ARTIFICIAL INTELLIGENCE & KNOWLEDGE BASED AUTOMATION ENHANCEMENT

• US 20190325341A1
• Filed: 03/31/2019
• Published: 10/24/2019
• Est. Priority Date: 05/26/2010
• Status: Active Grant

• Current Assignee: Automation Anywhere, Inc.
• Original Assignee: Automation Anywhere, Inc.
• Inventors: Kakhandiki, Abhijit, Gunapu, Sridhar
• Granted Patent: US 10,733,540 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

This invention generally relates to a process, system and computer code for updating of computer applications based on collecting automation information related to a current application such as processing power, load, footprint, and performance attributes, determining a system automation profile; using an artificial intelligence based modeler for analyzing data, applying the data to an artificial intelligence model for training and predicting performance, adjusting the artificial intelligence model to achieve an updated automation criteria with optimal values, wherein the optimal values provide input to an automation criteria library for storing and updating a prior automation criteria, and exporting the upgraded automation criteria values for incorporation in a computer-to-be-updated, to achieve a reliable automatic update.

Claims

1. A computerized method operable in a computer system having a service provider computer to enable automated updating of computer applications in a remote computer based on artificial intelligence methods comprising:

collecting automation information related to a current application by comparing system inputs and outputs to and from a service provider computer and a remote computer as a candidate for updating, respectively, to stored results in the remote computer to optimize automation fit in the remote computer, as a candidate for updating;

locating application controls;

scanning the computer application to extract data on processing power, load, footprint, and performance attributes;

using the data in an automation profile generator for determining a system automation profile;

using an artificial intelligence based modeler for analyzing input and output data;

applying the data to an artificial intelligence model for training and predicting performance, and wherein if the training and predicting function is not satisfied during test, the artificial intelligence model is adjusted and the process of creating a satisfactory performance model is repeated, until the training and predicting function is satisfied;

merging the artificial intelligence model within a cloud based database;

adjusting the artificial intelligence model to achieve an updated automation criteria with optimal values, wherein the optimal values provide input to an automation criteria library for storing and updating a prior automation criteria; and

exporting the upgraded automation criteria values for incorporation in the remote computer to achieve a reliable automatic update to optimize automation fit in the remote computer, and

wherein one or more tasks in the service provider computer execute (a) one or more of a series of commands, executable as variables by the remote computer'"'"'s application;

(b) one or more tasks assembled into execution files, which validate the tasks and organize nested tasks, including (i) collecting nested task information for each task, (ii) accounting for all dependencies to insure that files, tasks, and environments for operating the running on one or more remote computers to achieve optimal automation fit.

2. The method of claim 1, wherein the artificial intelligence model utilizes one of supervised and unsupervised learning methods, regression analysis, Bayesian statistical models, and clustering algorithms.

3. The method of claim 1, further including comparing system inputs and automation outputs from the service provider computer to stored results to obtain the optimal automation fit for the remote computer under examination, as a candidate for updating.

4. The method of claim 1, further including determining system automation profiles for the remote computer via one or more of a computer operating system, processing power, processing load, speed, processor type, manufacture application footprint, characteristics and specific application.

5. The method of claim 1, further including constructing artificial intelligence based learning mechanisms to derive key automation criteria.

6. The method of claim 1, further including combining artificial intelligence based learning on the service provider system a local system, with broader learning from multiple similar systems, to provide recommendations on automation criteria such as optimal automation speed, best automation technique, and recommendations for optimal delays.

7. The method of claim 1, further including creating portable automation criteria libraries, correlated to a system with a specific automation profiles.

8. The method of claim 1 wherein automation fit in the remote computer comprises one or more of processing power, load, footprint and performance attributes.

9. A computer system for creating adapters that enable updating new and updated applications in a remote computer comprising:

(a) means for collecting automation information related to a current application by comparing system inputs and outputs to and from a service provider computer system and a remote computer as a candidate for updating, respectively, to stored results in the remote computer as a candidate for updating to optimize to obtain the optimal automation fit in for the remote computer, as a candidate for updating such as processing power, load, footprint and performance attributes;

(b) means for locating application controls;

(c) means for scanning the computer application to extract data on processing power, load, footprint, and performance attributes;

(d) means for using the data in an automation profile generator for determining a system automation profile;

(e) means for using an artificial intelligence based modeler for analyzing input and output data;

(f) means for applying the data to an artificial intelligence model for training and predicting performance, and wherein if the training and predicting function is not satisfied during test, the artificial intelligence model is adjusted and the process of creating a satisfactory performance model is repeated, until the training and predicting function is satisfied;

(g) means for adjusting the artificial intelligence model to achieve an updated automation criteria with optimal values, wherein the optimal values provide input to an automation criteria library for storing and updating a prior automation criteria; and

(h) means for exporting the upgraded automation criteria values for incorporation in a remote computer to achieve a reliable automatic update and to optimize automation fit in the remote computer

wherein one or more tasks in the service provider computer have (a) one or more of a series of commands, executable as variables by the remote computer application;

(b) one or more tasks assembled into execution files, which validate the tasks and organize nested tasks, including (i) collecting nested task information for each task, (ii) accounting for all dependencies to insure that files, tasks, and environments for running on one or more of the remote computers to optimize automation fit in the remote computer.

10. The computer system of claim 9 wherein automation fit in the remote computer comprises one or more of processing power, load, footprint and performance attributes.

11. A computerized method for creating adapters to update a computer-to-be-updated based on artificial intelligence methods comprising:

(a) collecting automation information related to a current computer by comparing system inputs and outputs to and from a service provider computer and a computer-to-be-updated as a candidate for updating, respectively, to stored results in a remote computer to optimize automation fit in the computer-to-be-updated, as a candidate for updating;

(b) locating application controls;

(c) scanning the computer-to-be-updated to extract data on processing power, load, footprint, and performance attributes;

(d) using the data in an automation profile generator for determining a system automation profile;

(e) using an artificial intelligence based modeler for analyzing input and output data;

(f) and applying the data to an artificial intelligence model for training and predicting performance, and wherein if the training and predicting function is not satisfied during test, the artificial intelligence model is adjusted and the process of creating a satisfactory performance model is repeated, until the training and predicting function is satisfied;

(g) merging the artificial intelligence model within a cloud based database;

(h) adjusting the artificial intelligence model to achieve an updated automation criteria with optimal values, wherein the optimal values provide input to an automation criteria library for storing and updating a prior automation criteria; and

(i) exporting the upgraded automation criteria values for incorporation in the computer-to-be-updated to achieve a reliable automatic update to optimize automation fit in the computer-to-be-updated to achieve a reliable automatic update.

12. The computerized method of claim 11 wherein automation fit in the computer to be updated comprises one or more of processing power, load, footprint and performance attributes.

13. The computerized method of claim 11, wherein the artificial intelligence based modeler utilizes one of supervised and unsupervised learning methods, regression analysis, Bayesian statistical models, and clustering algorithms.

14. The computerized method of claim 11, further including comparing system inputs and automation outputs from the service provider computer to stored results to obtain the optimal automation fit for the remote computer under examination, as a candidate for updating.

15. The computerized method of claim 11, further including determining system automation profiles for the remote computer via one or more of a computer operating system, processing power, processing load, speed, processor type, manufacture application footprint, characteristics and specific application.

16. The computerized method of claim 11, further including constructing artificial intelligence based learning mechanisms to derive key automation criteria.

17. The computerized method of claim 11, further including combining artificial intelligence based learning on the service provider system a local system, with broader learning from multiple similar systems, to provide recommendations on automation criteria such as optimal automation speed, best automation technique, and recommendations for optimal delays.

18. The computerized method of claim 11, further including creating portable automation criteria libraries, correlated to a system with a specific automation profiles.