Abstract: A method is provided. The method is for detecting and automatically filling user interface (UI) elements of a page that are not visible on a screen. The method is executed by an interface engine implemented as a computer program within a computing environment. The method includes analyzing document object model (DOM) of the page to extract DOM text of relevant UI elements of the UI elements. The method includes performing computer vision (CV) analysis to determine types the relevant UI elements and extrapolating target UI elements of an invisible region of the page from the relevant UI elements.

Abstract: 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.

Abstract: Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and/or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.

Abstract: 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.

Abstract: 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.

Abstract: Artificial intelligence (AI)-driven, semantic, automatic data transfer between a source and a target using task mining is disclosed. Existing task mining technologies gather all the data pertaining to a screen and/or form, along with the fields and elements present therein. However, semantic meaning may be derived and correlations between fields/elements on the screen may be provided automatically. This may include automatic transformation and validation, and a robotic process automation (RPA) workflow/automation can automatically be generated for a user that performs data copy-and-paste functionality between a source and a target without the user indicating those steps. Also, this functionality may be provided despite the fact that the labels in the source and the target are not exactly the same.

Abstract: Multiple anchors may be utilized for robotic process automation (RPA) of a user interface (UI). The multiple anchors may be utilized to determine relationships between elements in the captured image of the UI for RPA. The results of the anchoring may be utilized for training or retraining of a machine learning (ML) component.

Abstract: A Computer Vision (CV) model generated by a Machine Learning (ML) system may be retrained for more accurate computer image analysis in Robotic Process Automation (RPA). A designer application may receive a selection of a misidentified or non-identified graphical component in an image form a user, determine representative data of an area of the image that includes the selection, and transmit the representative data and the image to an image database. A reviewer may execute the CV model, or cause the CV model to be executed, to confirm that the error exists, and if so, send the image and a correct label to an ML system for retraining. While the CV model is being retrained, an alternative image recognition model may be used to identify the misidentified or non-identified graphical component.

Abstract: A system and a method for verification of execution of an activity are provided. The method comprises receiving a user input indicative of enablement of the verification, and displaying, in response to the reception of the user input, a target element comprising a menu for selecting an edit action. The method further comprises receiving, in response to the selection of the edit action, a verification element, and determining a status of the activity, wherein the status of the activity comprises either of successful execution of the activity or non-successful execution of the activity. Further, the method comprises generating a verification response based on the status of the activity and the verification element.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and/or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.

Abstract: Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and/or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.

Abstract: A system and a method for verification of execution of an activity are provided. The method comprises receiving a user input indicative of enablement of the verification, and displaying, in response to the reception of the user input, a target element comprising a menu for selecting an edit action. The method further comprises receiving, in response to the selection of the edit action, a verification element, and determining a status of the activity, wherein the status of the activity comprises either of successful execution of the activity or non-successful execution of the activity. Further, the method comprises generating a verification response based on the status of the activity and the verification element.

Abstract: A Computer Vision (CV) model generated by a Machine Learning (ML) system may be retrained for more accurate computer image analysis in Robotic Process Automation (RPA). A designer application may receive a selection of a misidentified or non-identified graphical component in an image form a user, determine representative data of an area of the image that includes the selection, and transmit the representative data and the image to an image database. A reviewer may execute the CV model, or cause the CV model to be executed, to confirm that the error exists, and if so, send the image and a correct label to an ML system for retraining. While the CV model is being retrained, an alternative image recognition model may be used to identify the misidentified or non-identified graphical component.

Abstract: Multiple anchors may be utilized for robotic process automation (RPA) of a user interface (UI). The multiple anchors may be utilized to determine relationships between elements in the captured image of the Ul for RPA. The results of the anchoring may be utilized for training or retraining of a machine learning (ML) component.

Abstract: Multiple anchors may be utilized for robotic process automation (RPA) of a user interface (UI). The multiple anchors may be utilized to determine relationships between elements in the captured image of the UI for RPA. The results of the anchoring may be utilized for training or retraining of a machine learning (ML) component.

Abstract: A Computer Vision (CV) model generated by a Machine Learning (ML) system may be retrained for more accurate computer image analysis in Robotic Process Automation (RPA). A designer application may receive a selection of a misidentified or non-identified graphical component in an image form a user, determine representative data of an area of the image that includes the selection, and transmit the representative data and the image to an image database. A reviewer may execute the CV model, or cause the CV model to be executed, to confirm that the error exists, and if so, send the image and a correct label to an ML system for retraining. While the CV model is being retrained, an alternative image recognition model may be used to identify the misidentified or non-identified graphical component.

Abstract: A user interface (UI) mapper for robotic process automation (RPA) is disclosed. The UI mapper may initially capture UI elements to fetch UI elements faster for later use and allow an RPA developer to “map” the UI elements for automating an application. This may enable subsequent developers who potentially do not have programming knowledge to build RPA workflows using these predefined “target” UI elements.