Artificial intelligence layer-based process extraction for robotic process automation
Artificial intelligence (AI) layer-based process extraction for robotic process automation (RPA) is disclosed. Data collected by RPA robots and/or other sources may be analyzed to identify patterns that can be used to suggest or automatically generate RPA workflows. These AI layers may be used to recognize patterns of user or business system processes contained therein. Each AI layer may “sense” different characteristics in the data and be used individually or in concert with other AI layers to suggest RPA workflows.
1. A computer-implemented method for performing artificial intelligence (AI)-based process extraction for robotic process automation (RPA) using multiple AI layers, comprising:
retrieving data collected by listeners, by a computing system;
running the data collected by the listeners through the multiple AI layers, by the computing system, to process the collected data and identify one or more RPA processes in the collected data that exceed a confidence threshold; and
when an RPA process is identified that exceeds the confidence threshold:
generating an RPA workflow for the identified RPA process, by the computing system, and
generating a robot that is configured to execute the generated RPA workflow for the identified RPA process, by the computing system.
2. The computer-implemented method of claim 1 , wherein the data collected by the listeners comprises user interactions with respective user computing systems, audio, video, light, heat, motion, acceleration, radiation, or any combination thereof.
3. The computer-implemented method of claim 1 , wherein the multiple AI layers comprise a sequence extraction layer, a clustering detection later, a visual component detection layer, a text recognition layer, an audio-to-text translation layer, or any combination thereof.
4. The computer-implemented method of claim 1 , wherein each individual AI layer has an associated modifier based on an estimated accuracy of the respective individual AI layer.
5. The computer-implemented method of claim 1 , wherein an RPA workflow is only generated when a collective confidence threshold for all AI layers has been exceeded.
6. The computer-implemented method of claim 1 , wherein the multiple AI layers are configured to perform statistical modeling and utilize deep learning techniques to identify the one or more RPA processes in the collected that exceed a confidence threshold.
7. The computer-implemented method of claim 1 , further comprising:
identifying a similar existing RPA process to the identified RPA process that exceeds the confidence threshold, by the computing system;
determining that the existing RPA process works less optimally than the identified RPA process, by the computing system; and
replacing the existing RPA process with the identified RPA process by replacing an existing robot executing the existing RPA process with the generated robot, by the computing system.
8. The computer-implemented method of claim 7 , wherein similarity between the existing process and the identified RPA process is determined by entropy, minimization of a process detection objective function, or a combination thereof.
9. The computer-implemented method of claim 1 , wherein the collected data is run through the multiple AI layers in series.
10. The computer-implemented method of claim 1 , wherein the collected data is run through the multiple AI layers in parallel.
11. The computer-implemented method of claim 1 , wherein the collected data is fed through a combination of both series and parallel AI layers.
12. A non-transitory computer-readable medium storing a computer program, the computer program configured to cause at least one processor to:
run the data collected by a plurality of listeners through multiple artificial intelligence (AI) layers to process the collected data and identify one or more robotic process automation (RPA) processes in the collected data that exceed a confidence threshold; and
generate an RPA workflow for the identified RPA process when the identified RPA process exceeds the confidence threshold.
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:
generate a robot that is configured to execute the generated RPA workflow for the identified RPA process.
14. The non-transitory computer-readable medium of claim 13 , wherein the computer program is further configured to cause the at least one processor to:
identify a similar existing RPA process to the identified RPA process that exceeds the confidence threshold;
determine that the existing RPA process works less optimally than the identified RPA process; and
replace the existing RPA process with the identified RPA process by replacing an existing robot executing the existing RPA process with the generated robot.
15. The non-transitory computer-readable medium of claim 14 , wherein similarity between the existing process and the identified RPA process is determined by entropy, minimization of a process detection objective function, or a combination thereof.
16. The non-transitory computer-readable medium of claim 12 , wherein the data collected by the plurality of listeners comprises user interactions with respective user computing systems, audio, video, light, heat, motion, acceleration, radiation, or any combination thereof.
17. The non-transitory computer-readable medium of claim 12 , wherein the collected data is run through the multiple AI layers in series or the collected data is run through the multiple AI layers in parallel.
18. The non-transitory computer-readable medium of claim 12 , wherein the collected data is fed through a combination of both series and parallel AI layers.
19. An apparatus, comprising:
memory storing computer program instructions for performing artificial intelligence (AI)-based process extraction for robotic process automation (RPA) using multiple AI layers; and
at least one processor communicably coupled to the memory and configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to:
run the data collected by a plurality of listeners through multiple AI layers to process the collected data and identify one or more RPA processes in the collected data that exceed a confidence threshold,
generate an RPA workflow for an identified RPA process when the identified RPA process exceeds the confidence threshold, wherein
the collected data is run through the multiple AI layers in series, in parallel, or a combination of both series and in parallel.
20. The apparatus of claim 19 , wherein the computer program instructions are further configured to cause the at least one processor to:
generate a robot that is configured to execute the generated RPA workflow for the identified RPA process;
identify a similar existing RPA process to the identified RPA process that exceeds the confidence threshold;
determine that the existing RPA process works less optimally than the identified RPA process; and
replace the existing RPA process with the identified RPA process by replacing an existing robot executing the existing RPA process with the generated robot.