Abstract: Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.

Abstract: Intent-based automation that discovers automatable tasks and/or determines task variants in data is disclosed. Task capture data may be utilized to determine task variants in task mining data. Semantic understanding of user actions by artificial intelligence (AI)/machine learning (ML) model(s), for example, may be applied to determine the intent of the user rather than only focusing on what actions the user is performing on the computing system. Application logs and semantic understanding may be used to facilitate a more accurate determination of what the user actually intends to do. Task capture for individual user flows may be performed. Once these are captured, task capture algorithms and AI/ML models are used to determine which parts of the flows are similar and/or match and which parts are unique. The path through these flows can then be followed to build a process graph that includes decision points representing the unique flows.

Abstract: Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.

Abstract: A sequencing model may be utilized for captured steps of a workflow of an application. The sequencing model may determine processes and state transitions for the captured steps. A variance, a judgment action, or a rule-based action may be identified based on the sequencing model and changes of a log file of actions in relation to the captured steps. A hierarchy may be generated based on the identified variance, the judgment action, or the rule-based action to automatically generate an automated workflow for robotic process automation (RPA) by a robot.

Abstract: A sequencing model may be utilized for captured steps of a workflow of an application. The sequencing model may determine processes and state transitions for the captured steps. A variance, a judgment action, or a rule-based action may be identified based on the sequencing model and changes of a log file of actions in relation to the captured steps. A hierarchy may be generated based on the identified variance, the judgment action, or the rule-based action to automatically generate an automated workflow for robotic process automation (RPA) by a robot.