Generative AI Building Management Service Scheduling
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Solution Overview
Problem
Current building management systems face challenges in generating precise data for identifying appropriate response actions and sequences for servicing equipment, due to limitations in accessing timely and accurate data, especially when dealing with technical issues and varying equipment conditions.
Innovation Solution
Implementing a machine learning model, such as a generative AI system, trained on unstructured and structured data from service requests to predict responses, assign technicians, schedule activities, and provision resources based on patterns and trends identified from historical data, enabling real-time and accurate service operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional building management systems are used for service scheduling, then system simplicity is maintained, but data precision and response accuracy deteriorate
Solution Approach 1:
A generative AI model acts as an intermediary between building management systems and service scheduling operations. The model receives inputs from multiple data sources including building management systems, service request data, and historical service records, processes this information through pattern recognition, and generates optimized service scheduling outputs. This intermediary layer enables precise data processing without requiring complete system redesign.
Solution Approach 2:
The patent replaces traditional rule-based and manual service scheduling mechanisms with a generative AI model that uses machine learning algorithms to automatically generate service schedules. The system substitutes mechanical decision-making processes with intelligent algorithms that can analyze complex patterns in service request data, equipment status, technician availability, and historical performance to optimize scheduling decisions.
2Productivity
If manual service request processing is used, then system complexity is low, but productivity and response time deteriorate
Solution Approach 1:
The generative AI model enables self-service capabilities in service scheduling by automatically generating service schedules, assigning technicians, and optimizing resource allocation without requiring manual intervention. The system processes service requests, analyzes equipment status, and produces scheduling decisions autonomously based on learned patterns from historical data, significantly improving service operation efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-processing service request data, pre-identifying patterns from historical service records, and pre-generating optimized scheduling options before actual service execution. The generative AI model learns from past service requests and outcomes to prepare predictive insights and scheduling recommendations in advance, enabling faster response times when new service requests arrive.
3Reliability
If comprehensive service data is collected and processed, then service scheduling accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of comprehensive service data by pre-training the generative AI model on historical service requests, equipment data, and outcome records. This preliminary action extracts and stores learned patterns and relationships in the model's parameters, enabling rapid inference and accurate scheduling decisions for new service requests without re-processing the entire historical dataset each time.
Solution Approach 2:
The generative AI model creates simplified representations or copies of complex service data patterns through learned embeddings and feature extractions. Instead of processing raw comprehensive data repeatedly, the system uses the model's internal representations of service request patterns, equipment characteristics, and technician performance, which capture essential information in a compressed form that enables fast and accurate scheduling decisions.
Data Source
AI summary
A method includes training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment and outcome data indicating outcomes of the plurality of first service requests. The generative AI model may be trained to identify one or more patterns or trends between characteristics of the plurality of first service requests and the outcomes of the plurality of first service requests. The method may include receiving a second service request for servicing building equipment. The method may include automatically determining, using the generative AI model, one or more responses to the second service request based on characteristics of the second service request and the one or more patterns or trends between the characteristics of the plurality of first service requests and the outcomes of the plurality of first service requests identified using the generative AI model.