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:
May. 25, 2021

Filed:

Oct. 11, 2017
Applicant:

Accenture Global Solutions Limited, Dublin, IE;

Inventors:

Manoharan Ramasamy, Bangalore, IN;

Nitin Madhukar Sawant, Mumbai, IN;

Vijay Baskaran, Chennai, IN;

Ganesh Dadasaheb Waghmale, Pune, IN;

Abhishek Kumar Pandey, Bokaro, IN;

Balasubramanyam Besta, Papampeta, IN;

Rakesh Singh Kanyal, Gurgaon, IN;

Anil Kumar, Pune, IN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 12/24 (2006.01); G06K 9/62 (2006.01); G06F 30/00 (2020.01); G06F 30/18 (2020.01); G06F 111/12 (2020.01);
U.S. Cl.
CPC ...
H04L 41/142 (2013.01); G06F 30/00 (2020.01); G06F 30/18 (2020.01); G06K 9/6256 (2013.01); G06K 9/6267 (2013.01); H04L 41/12 (2013.01); H04L 41/145 (2013.01); H04L 41/16 (2013.01); H04L 41/22 (2013.01); G06F 2111/12 (2020.01);
Abstract

A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.


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