Inserting probabilistic models in deterministic workflows for robotic process automation and supervisor system

Patent metadata

Field	Value
Publication number	US-12306736-B2
Application number	US-202318497496-A
Country	US
Kind code	B2
Filing date	Oct 30, 2023
Priority date	Oct 15, 2019
Publication date	May 20, 2025
Grant date	May 20, 2025

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.

First claim

The invention claimed is:

1. A computer-implemented method for implementing probabilistic models in a deterministic workflow for robotic process automation (RPA), comprising:
automatically replacing a deterministic activity in a robotic process automation (RPA) workflow with a probabilistic activity, by an RPA designer application or another application stored in memory of a computing system and executed by at least one processor, responsive to a machine learning (ML) model called by the probabilistic activity reaching a confidence threshold; and
deploying an RPA robot stored in the memory of the computer system, the RPA robot configured to execute the RPA workflow at runtime, wherein RPA robot executes the RPA workflow at least in part using one or more drivers.

2. The computer-implemented method of claim 1 , further comprising:
combining the deterministic RPA workflow with a probabilistic RPA workflow.

3. The computer-implemented method of claim 1 , further comprising:
collecting data from the RPA robot while executing the deterministic RPA workflow, collecting data from computing systems on which the RPA robot is running, or both.

4. The computer-implemented method of claim 3 , further comprising:
periodically retraining the ML model using the collected data.

5. The computer-implemented method of claim 1 , further comprising:
generating the RPA robot.

6. The computer-implemented method of claim 5 , further comprising:
monitoring the ML model to ensure that the ML model is operating correctly using metrics on an input side of the ML model via a data drift detector and on an output side of the ML model using a concept drift detector.

7. The computer-implemented method of claim 6 , further comprising:
raising an alarm responsive to the data drift detector, the concept drift detector, or both, indicate an error.

8. The computer-implemented method of claim 6 , wherein responsive to the data drift detector, the concept drift detector, or both, indicating an error, the method further comprises:
disabling or bypassing the RPA robot.

9. The computer-implemented method of claim 6 , wherein responsive to the data drift detector, the concept drift detector, or both, indicating an error, the method further comprises:
rolling back to a previous version of the ML model.

10. A non-transitory computer-readable medium storing a computer program, the computer program configured to cause at least one processor to:
automatically replace a deterministic activity in a robotic process automation (RPA) workflow with a probabilistic activity responsive to a machine learning (ML) model called by the probabilistic activity reaching a confidence threshold; and
deploy an RPA robot that executes the RPA workflow at runtime, wherein RPA robot executes the RPA workflow at least in part using one or more drivers.

11. The non-transitory computer-readable medium of claim 10 , wherein the computer program is further configured to cause the at least one processor to:
combine the deterministic RPA workflow with a probabilistic RPA workflow.

12. The non-transitory computer-readable medium of claim 10 , wherein the computer program is further configured to cause the at least one processor to:
collect data from the RPA robot while executing the deterministic RPA workflow, collect data from computing systems on which the RPA robot is running, or both.

13. The non-transitory computer-readable medium of claim 12 , wherein the computer program is further configured to cause the at least one processor to:
periodically retrain the ML model using the collected data.

14. The non-transitory computer-readable medium of claim 10 , wherein the computer program is further configured to cause the at least one processor to:
monitor the ML model to ensure that the ML model is operating correctly using metrics on an input side of the ML model via a data drift detector and on an output side of the ML model using a concept drift detector.

15. The non-transitory computer-readable medium of claim 14 , wherein responsive to the data drift detector, the concept drift detector, or both, indicating an error, the computer program is further configured to cause the at least one processor to:
disable the RPA robot, bypass the RPA robot, or roll back to a previous version of the ML model.

16. 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:
automatically replace a deterministic activity in a robotic process automation (RPA) workflow with a probabilistic activity responsive to a machine learning (ML) model called by the probabilistic activity reaching a confidence threshold; and
deploy an RPA robot that executes the RPA workflow at runtime, wherein RPA robot executes the RPA workflow at least in part using one or more drivers.

17. The computing system of claim 16 , wherein the computer program instructions are further configured to cause the at least one processor to:
combine the deterministic RPA workflow with a probabilistic RPA workflow.

18. The computing system of claim 16 , wherein the computer program instructions are further configured to cause the at least one processor to:
collect data from the generated RPA robot while executing the deterministic RPA workflow, collect data from computing systems on which the RPA robot is running, or both.

19. The computing system of claim 16 , wherein the computer program instructions are further configured to cause the at least one processor to:
periodically retrain the ML model using the collected data.

20. The computing system of claim 16 , wherein the computer program instructions are further configured to cause the at least one processor to:
monitor the ML model to ensure that the ML model is operating correctly using metrics on an input side of the ML model via a data drift detector and on an output side of the ML model using a concept drift detector, and
responsive to the data drift detector, the concept drift detector, or both, indicating an error, disable the RPA robot, bypass the RPA robot, or roll back to a previous version of the ML model.

Assignees

• Uipath Inc

Inventors

• Singh Prabhdeep
• Mcgonnell Anton

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F11/302 Primary

  where the computing system component is a software system · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F11/0793

  Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40) · CPC title