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: 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: Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

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: 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: 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: 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.

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: 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: 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: 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.

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: 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: A document information extraction system determines a structure of an electronic document based on characteristics of the document's constituent elements. The system segments the document to generate elements with each element having similar characteristics. Elements may be clustered to assist in determining the document structure. The system determines directional relationships between elements (e.g., above, below, etc.). The system then employs a master comparator to determine familial relationships between adjacent elements. The master comparator includes a set of unit comparators and each unit comparator compares a specific characteristic between two elements. The master comparator sequentially applies the unit comparators to determine the familial relationship based on the comparisons. The system outputs a document hierarchy tree reflecting the determined familial relationships. The hierarchy tree represents the structure of the document.

Abstract: Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

Abstract: Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

Abstract: A document information extraction system determines a structure of an electronic document based on characteristics of the document's constituent elements. The system segments the document to generate elements with each element having similar characteristics. Elements may be clustered to assist in determining the document structure. The system determines directional relationships between elements (e.g., above, below, etc.). The system then employs a master comparator to determine familial relationships between adjacent elements. The master comparator includes a set of unit comparators and each unit comparator compares a specific characteristic between two elements. The master comparator sequentially applies the unit comparators to determine the familial relationship based on the comparisons. The system outputs a document hierarchy tree reflecting the determined familial relationships. The hierarchy tree represents the structure of the document.