Determining sequences of interactions, process extraction, and robot generation using generative artificial intelligence / machine learning models
AssigneeUiPath, Inc.
InventorPrabhdeep Singh
Life of this patent
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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.
Claims32
A computer-implemented method, comprising:
- providing, by a computing system, a generative artificial intelligence (AI)/machine learning (ML) model with data comprising time-ordered interactions of a plurality of users with respective user computing systems;
- training the generative AI/ML model, by the computing system, to recognize related sequences of user interactions that pertain to tasks in the time-ordered sequences of user interactions of the plurality of users by comparing n-grams of sequences of user interactions in recorded data from the computing systems over a sliding window to find the related sequences, the n-grams comprising two or more values of n;
- and
- deploying the trained generative AI/ML model.
A non-transitory computer-readable medium storing a computer program, the computer program configured to cause at least one processor to:
- analyze recorded real user interactions of a plurality of users with respective computing systems and determine sets of related sequences in the recorded real user interactions, by a generative artificial intelligence (AI)/machine learning (ML) model;
- and
- generate one or more respective robotic process automation (RPA) workflows comprising activities that implement user interactions of the determined sets of related sequences, wherein
- a minimum number of related sequences is required for the generative AI/ML model to determine that the sequence pertains to a task.
A computing system, comprising:
- memory storing computer program instructions;
- and
- at least one processor configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to:
- analyze recorded real user interactions of a plurality of users with respective computing systems and determine related sequences in the recorded real user interactions, by a generative artificial intelligence (AI)/machine learning (ML) model;
- determine a most efficient sequence of the related sequences;
- generate a graph or data model comprising associations between sequences of user interactions, by the generative AI/ML model, and
- generate one or more respective robotic process automation (RPA) workflows comprising activities that implement user interactions of the determined most efficient related sequence.
A computer-implemented method, comprising:
- providing, by a computing system, a generative artificial intelligence (AI)/machine learning (ML) model with data comprising time-ordered interactions of a plurality of users with respective user computing systems;
- training the generative AI/ML model, by the computing system, to recognize related sequences of user interactions that pertain to tasks in the time-ordered sequences of user interactions by comparing n-grams of sequences of user interactions in recorded data from the computing systems over a sliding window to find the related sequences, the n-grams comprising two or more values of n;
- and
- deploying the trained generative AI/ML model, wherein
- the training further comprises training the generative AI/ML model to determine a highest value of n such that n-grams above the highest value of n are not considered.
Dependencies are inferred from the claim text.