Neural Network Prediction for Optical Lens Manufacturing

The patent applies preliminary action by pre-training neural networks with historical data and computational results before actual manufacturing orders are processed. The neural network models are trained in advance to learn the complex relationships between prescription parameters and manufacturing parameters, enabling fast predictions during production without requiring real-time complex computations for each order.

The patent replaces the mechanical computation system (lens design software performing real-time calculations) with a neural network-based prediction system. Instead of executing complex mathematical optimizations and algorithms in real-time, the system uses pre-trained neural networks to predict manufacturing parameters directly from prescription data, substituting computational mechanics with pattern recognition and prediction.

The patent applies preliminary action by pre-processing and training neural networks in advance, so that when orders come in during peak periods, the system can immediately provide fast predictions without requiring massive computational resources. The heavy lifting of learning complex relationships is done beforehand during low-demand periods.

The patent replaces resource-intensive real-time computation systems with lightweight neural network inference systems. Once trained, neural networks require significantly fewer computational resources to make predictions compared to executing full lens design software algorithms, allowing the system to handle peak demands without proportionally increasing server infrastructure.

The patent replaces complex mathematical optimization algorithms and lens design software with neural network models. The neural networks encapsulate the complexity of optical calculations within their trained parameters, providing accurate predictions through pattern recognition rather than explicit mathematical computations, thereby simplifying the IT infrastructure.

The patent creates simplified copies of the complex computation system in the form of neural network models. These models capture the essential relationships and patterns from the original complex calculations, providing approximate but sufficiently accurate predictions with much simpler computational requirements, effectively copying the functionality at lower complexity.