Emotion Estimation Device Using Event-Segmented Tables
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Solution Overview
Problem
Existing emotion estimation devices face challenges in accurately estimating user emotions due to limitations in facial expression scoring methods, particularly when specific events like drowsiness or conversation occur, which can reduce estimation accuracy.
Innovation Solution
The emotion estimation device employs a system that includes multiple sensors and data processing units to detect events, calculate facial expression scores, and update emotion estimation tables, using machine learning algorithms to correlate facial expressions with emotions across consecutive events, thereby improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial expression scoring method is used to estimate user emotion, then emotion estimation can be performed, but estimation accuracy deteriorates when specific events like drowsiness or conversation occur
Solution Approach 1:
The patent segments the emotion estimation process by creating separate emotion estimation tables for different event conditions (e.g., drowsiness, conversation, normal states). Each table stores correspondence relationships specific to that event type, allowing the system to select the appropriate table based on detected events, thereby maintaining accuracy across diverse conditions without requiring a single generic model
Solution Approach 2:
The patent changes the parameters of the emotion estimation system by storing multiple emotion estimation tables with different correspondence relationships between facial expressions and emotions. The system dynamically selects which table to use based on the detected event condition, effectively changing the estimation parameters to match the current state and improving both accuracy and adaptability
2Measurement precision
If multiple emotion estimation tables are maintained for different events, then estimation accuracy improves across various conditions, but device complexity increases
Solution Approach 1:
The patent creates a universal emotion estimation framework that handles multiple event types (drowsiness, conversation, normal states) through a common architecture. The event detection unit and table selection mechanism serve multiple purposes, and the emotion estimation tables follow a unified structure, allowing the system to manage complexity while maintaining multi-condition accuracy
Solution Approach 2:
The patent performs preliminary actions by pre-storing multiple emotion estimation tables corresponding to different event conditions before actual emotion estimation begins. The event detection and table selection processes are prepared in advance, allowing the system to quickly switch between different estimation models without real-time computation overhead, thus managing complexity efficiently
Data Source
AI summary
An emotion estimation device includes: an emotion estimation unit estimating a user emotion as an emotion associated with an index value of a facial expression of a user between (i.e., involving/during a period having) a plurality of consecutive events, based on a correspondence between (A) the index value which is at least one of (i) a facial expression score of (i.e., representing) the facial expression of the user and (ii) a feature amount about a change of such facial expression score and (B) the facial expression of the user correlated with the plurality of consecutive events.