This paper presents a comprehensive framework for intelligent and personalized task scheduling based on Transformer architectures and contextual-behavioral feature modeling. The proposed system processes sequences of user activity enriched with temporal, spatial, and behavioral information to generate structured task representations. Each predicted task includes six key attributes: task type, execution time window, estimated duration, execution context, confidence score, and priority level. By leveraging Transformer encoders, the model effectively captures long-range temporal dependencies while enabling parallel processing, which significantly improves both scalability and responsiveness compared to recurrent approaches.
The system is designed to support real-time adaptation by integrating diverse data sources such as device activity, location, calendar status, and behavioral metrics. A modular architecture enables input encoding, multi-head self-attention, and global behavior summarization for downstream task generation. Experimental evaluation using artificially generated user data illustrates the model’s ability to maintain high accuracy in task type and timing prediction, with consistent performance under varying contextual conditions. The proposed approach is applicable in domains such as digital productivity, cognitive workload balancing, and proactive time management, where adaptive and interpretable planning is essential.