Uipath Inc.

Kartik Iyer

Radhakrishnan Iyer

Naveen Kumar M

Eshwar Ganesan

Mark Benyovszky

Gerd Weishaar

Christian Mayer

Thomas Stocker

Kruthika Ravi

Priya Dubey

Training and using artificial intelligence (AI)/machine learning (ML) models to automatically supplement and/or complete code of RPA workflows is disclosed. A trained AI/ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The AI/ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the AI/ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.

Training and using artificial intelligence (AI) / machine learning (ML) models to automatically supplement and/or complete code of robotic process automation workflows

A system and a computer-implemented method for validating label data includes receiving the label data and segmenting it into one or more parts using a first machine learning model. Further, from the segmented label data a first plurality of attributes, including text and images, are extracted. The method further includes receiving ground truth data associated with the label data and extracting a second plurality of attributes from the ground truth data. The first and second plurality of attributes are then compared using a second machine learning model and the result of comparison are displayed on a three pane user interface. Further, the label data is validated based on the displayed results.

System and computer-implemented method for validation of label data

A system for managing one or more robots is provided. The system is configured to resolve the one or more issues or faults that lead to failure of execution of one or more automation processes executed by the one or more robots. The system is configured to receive information of an issue associated with at least one robot of the one or more robots and further configured to obtain job log data, associated with the at least one robot, for the issue. The system is further configured to determine, using a trained machine learning model, a corrective action, and its associated confidence score for resolving the received issue, based on the job log data and an analysis performed by the trained machine learning model. Further, system performs the corrective action based on the confidence score and the analysis, for managing the one or more robots.

System and computer-implemented method for managing robotic process automation (RPA) robots

A system and a computer-implemented method for analyzing workflow of test automation associated with a robotic process automation (RPA) application are disclosed herein. The computer-implemented method includes receiving the workflow of the test automation associated with the RPA application and analyzing, via an Artificial Intelligence (AI) model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules. The computer-implemented method further includes determining one or more metrics associated with the analyzed workflow of the test automation and generating, via the AI model, corrective activity data based on the determined one or more metrics.

System and computer-implemented method for analyzing test automation workflow of robotic process automation (RPA)

A robotic process automation (RPA) workflow may be automatically created from text, an image, and/or a media file. A workflow sequence may be converted into a digital format using optical character recognition (OCR), and this information may then be analyzed by an artificial intelligence (AI) model and converted into a predicted RPA workflow. The predicted RPA workflow may be presented to a developer for approval, denial, or modification. Information pertaining to the selection by the developer may then be used for subsequent retraining of the AI model to improve prediction accuracy.

Media-to-workflow generation using artificial intelligence (AI)

Automatic completion of robotic process automation (RPA) workflows using machine learning (ML) is disclosed. A trained ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.

Growing community of inventors

Karnataka, India

Kartik Iyer