Abstract: Disclosed herein is a computing system that includes a memory and a processor coupled to the memory. The memory storing processor executable instructions for an interface engine that integrates robotic processes into a graphic user interface of the computing system. The processor executes the interface engine to cause the computing device to receive inputs via a menu of the graphic user interface and to automatically determine the robotic processes for display in response to the inputs. The interface engine further generates a list including selectable links corresponding to the robotic processes and displays the list in association with the menu.

Abstract: Automation windows for robotic process automation (RPA) using multiple desktops are disclosed. One or more robot desktops are launched, and one or more RPA robots operate in the robot desktop(s). The robot desktops may not initially be the active desktop. When the robot(s) execute their automations, if an activity in the RPA robot workflow needs the robot desktop to be the active desktop, the active desktop is switched to the appropriate robot desktop when the appropriate robot desktop is not already active, and activit(ies) of the RPA workflow that require the robot desktop to be the active desktop are executed. In some embodiments, after RPA workflow execution finishes, the robot desktop, the RPA robot, or both, are automatically closed.

Abstract: A robot design interface comprises tools for testing a robotic process automation (RPA) workflow. Some embodiments automatically generate a mock workflow comprising a duplicate of the original workflow wherein a set of RPA activities are replaced with substitute activities for testing purposes. Some embodiments expose an intuitive interface co-displaying the substitute activities in parallel to their respective original activities and enabling a user to configure various mock parameters. Testing is then carried out on the mock workflow.

Abstract: Systems and methods for executing a robotic process automation (RPA) workflow are provided. The RPA workflow is executed by a first robot. The execution of the RPA workflow is suspended by the first robot. A current context of the RPA workflow is serialized at a time of the suspension and the current context of the RPA workflow is stored. The execution of the RPA workflow is resumed by a second robot based on a triggering condition by retrieving the current context of the RPA workflow. The first robot and the second robot may be the same robot or different robots.

Abstract: A computing device including a memory and a processor is provided. The memory stores processor executable instructions for an entity engine. The processor is coupled to the memory. The processor executes the entity engine to cause the computing device to model entities, which hold or classify data. The processor executes the entity engine to cause the computing device to store in the memory a list identifying each of the entities and the entities themselves in correspondence with the list. The processor executes the entity engine to cause the computing device to provide, in response to a selection input from an external system, access to the entities based on the list. The access includes providing the list to the external system, receiving the selection input identifying a first entity of the entities, and exporting the first entity from the memory to the external system.

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: According to one or more embodiments, a method is provided. The method is implemented by a trigger engine stored on a memory as processor executable instructions. The processor executable instructions being executed by a processor. The trigger engine operates as an intermediary for robotic process automations of a software platform. The method includes tracking operations within external systems and registering available events with respect to the operations into a database accessible by the robotic process automations. The method also includes enabling an active event of the external systems to be visible via a trigger of the trigger engine to the robotic process automations.

Abstract: Multi-target libraries, projects, and activities for robotic process automation (RPA) are disclosed. Some embodiments multiple target platforms can be handled in the same project. The target platform(s) can be specified at the automation and/or activity level in order to provide the supported functionality for each. This may also allow previously built automations to be applied to new target frameworks without starting from scratch.

Abstract: A system and method provide an integrated automation solution that links multiple systems and applications of a contact center operation and provides a unified support interface and unified knowledge base that delivers relevant data in real-time to assist contact center personnel during a customer interaction. Robotic Process Automation (RPA) is used for automating workflows and processes with robots (e.g., attended and/or unattended) that perform various tasks and activities for capturing information (data, documents, etc.) from multiple front-end and/or back-end systems and applications to provide the necessary data and information in real-time during a contact center session.

Abstract: Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

Abstract: Systems and methods for integration of applications are provided. A request for data associated with a second application is received from a first application. The data associated with the second application is generated using one or more process extension APIs. The one or more process extension APIs generate the data using one or more native APIs of the second application. The data is transmitted to the first application.

Abstract: Graphical element search technique selection, fuzzy logic selection for anchors and targets, and hierarchical graphical element identification for robotic process automation (RPA) are disclosed. The fuzzy logic selection of anchors and targets may be part of a larger, tiered, or hierarchical process for identifying graphical elements in the UI. When a selector for a UI element is not found with at least a confidence threshold, similar elements potentially corresponding to the selector for a UI element target may be searched based on fuzzy matching of the target and corresponding anchor(s). Geometric matching may also be employed between the target UI element and its respective anchor(s). The combination of fuzzy matching and geometric matching may allow for more flexible and accurate identification of the exact selector with which an RPA robot is attempting to interact.

Abstract: Systems and methods for automatically assigning labels to one or more types of non-conforming behavior of execution of a process are provided. An aligned process defining non-conforming behavior of execution of a process is received. One or more types of the non-conforming behavior of the execution of the process is identified from the aligned process. Labels identifying the one or more types are assigned to the non-conforming behavior. The labels assigned to the non-conforming behavior are output.

Abstract: In some embodiments, a robotic process automation (RPA) design application provides a user-friendly graphical user interface that unifies the design of automation activities performed on desktop computers with the design of automation activities performed on mobile computing devices such as smartphones and wearable computers. Some embodiments connect to a model device acting as a substitute for an actual automation target device (e.g. smartphone of specific make and model) and display a model GUI mirroring the output of the respective model device. Some embodiments further enable the user to design an automation workflow by directly interacting with the model GUI.

Abstract: Artificial intelligence (AI)-based process identification, extraction, and automation for robotic process automation (RPA) is disclosed. Listeners may be deployed to user computing systems to collect data pertaining to user actions. The data collected by the listeners may then be sent to one or more servers and be stored in a database. This data may be analyzed by AI layers to recognize patterns of user behavioral processes therein. These recognized processes may then be distilled into respective RPA workflows and deployed to automate the processes.

Abstract: Anomaly detection and self-healing for robotic process automation (RPA) via artificial intelligence (AI)/machine learning (ML) is disclosed. RPA robots that utilize AI/ML models and computer vision (CV) may interpret and/or interact with most encountered graphical elements via normal learned interactions. However, such RPA robots may occasionally encounter new, unhandled anomalies where graphical elements cannot be identified and/or normal interactions will not work. Such anomalies may be processed by an anomaly handler. The RPA robots may have self-healing functionality that seeks to automatically find information that addresses anomalies.

Abstract: Disclosed herein is a computing device that includes a memory and a processor. The memory store processor executable instructions for an authentication system. The processor is coupled to the memory. The processor executes the authentication system to cause the computing device to generate a credential asset, which includes a unique name. The authentication system, also, fetches tokens for the credential asset using the unique name, calls a notification for each of the tokens, polls for a code of the credential asset, and utilizes the code for an authentication to run a job.

Abstract: Using long-running workflows with artificial intelligence flows to manage the training/retraining lifecycle of artificial intelligence (AI)/machine learning (ML) models is disclosed. Validation may be desired when an AI/ML model is called by a robotic process automation (RPA) robot executing the long-running workflow. This validation includes dynamic input from users. The RPA robot receives the dynamic input from the users and uses this data for training a replacement AI/ML model or retraining the called AI/ML model. The state of the long-running workflow may be preserved, both in training and serving. Long-running workflows may be used to keep track of where the current execution is in the ML model lifecycle.

Abstract: Systems and methods for mapping interactive UI (user interface) elements to an RPA (robotic process automation) object repository are provided. User input selecting a window of an application displayed on a display device is received. In response to receiving the user input selecting the window of the application, interactive UI elements in the window of the application are automatically identified. User input selecting one or more of the identified interactive UI elements in the window of the application is received. The one or more selected interactive UI elements are stored in an RPA object repository of an RPA system.

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.