Neural Network Configuration Scoring for Computing Devices

The patent pre-calculates and stores configuration scores for various device configurations in a database before actual testing occurs. When a testing request comes in, the system directly retrieves pre-computed scores instead of performing time-consuming tests, thus maintaining evaluation accuracy while dramatically reducing testing time.

The patent creates a virtual copy of the testing process by using machine learning models to predict test results based on device configuration parameters. Instead of physically executing tests on each device, the system generates predicted test results that mirror what actual tests would produce, eliminating the need for time-consuming physical testing while preserving evaluation accuracy.

The patent segments the evaluation process by creating separate machine learning models for different device functions (e.g., display, processor, memory, camera). Each model is trained on specific function-related data and can independently evaluate that function, allowing the system to provide comprehensive evaluation across all functions while quickly assessing specific functions by activating only the relevant model.

The patent applies different evaluation qualities to different functions by training specialized machine learning models for each device component. Each model has local expertise in evaluating its specific function, enabling the system to maintain comprehensive evaluation capability while providing fast, specialized assessment for any specific function requested by the user.