Generative AI for Building Equipment Service Recommendations
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Solution Overview
Problem
Existing building management systems face challenges in generating precise data for equipment servicing due to limitations in existing AI and machine learning models, which often produce incorrect, imprecise, or irrelevant data, requiring manual input adjustments and struggling with large datasets and unstructured data processing.
Innovation Solution
The implementation of generative AI models, such as language models and neural networks, to generate and fine-tune data pairs from service and warranty records, creating question-and-answer pairs for precise service recommendations and reports, and integrating these models with feedback loops for improved accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing AI and machine learning models are used to generate service data, then automation is achieved, but the data generated is often incorrect, imprecise, or irrelevant
Solution Approach 1:
The system implements feedback loops where service technicians verify and correct AI-generated service data, and this corrected data is fed back to retrain and improve the AI model. This continuous feedback mechanism resolves the contradiction by maintaining automation while progressively improving data precision through learned corrections.
Solution Approach 2:
The patent introduces an intermediary verification layer between the AI model and the final service data output. This intermediary consists of validation rules, expert knowledge bases, and technician review processes that filter and refine AI-generated content, ensuring precision while preserving automation benefits.
2Measurement precision
If manual input adjustments are made to correct AI-generated data, then data precision is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-training AI models on historical service data and pre-establishing validation rules before actual service operations. This preparation reduces the need for manual adjustments during time-critical service situations, improving precision without proportionally increasing time loss.
Solution Approach 2:
The AI model progressively performs self-correction through automated retraining on verified data, reducing the need for manual interventions over time. The system serves itself by automatically learning from corrections, thereby improving precision while decreasing the time and effort required for manual adjustments as the system matures.
3Quantity of substance
If existing AI models process large datasets, then comprehensive analysis is achieved, but the models struggle with unstructured data and processing efficiency
Solution Approach 1:
The patent segments the processing of large service datasets by organizing data into structured categories (equipment type, service category, fault codes, etc.) and processing different segments through specialized AI models or rules. This segmentation improves processing efficiency while maintaining comprehensive analysis capabilities across the entire dataset.
4Reliability
If generative AI models are fine-tuned with service records, then service recommendation accuracy is improved, but system complexity and training requirements increase
Solution Approach 1:
The system implements dynamic complexity management where the AI model structure and training requirements adapt based on the volume and quality of available service records. As more data becomes available, the system dynamically adjusts its model complexity and fine-tuning depth, improving recommendation accuracy while managing system complexity through adaptive scaling.
Data Source
AI summary
A method includes generating a plurality of data pairs by prompting a generative AI model to output, for each of a plurality of service records, a data pair comprising a question and an answer. The question relates to an equipment issue indicated in the service record and the answer relates to a service task indicated in the service record. The method also includes fine-tuning the generative AI model using the plurality of data pairs and providing a service recommendation using the generative AI model.