ARTIFICIAL INTELLIGENCE & KNOWLEDGE BASED AUTOMATION ENHANCEMENT

• US 20160078368A1
• Filed: 11/12/2015
• Published: 03/17/2016
• Est. Priority Date: 05/26/2010
• Status: Active Application

• Current Assignee: Automation Anywhere, Inc.
• Original Assignee: Automation Anywhere, Inc.
• Inventors: Kakhandiki, Abhijit, Gunapu, Sridhar
• Granted Patent: US 10,289,959 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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

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 to enable an automated updating of computer applications based on artificial intelligence methods comprises the steps of:

(a) collecting automation information related to a current application;

(b) locating application controls;

(c) scanning the computer application 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) then 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 a computer-to-be-updated, to achieve a reliable automatic update.

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 a local system to stored results to obtain the optimal automation fit for the computer-to-be-updated under examination, as a candidate for updating.

4. The method of claim 1, further including determining system automation profiles 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 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. A non-transitory computer-readable medium having stored thereon computer-readable instructions for creating adapters that enable new and updated applications including steps of:

(a) collecting automation information related to a current application;

(b) locating application controls;

(c) scanning the computer application 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) 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) exporting the upgraded automation criteria values for incorporation in a computer-to-be-updated, to achieve a reliable automatic update.

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

(a) an automation profile generator for determining a computer to be updated computer automation profile as an aggregate function of the data provided by the system'"'"'s application inspector;

(b) an artificial intelligence based modeler for applying artificial intelligence mechanisms and techniques to determine optimal automation criteria based on the system automation profile;

(c) a recommendation engine for merging one or more artificial intelligence based modeler results, for performing an aggregate analysis, for adjusting an automation criteria to optimize values;

(d) an automation criteria library for storage, update and retrieval;

(e) a learning and update mechanism for updating the automation criteria over the entire period of time during which automation is performed on the computer to be updated;

(f) a library porting mechanism for exporting the automation criteria library to a computer-to-be-updated, to achieve a reliable automatic update.