The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 31, 2021

Filed:

Jul. 06, 2018
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Jayanth Gangadhar, Bangalore, IN;

Karthick Ramanujam, Chennai, IN;

Ullas M. Basavaraj, Bangalore, IN;

Ankur B. Shah, Pune, IN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 15/16 (2006.01); H04L 12/24 (2006.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
H04L 41/5054 (2013.01); H04L 41/16 (2013.01); H04L 67/10 (2013.01);
Abstract

Techniques are described relating to automated deployment of an application in a managed services domain of a cloud computing environment. One or more of such techniques may minimize human interaction or intervention during deployment of the application. An associated method includes receiving from a client system a request to deploy the application in a target environment and analyzing the request via a machine learning knowledge model. Additionally, the method includes requesting from the client system access to the target environment and, upon receiving access to the target environment, validating the target environment through inspection. Upon validating the target environment, the method further includes facilitating presentation of an application deployment plan through an interface of the client system. Responsive to client approval of the application deployment plan, the method further includes deploying the application by facilitating application installation in the target environment via the machine learning knowledge model.


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