Abstract: In an embodiment, a method of performance-enhanced machine-learning model creation is performed by a computer system. The method includes receiving a command to port a first bot from a first RPA platform to a second RPA platform, where the first bot executes a robotic process in a computing environment provided by a particular computer system using the first RPA platform. The method further includes extracting bot configurations for the first bot from the first RPA platform, where the bot configurations include an instruction set that at least partially defines the robotic process. The method also includes creating a second bot for the second RPA platform, where the creating includes transforming the instruction set to a format of the second RPA platform. In addition, the method includes deploying the second bot on the second RPA platform, wherein the deployed second bot executes the robotic process.

Abstract: In an embodiment, a method includes receiving information identifying an input bot for testing. The method also includes detecting a functionality performed by the input bot. The method also includes creating a plurality of inputs for simulation of the functionality of the input bot. The method also includes executing the input bot a plurality of times using a same sample of actions. The method also includes checking for consistency of at least one of behavior and output for the same sample of actions. The method also includes executing the input bot a plurality of times using different samples of actions. The method also includes generating an execution plan for the input bot. The method also includes automatically validating the input bot, where the validation results in an automated determination of whether the input bot is defective.

Abstract: In an embodiment, a method includes deploying a learning bot onto a system of bots, where the learning bot monitors a first bot of the system of bots, the first bot executing a first automated process. The method further includes determining a learning phase of the learning bot. The learning bot utilizes a plurality of learning phases including a first learning phase, a second learning phase and a third learning phase. The method also includes, responsive to a determination that the learning bot is in the third learning phase, the learning bot: monitoring activity related to the first automated process; collecting data related to the monitored activity; analyzing at least a portion of the collected data; identifying an automatic tuning adjustment responsive to the analyzing; and automatically making the automatic tuning adjustment to the first automated process.

Abstract: In an embodiment, another general aspect includes a method including, by a compliance bot deployed on a computer system including a system of bots, monitoring the system of bots for deployment activity. The method also includes, responsive to the monitoring, identifying activity indicative of deployment of a particular bot. The method also includes determining an automation type of the particular bot. The method also includes retrieving compliance rules corresponding to the automation type of the particular bot. The method also includes retrieving data from the particular bot. The method also includes automatically checking compliance of the particular bot with the compliance rules based on the retrieved data. The method also includes, responsive to a determination that the particular bot is noncompliant, automatically invalidating the particular bot.

Abstract: In one general aspect, in an embodiment, a method of performance-enhanced machine-learning model creation is performed by a computer system. The method includes receiving a command to record user interface (UI) activity in a computing environment. The method further includes, responsive to the command: receiving video frames of a live screen output of the computing environment; detecting UI events in the computing environment in relation to the video frames of the live screen output; and determining target applications for the UI events, wherein the target applications are executing in the computing environment. The method also includes generating UI metadata comprising information identifying the UI events and the target applications in relation to the video frames. In addition, the method includes sequentially encoding, in a video file, the video frames together with information sufficient to derive the UI metadata.

Abstract: In one embodiment, a method includes monitoring, in real-time, a plurality of resources including a first robotic process resident on a first RPA platform and a second robotic process resident on a second RPA platform. The first RPA platform and the second RPA platform provide robotic process data in heterogeneous data formats via heterogeneous interfaces. The method also includes, responsive to a trigger, invoking at least one function on a unified interface. The method also includes receiving at least one function call reply from the unified interface responsive to the invoking, the at least one function call reply including homogeneous data related to the first robotic process and the second robotic process. In addition, the method includes determining real-time statuses of the first robotic process and the second robotic process using the homogeneous data. The method also includes updating a real-time dashboard with the real-time statuses.