Point-of-Interest Identification System Using Neural Network Scoring

The patent replaces conventional simple classification methods with a machine learning-based scoring system. A trained neural network model processes multiple features of points of interest (such as name, address, category, and user feedback) to generate scores for different POI types, automatically selecting the type with the highest score. This substitution of mechanical classification with intelligent algorithms significantly improves classification accuracy and reliability while maintaining system manageability through modular architecture.

The patent implements a pre-trained neural network model that has been trained offline on extensive POI data. During runtime, the model receives pre-processed feature data (name, address, category, user feedback) and quickly generates scores for different POI types. The pre-training phase separates heavy computational work from real-time operation, allowing the system to achieve high reliability in POI type identification while keeping online computational complexity manageable. The model caches learned patterns from training data, enabling fast inference without reprocessing all training data during operation.