Building Management Generative AI for Interactive Service Requests

The patent introduces a specialized AI processing layer as an intermediary between conventional building management systems and equipment data. This layer uses trained machine learning models to bridge the gap between raw equipment data and actionable insights, enabling precise response action generation without requiring complete system redesign. The AI intermediary handles complex pattern recognition while the underlying system maintains its existing architecture.

The system segments the data processing function by separating conventional rule-based processing from AI-driven analytical processing. Different AI models are assigned to specific tasks (anomaly detection, predictive maintenance, energy optimization), allowing each segment to specialize without increasing overall system complexity. This modular approach enables precise response actions while maintaining manageable system architecture.

The patent creates simplified representations (copies) of complex equipment states through AI-generated anomalies and patterns. Instead of directly analyzing raw unstructured data from multiple sources, the system uses trained models to produce standardized anomaly detections and predictive indicators that are easier to interpret and act upon quickly, reducing both time loss and detection difficulty.

The system transforms diverse data types into standardized parameters that AI models can process efficiently. By converting unstructured data, sensor readings, and operational data into unified feature representations, the system enables timely pattern detection without being hindered by data diversity. The AI models learn optimal parameter transformations during training to balance timeliness and detectability.