Abstract: Trigger(s) administration, development, distribution, coordination, registration, or coding for a software automation process or robotic process automation (RPA) is disclosed. A trigger(s) may be configured to run or execute by an outside service or component for an automation process in relation to an application and associated event(s). A definition file(s) or configuration file(s) may be created or utilized for a trigger(s) for an event. A separate or outside service or component may be configured to listen for the event in the process. Once a trigger(s) is met, an action or activity may run for the automation process on a client device using the outside service or component.

Abstract: Disclosed herein is a computing device that includes a memory and a processor. The memory stores processor executable for a robotic process engine. The robotic process engine accesses a distributed packaged robotic process to procure code and generate a local robotic process. The code includes parameters, while local robotic process includes input fields in accordance with the parameters. The robotic process engine receives input arguments via the input fields of the local robotic process to generate a configuration and executes the local robotic process utilizing the configuration. The execution of the local robotic process mirrors an execution of the distributed packaged robotic process without changing the distributed packaged robotic process.

Abstract: Dynamically updating, or retraining and updating, artificial intelligence (AI)/machine learning (ML) models in digital processes at runtime is disclosed. Production operation may not need to be stopped for AI/ML model update or retraining and update. The update steps and/or retraining steps for the AI/ML model may be included as part of the digital process. The AI/ML model update may be requested from internal logic (e.g., from the evaluation of a condition, by an that expression calls for the AI/ML model, etc.), external requests (e.g., from external triggers in a finite state machine (FSM), such as a file change, database data, a service call, etc.), or both. Automation of AI/ML model updates or retraining and updates may be provided, where the software reloads/reinitializes/re-instantiates with a retrained and/or updated AI/ML model after (and possibly immediately after) the AI/ML model becomes available.

Abstract: Human-in-the-loop robot training using artificial intelligence (AI) for robotic process automation (RPA) is disclosed. This may be accomplished by a listener robot watching interactions of a user or another robot with a computing system. Based on the interactions by the user or robot with the computing system, the robot may be improved and/or personalized for the user or a group of users.

Abstract: Inter-session automation for RPA is disclosed. A robot or another application or process running in the user session may interact with an application, but one or more attended RPA robots in one or more child sessions perform operations and fetch data that the user session robot will then use to interact with the application in the user session. Attended RPA robots in client sessions may share data through an IPC protocol, by storing data in a persistent data store, such as a spreadsheet, an object-oriented database, a plain text file, another data store or file, etc. The user session robot or another application or process running in the parent session can then read this information and respond accordingly.

Abstract: Techniques for training a generative artificial intelligence (AI) / machine learning (ML) model to recognize applications, screens, and UI elements using computer vision (CV) and to recognize user interactions with the applications, screens, and UI elements are disclosed. Optical character recognition (OCR) may also be used to assist in training the generative AI/ML model. Training of the generative AI/ML model may be performed without other system inputs such as system-level information (e.g., key presses, mouse clicks, locations, operating system operations, etc.) or application-level information (e.g., information from an application programming interface (API) from a software application executing on a computing system), or the training of the generative AI/ML model may be supplemented by other information, such as browser history, heat maps, file information, currently running applications and locations, system level and/or application-level information, etc.

Abstract: Techniques for training an optical character recognition (OCR) model to detect and recognize text in images for robotic process automation (RPA) are disclosed. A text detection model and a text recognition model may be trained separately and then combined to produce the OCR model. Synthetic data and a smaller amount of real, human-labeled data may be used for training to increase the speed and accuracy with which the OCR text detection model and the text recognition model can be trained. After the OCR model has been trained, a workflow may be generated that includes an activity calling the OCR model, and a robot implementing the workflow may be generated and deployed.

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: A computing device includes a processor and a memory configured to create one or more forms for an application in an environment. The processor and the memory are further configured to create one or more environment variables related to the one or more forms. The processor is further configured to utilize one or more paths to track a dependency reference between the one or more environment variables, wherein a data model includes the one or more paths and the one or more environment variables. The processor is further configured to execute the data model to recreate the dependency reference, between the one or more environment variables, for the application in a target environment.

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: Use of 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 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: 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: Systems and methods for implementing robotic process automation (RPA) in the cloud are provided. An instruction for managing an RPA robot is received at an orchestrator in a cloud computing environment from a user in a local computing environment. In response to receiving the instruction, the instruction for managing the RPA robot is effectuated.

Abstract: Development and deployment of multi-platform automations for robotic process automation (RPA) are disclosed. Hardware level commands, driver level commands, and/or application programming interface (API) calls are automatically and seamlessly substituted within an automation and/or within an RPA workflow at design time. Development of an RPA automation may occur on a first operating system, and the automated reconfiguration and deployment of the RPA automation may occur in a second, distinct operating system. An automation including a first set of hardware level commands, driver level commands, and/or API calls native to a first operating system may be received, ingested, or retrieve and the automation may be automatically reconfigured to include a second set of hardware level commands, driver level commands, and/or API calls native to an operating system.

Abstract: Systems and methods for classifying and splitting an electronic file into a plurality of extracted documents are provided. The electronic file is received. An initial portion of the electronic file is classified using a trained classifier and extracted from the electronic file as an extracted document associated with the classification. It is iteratively determined whether each respective next portion of the electronic file should be added to the extracted document until it is determined that the respective next portion should not be added to the extracted document. In response to determining that the respective next portion should be added to the extracted document, the respective next portion is extracted from the electronic file and added to the extracted document. In response to determining that the respective next portion should not be added to the extracted document, the classifying and the iteratively determining are repeated using the respective next portion as the initial portion.

Abstract: A robotic process automation (RPA) robot performs fuzzing on a workflow. The robot provides a randomized typed data input in a workflow, and executes the workflow as a black box with the randomized typed data input. The robot creates test case when a new path is discovered based on an output of the workflow, and terminates the fuzzing when a desired path coverage has been reached.

Abstract: Test cases for existing workflows (or workflows under test) may be created and executed. A test case may be created for a workflow in production or one or more parts of the workflow, and the created test case for the workflow, or the one or more parts of the workflow, may be executed to identify environmental and/or automation issues for the workflow. A failed workflow test may be reported when the environmental and/or automation issues are identified.

Abstract: Web-based robotic process automation (RPA) designer systems that allow RPA developers to design and implement web serverless automations, user interface (UI) automations, and other automations are disclosed. Such web-based RPA designer systems may allow a developer to sign in through the cloud and obtain a list of template projects, developer-designed projects, services, activities, etc. Thus, RPA development may be centralized and cloud-based, reducing the local processing and memory requirements on a user's computing system and centralizing RPA designer functionality, enabling better compliance. Automations generated by the web-based RPA designer systems may be deployed and executed in virtual machines (VMs), containers, or operating system sessions.

Abstract: Techniques for training an artificial intelligence (AI)/machine learning (ML) model to recognize applications, screens, and UI elements using computer vision (CV) and to recognize user interactions with the applications, screens, and UI elements. Optical character recognition (OCR) may also be used to assist in training the AI/ML model. Training of the AI/ML model may be performed without other system inputs such as system-level information (e.g., key presses, mouse clicks, locations, operating system operations, etc.) or application-level information (e.g., information from an application programming interface (API) from a software application executing on a computing system), or the training of the AI/ML model may be supplemented by other information, such as browser history, heat maps, file information, currently running applications and locations, system level and/or application-level information, etc.

Abstract: Use of 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 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.