Communication Device Radio Switching With User-Intent Prediction
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Solution Overview
Problem
Communication devices automatically switching communication paths based on quality estimation can lead to user discomfort when the user prefers to continue using the current path due to factors like price, and the reason for switching is not transparent to the user.
Innovation Solution
A communication device uses machine learning-based prediction models to anticipate user preference by training on action and environmental parameters, predicting the desired radio communication scheme before switching, thereby aligning with user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the communication device automatically switches communication paths based on quality estimation, then the communication reliability is improved, but the user comfort deteriorates when the user prefers to continue using the current path
Solution Approach 1:
The system performs preliminary action by predicting user connection intentions before automatic switching occurs. The prediction model analyzes action parameters and environmental parameters to forecast whether the user wants to maintain the current communication path, allowing the system to prepare for or avoid unwanted switching actions.
Solution Approach 2:
The system implements feedback by using the prediction results to adjust the automatic switching behavior. When the prediction model indicates the user likely wants to continue using the current path, the system suppresses automatic switching, creating a feedback loop that adapts to user preferences and improves comfort while maintaining reliability.
2Reliability
If the communication device performs automatic communication path switching, then the communication quality is improved, but the transparency of switching reason to the user deteriorates
Solution Approach 1:
The prediction model acts as an intermediary between the automatic switching mechanism and the user. It processes action parameters and environmental parameters to generate predictions about user intentions, providing a rationale that bridges the gap between automated decisions and user understanding, thereby improving transparency.
3Ease of operation
If the communication device uses machine learning prediction models to predict user preferences, then the user comfort is improved, but the device complexity increases
Solution Approach 1:
The system applies self-service by automatically collecting action parameters and environmental parameters, and by having the prediction model autonomously process this data to generate predictions. This reduces the need for manual user input or complex external systems, allowing the device to serve itself in predicting and adapting to user preferences.
Data Source
AI summary
A communication device includes circuitry configured to use a first radio communication scheme for a first radio communication. In a case where a quality of the radio communication using the first radio communication scheme satisfies a condition, the circuitry may be configured to use a prediction model of an application's action related to the radio communication to predict a second radio communication scheme desired by the application. The prediction model may be a learning model generated based on at least one of an action parameter related to the radio communication of the application and an environmental parameter related to the radio communication. The second radio communication scheme desired by the application may be predicted at a time interval after the quality of the radio communication using the first radio communication scheme satisfies the condition.