Machine learning model retraining pipeline for robotic process automation
AssigneeUiPath, Inc.
InventorsPrabhdeep SINGH, Marco Alban HIDALGO, Anton MCGONNELL
Life of this patent
Estimating the term…
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.
Claims20
A computer-implemented method for implementing a machine learning (ML) model retraining pipeline for robotic process automation (RPA), comprising:
- calling a machine learning (ML) model, by an RPA robot;
- receiving a result from the execution of the ML model, by the RPA robot;
- determining whether one or more trigger conditions are met for labeling of data for the ML model, by the RPA robot;
- and
- when the one or more trigger conditions are met:
- prompting a user to provide labeled data for training or retraining the ML model and sending the labeled data to a server for training or retraining of the ML model, by the RPA robot, or
- adding information pertaining to the result from the ML model to a queue for subsequent labeling, by the RPA robot.
A computer-implemented method for implementing a machine learning (ML) model retraining pipeline for robotic process automation (RPA), comprising,
- determining whether one or more trigger conditions are met for labeling of data for an ML model, by the RPA robot;
- and
- when the one or more trigger conditions are met:
- prompting a user to provide labeled data for training or retraining the ML model and sending the labeled data to a server for training or retraining of the ML model, by the RPA robot, or
- adding information pertaining to the result from the ML model to a queue for subsequent labeling, by the RPA robot.
A computer-implemented method for implementing a machine learning (ML) model retraining pipeline for robotic process automation (RPA), comprising,
- calling a machine learning (ML) model, by an RPA robot;
- receiving a result from the execution of the ML model, by the RPA robot;
- determining whether one or more trigger conditions are met for labeling of data for the ML model, by the RPA robot;
- when the one or more trigger conditions are met:
- prompting a user to provide labeled data for training or retraining the ML model and sending the labeled data to a server for training or retraining of the ML model, by the RPA robot, or
- adding information pertaining to the result from the ML model to a queue for subsequent labeling, by the RPA robot;
- when one or more training criteria are met, training the ML model, by the server, using the labeled data to produce a new version of the ML model;
- and
- deploying the new version of the ML model, by the server.
Dependencies are inferred from the claim text.