Neural Network Spectral Response Generation

The patent creates a computational copy of the spectroscopy measurement process using neural networks. Instead of physically measuring spectral responses with complex spectroscopy tools, the system trains a neural network model on historical measurement-spectral response pairs, then uses this trained model to generate synthetic spectral responses from new measurements. This copying approach eliminates the need for expensive, complex on-site spectroscopy equipment while maintaining measurement precision.

The patent replaces the mechanical/physical spectroscopy measurement system with a computational information processing system. Instead of deploying physical spectroscopy instruments to remote locations, the system uses readily available measurements combined with a trained neural network model to generate spectral responses. This substitution transforms a complex field operation into a simple computational process that can be performed with standard equipment.

The patent applies partial action by using only a subset of information that would be obtained from full spectroscopy measurements. Instead of requiring complete spectral data acquisition that may consume significant material or require extensive sampling, the system uses available measurements combined with the trained neural network model to generate the necessary spectral response information, reducing material consumption while maintaining adequate measurement precision.