Environmental Material Recommendation Using Knowledge-Graph AI
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Solution Overview
Problem
Current systems lack automated tools for identifying and tracking environmentally friendly materials and reducing carbon emissions, failing to scale due to the complexity of parameters involved, and existing methods like syntax search and supervised learning provide inaccurate results.
Innovation Solution
A private generative AI system utilizing a domain-specific large language model (LLM) trained on a knowledge graph, which integrates diverse data sources to recommend environmentally friendly materials and processes, calculates an Environmental Cost Indicator (ECI) and Ecoscore, and updates dynamically based on user interactions and new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tools and analytics are implemented to identify environmentally friendly materials, then productivity and decision-making efficiency are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of environmental material identification into distinct functional modules: data acquisition module, data processing module, analytics engine, and recommendation generation module. Each module handles specific aspects of the workflow, managing complexity through functional decomposition while maintaining high productivity through automated processing pipelines.
Solution Approach 2:
The patent introduces an intermediary layer of AI/ML models and analytics engines that mediate between raw data sources and decision-making processes. This intermediary layer processes and transforms complex environmental data into actionable insights, reducing the burden on users while maintaining high productivity through intelligent automation.
2Measurement precision
If comprehensive data from multiple sources is collected and analyzed, then measurement precision and recommendation accuracy are improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-analyzing data from multiple sources before actual material selection occurs. Environmental impact factors, material properties, and compatibility data are pre-computed and stored in accessible formats, enabling rapid querying and analysis during the actual decision-making process without time-consuming real-time computation.
Solution Approach 2:
The analytics engine dynamically changes processing parameters based on the specific query and data availability. It adjusts the depth of analysis, data sources consulted, and computational methods applied to balance accuracy requirements with time constraints, providing high precision when needed and faster results when time is limited.
3Ease of manufacture
If existing methods like syntax search and supervised learning are used, then ease of implementation is maintained, but manufacturing precision and recommendation accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical search methods and basic supervised learning with advanced AI/ML techniques including natural language processing, knowledge graphs, and predictive analytics. This substitution significantly improves recommendation accuracy by understanding semantic relationships and environmental impact patterns that syntax-based methods cannot capture, while maintaining ease of use through intuitive interfaces.
Solution Approach 2:
The system uses a composite approach combining multiple AI/ML methodologies (NLP, knowledge graphs, predictive modeling) rather than relying on a single technique. This composite methodology integrates the strengths of different approaches to achieve high accuracy in material recommendations while maintaining practical implementability through modular architecture.
Data Source
AI summary
A system and method for recommending environmentally conscious materials for construction and manufacturing projects is disclosed. The system addresses the need for tools to identify materials that meet decarbonization goals and comply with climate disclosure rules. The system includes a data acquisition module, a data ingestion module, and a private generative AI large language model trained on a knowledge graph of material and chemical relationships. This system generates recommendations for alternative materials, calculates an Environmental Cost Indicator (ECI) and Ecoscore, and records data on a blockchain ledger. The primary use of the system is to provide decision support for selecting low-carbon materials, thereby reducing environmental impacts. Additionally, the system includes a user interface for exploring options and simulating impacts on ECI and Ecoscore. This system is particularly useful for investors, architects, and organizations aiming to achieve net-zero decarbonization goals.