Neural Network Device Configuration Automation
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Solution Overview
Problem
Users face difficulties in configuring new electronic devices due to complex setup menus and unfamiliarity with the device's capabilities, leading to a cumbersome configuration process.
Innovation Solution
A neural network model-based apparatus and method for automatic configuration of electronic devices, which trains on usage data from similar devices to determine optimal settings for new devices based on their capabilities, reducing manual effort and enhancing configuration efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration through setup menus is used, then user control over device settings is maintained, but configuration time and user effort increase significantly
Solution Approach 1:
The system performs automatic configuration of device settings without requiring manual user input. The neural network model autonomously analyzes device capabilities and generates appropriate configuration parameters, enabling the device to configure itself based on learned patterns from similar devices and usage scenarios.
Solution Approach 2:
The neural network model is pre-trained on extensive device capability data and configuration patterns before deployment. This preliminary training enables the model to rapidly determine optimal settings during actual device configuration, eliminating the need for users to manually navigate setup menus and significantly reducing configuration time.
2Adaptability or versatility
If comprehensive setup menus with all capability options are provided, then complete device functionality can be configured, but user complexity and difficulty in understanding increase
Solution Approach 1:
The system extracts only the essential configuration parameters needed for optimal device operation based on the specific device capabilities and usage scenario. The neural network model identifies and configures only the most relevant settings, eliminating the need for users to navigate through comprehensive but overwhelming setup menus while still achieving complete functional configuration.
Solution Approach 2:
The configuration approach tailors the setup process to the specific device type, capabilities, and intended usage scenario. Rather than presenting a generic comprehensive menu for all devices, the system adapts the configuration parameters and options to match the local requirements of each specific device instance, simplifying the interface while maintaining completeness.
3Productivity
If automatic configuration systems are implemented, then user effort and configuration time are reduced, but system complexity and training data requirements increase
Solution Approach 1:
The neural network model serves multiple functions: it analyzes device capabilities, determines optimal configuration parameters, and adapts to different device types and usage scenarios. This multi-functional approach consolidates what would otherwise require multiple separate configuration systems into a single unified model, managing system complexity while maintaining high configuration efficiency across diverse device instances.
Data Source
AI summary
A server that includes circuitry and memory is provided. The memory stores a neural network model trained based on first device-usage information and a first set of configuration values for a first plurality of settings, associated with at least one first electronic device. The circuitry receives second capability information of a second electronic device from the second electronic device and compares the second capability information with first capability information of the at least one first electronic device. The circuitry further determines a second set of configuration values for a second plurality of settings of the first plurality of settings based on the comparison of the second capability information with the first capability information. Further, the circuitry transmits the second set of configuration values for the second plurality of settings and the corresponding first device-usage information to the second electronic device for configuration of the second electronic device.