AUTONOMOUS PERSONAL CALENDAR MANAGEMENT THROUGH PREFERENCE LEARNING

Personal calendar management involves highly individual, implicit preferences that vary drastically between individuals and are poorly served by current one-size-fits-all calendar applications. Unlike enterprise systems governed by organizational policies, personal calendar management requires understanding individual preferences about work-life balance, personal time protection, energy patterns, and other priority trade-offs. I present a novel machine learning approach that analyzes how individuals generally resolve scheduling conflicts in their personal calendars to build personalized preference models for autonomous personal scheduling assistance. The system extracts multi-dimensional features from personal calendar conflicts—including work-life boundary preferences, individual energy patterns, personal relationship priorities, and lifestyle factors like fitness and other engagements—and learns to predict user preferences accordingly. It also learns through feedback from the individuals on these preferences. After evaluating realistic personal scheduling scenarios, I demonstrate that this approach achieves 78% accuracy in predicting individual scheduling decisions, with confidence calibration enabling autonomous action for high-certainty personal decisions (>80% confidence) while preserving user control for complex personal trade-offs. This work establishes the foundation for truly personal calendar assistants that can learn individual scheduling patterns and make intelligent decisions that respect personal boundaries and individual lifestyle preferences.