qPCR Amplification Curve Analysis Using Convolutional Neural Networks

The patent replaces traditional algorithmic techniques and visual analysis methods with deep learning-based artificial neural networks. The neural network model automatically learns complex patterns from amplification curve data, substituting manual or simple algorithmic evaluation with an intelligent system that achieves superior accuracy in determining amplification while automatically handling the complexity of pattern recognition.

The patent implements an automated deep learning system that performs amplification determination independently without requiring manual review. The neural network model processes amplification curves automatically, extracting features and making determinations autonomously. This self-service approach enables high-throughput analysis while maintaining the flexibility and accuracy previously associated with manual expert review.

The patent employs a pre-trained deep learning model that has been trained on extensive amplification curve data before deployment. This preliminary training action enables the model to automatically recognize complex patterns and make reliable predictions when applied to new samples. The pre-trained system achieves clinical-grade reliability by learning from diverse training data, handling the complexity of accurate prediction through advance preparation rather than during actual sample analysis.