Distributed robotic processing automation flow corrections

1 . A method, executed by one or more processors, of updating a robotic process automation (RPA) bot flow, comprising:

receiving an indication that an RPA bot executing RPA operations, in accordance with an RPA bot flow, encountered an exception;

in response to receiving the indication that the RPA bot executing the RPA operations encountered the exception, determining further RPA operations that allow the RPA bot to successfully complete the RPA operations without any exception by iteratively, until either the RPA operations have successfully completed without any exception or a maximum level of the RPA bot flow is reached:

determining an RPA bot flow of a then-next level that is similar to an RPA bot flow at a then-current level at an operation that resulted in a failure to successfully complete, wherein a similarity between a pair of RPA bot flows is based on a determination that the pair of RPA bot flows are associated with at least one shared data entry requirement;

executing RPA operations of the determined RPA bot flow at the then-next level; and

determining whether the RPA operations of the determined RPA bot flow at the then-next level have successfully completed without any exception; and

in response to determining that the RPA operations of the determined RPA bot flow at the then-next level have successfully completed without any exception, updating the RPA bot flow with the further RPA operations that allow the RPA bot to successfully complete the RPA operations without any exception.

2 . The method of claim 1 , wherein the determined RPA bot flow at the then-next level is an RPA bot flow whose RPA operations have not yet been executed at the then-next level.

3 . The method of claim 1 , wherein the maximum level of the RPA bot flow is a predetermined level.

4 . The method of claim 1 , wherein the maximum level of the RPA bot flow is a level at which there are no RPA bot flows of the then-next level that are similar to the RPA bot flow at the then-current level at the operation that resulted in the failure to successfully complete, and wherein a similarity between a pair of RPA bot flows is based on a determination that the pair of RPA bot flows are associated with at least one shared data entry requirement.

5 . The method of claim 1 , wherein determining the RPA bot flow of the then-next level comprises:

commanding presentation, on a display, of a request to a user to perform data entry; and

recording entries provided by the user.

6 . A method, executed by one or more processors, of updating a robotic process automation (RPA) bot flow, comprising:

receiving an indication that an RPA bot executing RPA operations, in accordance with an RPA bot flow, encountered an exception;

in response to receiving the indication that the RPA bot executing the RPA operations encountered the exception, determining further RPA operations that allow the RPA bot to successfully complete the RPA operations without any exception, wherein determining the further RPA operations comprises:

determining other RPA bot flows that are similar to the RPA bot flow at an operation that resulted in a failure to successfully complete, wherein a similarity between a pair of RPA bot flows is based on a determination that the pair of RPA bot flows are associated with shared visual elements, identified using an image analysis process, between:

at least portions of display images provided as part of the other RPA bot flows; and

at least a portion of a display image provided by the RPA bot flow at the operation that resulted in the failure to successfully complete;

determining portions of at least one RPA bot flow of the other RPA bot flows that allow for the successful completion of the RPA operations without any exception; and

executing the RPA operations of the at least one RPA bot flow of the other RPA bot flows until the at least one RPA bot flow of the other RPA bot flows successfully completes the RPA operations without any exception; and

updating the RPA bot flow with the further RPA operations that allow the RPA bot to successfully complete the RPA operations without any exception.

7 . The method of claim 6 , wherein the image analysis process is executed by a neural network.

8 . The method of claim 7 , wherein the neural network has been trained to determine when display images share data entry requirements.

9 . The method of claim 6 , wherein determining the other RPA bot flows comprises:

determining, by a neural network, document object model (DOM) elements indicated by:

the at least a portion of the display image provided by the RPA bot flow at the operation that resulted in the failure to successfully complete; and

the at least portions of the display images provided as part of the other RPA bot flows; and

determining shared data fields between the DOM elements indicated by:

the at least a portion of the display image provided by the RPA bot flow at the operation that resulted in the failure to successfully complete; and

the at least portions of the display images provided as part of the other RPA bot flows.

10 . The method of claim 6 , wherein determining the portions of the at least one RPA bot flow of the other RPA bot flows that allow for the successful completion of the RPA operations without any exception comprises determining that the executed RPA operations are the portions of the at least one RPA bot flow of the other RPA bot flows that allow for the successful completion of the RPA operations without any exception.

11 . The method of claim 10 , wherein the at least one RPA bot flow of the other RPA bot flows comprises a single RPA bot flow.

12 . The method of claim 10 , wherein the at least one RPA bot flow of the other RPA bot flows comprises multiple RPA bot flows in the at least one RPA bot flow of the other RPA bot flows.

13 . The method of claim 12 , wherein determining the portions of the at least one RPA bot flow of the other RPA bot flows that allow for the successful completion of the RPA operations without any exception comprises:

executing RPA operations of a first RPA bot flow of the other RPA bot flows until the RPA operations encounter any exception;

determining yet other RPA bot flows that are similar to the first RPA bot flow of the other RPA bot flows at an operation that resulted in the failure to successfully complete, wherein a similarity between a pair of RPA bot flows is based on a determination that the pair of RPA bot flows are associated with shared visual elements, identified using the image analysis process; and

executing RPA operations of a first RPA bot flow of the yet other RPA bot flows.

14 . A system for updating a robotic process automation (RPA) bot flow, comprising:

a network interface;

a memory storing instructions; and

one or more processors configured to communicate data with the network interface and the memory, the one or more processors further configured to execute the instructions to perform a process comprising:

receiving, using the network interface, an indication that an RPA bot executing RPA operations, in accordance with a first RPA bot flow, encountered an exception;

in response to receiving the indication that the RPA bot executing the RPA operations encountered the exception, determining further RPA operations that allow the RPA bot to successfully complete RPA operations without any exception, wherein determining the further RPA operations comprises:

determining a second RPA bot flow at an operation in the first RPA bot flow that resulted in a failure to successfully complete, wherein the second RPA bot flow is determined based on shared visual elements, identified using an image analysis process, between:

at least a portion of a display image generated during an execution of the second RPA bot flow; and

at least a portion of a display image generated during an execution of the first RPA bot flow at the operation in the first RPA bot flow that resulted in the failure to successfully complete;

determining at least one portion of the second RPA bot flow that allows for the successful completion of the RPA operations without any exception; and

executing the RPA operations of the second RPA bot flow until the second RPA bot flow successfully completes the RPA operations without any exception; and

updating the first RPA bot flow with the further RPA operations that allow the RPA bot to successfully complete the RPA operations without any exception.

15 . The system of claim 14 , wherein the image analysis process is executed by a neural network.

16 . The system of claim 15 , wherein the neural network has been trained to determine when display images share data entry requirements.

17 . The system of claim 14 , wherein determining the second RPA bot flow comprises:

determining, by a neural network, document object model (DOM) elements indicated by:

the at least a portion of the display image generated during the execution of the first RPA bot flow at the operation in the first RPA bot flow that resulted in the failure to successfully complete; and

the at least a portion of the display image generated during the execution of the second RPA bot flow; and

determining shared data fields between the DOM elements indicated by:

the at least a portion of the display image generated during the execution of the first RPA bot flow at the operation in the first RPA bot flow that resulted in the failure to successfully complete; and

the at least a portion of the display image generated during the execution of the second RPA bot flow.

18 . The system of claim 14 , wherein determining the at least one portion of the second RPA bot flow that allows for the successful completion of the RPA operations without any exception comprises determining that the executed RPA operations are the at least one portion of the second RPA bot flow that allow for the successful completion of the RPA operations without any exception.

19 . The system of claim 18 , wherein the at least one portion of the second RPA bot flow comprises multiple portions in the at least one portion of the second RPA bot flow.

20 . The system of claim 19 , wherein determining the at least one portion of the second RPA bot flow that allows for the successful completion of the RPA operations without any exception comprises:

executing RPA operations of a first portion of the second RPA bot flow until the RPA operations encounter any exception;

determining a second portion of the second RPA bot flow that is similar to the first portion of the second RPA bot flow at an operation that resulted in the failure to successfully complete; and

executing RPA operations of the second portion of the second RPA bot flow.