Graph Neural Network Module Recommender with Topology Calibration
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Solution Overview
Problem
Complex engineering projects, such as industrial automation systems, require selecting and configuring multiple modules, which is a time-consuming and knowledge-intensive process, often leading to information overload and potential errors due to insufficient evidence for confident decision-making in existing recommender systems.
Innovation Solution
A method and system utilizing a graph neural network to compute embeddings for selected modules, determining preliminary confidence scores, and applying a topology-aware calibration step to scale scores based on the graph structure, providing more interpretable and trustworthy recommendations by binning and calibrating confidence scores, ensuring compatibility and functional correctness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a recommender system is used to speed up the design process, then productivity is improved, but the confidence score accuracy deteriorates due to insufficient evidence
Solution Approach 1:
The patent introduces an evidence accumulation mechanism as an intermediary between the recommendation generation and confidence scoring. The system collects multiple types of evidence (module compatibility, connection validity, functional requirements) and aggregates them to form a robust confidence score. This evidence layer mediates between the fast recommendation generation and accurate confidence assessment, resolving the contradiction between speed and precision.
2Adaptability or versatility
If multiple modules are selected to fulfill functional requirements, then the system complexity increases, but the difficulty of detecting and measuring compatibility increases
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously evaluates module compatibility as modules are added to the engineering project. The recommender system provides feedback on compatibility issues, connection validity, and functional requirement satisfaction. This feedback loop enables real-time detection and measurement of compatibility across multiple modules, making the complex verification process manageable and systematic.
Solution Approach 2:
The system performs preliminary compatibility checks and evidence collection before final module selection. By pre-evaluating module interactions, connection requirements, and functional compatibility, the system prepares compatibility information in advance. This preliminary action reduces the complexity of real-time compatibility detection when multiple modules need to be integrated.
3Ease of operation
If configuration software is used to select modules, then the ease of operation is improved, but information overload occurs due to the multitude of modules
Solution Approach 1:
The patent extracts and filters only the most relevant information for module selection from the vast available data. The recommender system identifies and presents key compatibility criteria, essential connection requirements, and critical functional constraints, while filtering out redundant information. This extraction approach maintains ease of operation by presenting only necessary information, preventing user information overload while preserving decision-making quality.
Data Source
AI summary
Based on a graph storing a current state of an engineering project consisting of modules, a graph neural network computes an embedding for each node. For each node embedding, a classifier determines a preliminary confidence score for each class, which represents a type of module that could be added to the engineering project. A topology-based measure is calculated at least for a current center node. A blank node is assigned to a bin depending on the topology-based measure that has been computed for the current center node. A post-processor calibrates all preliminary confidence scores for the blank node by applying a scaling factor depending on the assigned bin. Finally, a user interface outputs at least the class with the highest calibrated confidence score for the blank node as well as the respective calibrated confidence score. The binning scheme takes the graph structure into account and allows for adaptive calibration.