Language Model User State Detection for Environmental Adjustment
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Solution Overview
Problem
Current human-machine interaction technologies lack the ability to effectively adjust environmental conditions based on user states to improve user safety, health, and well-being, often relying on standardized solutions rather than personalized approaches.
Innovation Solution
A computing system uses a language model to determine if a user's current state meets preset criteria by analyzing user and environmental data from past interactions, then adjusts environmental conditions, such as through IoT devices, to achieve a target state, enhancing user experience and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standardized solutions are used for human-machine interaction, then implementation is simple and fast, but user safety, health, and well-being cannot be effectively improved due to lack of personalization
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user data from past interaction sessions before the current interaction occurs. This includes gathering physiological data, environmental data, and interaction patterns to establish a user profile that enables personalized responses in future interactions, thereby improving adaptability without increasing real-time system complexity
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user state during interactions and using language models to analyze whether the current state meets preset criteria. Based on this feedback, the system adjusts environmental conditions through IoT devices to achieve target user states, creating a closed-loop system that improves personalization capability while maintaining manageable complexity through automated decision-making
2Reliability
If environmental conditions are adjusted based on user state analysis, then user safety and well-being are improved, but data processing complexity and time increase
Solution Approach 1:
The system performs preliminary analysis of user data from past interaction sessions to establish baseline profiles and preset criteria before real-time interactions occur. This pre-processing reduces the computational burden during actual interactions, enabling faster decision-making about whether environmental adjustments are needed while maintaining high reliability in safety assessments
Solution Approach 2:
The system changes parameters by selectively analyzing specific user state parameters (physiological data, environmental conditions, interaction patterns) rather than processing all available data. The language model evaluates whether the current state meets preset criteria based on key parameters, enabling rapid decision-making that balances thorough analysis with time efficiency
3Adaptability or versatility
If comprehensive user data from past interactions is analyzed, then personalized solutions are achieved, but information processing load increases
Solution Approach 1:
The system extracts only the essential and relevant information from comprehensive user data collected during past interactions. Instead of processing all raw data, the language model identifies and extracts key patterns, user preferences, and state indicators that are most relevant for personalization, thereby reducing computational energy requirements while maintaining high personalization accuracy
Solution Approach 2:
The system applies local quality by focusing computational resources on analyzing specific aspects of user data that are most relevant to the current interaction context. Rather than uniformly processing all user data, the language model selectively analyzes particular data elements based on their relevance to achieving target user states, optimizing the balance between personalization accuracy and energy consumption
Data Source
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AI summary
A human-machine interaction method implemented by a computing system. The method includes: determining, by using a language model, whether a current state of a user meets a preset criteria based on current user data associated with one or more physical actions of the user during a current interaction session between the user and a machine; if a result of the determining step indicates that the current state of the user meets the preset criteria, performing following steps: obtaining, from data associated with a past interaction session between the user and a machine, data representing an environmental condition contributing to a target state of the user that is different from the current state; determining, by using the language model, an action to be performed by a device in a current environment where the user is located during the current interaction session to achieve or approximate the environmental condition based on current environmental data associated with the current environment; and sending a command to enable the device to perform the action.