Abstract: A computing device may execute a robot service that receives process requests to store in a process queue in memory. The robot service may utilize user-defined preferences to prioritize the process requests in the process queue. The process requests may be scheduled based on the user-defined preferences. The robot service may initiate the scheduled process requests for robotic automation of the application.

Abstract: Probabilistic models may be used in a deterministic workflow for robotic process automation (RPA). Machine learning (ML) introduces a probabilistic framework where the outcome is not deterministic, and therefore, the steps are not deterministic. Deterministic workflows may be mixed with probabilistic workflows, or probabilistic activities may be inserted into deterministic workflows, in order to create more dynamic workflows. A supervisor system may be used to monitor an ML model and raise an alarm, disable an RPA robot, bypass an RPA robot, or roll back to a previous version of the ML model when an error is detected by a data drift detector, a concept drift detector, or both.

Abstract: User interface (UI) object descriptors, UI object libraries, UI object repositories, and UI object browsers for robotic process automation (RPA) are disclosed. A UI object browser may be used for managing, reusing, and increasing the reliability of UI descriptors in a project. UI descriptors may be added to UI object libraries and be published or republished as UI object libraries for global reuse in a UI object repository. The UI object browser, UI object libraries, and UI object repository may facilitate reusability of UI element identification frameworks and derivatives thereof.

Abstract: According to one or more embodiments, a method executed by a type cache service implemented as a computer program within a computing environment is provided. The method includes scanning feeds publishing unpacked packages and automatically detecting new activities on the feeds. The method includes indexing the new activities and all previously detected activities according to type and generating a type cache for the unpacked packages according to the indexing of the new activities and all previously detected activities.

Abstract: Intent-based automation that discovers automatable tasks and/or determines task variants in data is disclosed. Task capture data may be utilized to determine task variants in task mining data. Semantic understanding of user actions by artificial intelligence (AI)/machine learning (ML) model(s), for example, may be applied to determine the intent of the user rather than only focusing on what actions the user is performing on the computing system. Application logs and semantic understanding may be used to facilitate a more accurate determination of what the user actually intends to do. Task capture for individual user flows may be performed. Once these are captured, task capture algorithms and AI/ML models are used to determine which parts of the flows are similar and/or match and which parts are unique. The path through these flows can then be followed to build a process graph that includes decision points representing the unique flows.

Abstract: A method or system for executing one or more digital assistant tasks using robotic processing automation (RPA) includes populating, by a next generation digital assistant application, a drop down menu comprising of a series of options when a user performs an action in a software application. The method or system also includes populating, by the next generation digital assistant application, a side panel comprising a task to be performed by the next generation digital assistant application and a series of options for the user to select from. The method or system further includes updating, by the next generation digital assistant application, the side panel with additional options in response to a selected option from the series of options. The additional options are recommended actions for the user to select from in addition to the task performed by the next generation digital assistant application.

Abstract: Using artificial intelligence (AI) to select and/or chain robotic process automation (RPA) models a given problem is disclosed. A model of models (e.g., an RPA robot or an ML model) may serve as an additional layer on an existing system that makes the existing models more effective. This model of models may incorporate AI that learns an improved or best set of rules or an order from existing models, potentially taking certain activities from a model, feeding input from one model into another, and/or chaining models in some embodiments.

Abstract: Systems and methods for generating a process tree of a process are provided. An event log of the process is received. It is determined whether a base case applies to the event log and, in response to determining that the base case applies to the event log, one or more nodes are added to the process tree. In response to determining that the base case does not apply to the event log, the event log is split into sub-event logs and one or more nodes are added to the process tree. The steps of determining whether a base case applies and splitting the event log are repeatedly performed for each respective sub-event log using the respective sub-event log as the event log until it is determined that the base case applies to the event log. The process tree is output. The process may be a robotic process automation process.

Abstract: A software robot is configured to automatically identify a target element (e.g., a button, a form field, etc.) within a user interface (UI) according to a set of attributes of the target element specified in the source-code of the respective UI. The robot's code specification includes a multiplicity flag which, when set, causes the robot to search for the target element within multiple instances of a UI object matching a subset of the set attributes (for instance, within all windows having a specific name, within all browser tabs, etc.

Abstract: Disclosed herein is a computing system. The computing system includes a memory and a processor. The memory stores processor executable instructions for a workflow recommendation assistant engine. The processor is coupled to the memory. The processor executes the workflow recommendation assistant engine to cause the computing device to analyze images of a user interface corresponding to user activity, execute a pattern matching of the images with respect to existing automations, and provide a prompt indicating that an existing automation matches the user activity.

Abstract: Systems and methods for presenting video of execution of a robotic process automation (RPA) workflow at a remote computing system are provided. Execution of the RPA workflow by a remote computing system is initiated. Video of the execution of the RPA workflow by the remote computing system is received at a local computing system. The video is presented at the local computing system.

Abstract: Target-based schema identification and semantic mapping for robotic process automation (RPA) are disclosed. When looking at a source, such as a document, a web form, a user interface of a software application, a data file, etc., it is often difficult for software to determine which fields are labels and which are values associated with those labels. Since values have not yet been entered for various labels (e.g., first name, company, customer number, etc.), these labels are easier to detect than when the target also includes various values associated with the labels. A selection of an empty target may be received and target-based schema identification may be performed on the empty target, determining labels and a type of the target. Semantic matching may then be performed between a source and the target. These features may be performed at design time or runtime.

Abstract: Target-based schema identification and semantic mapping for robotic process automation (RPA) are disclosed. When looking at a source, such as a document, a web form, a user interface of a software application, a data file, etc., it is often difficult for software to determine which fields are labels and which are values associated with those labels. Since values have not yet been entered for various labels (e.g., first name, company, customer number, etc.), these labels are easier to detect than when the target also includes various values associated with the labels. A selection of an empty target may be received and target-based schema identification may be performed on the empty target, determining labels and a type of the target. Semantic matching may then be performed between a source and the target. These features may be performed at design time or runtime.

Abstract: A method for visualizing a process map is executed by a process map server. The method includes receiving a flowchart and a step-by-step recording related to a process. Generating a process map by combining the flowchart and the step-by-step recording and displaying the process map. The process map displays a task, step, and action related to the process. A detail window shows information associated with the process, and portions of the process, in response to user input. The action is based on information from the step-by-step recording.

Abstract: Use of generative artificial intelligence (AI)/machine learning (ML) models is disclosed to determine sequences of user interactions with computing systems, extract common processes, and generate robotic process automation (RPA) robots. The generative AI/ML model may be trained to recognize matching n-grams of user interactions and/or a beneficial end state. Recorded real user interactions may be analyzed, and matching sequences may be implemented as corresponding activities in an RPA workflow.

Abstract: Systems and methods for annotating exclusive-or nodes or the exclusive outgoing paths of a process model of a process are provided. A process model representing execution of a process is received. Exclusive-or blocks in the process model are identified. Attribute data relating to an exclusive outgoing path from an exclusive-or node in each of the identified exclusive-or blocks are identified. At least one of the exclusive-or node or the exclusive outgoing paths are annotated based on the attribute data. The annotated at least one of the exclusive-or node or the exclusive outgoing paths are output.

Abstract: Systems and methods for analyzing an event log for a plurality of instances of execution of a process to identify a bottleneck are provided. An event log for a plurality of instances of execution of a process is received and segments executed during one or more of the plurality of instances of execution are identified from the event log. The segments represent a pair of activities of the process. For each particular segment of the identified segments, a measure of performance is calculated for each of the one or more instances of execution of the particular segment based on the event log, each of the one or more instances of execution of the particular segment is classified based on the calculated measures of performance, and one or more metrics are computed for the particular segment based on the classified one or more instances of execution of the particular segment.

Abstract: A method and system are provided in which predictions are generated, using one or more machine learning-based prediction models, for one or more process parameters associated with a running process. Explanation-oriented data elements are generated that correspond to the generated predictions and include confidence indicators associated with the generated predictions. The explanation-oriented data elements are presented in one or more dashboards of a visualization platform. The explanation-oriented data elements are representative of an explanation framework for explaining the predicted business process parameters generated by a machine learning-based prediction model and in a manner so that a user can understand and trust the basis for the predictions to facilitate effective and appropriate intervention in a running process.

Abstract: Artificial intelligence (AI) layer-based process extraction for robotic process automation (RPA) is disclosed. Data collected by RPA robots and/or other sources may be analyzed to identify patterns that can be used to suggest or automatically generate RPA workflows. These AI layers may be used to recognize patterns of user or business system processes contained therein. Each AI layer may “sense” different characteristics in the data and be used individually or in concert with other AI layers to suggest RPA workflows.

Abstract: Use of generative artificial intelligence (AI)/machine learning (ML) models is disclosed to determine sequences of user interactions with computing systems, extract common processes, and generate robotic process automation (RPA) robots. The generative AI/ML model may be trained to recognize matching n-grams of user interactions and/or a beneficial end state. Recorded real user interactions may be analyzed, and matching sequences may be implemented as corresponding activities in an RPA workflow.