Calibrated Location Prediction Model for Mobile Devices

The patent segments the mobile device dataset into multiple clusters based on location patterns and behaviors. Each cluster represents a group of devices with similar characteristics, allowing the system to process and store data more efficiently while maintaining prediction accuracy. The segmentation reduces the overall complexity of the dataset by organizing it into manageable groups.

The patent creates simplified representations (copies) of location data through feature engineering, where complex raw location data is transformed into condensed feature vectors. These feature copies capture the essential patterns needed for prediction while requiring significantly less memory and processing power than the original detailed location data.

The patent performs feature engineering and data preprocessing in advance before model training begins. Training feature spaces are constructed beforehand from the raw mobile device datasets, and feature extraction is completed prior to the training process. This preliminary action allows the actual model training to proceed more efficiently without the overhead of real-time feature processing.

The patent implements optimized feature extraction processes that quickly transform raw data into useful features by skipping unnecessary intermediate processing steps. The system rushes through the feature engineering phase using efficient algorithms that produce the necessary feature spaces without excessive processing time, enabling faster model training overall.