Human-in-the-loop robot training for robotic process automation

1. A cloud robotic process automation (RPA) system, comprising:

a user computing system comprising an RPA robot and a listener; and

one or more cloud computing systems configured to perform human-in-the-loop RPA robot training using artificial intelligence (AI), wherein

the listener is configured to:

monitor user interactions with the RPA robot via the user computing system and log data pertaining to the interactions, and

transmit the logged data pertaining to the user interactions to the one or more cloud computing systems, and

the one or more cloud computing systems are configured to:

receive the logged data pertaining to the user interactions,

determine whether a modification should be made to an RPA workflow for the RPA robot based on a predetermined number of exceptions of a same type being received by one or more users in the logged data, based on an exception frequency, or both, and

when the one or more cloud computing systems determine that the modification should be made and the modification is addressable by inserting an activity or sequence of activities into the RPA workflow for the RPA robot, insert the activity or sequence of activities into the RPA workflow for the RPA robot that makes the determined modification.

2. The cloud RPA system of claim 1 , wherein the one or more cloud computing systems are further configured to:

generate a new version of the RPA robot using the modified RPA workflow; and

deploy the new version of the RPA robot to the user computing system.

3. The cloud RPA system of claim 1 , wherein the user computing system is configured to:

receive a new version of the RPA robot from the one or more cloud computing systems; and

deploy the new version of the RPA robot.

4. The cloud RPA system of claim 1 , wherein the logged data comprises exceptions noted by the user via the user computing system during operation of the RPA robot.

5. The cloud RPA system of claim 4 , wherein the exceptions pertain to errors by the RPA robot, user preferences, or both.

6. The cloud RPA system of claim 1 , wherein when the modification is not addressable by inserting the activity or sequence of activities into the RPA workflow, the one or more cloud computing systems are further configured to:

train a local machine learning (ML) model based on the logged data; and

modify the RPA workflow to call the trained ML model.

7. The cloud RPA system of claim 1 , wherein the one or more cloud computing systems are further configured to:

collect logged data pertaining to interactions of other users of other computing systems with respective RPA robots,

when exceptions for the user are similar to those in the collected logged data for a group of the other users that is a subset of all of the other users:

train a community ML model for the subset of users, and

modify the RPA workflow to call the community model, and

when exceptions for the user are similar to those in the collected logged data for a group of the other users and exceeds a global retraining threshold:

train a global ML model for all users, and

modify the RPA workflow to call the global model.

8. The cloud RPA system of claim 1 , wherein the logged data is transmitted to the one or more cloud computing systems by the listener as part of a heartbeat message to a conductor application running on one or more cloud computing systems.

9. A non-transitory computer-readable medium storing a computer program, the computer program configured to cause at least one processor to:

monitor user interactions with an RPA robot via a user computing system and log data pertaining to the interactions, the logged data comprising exceptions;

transmit the logged data pertaining to the user interactions to one or more cloud computing systems of a cloud RPA system;

receive a new version of the RPA robot from the one or more cloud computing systems of the cloud RPA system that has been modified to address the exceptions in the logged data responsive to receiving a predetermined number of exceptions of a same type, an exception frequency, or both; and

deploy the new version of the RPA robot.

10. The non-transitory computer-readable medium of claim 9 , wherein the exceptions pertain to errors by the RPA robot, user preferences, or both.

11. The non-transitory computer-readable medium of claim 9 , wherein the logged data is transmitted to the one or more cloud computing systems of the cloud RPA system as part of a heartbeat message to a conductor application running on the one or more cloud computing systems.

12. The non-transitory computer-readable medium of claim 9 , wherein the logged data is sent to the one or more cloud computing systems of the cloud RPA system after a predetermined amount of data has been collected, after a predetermined time period has elapsed, or both.

13. A computer-implemented method for performing human-in-the-loop robotic process automation (RPA) robot training using artificial intelligence (AI), comprising:

receiving, by one or more cloud computing systems of a cloud RPA system, logged data pertaining to interactions of a user with an RPA robot;

determining, by the one or more cloud computing systems, whether a modification should be made to an RPA workflow for the RPA robot based on a predetermined number of exceptions of a same type being received by one or more users in the logged data, based on an exception frequency, or both; and

when the one or more cloud computing systems determine that the modification should be made and the modification is addressable by inserting an activity or sequence of activities into the RPA workflow for the RPA robot, inserting the activity or sequence of activities into the RPA workflow for the RPA robot that makes the determined modification, by the one or more cloud computing systems.

14. The computer-implemented method of claim 13 , further comprising:

generating a new version of the RPA robot, by the one or more cloud computing systems, using the modified RPA workflow; and

deploying the new version of the RPA robot, by the one or more cloud computing systems.

15. The computer-implemented method of claim 13 , wherein the logged data comprises exceptions noted by the user during operation of the RPA robot.

16. The computer-implemented method of claim 15 , wherein the exceptions pertain to errors by the RPA robot, user preferences, or both.

17. The computer-implemented method of claim 13 , wherein when the modification is not addressable by inserting the activity or sequence of activities into the RPA workflow, the method further comprises:

training a local machine learning (ML) model based on the logged data, by the one or more cloud computing systems; and

modifying the RPA workflow to call the trained ML model, by the one or more cloud computing systems.

18. The computer-implemented method of claim 13 , further comprising:

collecting logged data pertaining to interactions of other users with respective RPA robots, by the one or more cloud computing systems;

when exceptions for the user are similar to those in the collected logged data for a group of the other users that is a subset of all of the other users:

training a community ML model for the subset of users, by the one or more cloud computing systems, and

modifying the RPA workflow to call the community model, by the one or more cloud computing systems; and

when exceptions for the user are similar to those in the collected logged data for a group of the other users and exceeds a global retraining threshold:

training a global ML model for all users, by the one or more cloud computing systems, and

modifying the RPA workflow to call the global model, by the one or more cloud computing systems.