI summary
An emotion estimation method includes: obtaining a first parameter indicating a user's subjective mood, a second parameter indicating the user's subjective excitement degree, and a third parameter indicating the user's subjective relaxation degree; estimating an emotion parameter indicating the user's emotion based on the obtained first parameter, second parameter, and third parameter; and outputting information related to the emotion parameter.