Abstract: Reinforcement learning may be used to train machine learning (ML) models for robotic process automation (RPA) that are implemented by robots. A policy network may be employed, which learns to achieve a definite output by providing a particular input. In other words, the policy network informs the system whether it is getting closer to the winning state. The policy network may be refined by the robots automatically or with the periodic assistance of a human in order to reach the winning state, or to achieve a more optimal winning state. Robots may also create other robots that utilize reinforcement learning.

Abstract: Systems and methods for automatically benchmarking a process of an organization are provided. A process of an organization is extracted from a database of process data. A semantic understanding of the process of the organization is determined. The process of the organization is benchmarked to a standardized process based on the semantic understanding. Results of the benchmarking are output.

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: 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: Some embodiments address unique challenges of provisioning RPA software to airgapped hosts, and in particular, provisioning RPA machine learning components and training corpora of substantial size, and provisioning to multiple airgapped hosts having distinct hardware and/or software specifications. To reduce costs associated with data traffic and manipulation, some embodiments bundle together multiple RPA components and/or training corpora into an aggregate package comprising a deduplicated collection of software libraries. Individual RPA components are then automatically reconstructed from the aggregate package and distributed to airgapped hosts.

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: Systems and methods for automatically provisioning recommendations for optimizing a process are provided. A knowledge graph for an organization is generated. The knowledge graph for the organization is compared with an optimized knowledge graph. Recommendations for optimizing a process of the organization are generated based on the comparing. The recommendations for optimizing the process are output.

Abstract: Systems and methods for automatically creating a data model are provided. A semantic understanding of entities stored in one or more business data sources is determined. The entities are extracted from the one or more business data sources based on the semantic understanding. A data model for the entities is created. The data model is output.

Abstract: Systems and methods for automatically generating a knowledge graph are provided. Entity data, process data, user data, and system data of an organization are extracted from one or more business data sources. A knowledge graph defining relationships between the entities data, the process data, the user data, and the system data is generated. The knowledge graph is output.

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: 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 centralized platform for validation of machine learning (ML) models for robotic process automation (RPA) before deployment is provided. The validation platform may support multiple programming languages and build platforms in a single centralized platform. The platform may allow the user to upload the model in a predefined package structure. The platform may then validate the package for deployment.

Abstract: A computer system that generates a universal resource locator (URL). The URL is associated with resuming at least one automated process of a running workflow process that includes a plurality of automated processes. The URL is provided to an external system/application. An input of the URL is received from the external system/application, in response to a trigger event at the external system/application. Based on the input of the URL, the at least one automated process is resumed by at least one headless robot.

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: Frameworks and techniques for integration of heterogeneous machine learning (ML) models into robotic process automation (RPA) workflows are provided. This may be accomplished via a seamless drag-and-drop interface that allows deployment of ML models into an RPA workflow. Via a framework, these heterogeneous models may be provided by customers, third parties, and/or partners and integrated into the RPA workflow. The framework may provide a straightforward way to deploy machine learning models via a conductor and to manage model versioning and create/retrieve/update/delete (CRUD). The framework may facilitate integration of different models into the RPA workflow through the steps of uploading, validating, publishing, and deploying models.

Abstract: Automation of a process running in a first session via robotic process automation (RPA) robot(s) running in a second session is disclosed. In some aspects, a form is displayed in a user session, but one or more attended RPA robots that retrieve and/or interact with data for an application in the first session run in one or more other sessions. In this manner, the operation of the RPA robot(s) may not prevent the user from using other applications or instances when the RPA robot(s) are running, but the data modifications made or facilitated by the RPA robot(s) may be visible to the user in the first session window.

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: In some embodiments, a robotic process automation (RPA) agent executing within a browser window/tab interacts with an RPA driver executing outside of the browser. A bridge module establishes a communication channel between the RPA agent and the RPA driver. In one exemplary use case, the RPA agent receives a robot specification from a remote server, the specification indicating at least one RPA activity, and communicates details of the respective activity to the RPA driver via the communication channel. The RPA driver identifies a runtime target for the RPA activity within the target web page and executes the respective activity.

Abstract: Screen response validation of robot execution for robotic process automation (RPA) is disclosed. Whether text, screen changes, images, and/or other expected visual actions occur in an application executing on a computing system that an RPA robot is interacting with may be recognized. Where the robot has been typing may be determined and the physical position on the screen based on the current resolution of where one or more characters, images, windows, etc. appeared may be provided. The physical position of these elements, or the lack thereof, may allow determination of which field(s) the robot is typing in and what the associated application is for the purpose of validation that the application and computing system are responding as intended. When the expected screen changes do not occur, the robot can stop and throw an exception, go back and attempt the intended interaction again, restart the workflow, or take another suitable action.

Abstract: Multi-session automation windows for robotic process automation (RPA) for attended or unattended robots are disclosed. The sessions use the same credentials. A robot session is created and hosted as a window including the user interfaces (UIs) of applications of a window associated with a user session. Running multiple sessions allows a robot to operate in this robot session while the user interacts with the user session. The user may thus be able to interact with applications that the robot is not using or the user and the robot may be able to interact with the same application if that application is capable of this functionality. The user and the robot may both be interacting with the same application instances and file system.