Building Management LLM Service Query Interface
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Solution Overview
Problem
Building management systems face challenges in generating precise data for service operations due to limitations in existing AI and machine learning models, which often produce incorrect, imprecise, or irrelevant outputs, requiring manual adjustments and struggling with large amounts of raw or unstructured data.
Innovation Solution
The implementation of fine-tuned large language models (LLMs) within building management systems, using an interactive interface to guide user input and generate targeted queries, coupled with data from various sources like engineering, operational, and warranty data, to provide accurate and relevant responses for equipment servicing, diagnostics, and troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing AI and machine learning models are used to generate service operation data, then automated data generation is achieved, but the output precision and relevance deteriorate due to incorrect or irrelevant responses
Solution Approach 1:
The patent introduces an interactive interface as an intermediary between the user and the LLM system. This interface guides users through structured input fields (equipment type, parameter, output condition, request type) to formulate precise queries, which then serve as effective prompts for the LLM to generate accurate service operation data, resolving the contradiction between automation and precision
Solution Approach 2:
The system fine-tunes the large language model using building domain data, changing the model's parameters and knowledge base to specialize in building equipment service operations. This parameter change enables the LLM to generate precise and relevant outputs for service operations while maintaining automation
2Measurement precision
If manual adjustments are made to AI model outputs to improve precision, then output quality improves, but operation time and complexity increase
Solution Approach 1:
The system enables self-service by having the LLM generate accurate service operation data automatically through fine-tuning on domain-specific data. The interactive interface guides users to provide necessary information, and the LLM processes this to produce precise outputs without requiring manual adjustments, thus improving output quality while reducing operation time
3Measurement precision
If structured input fields are used to guide user input, then query precision improves, but interface complexity increases
Solution Approach 1:
The interactive interface is segmented into distinct input fields (equipment type, equipment parameter, output condition, request type), each handling a specific aspect of the query. This segmentation organizes the complexity into manageable parts, guiding users to provide precise information for each component while maintaining overall interface structure
4Measurement precision
If fine-tuned LLMs are implemented to improve data accuracy, then service operation precision improves, but system complexity and computational resources increase
Solution Approach 1:
The LLM is fine-tuned in advance using building domain data before deployment. This preliminary action prepares the model with specialized knowledge for building equipment service operations, enabling it to generate precise outputs without requiring complex real-time processing or additional system complexity during operation
Data Source
AI summary
A method includes fine-tuning at least one large language model (LLM) using building domain data comprising information regarding equipment types, equipment parameters, and output conditions, facilitating generation of an input query for the at least one LLM by providing an interactive interface configured to guide input of a relevant equipment type, a relevant equipment parameter, a relevant output condition, and a request type by a user and generating the input query based on the input of the relevant equipment type, the relevant equipment parameter, the relevant output condition, and the request type, and providing a response to the input query as an output of the at least one LLM by using the input query as an input to the LLM.