公开(公告)号：US12124806B2

公开(公告)日：2024-10-22

申请号：US17494744

申请日：2021-10-05

申请人： UiPath, Inc.

发明人： Christian Mayer ,  Mircea Neagovici ,  Cosmin Voicu

IPC分类号： G06F40/30 ,  G06F3/0481 ,  G06F40/289 ,  G06N20/00 ,  G06V30/148 ,  G06F17/10 ,  G06F17/18 ,  G06F30/20 ,  G06V30/10

CPC分类号： G06F40/30 ,  G06F3/0481 ,  G06F40/289 ,  G06N20/00 ,  G06V30/153 ,  G06F17/10 ,  G06F17/18 ,  G06F30/20 ,  G06V30/10

摘要： Semantic matching between a source screen or source data and a target screen using semantic artificial intelligence (AI) for robotic process automation (RPA) workflows is disclosed. The source data or source screen and the target screen are selected on a matching interface, semantic matching is performed between the source data/screen and the target screen using an artificial intelligence/machine learning (AI/ML) model, and matching graphical elements and unmatched graphical elements are highlighted, allowing the developer to see which graphical elements match and which do not. The matching interface may also provide a confidence score of the individual matches, provide an overall mapping score, and allow the developer to hide/unhide the matched/unmatched graphical elements. Activities of an RPA workflow may be automatically created based on the semantic mapping that can be executed to perform the automation.

公开(公告)号：US20210209449A1

公开(公告)日：2021-07-08

申请号：US17209601

申请日：2021-03-23

申请人： UiPath, Inc.

发明人： Mircea Neagovici ,  Stefan ADAM ,  Virgil Tudor ,  Dragos Bobolea

IPC分类号： G06N3/04 ,  G06F17/18 ,  G06F17/15 ,  G06T7/11 ,  G06F9/455

摘要： Graphical elements in a user interface (UI) may be detected in robotic process automation (RPA) using convolutional neural networks (CNNs). Such processes may be particularly well-suited for detecting graphical elements that are too small to be detected using conventional techniques. The accuracy of detecting graphical elements (e.g., control objects) may be enhanced by providing neural network-based processing that is robust to changes in various UI factors, such as different resolutions, different operating system (OS) scaling factors, different dots-per-inch (DPI) settings, and changes due to UI customization of applications and websites, for example.

公开(公告)号：US12124807B2

公开(公告)日：2024-10-22

申请号：US18052378

申请日：2022-11-03

申请人： UiPath, Inc.

发明人： Christian Mayer ,  Mircea Neagovici ,  Cosmin Voicu ,  Bogdan Ripa ,  Ion Miron

IPC分类号： G06F40/30 ,  G06F3/0481 ,  G06F40/289 ,  G06N20/00 ,  G06V30/148 ,  G06F17/10 ,  G06F17/18 ,  G06F30/20 ,  G06V30/10

CPC分类号： G06F40/30 ,  G06F3/0481 ,  G06F40/289 ,  G06N20/00 ,  G06V30/153 ,  G06F17/10 ,  G06F17/18 ,  G06F30/20 ,  G06V30/10

摘要： Semantic matching between a source screen or source data and a target screen using semantic artificial intelligence (AI) for robotic process automation (RPA) workflows is disclosed. The source data or source screen and the target screen are selected on a matching interface, semantic matching is performed between the source data/screen and the target screen using an artificial intelligence/machine learning (AI/ML) model, and matching graphical elements and unmatched graphical elements are highlighted, allowing the developer to see which graphical elements match and which do not. The matching interface may also provide a confidence score of the individual matches, provide an overall mapping score, and allow the developer to hide/unhide the matched/unmatched graphical elements. Activities of an RPA workflow may be automatically created based on the semantic mapping that can be executed to perform the automation.

公开(公告)号：US11599775B2

公开(公告)日：2023-03-07

申请号：US17209601

申请日：2021-03-23

申请人： UiPath, Inc.

发明人： Mircea Neagovici ,  Stefan Adam ,  Virgil Tudor ,  Dragos Bobolea

IPC分类号： G06F17/18 ,  G06N3/04 ,  G06F9/455 ,  G06F17/15 ,  G06T7/11

摘要： Graphical elements in a user interface (UI) may be detected in robotic process automation (RPA) using convolutional neural networks (CNNs). Such processes may be particularly well-suited for detecting graphical elements that are too small to be detected using conventional techniques. The accuracy of detecting graphical elements (e.g., control objects) may be enhanced by providing neural network-based processing that is robust to changes in various UI factors, such as different resolutions, different operating system (OS) scaling factors, different dots-per-inch (DPI) settings, and changes due to UI customization of applications and websites, for example.

公开(公告)号：US11328524B2

公开(公告)日：2022-05-10

申请号：US16504838

申请日：2019-07-08

申请人： UiPath Inc.

发明人： Horia Cristescu ,  Stefan A. Adam ,  Mircea Neagovici

IPC分类号： G06K9/00 ,  G06F16/56 ,  G06F16/583 ,  G06F17/27 ,  G06K9/46 ,  G06V30/414 ,  G06F40/284 ,  G06V10/40 ,  G06V30/413 ,  G06V30/416

摘要： Described systems and methods allow the automatic extraction of structured information from images of structured text documents such as invoices and receipts. Some embodiments employ optical character recognition (OCR) technology to extract individual text tokens (e.g., words) and token bounding boxes from a document image. A feature vector of each text token comprises a first part determined according to a character content of the text token, and a second part determined according to an image content of the token's bounding box. A neural network classifier produces a label indicative of a type of information (e.g. “billing address”, “due date”, etc.) carried by each text token. In some embodiments, documents are linearized by ordering text tokens in a sequence according to a reading order of a natural language (e.g., English, Arabic) in which the respective document is formulated. Token feature vectors are fed to the classifier in the order indicated by the token sequence.

公开(公告)号：US10990876B1

公开(公告)日：2021-04-27

申请号：US16595727

申请日：2019-10-08

申请人： UiPath, Inc.

发明人： Mircea Neagovici ,  Stefan Adam ,  Virgil Tudor ,  Dragos Bobolea

IPC分类号： G06N3/02 ,  G06N3/04 ,  G06F9/455 ,  G06F17/18 ,  G06F17/15 ,  G06T7/11

摘要： Graphical elements in a user interface (UI) may be detected in robotic process automation (RPA) using convolutional neural networks (CNNs). Such processes may be particularly well-suited for detecting graphical elements that are too small to be detected using conventional techniques. The accuracy of detecting graphical elements (e.g., control objects) may be enhanced by providing neural network-based processing that is robust to changes in various UI factors, such as different resolutions, different operating system (OS) scaling factors, different dots-per-inch (DPI) settings, and changes due to UI customization of applications and websites, for example.