AI Product Decomposition for Sparse-Data Life Cycle Impact Estimation
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Solution Overview
Problem
Conventional Life Cycle Assessment (LCA) methods require complete and error-free Bills of Materials (BOMs, which are often unavailable, impractical, or prohibitively expensive, and existing automation attempts fail to address data sparsity, making it difficult to accurately estimate carbon footprints from sparse or unstructured data.
Innovation Solution
A multi-stage generative AI architecture that deconstructs products into constituent components using Large Language Models (LLMs) and Large Vision Models (LVMs), constraining outputs to ISO 14067 standards, enabling accurate carbon footprint estimation from sparse inputs like text descriptions or images without complete BOMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional LCA methods are used, then measurement precision is improved, but device complexity and data requirements increase
Solution Approach 1:
The patent replaces complex mechanical data collection systems with machine learning models that automatically extract product composition information from images and text descriptions. This substitution reduces the need for manual BOM compilation while maintaining estimation accuracy through automated image recognition and natural language processing.
Solution Approach 2:
The system creates simplified digital representations (copies) of product bills of materials through image-to-data conversion. Instead of requiring complete original BOM documents, the system generates approximate material composition data from product images, enabling LCA calculations with reduced data collection complexity.
2Measurement precision
If complete Bills of Materials are required, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically generating product decomposition and material composition data from images and descriptions without requiring user-provided BOMs. The machine learning models independently complete the data collection task that would otherwise require extensive manual input from users.
Solution Approach 2:
The system performs preliminary data preparation by pre-processing images and text to extract material information before the actual carbon footprint calculation. This preliminary action of automatic data extraction eliminates the need for users to manually prepare complete BOMs, simplifying the operation while maintaining calculation accuracy.
3Measurement precision
If manual data collection is used, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual mechanical data collection processes with automated machine learning systems that rapidly process images and generate material composition data. This substitution increases productivity by eliminating time-consuming manual BOM compilation while maintaining data accuracy through sophisticated algorithms.
Solution Approach 2:
The system enables continuous automated data extraction and carbon footprint calculation without the interruptions inherent in manual data collection. The machine learning models process multiple products sequentially or in parallel, maintaining continuous productive operation while ensuring accurate emissions data through consistent algorithmic application.
Data Source
AI summary
An apparatus and a method for calculating the environmental impact of a product from sparse input data are described. The system receives input data, such as a textual description or an image, that lacks a complete bill of materials. A generative artificial intelligence model automatically deconstructs the product into a plurality of constituent components and associated lifecycle activities. An environmental impact is determined for each component, and the contributions are aggregated to estimate the total impact for the product. The system may further store and compare calculated impacts with previous estimates in a database to return the value associated with the lowest error.