Artificial intelligence / machine learning model drift detection and correction for robotic process automation
AssigneeUiPath, Inc.
InventorsPrabhdeep SINGH, Anton MCGONNELL, Marco Alban HIDALGO
Life of this patent
Estimating the term…
Abstract
Artificial intelligence (AI)/machine learning (ML) model drift detection and correction for robotic process automation (RPA) is disclosed. Information is analyzed pertaining to input data for an AI/ML model to determine whether data drift has occurred, analyze information pertaining to results from execution of the AI/ML model to determine whether model drift has occurred, or both. When, based on the analysis of the information, a change condition is found, a change threshold is met or exceeded, or both, the AI/ML model is retrained. The retrained AI/ML model may then be deployed to provide better predictions on real world data.
Claims32
A computer program for performing artificial intelligence (AI)/machine learning (ML) model drift detection and correction for robotic process automation (RPA) embodied on a non-transitory computer-readable medium, the computer program configured to cause at least one processor to:
- analyze information pertaining to input data for an AI/ML model to determine whether data drift has occurred, analyze information pertaining to results from execution of the AI/ML model to determine whether model drift has occurred, or both;
- and
- when, based on the analysis of the information, a change condition is found, a change threshold is met or exceeded, or both, retrain the AI/ML model.
A computer-implemented method for performing artificial intelligence (AI)/machine learning (ML) model drift detection and correction for robotic process automation (RPA), comprising:
- analyzing information pertaining to input data for an AI/ML model to determine whether data drift has occurred, analyzing information pertaining to results from execution of the AI/ML model to determine whether model drift has occurred, or both;
- and
- when, based on the analysis of the information, a change condition is found, a change threshold is met or exceeded, or both:
- retraining the AI/ML model,
- verifying that the retrained AI/ML model meets one or more performance thresholds, and
- when the retrained AI/ML model meets the one or more performance thresholds, deploying the retrained AI/ML model.
A computer-implemented method for performing artificial intelligence (AI)/machine learning (ML) model drift detection and correction for robotic process automation (RPA), comprising:
- analyzing information comprising one or more statistical distributions of predictions made by the AI/ML model, the information pertaining to input data for an AI/ML model to determine whether data drift has occurred, results from execution of the AI/ML model to determine whether model drift has occurred, or both;
- mapping the one or more statistical distributions to a respective action taken by a plurality of RPA robots;
- when, based on the analysis of the information, a change condition is found, a change threshold is met or exceeded, or both, for at least a period of time, retraining the AI/ML model, wherein
- the action is taken in a respective activity of an RPA workflow of the respective RPA robot.
Dependencies are inferred from the claim text.