Machine learning model retraining pipeline for robotic process automation

Abstract

A machine learning (ML) model retraining pipeline for robotic process automation (RPA) is disclosed. When an ML model is deployed in a production or development environment, RPA robots send requests to the ML model when executing their workflows. When a confidence level of the ML model falls below a certain confidence, training data is collected, potentially from a large number of computing systems. The ML model is then trained using at least in part the collected training data, and a new version of the ML model is deployed.

Description

[0001] Cross References to Related Applications [0002] This application claims the benefit of U.S. Nonprovisional Patent Application Serial No. 16 / 864,000, filed April 30, 2020. The subject matter of this earlier filed application is hereby incorporated by reference in its entirety. technical field [0003] The present invention relates generally to robotic process automation (RPA), and more particularly to machine learning (ML) model retraining pipelines for RPA. Background technique [0004] Currently, there is no standard mechanism or process for automating the feedback loop to retrain ML models. Therefore, an improved solution could be beneficial. Contents of the invention [0005] Certain embodiments of the present invention may provide solutions to problems and needs in the art that have not been fully identified, understood or addressed by current RPA technology. For example, some embodiments of the invention relate to ML model retraining pipelines for RPA. ...

AI Technical Summary

Problems solved by technology

[0004] Currently, there is no standard mechanism or process for automating the feedback loop to retrain ML models

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