Dynamic artificial intelligence / machine learning model update, or retrain and update, in digital processes at runtime

Patent metadata

Field	Value
Publication number	US-2020134374-A1
Application number	US-201916722301-A
Country	US
Kind code	A1
Filing date	Dec 20, 2019
Priority date	Dec 20, 2019
Publication date	Apr 30, 2020
Grant date	—

Abstract

Dynamically updating, or retraining and updating, artificial intelligence (AI)/machine learning (ML) models in digital processes at runtime is disclosed. Production operation may not need to be stopped for AI/ML model update or retraining and update. The update steps and/or retraining steps for the AWL model may be included as part of the digital process. The AI/ML model update may be requested from internal logic (e.g., from the evaluation of a condition, by an that expression calls for the AI/ML model, etc.), external requests (e.g., from external triggers in a finite state machine (FSM), such as a file change, database data, a service call, etc.), or both. Automation of AI/ML model updates or retraining and updates may be provided, where the software reloads/reinitializes/re-instantiates with a retrained and/or updated AWL model after (and possibly immediately after) the AI/ML model becomes available.

First claim

1 . A computer-implemented method, comprising:
listening for an update request for an artificial intelligence (AI)/machine learning (ML) model, by a digital process executing on a computing system; and when the update request is received to update the AWL model, reinitializing or re-instantiating the digital process to call an updated version of the AWL model and listening for another update request, by the digital process executing on the computing system, wherein the updating of the AI/ML, model occurs during runtime of the digital process.

2 . The computer-implemented method of claim 1 , further comprising:
listening for a retraining request for the AI/ML model, by the digital process executing on the computing system; and when the retraining request is received to retrain the AWL model, initiating retraining of the AWL model, by the digital process executing on the computing system, wherein the retraining of the AI/ML model occurs during runtime of the digital process.

3 . The computer-implemented method of claim 2 , wherein the retraining of the AI/ML model occurs on one or more other computing systems different than the computing system executing the digital process.

4 . The computer-implemented method of claim 2 , further comprising:
using a current version of the AI/ML, model during the retraining, by the digital process running on the computing system, until the update request is received.

5 . The computer-implemented method of claim 2 , further comprising:
comparing performance of the retrained AI/ML model to a performance threshold, against performance of a previous version of the AI/ML model, or both, by the digital process executing on the computing system; and updating the AI/ML model when the retrained AI/ML model exceeds the performance threshold, the performance of the previous version of the AI/ML model, or both, by the digital process executing on the computing system.

6 . The computer-implemented method of claim 1 , further comprising:
automatically initiating retraining of the AI/ML model, by the digital process executing on the computing system, after a predetermined amount of training data is received, after a predetermined amount of time has elapsed since a last retraining, or both.

7 . The computer-implemented method of claim 1 , wherein the digital process comprises a robotic process automation (RPA) workflow, a business process management (BPM) flowchart, a sequential flow, or a finite state machine (FSM).

8 . The computer-implemented method of claim 1 , wherein the digital process comprises an RPA workflow and the AWL model is called by an activity of the RPA workflow.

9 . The computer-implemented method of claim 8 , wherein the AWL model is embedded directly in the activity of the RPA workflow.

10 . The computer-implemented method of claim 1 , wherein the digital process comprises an initialization state that loads the AWL model from storage or makes the AI/ML model callable by the digital process.

11 . The computer-implemented method of claim 1 , further comprising:
receiving a request to run the AI/ML model, by the digital process running on the computing system; executing the AWL model, or causing the AWL model to be executed, by the digital process running on the computing system; and returning results of the execution of the AI/ML model, by the digital process running on the computing system.

12 . A computer program comprising a digital process and embodied on a non-transitory computer-readable medium, the program configured to cause at least one processor to:
listen for a retraining request or an update request for an artificial intelligence (AI)/machine learning (ML) model; when the retraining request is received to retrain the AWL model, initiate retraining of the AWL model; and when the update request is received to update the AWL model, reinitialize or re-instantiate the digital process to call an updated version of the AI/ML model and listen for another retraining request or update request, wherein the retraining or updating of the AI/ML model occurs during runtime of the digital process.

13 . The computer program of claim 12 , wherein the program is further configured to cause the at least one processor to:
use a current version of the AWL model during the retraining until the update request is received.

14 . The computer program of claim 12 , wherein the program is further configured to cause the at least one processor to:
compare performance of the retrained AI/ML model to a performance threshold, against performance of a previous version of the AI/ML model, or both; and update the AWL model when the retrained AWL model exceeds the performance threshold, the performance of the previous version of the AI/ML model, or both.

15 . The computer program of claim 12 , wherein the program is further configured to cause the at least one processor to:
automatically initiate retraining of the AI/ML model after a predetermined amount of training data is received, after a predetermined amount of time has elapsed since a last retraining, or both.

16 . The computer program of claim 12 , wherein the digital process comprises an RPA workflow and the AI/ML model is called by an activity of the RPA workflow.

17 . The computer program of claim 12 , wherein the program is further configured to cause the at least one processor to:
receive a request to run the AI/ML model; execute the AI/ML model or cause the AI/ML model to be executed; and return results of the execution of the AI/ML model.

18 . A computing system, comprising:
memory storing computer program instructions comprising a digital process; and at least one processor configured to execute the computer program instructions, the instructions configured to cause the at least one processor to:
listen for a retraining request for an artificial intelligence (AI)/machine learning (ML) model, and
when the retraining request is received to retrain the AWL model, initiate retraining of the AI/ML model, wherein
the retraining of the AI/ML model occurs during runtime of the digital process.

19 . The computing system of claim 18 , wherein the instructions are further configured to cause the at least one processor to:
listen for an update request for the AI/ML model; and when the update request is received to update the AWL model, reinitialize or re-instantiate the digital process to call an updated version of the AI/ML model and listen for another update request, by the digital process executing on the computing system, wherein the updating of the AI/ML model occurs during runtime of the digital process.

20 . The computing system of claim 18 , wherein the program is further configured to cause the at least one processor to:
use a current version of the AWL model during the retraining until the update request is received.

21 . The computing system of claim 18 , wherein the program is further configured to cause the at least one processor to:
compare performance of the retrained AI/ML model to a performance threshold, against performance of a previous version of the AI/ML model, or both; and update the AWL model when the retrained AWL model exceeds the performance threshold, the performance of the previous version of the AI/ML model, or both.

22 . The computing system of claim 18 , wherein the program is further configured to cause the at least one processor to:
automatically initiate retraining of the AI/ML model after a pre

Assignees

• Uipath Inc

Inventors

• Oros Andrei Robert

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06F8/71

  Version control (security arrangements therefor G06F21/57); Configuration management · CPC title

• G06F8/65 Primary

  Updates (security arrangements therefor G06F21/57) · CPC title

• G06K9/6256 Primary

  Physics · mapped topic

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title