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 of real-time process monitoring includes initiating monitoring of user interface (UI) activity in a plurality of user environments in which a user-executed process is performed. The user-executed process is defined in a stored instruction set that identifies a plurality of steps of the user-executed process. The plurality of user environments include a first environment operated by a human worker and a second environment operated by a bot. The method also includes, responsive to the initiating, detecting a new process scenario for the user-executed process. The method also includes determining new bot logic for the new process scenario. The method also includes causing the bot to implement the new bot logic.

Abstract: In an embodiment, a method is performed by a computer system and includes intercepting machine learning (ML) input data before the ML input data flows into a ML model. The method also includes scanning the ML input data against a plurality of ML threat signatures, the scanning yielding at least a first result. The method also includes examining a correlation between values of first and second variables in the ML input data, the examining yielding at least a second result. The method also includes validating at least one of the first and second results via a variability analysis of error instances in the ML input data, the validating yielding at least a third result. The method also includes applying thresholding to the ML input data via the third result, where the applying thresholding results in at least a portion of the ML input data being filtered.

Abstract: In an embodiment, a method includes receiving a trigger of machine learning model generation. In addition, the method includes algorithmically eliminating at least some of rows and at least some of columns of a training dataset, the algorithmically eliminating yielding a size-reduced training dataset. The method additionally includes generating, for a prediction target, a plurality of machine learning models via a plurality of machine learning algorithms. The method also includes measuring prediction accuracies of the plurality of machine learning models relative to the prediction target. Furthermore, the method includes selecting a particular machine learning model. Moreover, the method includes applying the particular machine learning model to a data source.

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: A method of optimizing automatic schema-based metadata generation, the method including, receiving a trigger to automatically generate metadata for unstructured data, dynamically detecting a metadata optimization parameter for the unstructured data, automatically selecting a metadata schema for the unstructured data, identifying a pre-optimized metadata-generation format for the selected metadata schema, automatically transforming the unstructured data into an optimized metadata-generation structure of the pre-optimized metadata-generation format, identifying a metadata-generation logic set for the detected metadata optimization parameter, the identification based on a programmed association between the metadata-generation logic set and the detected metadata optimization parameter, and a stored pre-programmed logic set of computer-executable actions, executing the computer-executable actions in relation to the optimized metadata-generation structure, generating a search phrase responsive to the execution of t

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.