Digital Human Vehicle Personalization Using Biometrics and Preferences
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Solution Overview
Problem
Digital humans lack the ability to predict user questions through observation and effectively engage users due to reduced physical interactions, leading to decreased engagement and difficulty in assessing user interests.
Innovation Solution
Implement a digital human adaptation system using machine learning algorithms and language models to analyze user biometrics and device proximity for automatic vehicle configuration based on stored user preferences, enhancing user interaction through intelligent prompt generation and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital humans are used to interact with users in vehicle environments, then user engagement is improved, but the ability to predict user questions and assess user interests deteriorates due to reduced physical interactions
Solution Approach 1:
The system performs preliminary actions by detecting user identifiers and obtaining stored preferences before the user actually requests them. This anticipatory approach allows the digital human to pre-load user profile data, biometric information, and preference settings, enabling it to predict user questions and needs without requiring explicit user input, thereby compensating for the lack of physical interaction cues
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user biometrics, device proximity, and interaction patterns. This feedback loop allows the digital human to assess user interests in real-time, adjust its responses dynamically, and improve its prediction accuracy over time, addressing the information loss caused by reduced physical interactions
2Adaptability or versatility
If digital humans automatically configure vehicles using stored preferences, then personalization is improved, but the complexity of the system increases
Solution Approach 1:
The system applies universality by using a multi-functional digital human architecture that integrates multiple capabilities: user identification through biometrics, preference management, vehicle configuration control, and interactive communication. This single digital human entity performs what would otherwise require multiple separate systems, achieving high personalization adaptability while managing system complexity through functional integration
Solution Approach 2:
The system uses copying by storing and replicating user preference profiles in a centralized database. Instead of implementing complex real-time analysis of each user's preferences during interaction, the system creates copies of pre-stored preference data and applies them to vehicle configuration, simplifying the real-time processing requirements while maintaining high personalization capability
Data Source
AI summary
Techniques are provided for personalizing vehicles using a digital human to administer user preferences. One method comprises automatically detecting an identifier of a user in a vehicle, wherein the identifier of the user is automatically detected based on obtained biometrics of the at least one user and/or a proximity of a device of the user; obtaining stored preferences of the user using the identifier of the user; and automatically configuring the vehicle, by a digital human, using the obtained stored preferences of the user. One or more updated preferences of the user may be stored in one or more persistent slots of a conversation system using the identifier of the user.