AI/ML Engineering Library Mapping for Schema Integration
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Solution Overview
Problem
The challenge of mapping or classifying contents between different engineering libraries is resource-intensive and error-prone, particularly due to the lack of a unified library standard, leading to significant costs and errors in manual matching processes.
Innovation Solution
Employing an artificial intelligence/machine learning (AI/ML) model to automate the mapping and transformation of libraries, schemas, and file formats, utilizing methods such as joint embedding representations, Named Entity Recognition, and Graph-Neural Networks, with a feedback mechanism to improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual matching and mapping is used to map content between different engineering libraries, then flexibility and adaptability are maintained, but resource consumption increases and error rates increase
Solution Approach 1:
The patent replaces manual mechanical matching processes with an AI/ML-based automated system. The AI/ML model processes library content automatically to perform mapping and classification tasks, eliminating the need for manual human intervention while improving both accuracy and efficiency.
Solution Approach 2:
The system enables self-service mapping where the AI/ML model autonomously performs content mapping between different engineering libraries without requiring human expertise. The model independently processes, compares, and maps content elements across libraries, making the process self-sufficient.
2Adaptability or versatility
If manual matching is used to map content between libraries, then adaptability to different library formats is maintained, but cost and error rates increase
Solution Approach 1:
The AI/ML model processes and transforms library content by changing and normalizing parameters such as naming conventions, data types, and formatting styles. This enables the system to adapt to different library formats automatically by transforming content into a standardized representation.
Solution Approach 2:
The AI/ML model serves as a universal mapping engine that can handle multiple different engineering library formats simultaneously. The single model performs multiple mapping tasks across different library types, eliminating the need for separate manual processes for each library format.
3Productivity
If automated AI/ML mapping is implemented, then resource consumption and errors are reduced, but system complexity increases
Solution Approach 1:
The AI/ML model acts as an intermediary layer between different engineering libraries. Rather than directly manipulating complex library structures, the model processes and transforms content through intermediate representations, simplifying the overall system architecture while maintaining high mapping efficiency.
Data Source
AI summary
A method includes using an artificial intelligence/machine learning, AI/ML, model to map content between an interface of a first entity for interaction with other entities and an interface of a second entity for interaction with other entities, and/or classify content of the interface of the first entity and/or of the interface of the second entity; and obtaining, from the AI/ML model, a first output indicative of a result of the mapping of the content, and/or of a result of the classification of the content.