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:
Feb. 23, 2021

Filed:

Nov. 01, 2019
Applicant:

Emc Ip Holding Company Llc, Hopkinton, MA (US);

Inventors:

Hung T. Dinh, Austin, TX (US);

Kiran Kumar Pidugu, SangaReddy, IN;

Sabu K. Syed, Austin, TX (US);

Lakshman Kumar Tiwari, Uttar Pradesh, IN;

Rajesh Krishnan, Bangalore, IN;

Seshadri Srinivasan, Shrewsbury, MA (US);

Puneet Arora, New Delhi, IN;

Geetha Venkatesan, Bangalore, IN;

Sourav Datta, Bangalore, IN;

Vijaya P. Sekhar, Bangalore, IN;

Manikandan Rathinavelu, Cedar Park, TX (US);

Ranjani M. Venkata, Leander, TX (US);

Muhammed Mohiuddin, Cedar Park, TX (US);

Assignee:

EMC IP Holding Company LLC, Hopkinton, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 3/0482 (2013.01); G06F 16/248 (2019.01); G06F 16/26 (2019.01); G06Q 10/06 (2012.01); G06Q 20/40 (2012.01); G06N 20/00 (2019.01); G06T 11/20 (2006.01); G06F 21/55 (2013.01); G06F 3/0484 (2013.01);
U.S. Cl.
CPC ...
G06F 3/0482 (2013.01); G06F 3/04842 (2013.01); G06F 16/248 (2019.01); G06F 16/26 (2019.01); G06F 21/55 (2013.01); G06N 20/00 (2019.01); G06Q 10/06 (2013.01); G06Q 20/4016 (2013.01); G06T 11/206 (2013.01);
Abstract

Methods, apparatus, and processor-readable storage media for transaction visibility framework implemented using artificial intelligence are provided herein. An example computer-implemented method includes monitoring data related to a transaction flow across multiple layers of an enterprise system; determining which of the multiple layers correspond to at least one detected problem within the transaction flow by applying artificial intelligence techniques to the data; generating a visualization of the transaction flow, wherein generating the visualization comprises generating a sequential view of the transaction flow, generating one or more parallel sub-transaction flows pertaining to one or more dependencies of the transaction flow, and producing a visual indication of the detected problem; determining, based on analyzing the generated visualization, one or more automated actions related to the detected problem and the determined layer of the enterprise system corresponding to the detected problem; and automatically initiating the one or more automated actions.


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