Robot User Recognition via Stored Stimulus Feature Similarity
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Solution Overview
Problem
Current robots lack the ability to effectively recognize and respond to individual users through unique voice and touch patterns, leading to inadequate attachment and interaction experiences.
Innovation Solution
A robot equipped with a processor that acquires and stores outside stimulus feature amounts, calculates similarity degrees, and controls operations based on these calculations to differentiate between users through voice and touch recognition, enabling personalized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot uses generic response patterns for all users, then the device complexity is low, but the user attachment and interaction quality deteriorate
Solution Approach 1:
The robot performs preliminary actions by storing outside stimulus feature amounts (voice patterns, touch patterns) in advance as historical data. This preliminary storage enables later similarity comparisons to identify individual users, creating personalized responses without requiring complex real-time analysis infrastructure
Solution Approach 2:
The robot implements feedback by calculating similarity degrees between current outside stimulus feature amounts and stored historical data. This feedback mechanism identifies recognized users and enables the robot to perform differentiated friendly actions based on the similarity degree, thereby enhancing user attachment through personalized interaction
2Measurement precision
If the robot stores and analyzes detailed outside stimulus feature amounts, then the user recognition accuracy improves, but the information processing load increases
Solution Approach 1:
The robot extracts only the essential outside stimulus feature amounts (voice characteristics, touch patterns) from the complex sensory input. By storing only these extracted feature amounts rather than raw sensory data, the system achieves accurate user recognition while minimizing information processing load and storage requirements
3Adaptability or versatility
If the robot performs similarity calculations with stored data, then the personalized response capability improves, but the calculation time increases
Solution Approach 1:
The robot performs partial similarity calculations by comparing current outside stimulus feature amounts with stored historical data to determine similarity degrees. Rather than analyzing all possible parameters, the system focuses on key feature amounts, achieving personalized response capability while keeping calculation time acceptable for real-time interaction
Data Source
AI summary
The robot includes a storage unit and a control unit. The control unit acquires outside stimulus feature amounts that are feature amounts of an outside stimulus acting from outside, stores the acquired outside stimulus feature amounts in the storage unit as a history, compares outside stimulus feature amounts acquired at a certain timing with outside stimulus feature amounts stored in the storage unit to calculate a first similarity degree, and controls operations based on the calculated first similarity degree.