Electronic Device Operation Mode Adaptation via User History
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Solution Overview
Problem
Existing electronic devices lack a technology to efficiently identify and update operation modes based on user preferences, leading to suboptimal settings for various modes.
Innovation Solution
An electronic device and method that identify the current operation mode, sense changes in settings, and update setting information based on changing patterns or user history, using reinforcement learning to optimize settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the electronic device provides multiple operation modes with different settings, then the adaptability to user needs is improved, but the complexity of managing and updating settings increases
Solution Approach 1:
The electronic device automatically identifies the current operation mode and updates settings based on sensed changes without requiring manual user intervention. The system self-manages the complexity of tracking which settings correspond to which operation modes and automatically applies updates, thereby maintaining high adaptability while reducing the burden on the user.
Solution Approach 2:
The system senses changes in settings and uses this feedback to determine whether to update setting information based on changing patterns or user change history. This feedback mechanism allows the device to adapt to user preferences automatically, improving versatility while managing complexity through intelligent decision-making about when updates are necessary.
2Ease of operation
If the electronic device automatically updates settings based on changing patterns, then the ease of operation is improved, but the loss of time for processing and determining updates increases
Solution Approach 1:
The system updates setting information only when necessary, based on determined changing patterns or user change history, rather than continuously processing all possible changes. This partial action approach maintains ease of operation by applying updates only when beneficial, while reducing time loss by avoiding unnecessary processing of every setting change.
3Adaptability or versatility
If the electronic device uses reinforcement learning models to optimize settings, then the adaptability to user preferences is improved, but the device complexity increases
Solution Approach 1:
The reinforcement learning model operates autonomously within the electronic device, self-training on user behavior patterns and change history to optimize settings without requiring external intervention. This self-service capability improves adaptability to user preferences while managing complexity by containing the learning model within the device's existing processing architecture.
Data Source
AI summary
A method including identifying the operation mode of the electronic device as a first operation mode, sensing a change of setting corresponding to the first operation mode, determining whether to update setting information about the setting, based on at least one of a changing pattern or a change history of a user, and when the operation mode is identified later as the first operation mode, providing the user with the first operation mode with the plurality of updated settings including the setting information about the updated setting is provided.


