Guardrail Component for Model Transfer Learning Across Evolving Processes
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Solution Overview
Problem
Existing model transfer learning technologies are not process-aware, failing to consider underlying changes in context such as process model changes when applied to evolving processes, leading to ineffective predictions in process flow applications.
Innovation Solution
A system comprising a condition definition component and a guardrail component that defines conditions for using a model trained on first process traces to make predictions on second process traces, determining whether to use the model and estimating its ability to service future traces without requiring retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model transfer learning is applied without considering process changes, then model reusability is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent implements dynamic guardrail conditions that adapt to process changes. The system continuously monitors process model changes and adjusts the conditions under which the transferred model can be applied, allowing the model to remain reusable while maintaining accuracy by dynamically restricting its application to appropriate contexts.
Solution Approach 2:
The patent introduces guardrail conditions as an intermediary layer between the transferred model and the new process traces. These guardrails act as a mediator that evaluates whether process changes have occurred and determines if the model should be applied, thereby preserving both reusability and accuracy.
2Measurement precision
If model retraining is performed on second process traces, then prediction accuracy is improved, but computational cost increases
Solution Approach 1:
Instead of fully retraining the model on all second process traces, the patent applies partial action by using guardrail conditions to selectively determine when the transferred model is sufficient. This avoids the excessive computational cost of complete retraining while maintaining adequate accuracy for appropriate cases.
Solution Approach 2:
The system implements feedback mechanisms that monitor process changes and model performance. When guardrail conditions indicate that process changes exceed thresholds, the system triggers selective retraining or model updates rather than continuous full retraining, optimizing computational resource usage based on actual needs.
3Ease of operation
If existing transfer learning technologies are used without process awareness, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The patent performs preliminary analysis of process model changes before applying the transferred model. Guardrail conditions are established in advance to define when the model can be reliably applied, ensuring that reliability checks are performed automatically without complicating the ease of operation for users.
Data Source
AI summary
Systems, computer-implemented methods, and computer program products to facilitate model transfer learning across evolving processes are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a condition definition component that defines one or more conditions associated with use of a model trained on first traces of a first process to make a prediction on one or more second traces of a second process. The computer executable components can further comprise a guardrail component that determines whether to use the model to make the prediction.