Intelligent control code update for robotic process automation

• US 10,710,239 B2
• Filed: 11/08/2018
• Issued: 07/14/2020
• Est. Priority Date: 11/08/2018
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Safary, Jigesh Rajendra, Mamadapur, Krishna Rangarao, Sinha, Gyanendra Mohan
• Primary Examiner(s): Khatri, Anil
• Application Number:
  US16/184,746
• Publication Number:
  US 20200147791A1
• Time in Patent Office: 614 Days
• Field of Search: 717124-140, 717168-174, 717168-140, 706 45- 50
• CPC Class Codes: B25J 9/1602 characterised by the control system, structure, architecture
• CPC Class Codes: B25J 9/1605 Simulation of manipulator lay-out, design, modelling of manipulator
• CPC Class Codes: G05B 19/4155 characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
• CPC Class Codes: G05B 2219/40528 Ann for learning robot contact surface shape
• CPC Class Codes: G06F 11/00 Error detection; Error correction; Monitoring error detection, correction or monitoring in information storage based on relative movement between record carrier and transducer G11B20/18; monitoring, i.e. supervising the progress of recording or reproducing G11B27/36; in static stores G11C29/00
• CPC Class Codes: G06F 11/0736 in functional embedded systems, i.e. in a data processing system designed as a combination of hardware and software dedicated to performing a certain function testing or monitoring of automated control systems G05B23/02
• CPC Class Codes: G06F 11/0793 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 Class Codes: G06F 8/31 Programming languages or programming paradigms
• CPC Class Codes: G06F 8/65 Updates security arrangements therefor G06F21/57
• CPC Class Codes: G06F 8/654 using techniques specially adapted for alterable solid state memories, e.g. for EEPROM or flash memories
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/045 Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Systems, computer program products, and methods are described herein for intelligent control code update for robotic process automation. The present invention is configured to retrieve execution logs associated with robotic process automation (RPA) sessions, wherein the execution logs comprises exceptions. Next, the present invention is configured to initiate machine learning algorithms configured to process the one or more execution logs and classify the exceptions into predetermined classes. Next, the present invention is configured to deploy automated exception handling subroutines to address the exceptions based on at least classifying the exceptions into the predetermined classes.

Claims

1. A system for intelligent control code update for robotic process automation, the system comprising:

at least one non-transitory storage device; and

at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to;

electronically retrieve one or more execution logs associated with one or more robotic process automation (RPA) sessions, wherein the one or more execution logs comprises one or more exceptions;

initiate one or more machine learning algorithms configured to process the one or more execution logs;

classify the one or more exceptions into one or more predetermined classes based on at least initiating the one or more machine learning algorithms on the one or more execution logs;

deploy one or more automated exception handling subroutines to address the one or more exceptions based on at least classifying the one or more exceptions into the one or more predetermined classes;

dynamically retrieve the one or more execution logs from the one or more RPA sessions;

store the one or more execution logs in a secure database; and

transmit control signals configured to intiate one or more reporting subroutines based on at least storing the one or more execution logs in the secure database.

2. The system of claim 1, wherein addressing the one or more exceptions further comprises deploying an update to a software and/or a firmware code associated with at least one of the one or more RPA sessions.

3. The system of claim 1, wherein the at least one processing device is further configured to:

retrieve at least a portion of the one or more execution logs;

retrieve the one or more exceptions associated with at least the portion of the one or more execution logs;

electronically receive, from a user device, a user input assigning one or more classification labels to the one or more exceptions associated with at least the portion of the one or more execution logs; and

initiate a training session associated with the one or more machine learning algorithms based on at least the one or more exceptions associated with at least the portion of the one or more execution logs and the one or more classification labels.

4. The system of claim 3, wherein the one or more initiated reporting subroutines are configured to display on the user device, a first dashboard page comprising a first graphical representation indicating a total number of exceptions arising from each RPA session during a predetermined time period based on at least the retrieved one or more execution logs.

5. The system of claim 1, wherein each of the one or more automated exception handling subroutines is associated with the one or more predetermined classes.

6. The system of claim 1, wherein the at least one processing device is further configured to:

determine a generation of at least one of the one or more execution logs by at least one of the one or more RPA sessions;

electronically receive an indication of the generation of at least one of the one or more execution logs;

dynamically retrieve the generated at least one of the one or more execution logs;

store the generated at least one of the one or more execution logs in the secure database; and

transmit control signals configured to dynamically initiate the one or more reporting subroutines based on at least storing the generated at least one of the one or more execution logs in the secure database.

7. The system of claim 1, wherein the one or more initiated reporting subroutines are configured to display on the user device, a second dashboard page comprising a second graphical representation indicating one or more exception types associated with the one or more exceptions based on at least the retrieved one or more execution logs.

8. A computer implemented method for intelligent control code update for robotic process automation, the method comprising:

electronically retrieving one or more execution logs associated with one or more robotic process automation (RPA) sessions, wherein the one or more execution logs comprises one or more exceptions;

initiating one or more machine learning algorithms configured to process the one or more execution logs;

classifying the one or more exceptions into one or more predetermined classes based on at least initiating the one or more machine learning algorithms on the one or more execution logs;

deploying one or more automated exception handling subroutines to address the one or more exceptions based on at least classifying the one or more exceptions into the one or more predetermined classes;

dynamically retrieving the one or more execution logs from the one or more RPA sessions;

storing the one or more execution logs in a secure database; and

transmitting control signals configured to initiate one or more reporting subroutines based on at least storing the one or more execution logs in the secure database.

9. The computer implemented method of claim 8, wherein addressing the one or more exceptions further comprises deploying an update to a software and/or a firmware code associated with at least one of the one or more RPA sessions.

10. The computer implemented method of claim 8, wherein the method further comprises:

retrieving at least a portion of the one or more execution logs;

retrieving the one or more exceptions associated with at least the portion of the one or more execution logs;

electronically receiving, from a user device, a user input assigning one or more classification labels to the one or more exceptions associated with at least the portion of the one or more execution logs; and

initiating a training session associated with the one or more machine learning algorithms based on at least the one or more exceptions associated with at least the portion of the one or more execution logs and the one or more classification labels.

11. The computer implemented method of claim 10, wherein the one or more initiated reporting subroutines are configured to display on the user device, a first dashboard page comprising a first graphical representation indicating a total number of exceptions arising from each RPA session during a predetermined time period based on at least the retrieved one or more execution logs.

12. The computer implemented method of claim 8, wherein each of the one or more automated exception handling subroutines is associated with the one or more predetermined classes.

13. The computer implemented method of claim 8, wherein the method further comprises:

determining a generation of at least one of the one or more execution logs by at least one of the one or more RPA sessions;

electronically receiving an indication of the generation of at least one of the one or more execution logs;

dynamically retrieving the generated at least one of the one or more execution logs;

storing the generated at least one of the one or more execution logs in the secure database; and

transmitting control signals configured to dynamically initiate the one or more reporting subroutines based on at least storing the generated at least one of the one or more execution logs in the secure database.

14. The computer implemented method of claim 8, wherein the one or more initiated reporting subroutines are configured to display on the user device, a second dashboard page comprising a second graphical representation indicating one or more exception types associated with the one or more exceptions based on at least the retrieved one or more execution logs.

15. A computer program product for intelligent control code update for robotic process automation, the computer program product comprising a non-transitory computer-readable medium comprising code causing a first apparatus to:

electronically retrieve one or more execution logs associated with one or more robotic process automation (RPA) sessions, wherein the one or more execution logs comprises one or more exceptions;

initiate one or more machine learning algorithms configured to process the one or more execution logs;

classify the one or more exceptions into one or more predetermined classes based on at least initiating the one or more machine learning algorithms on the one or more execution logs;

deploy one or more automated exception handling subroutines to address the one or more exceptions based on at least classifying the one or more exceptions into the one or more predetermined classes;

dynamically retrieve the one or more execution logs from the one or more RPA sessions;

store the one or more execution logs in a secure database; and

transmit control signals configured to initiate one or more reporting subroutines based on at least storing the one or more execution logs in the secure database.

16. The computer program product of claim 15, wherein addressing the one or more exceptions further comprises deploying an update to a software and/or a firmware code associated with at least one of the one or more RPA sessions.

17. The computer program product of claim 15, wherein the first apparatus is further configured to:

retrieve at least a portion of the one or more execution logs;

retrieve the one or more exceptions associated with at least the portion of the one or more execution logs;

electronically receive, from a user device, a user input assigning one or more classification labels to the one or more exceptions associated with at least the portion of the one or more execution logs; and

initiate a training session associated with the one or more machine learning algorithms based on at least the one or more exceptions associated with at least the portion of the one or more execution logs and the one or more classification labels.

18. The computer program product of claim 15, wherein each of the one or more automated exception handling subroutines is associated with the one or more predetermined classes.

19. The computer program product of claim 15, wherein the first apparatus is further configured to:

determine a generation of at least one of the one or more execution logs by at least one of the one or more RPA sessions;

electonically receive an indication of the generation of at least one of the one or more execution logs;

dynamically retrieve the generated at least one of the one or more execution logs;

store the generated at least one of the one or more execution logs in the secure database; and

transmit control signals configured to dynamically initiate the one or more reporting subroutines based on at least storing the generated at least one of the one or more execution logs in the secure database.

20. The computer program product of claim 15, wherein the one or more initiated reporting subroutines are configured to display on a user device, a first dashboard page comprising a first graphical representation indicating a total number of exceptions arising from each RPA session during a predetermined time period based on at least the retrieved one or more execution logs.