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:
Jun. 06, 2023

Filed:

Sep. 30, 2020
Applicant:

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

Inventors:

Dustin Garvey, Oakland, CA (US);

Uri Shaft, Fremont, CA (US);

Edwina Ming-Yue Lu, Palo Alto, CA (US);

Sampanna Shahaji Salunke, Dublin, CA (US);

Lik Wong, Palo Alto, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/0202 (2023.01); G06Q 10/04 (2023.01); G06Q 10/0631 (2023.01); G06T 11/20 (2006.01); G06N 20/00 (2019.01); G06F 17/18 (2006.01); G06F 21/55 (2013.01); G06Q 10/06 (2023.01); G06F 11/34 (2006.01); G06F 18/2431 (2023.01); G06Q 10/1093 (2023.01); G06T 11/00 (2006.01); H04L 41/0896 (2022.01); G06F 9/50 (2006.01);
U.S. Cl.
CPC ...
G06T 11/206 (2013.01); G06F 11/3452 (2013.01); G06F 17/18 (2013.01); G06F 18/2431 (2023.01); G06F 21/55 (2013.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G06Q 10/06 (2013.01); G06Q 10/0631 (2013.01); G06Q 10/1093 (2013.01); G06Q 30/0202 (2013.01); G06T 11/001 (2013.01); G06F 9/505 (2013.01); G06F 2218/12 (2023.01); G06Q 10/06315 (2013.01); H04L 41/0896 (2013.01);
Abstract

Techniques are described for generating seasonal forecasts. According to an embodiment, a set of time-series data is associated with one or more classes, which may include a first class that represent a dense pattern that repeats over multiple instances of a season in the set of time-series data and a second class that represent another pattern that repeats over multiple instances of the season in the set of time-series data. A particular class of data is associated with at least two sub-classes of data, where a first sub-class represents high data points from the first class, and a second sub-class represents another set of data points from the first class. A trend rate is determined for a particular sub-class. Based at least in part on the trend rate, a forecast is generated.


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