AI Rule Generation for Building Equipment Management
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Solution Overview
Problem
Generating rules for building management systems is cumbersome and requires substantial knowledge and time, as users often lack specific conditions for triggering rules and outcomes.
Innovation Solution
A method and system that utilize machine learning models, specifically generative AI, to receive natural language prompts and generate representations of rules for building management systems, allowing for intelligent and automated rule generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual rule generation is used, then rule accuracy can be maintained through expert knowledge, but the process becomes time-consuming and complex
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between natural language input and rule generation. The model translates user-friendly natural language prompts into structured rules, eliminating the need for users to manually craft complex rule syntax while maintaining accuracy through the model's learned understanding of rule patterns and conditions.
Solution Approach 2:
The patent replaces the mechanical process of manual rule creation with an automated machine learning-based system. Instead of requiring users to manually define rules through complex interfaces, the system uses natural language processing and generative AI to automatically generate rules, significantly reducing time and complexity while maintaining or improving accuracy.
2Adaptability or versatility
If manual rule generation is used, then rules can be customized according to specific needs, but the process requires substantial expertise and becomes cumbersome
Solution Approach 1:
The patent enables users to generate customized rules through simple natural language prompts without requiring expert knowledge. The machine learning model interprets the user's intent and automatically generates appropriate rules, allowing users to serve themselves without needing to understand complex rule syntax or logic, thus improving ease of operation while maintaining adaptability.
Solution Approach 2:
The patent creates a universal system that handles diverse rule generation needs through a single interface. The machine learning model is trained to understand various types of rules and conditions, allowing it to generate different kinds of rules (temperature control, equipment scheduling, energy management, etc.) from a unified natural language input mechanism, making the system both adaptable and easy to use.
3Productivity
If automated rule generation is implemented, then time and complexity are reduced, but the system requires sophisticated machine learning models
Solution Approach 1:
The patent uses the machine learning model to copy and adapt proven rule patterns from training data. Instead of requiring users to create rules from scratch or the system to invent new rule logic, the model copies existing successful rule structures and adapts them to new situations based on natural language input, achieving high productivity while managing complexity through pattern recognition rather than complex algorithm design.
Data Source
AI summary
Systems and methods of the present disclosure relate to a machine learning model-based rules generator for generating rules for building management systems. A method can include receiving, by one or more processors, a prompt comprising natural language data regarding a rule associated with operation of an item of equipment of a building; providing, by the one or more processors, the prompt as input to a machine learning model to cause the machine learning model to generate a representation of the rule; and activating, by the one or more processors, the rule for the item of equipment.