Emission Factor Mapping With ML and Fallback Search Logic
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Solution Overview
Problem
The challenge of calculating a comprehensive product footprint is hindered by the inability to efficiently map extensive business data from enterprise systems to sustainability reference content, which often uses different formats and nomenclature, making it difficult to accurately manage sustainability metrics.
Innovation Solution
Implementing intelligent mapping services that utilize a progression of three horizons: manual matching, assisted search with fuzzy/semantic matching, and machine learning to predict mappings with confidence scores, reducing the manual effort required in mapping product components to emission factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching is used to map product components to emission factors, then mapping accuracy can be controlled, but mapping time and labor effort increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing product component data and emission factor data, creating standardized representations and pre-established mapping rules. This preparation enables faster automated matching during actual mapping operations, reducing the time required while maintaining accuracy through the pre-processed structured data.
Solution Approach 2:
The patent replaces manual mechanical matching processes with automated computer-based systems that use algorithms for fuzzy logic matching, semantic similarity calculation, and machine learning predictions. This substitution eliminates human labor while maintaining or improving mapping accuracy through sophisticated computational methods.
2Productivity
If automated search algorithms are used to map product components to emission factors, then mapping speed increases, but mapping precision may decrease due to algorithm limitations
Solution Approach 1:
The system incorporates feedback mechanisms where automated mapping results are reviewed and validated, with corrections fed back into the system to improve future automated mappings. Machine learning models continuously learn from confirmed mappings to enhance their prediction accuracy, creating a feedback loop that improves both speed and precision over time.
Solution Approach 2:
The patent employs parameter changes by adjusting matching thresholds, weightings for different attributes (classification code vs. description), and algorithm parameters dynamically. This allows the system to optimize between speed and precision by changing parameters based on data characteristics and mapping context.
3Reliability
If extensive manual mapping is performed to ensure accuracy, then mapping reliability improves, but system complexity and operational difficulty increase
Solution Approach 1:
The mapping system is segmented into distinct functional modules: data collection modules for product components and emission factors, preprocessing modules for data standardization, matching modules for automated pairing, validation modules for reliability checking, and presentation modules for output. This segmentation makes the complex system more manageable and easier to operate while maintaining reliability through specialized functional components.
Solution Approach 2:
The patent introduces intermediary components such as standardized data formats, classification code bridges, and semantic representation layers that mediate between product component data and emission factor data. These intermediaries simplify the mapping process by providing standardized translation layers, reducing the complexity of direct mapping while ensuring reliable connections.
4Adaptability or versatility
If traditional data formats are used for emission factors, then data compatibility is maintained, but data integration difficulty increases
Solution Approach 1:
The system implements a universal data representation framework that can accommodate multiple emission factor data formats and sources through a common standardized structure. This multi-functional approach allows the system to integrate diverse data sources without requiring separate processing paths, improving both compatibility and integration ease by treating all data sources uniformly through standardization.
Data Source
AI summary
Intelligent mapping from created item information to sustainability reference content from a variety of sources can be implemented to facilitate created item footprint management and other sustainability applications. The difficult task of finding appropriate emission factors across a portfolio can be automated. Assisted search can be implemented using enhanced search techniques. Fallback mappings can be implemented to accommodate different levels of granularity during search. A machine learning model can be trained based on a variety of input data, including confirmed mappings, mapping history, and rules. The process of mapping to emission datasets can thus be simplified, enabling footprint calculations to proceed.