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. 02, 2021

Filed:

Sep. 18, 2017
Applicant:

Oracle International Corporation, Redwood Shores, CA (US);

Inventors:

Yaser I. Suleiman, Santa Clara, CA (US);

Michael Zoll, Foster City, CA (US);

Subhransu Basu, Fremont, CA (US);

Angelo Pruscino, Los Altos, CA (US);

Wolfgang Lohwasser, Munich, DE;

Wataru Miyoshi, Redwood City, CA (US);

Thomas Breidt, Munich, DE;

Thomas Herter, Santa Clara, CA (US);

Klaus Thielen, Munich, DE;

Sahil Kumar, Redwood City, CA (US);

Assignee:

Oracle International Corporation, Redwood Shores, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/215 (2019.01); G06K 9/62 (2006.01); G06F 11/34 (2006.01); G06N 20/20 (2019.01); H04L 12/24 (2006.01); G06N 7/00 (2006.01); G06N 20/00 (2019.01); H04L 12/26 (2006.01);
U.S. Cl.
CPC ...
G06F 16/215 (2019.01); G06F 11/3409 (2013.01); G06F 11/3452 (2013.01); G06K 9/6256 (2013.01); G06N 7/005 (2013.01); G06N 20/20 (2019.01); H04L 41/069 (2013.01); H04L 41/142 (2013.01); G06N 20/00 (2019.01); H04L 41/0695 (2013.01); H04L 41/0823 (2013.01); H04L 43/02 (2013.01);
Abstract

Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.


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