Building Data Query Structuring for Precise Natural Language Retrieval
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Solution Overview
Problem
Existing building management systems face challenges in generating accurate and relevant data in response to user prompts due to limitations in language models, such as imprecision, lack of transparency, and inefficiencies in processing large amounts of unstructured data, which require manual input adjustments and increased computational resources.
Innovation Solution
Implementing a building management system with language model-based data structure generation using machine learning models, including LLMs, to process unstructured data from various sources, apply automated and expert-based thresholds, and leverage building knowledge graphs for precise output generation, enabling real-time messaging and conversational interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If language models are used to process unstructured data from multiple sources, then the system can generate relevant information in response to user prompts, but the computational resources required increase significantly
Solution Approach 1:
The system segments the data processing task by separating structured data retrieval from unstructured data generation. The machine learning model retrieves structured data from databases using efficient queries, then only applies language model processing to generate natural language responses from this pre-filtered structured data, rather than processing all unstructured data directly through the language model.
Solution Approach 2:
Structured data acts as an intermediary between the user's natural language prompt and the final response. The system translates user prompts into structured queries, retrieves precise data from databases, then uses language models to translate this structured data back into natural language responses, reducing the computational burden on the language model.
2Measurement precision
If expert analysis or specific query languages are used to obtain relevant information, then the precision of data retrieval improves, but the ease of operation decreases due to manual input adjustments
Solution Approach 1:
The machine learning model performs self-service by automatically translating natural language user prompts into structured queries and retrieving relevant data without requiring manual expert analysis or complex query language input. The system autonomously handles the transformation from natural language to precise data retrieval and back.
Solution Approach 2:
The machine learning model serves multiple functions: it acts as a natural language interpreter, a query generator, and a response formatter. This multi-functional approach eliminates the need for separate expert analysis tools and query language interfaces, allowing users to interact with the system using only natural language while maintaining high retrieval precision.
3Reliability
If manual input adjustments are made to improve query accuracy, then the reliability of information generation increases, but the productivity decreases due to increased time requirements
Solution Approach 1:
The system performs preliminary action by pre-processing and structuring data from multiple sources before it is needed for response generation. Data is organized into structured formats and stored in databases in advance, so when a user prompt arrives, the system can quickly retrieve relevant structured data without requiring manual adjustments or time-consuming processing at query time.
Solution Approach 2:
The system uses feedback mechanisms where the machine learning model learns from the structure and content of retrieved data to improve future query generation. The model receives feedback on the effectiveness of its queries and adjusts its strategy to improve accuracy over time without requiring manual intervention, thus maintaining both reliability and productivity.
Data Source
AI summary
Systems and methods are disclosed relating to building management systems with language model-based data structure generation. For example, a method can include receiving a query to select, from a plurality of data sources of a building management system, a selected one or more data sources according to a characteristic indicated by the query in at least one of a natural language representation or a semantic representation. The method can further include applying the query as input to a machine learning model to cause the machine learning model to generate an output indicating the selected one or more data sources, the machine learning model configured using training data comprising sample data and metadata from the plurality of data sources. The method can further include presenting, using at least one of a display device or an audio output device, the output.