Machine Learning Model for LED Structure Performance Prediction

The patent applies preliminary action by using machine learning models to predict LED performance before actual fabrication and testing. The model is trained in advance on existing data to provide rapid predictions, allowing designers to evaluate multiple structures quickly without waiting for physical prototypes. This pre-computation approach significantly reduces the time required for design iteration while maintaining optimization reliability.

The patent uses copying by creating a virtual replica of the LED structure through machine learning models. Instead of physically manufacturing and testing each design variant, the system creates digital copies (predictions) of performance characteristics. This virtual copying allows unlimited iterations without additional physical resource consumption, solving the time and resource constraints of traditional trial-and-error methods.

The patent replaces physical resource consumption with digital copying through machine learning predictions. Instead of manufacturing physical LED structures for each design iteration, the system creates virtual predictions of performance characteristics. This eliminates consumption of materials, equipment time, and manpower for each trial, while still achieving reliable performance optimization through accurate model predictions.

The patent substitutes the mechanical trial-and-error process with an information-based machine learning system. Rather than physically building and testing structures, the system uses computational models to simulate and predict performance. This substitution replaces material and equipment resources with computational resources (data processing, algorithms), dramatically reducing overall resource consumption while maintaining optimization effectiveness.