Mobile Terminal IoT Control During Call Connections
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Solution Overview
Problem
Current technologies for controlling IoT devices in a home environment during a smartphone call connection primarily focus on temperature and air quality, but fail to adequately enhance the call environment, lacking comprehensive control solutions.
Innovation Solution
An intelligent device controlling method and system that utilizes pre-learned control models to select and control IoT devices based on the mobile terminal's location, including sound and light output devices, during a call connection, using both global and personalized control models learned from device histories and caller information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If IoT devices are controlled during call connection, then call environment is improved, but device complexity increases
Solution Approach 1:
The system automatically controls IoT devices during call connections without requiring manual user intervention. The mobile terminal autonomously determines which devices to control and adjusts their settings based on the call state, eliminating the need for complex user interactions while improving call environment quality.
Solution Approach 2:
The control system is designed to work with multiple types of IoT devices (sound output devices, light output devices, etc.) through a unified control mechanism. This multi-functional approach allows the system to handle various device types without increasing operational complexity, as the same control logic applies across different device categories.
2Adaptability or versatility
If multiple control models are used for personalized control, then adaptability improves, but computing resources increase
Solution Approach 1:
Control models are pre-trained and stored in the mobile terminal before actual use. The system performs the computationally intensive model training process in advance, allowing the terminal to only execute inference during call connections. This preliminary action significantly reduces real-time computing resource consumption while maintaining high adaptability through personalized control models.
3Ease of operation
If automated device control is implemented, then ease of operation improves, but control precision requirements increase
Solution Approach 1:
The system incorporates feedback mechanisms where control results and user responses are continuously monitored. Based on this feedback, the system adjusts and optimizes control decisions for subsequent operations. This feedback loop enables the automated control system to improve its precision over time without requiring manual intervention, balancing automation with accurate control item selection.
Data Source
AI summary
An intelligent device controlling method, a mobile terminal, and a computing device are provided. A mobile terminal for controlling intelligently a device according to an embodiment of the present disclosure receives a call for a call connection, selects at least one control target device to control an operation while the call is connected, based on a location of the mobile terminal, selects a control item of the at least one control target device using a plurality of pre-learned control methods, and controls the control item for the at least one control target device in a state where the call is connected. Accordingly, it is possible to improve a call environment by controlling an operation of a device around a smart phone at the time of a call connection of the smart phone. At least one of a mobile terminal and an intelligent computing device of the present disclosure is associated with an artificial intelligence module, an unmmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, and a device related to a 5G service.