Building Data Querying With LLM-Based Structure Generation
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Solution Overview
Problem
Building management systems face challenges in generating precise and relevant data in response to user queries due to limitations in existing language models, such as imprecision, lack of transparency, and inefficiencies in processing large amounts of unstructured data, which often require manual adjustments and increased computational resources.
Innovation Solution
The implementation of a language model-based system that uses machine learning models, including LLMs, to generate data structures and process unstructured knowledge from various sources, leveraging building knowledge graphs to improve accuracy and efficiency, allowing for real-time messaging and conversational interfaces that can understand natural language queries without requiring specific query languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional query language and predetermined schema vocabulary are used to obtain relevant information, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into structured query language and schema vocabulary. This mediator enables users to query without learning complex query languages while maintaining precise information retrieval through the translation to predetermined schema vocabulary.
Solution Approach 2:
The system segments the querying process into distinct components: natural language input processing, query translation to structured language, schema vocabulary mapping, and result generation. This segmentation allows each component to specialize in one function, improving both precision through structured processing and ease of operation through natural language interfaces.
2Measurement precision
If expert analysis is used to generate relevant data in response to user queries, then measurement precision is improved, but device complexity deteriorates
Solution Approach 1:
The system implements self-service through automated natural language processing and machine learning models that generate relevant data without requiring expert human analysis. The AI models automatically interpret queries, map to schema vocabulary, and retrieve or generate appropriate information, eliminating the need for expert analysts while maintaining accuracy.
Solution Approach 2:
The patent replaces the mechanical system of expert human analysis with an automated computational system using natural language processing and machine learning. This substitution maintains measurement precision through algorithmic consistency while reducing device complexity by eliminating manual expert intervention and associated organizational overhead.
3Manufacturing precision
If manual adjustments are made to process unstructured data, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The system replaces manual data processing adjustments with automated natural language processing and machine learning algorithms. These automated systems maintain manufacturing precision through consistent application of processing rules while dramatically improving productivity by handling large volumes of unstructured data simultaneously without human intervention.
Solution Approach 2:
The patent applies parameter changes by transforming unstructured data into structured formats through automated processing. The system changes the state of data from unstructured to structured, applying consistent transformation parameters that ensure manufacturing precision while enabling high-speed automated processing that improves productivity compared to manual adjustments.
4Measurement precision
If increased computational resources are allocated to process queries, then measurement precision is improved, but loss of energy deteriorates
Solution Approach 1:
The system segments computational resources into specialized components: natural language processing modules, schema vocabulary mapping engines, and data retrieval systems. This segmentation allows each component to use optimized algorithms for its specific function, improving measurement precision through specialized processing while reducing overall energy consumption by avoiding redundant computational operations.
Solution Approach 2:
The patent implements preliminary action by pre-processing and indexing data according to schema vocabulary before query execution. This preliminary organization enables faster, more energy-efficient query processing while maintaining measurement precision through pre-validated data structures, reducing the computational energy required during actual query execution.
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.