Building Management Generative AI for Service Report Standardization
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Solution Overview
Problem
Existing building management systems face challenges in generating precise and timely data for equipment servicing, including identifying proper response actions and sequences, due to the variability in equipment types and the unstructured nature of service reports.
Innovation Solution
Implementing a building management system that utilizes machine learning models, such as generative AI, to process unstructured service reports, extract standards from technical documents, and generate standardized service reports that comply with predetermined formats, incorporating additional data sources for enhanced accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unstructured service reports are used to document equipment servicing, then flexibility in data collection is improved, but data standardization and precision are worsened
Solution Approach 1:
A machine learning model acts as an intermediary between unstructured service reports and structured data requirements. The model automatically processes unstructured text inputs from technicians and converts them into standardized structured data formats, enabling both flexibility in data collection and standardization in output without requiring manual intervention.
Solution Approach 2:
The patent replaces manual data processing and formatting mechanisms with an automated machine learning system. Instead of technicians manually formatting reports according to strict templates, the ML model automatically extracts relevant information and structures it according to predefined schemas, eliminating the need for mechanical adherence to formatting rules while maintaining standardization.
2Ease of operation
If multiple different formats are accepted for service reports, then ease of operation is improved, but data processing complexity is worsened
Solution Approach 1:
The machine learning model serves as an intermediary processing layer that accepts multiple input formats from service reports and automatically converts them into a unified structured format. This intermediary layer handles the complexity of format conversion, allowing technicians to submit reports in their preferred format while the system manages the processing complexity centrally.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the input format detected. When different formats are received, the ML model automatically modifies its processing approach to extract relevant information effectively from each format type, then standardizes the output. This parameter adaptation handles format variability without requiring complex manual processing rules.
3Device complexity
If manual processing of service reports is used, then system complexity is reduced, but productivity and timeliness are worsened
Solution Approach 1:
The machine learning model enables self-service processing of service reports. The system automatically extracts information, structures data, and generates standardized reports without requiring manual intervention for each report. This self-service capability dramatically improves productivity while the complexity is encapsulated within the automated ML processing rather than requiring complex human workflows.
4Loss of information
If unstructured data is collected from service reports, then information completeness is improved, but measurement precision and fault detection accuracy are worsened
Solution Approach 1:
The machine learning model selectively extracts relevant information from unstructured service report data and isolates it into structured fields. By extracting specific diagnostic elements, fault indicators, and service details from the unstructured text, the system maintains information completeness while organizing it into precise, queryable formats that improve measurement precision and fault detection accuracy.
Solution Approach 2:
The patent replaces manual analysis of unstructured data with automated machine learning processing. The ML model applies consistent analytical rules and patterns to extract meaningful information from unstructured text, eliminating human variability in data interpretation while maintaining information completeness. This substitution improves measurement precision through consistent, repeatable analysis of the same data elements.
Data Source
AI summary
A method includes receiving, by one or more processors, an unstructured service report corresponding to a service request handled by one or more technicians for servicing building equipment. The unstructured service report may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method may include extracting, by the one or more processors, a set of standards from one or more technical documents associated with the building equipment. The method may include automatically generating, by the one or more processors using a generative AI model, a structured service report in the predetermined format for delivery to a customer associated with the building equipment. Generating the structured service report may include standardizing the structured service report to comply with the set of standards extracted from the one or more technical documents associated with the building equipment.