Abstract: Systems and methods are provided for determining variants of an automatable task. Task flow data of a performance of an automatable task by one or more users is received. The task flow data is generated based on user input using task mining. User interaction data is identified from the task flow data. One or more variants of the automatable task are determined based on the user interaction data using a machine learning based model. The one or more variants of the automatable task are output.

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: Systems and methods are provided for merging task graphs representing variants of an automatable task into a merged task graph. A plurality of task graphs each representing a variant of an automatable task is received. Similarities between the plurality of task graphs are identified. The plurality of task graphs is merged into a merged task graph of the automatable task based on the identified similarities. The merged task graph is output.

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: Controlling and provisioning a robot of a virtual machine (VM) includes transmitting a connection request between a first service installed in a virtual machine and a second service. The robot is associated with at least one process running on the virtual machine. The virtual machine is authenticated based on a token associated with the second service and the virtual machine. A connection is established between the first service and the second service. A command is transmitted associated with the controlling of the robot from the second service to the first service based on the authentication of the virtual machine. The command is associated with a corresponding command identifier for identifying a type of the command. The command is then executed for controlling the robot.

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: Systems and methods for extracting features from screen images for performing a task mining task are provided. A screen image depicting a user interface of a computing system is received. The screen image is preprocessed to generate a preprocessed screen image and processing results. Image features are extracted from the preprocessed screen image using a first machine learning based network. Text features and control element features are extracted from the processing results using a second machine learning based network. The text features and the control element features are encoded using a third machine learning based network to generate representative features of the screen image. A task is performed on the screen image based on one or more of the image features, the text features, the control element features, or the representative features. Results of the task are output.

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: Robot access control and governance for robotic process automation (RPA) is disclosed. A code analyzer of an RPA designer application, such as a workflow analyzer, may read access control and governance policy rules for an RPA designer application and analyze activities of an RPA workflow of the RPA designer application against the access control and governance policy rules. When one or more analyzed activities of the RPA workflow violate the access control and governance policy rules, the code analyzer prevents generation of an RPA robot or publication of the RPA workflow until the RPA workflow satisfies the access control and governance policy rules. When the analyzed activities of the RPA workflow comply with all required access control and governance policy rules, the RPA designer application may generate an RPA robot implementing the RPA workflow or publish the RPA workflow.

Abstract: An intelligent workflow design solution is provided that assists a user (e.g., developer of RPA workflows) by automatically and intelligently recommending suggested activities for use in building sequences of activities in an RPA workflow. The solution utilizes a predictive learning model to customize and personalize the workflow design process for a user, thereby shortening design cycle time and improving efficiency. A system and method for developing an RPA workflow includes monitoring one or more activities that are selected by a user and identifying one or more recommended activities as candidate next activities in a sequence based on a predictive learning model. The candidate next activities are generated for selection by the user and the predictive learning model is trained based on an actual selection by the user of a next activity for the sequence.

Abstract: A method is provided. The method is executed by a listener service implemented as a computer program executed by a processor. The listener service provides event listeners for change event management. The method includes initiating the event listeners specific to a target application and specifying change events for the event listeners that occur in the target application. The method also includes receiving, by the listener service from the event listeners, the change events in the target application, based on the specified change events, to capture the change events.

Abstract: Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.

Abstract: Systems and methods for object centric process mining are provided. Execution data of a process having a plurality of entities is received. A plurality of object networks representing relationships between objects of the plurality of entities are generated based on the execution data. A set of transitions is determined for each of the plurality of object networks. A process graph of execution of the process is generated based on the sets of transitions. The process graph is output.

Abstract: Systems and methods for allocating computing environments for completing an RPA (robotic process automation) workload are provided. A request for completing an RPA workload is received. A number of computing environments to allocate for completing the RPA workload is calculated based on a selected one of a plurality of RPA autoscaling strategies. The calculated number of computing environments is allocated for allocating one or more RPA robots to complete the RPA workload. The computing environments may be virtual machines.

Abstract: A method is provided. The method is implemented by a robotic process automation workflow designer application that includes a hypertext transfer protocol recorder feature. The robotic process automation workflow designer application is embodied in software stored on a memory and executed by a processor. The method includes filtering hypertext transfer protocol requests to provide filtered hypertext transfer protocol requests. The method includes recording the filtered hypertext transfer protocol request to provide recorded hypertext transfer protocol requests. The method includes converting the recorded hypertext transfer protocol requests into hypertext transfer protocol request activities. The method includes generating a sequence from the hypertext transfer protocol request activities.

Abstract: A method is provided. The method is implemented by an augmentation engine that is executed by a processor. The method includes detecting a target application hosted by a browser. The method includes augmenting features or functionalities into the target application by automatically embedding predefined automation augmentations into the target application without modifying a source code of the target application. The method includes executing a selected number of the predefined automation augmentations.

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: 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: Systems and methods for monitoring a robotic process automation (RPA) system are provided. Job execution data for one or more jobs in the RPA system is determined based on logs of the RPA system. The job execution data for the one or more jobs in the RPA system is caused to be displayed in substantially real time.

Abstract: A method is provided. The method is implemented by a semantic automation builder executed on a processor. The method generating semantic automations of a robotic process automation. The method includes receiving user inputs corresponding to a target application, a written automation task, or steps to identify actions of the robotic process automation. The method also includes mapping each action to an activity to generate mapped activities, transforming the actions into the semantic automations based on the mapped activities, and providing the semantic automations in a user interface of the semantic automation builder to enable editing of the actions.