External Device Recognition Using Machine Learning and Connection Environment Data
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Solution Overview
Problem
The challenge is to improve the recognition success rate of external devices connected to a display device, particularly in adapting priority between control schemes based on device type and connection environment.
Innovation Solution
An electronic device is designed with a connection unit and a processor that identifies connected external devices, determines their controllability, and selects a priority control scheme using machine learning models and connection information, including device, image, and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional connection recognition methods are used, then the device can identify connected external devices, but the recognition success rate is low and false recognition occurs
Solution Approach 1:
The patent changes the parameters used for device identification from traditional connection status alone to a multi-parameter approach including device information, image information, and connection environment information. This parameter expansion enables more accurate device recognition and reduces false identification by comparing multiple characteristics simultaneously.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that process and analyze the collected device information, image information, and connection environment information. These models act as mediators between raw data and final device identification, enabling intelligent recognition decisions that improve both success rate and accuracy.
2Adaptability or versatility
If multiple control schemes are supported, then the device can adapt to different external devices, but determining the priority control scheme becomes complex
Solution Approach 1:
The patent enables the electronic device to automatically determine the priority control scheme through machine learning models without requiring manual user configuration. The system self-services by autonomously analyzing connection information and making intelligent decisions about which control scheme to prioritize, simplifying the user experience while maintaining adaptability.
Solution Approach 2:
The patent performs preliminary analysis of device information, image information, and connection environment information using machine learning models before establishing control schemes. This preliminary action pre-determines the most suitable control scheme based on predicted device characteristics and connection conditions, reducing the complexity of real-time control scheme selection.
Data Source
AI summary
An electronic device including a connection unit; and a processor configured to identify that a first external device is physically connected to the electronic device through the connection unit, identify connection information about the first external device, the identified connection information including device information about the first external device, image information from the first external device, and information about a connection environment of the first external device, determine whether the first external device is a controllable device based on the identified connection information about the first external device and at least one machine learning model for identifying a plurality of external devices, identify at least one control scheme of the first external device in response to determining that the first external device is a controllable device, and determine a control scheme of the identified at least one control scheme as a priority control scheme.