Calendar Event Peer Identification via Relationship Evaluation

The patent merges data from multiple sources (messaging application contacts and calendar application events) into a unified peer identification system. By combining these data sources, the system enhances its ability to identify peers and provide relevant feedback without proportionally increasing complexity, as the calendar data integration follows a systematic approach using predefined parameters and machine learning models.

The system is designed to work with multiple types of personal data (contacts from messaging apps and events from calendar apps) using a universal evaluation framework. The peer evaluation system uses a standardized set of parameters and machine learning models that can process different data types uniformly, allowing the system to handle diverse input sources without requiring separate processing mechanisms for each data type.

The system performs preliminary actions by pre-defining evaluation parameters and criteria for assessing peer relationships from calendar events. The machine learning models are trained in advance on historical data, and the evaluation framework is established beforehand. This allows the system to quickly evaluate new calendar events without performing complex computations in real-time, thus reducing data processing time while maintaining high identification accuracy.

The system employs parameter changes by using a standardized set of evaluation parameters (such as interaction frequency, event types, and relationship strength metrics) that can be adjusted based on the specific context. The machine learning models process calendar event data by transforming raw event information into standardized parameters, enabling efficient computation while preserving the nuanced information needed for accurate peer identification.