Building Equipment Root Cause Prediction From Unstructured Service Data
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Solution Overview
Problem
Existing building management systems face challenges in generating precise data for timely and effective response actions due to unstructured and varied data formats, making it difficult to identify appropriate service operations for building equipment.
Innovation Solution
Implementing a machine learning model, such as a generative AI model, to process unstructured data from various sources, including service reports, engineering data, and sensor data, to generate structured responses for equipment servicing, and guide technicians through service operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional building management systems process data using conventional methods, then the system structure remains simple, but the precision of data analysis and response generation is insufficient
Solution Approach 1:
The patent replaces conventional data processing methods with machine learning models, specifically using neural networks to analyze building equipment data. The system ingests unstructured data (service reports, technician notes) and structured data (sensor readings, equipment logs) through ML algorithms that automatically identify patterns and generate response actions, substituting manual or rule-based analysis with intelligent automated processing.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw building management data and actionable insights. The ML models act as mediators that process and transform unstructured service data into structured responses, bridging the gap between disparate data formats and decision-making requirements without requiring complex manual integration logic.
2Loss of information
If building management systems collect and process diverse unstructured data from multiple sources, then the completeness of service information improves, but the difficulty of data processing and analysis increases
Solution Approach 1:
The patent transforms unstructured service data (text, images, audio from technicians) into structured parameters that machine learning models can process. The system changes the state of diverse data formats into standardized representations, converting variable-length unstructured inputs into fixed-format features that maintain information completeness while enabling systematic analysis through parameter standardization.
Solution Approach 2:
The patent segments diverse unstructured service data into distinct processing streams handled by specialized machine learning components. Different ML models process different data types (text analysis, image recognition, audio processing) separately, then integrate results comprehensively. This segmentation reduces processing difficulty by dividing complex heterogeneous data into manageable specialized tasks.
3Productivity
If machine learning models are used to generate service responses, then the accuracy and timeliness of service operations improve, but the computational resources and processing time required increase
Solution Approach 1:
The patent pre-trains machine learning models on extensive building equipment service data before deployment. This preliminary training enables the models to quickly process new service requests with minimal real-time computation. The models learn patterns and relationships in advance, so during actual service operations, they can generate accurate responses rapidly without requiring extensive computational resources during critical service moments.
Data Source
AI summary
A method including training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment. The generative AI model may be trained to predict root causes of a plurality of first problems corresponding to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request for servicing building equipment. The method may include predicting, by the one or more processors using the generative AI model, a root cause of a second problem corresponding to the second service request based on characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests using the generative AI model.