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:
Mar. 12, 2024

Filed:

Feb. 26, 2021
Applicant:

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

Inventors:

Dustin Garvey, Exeter, NH (US);

Uri Shaft, Fremont, 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 ...
G06N 20/00 (2019.01); G06F 9/50 (2006.01); G06F 11/34 (2006.01); G06F 17/18 (2006.01); G06F 18/2431 (2023.01); G06F 21/55 (2013.01); G06Q 10/04 (2023.01); G06Q 10/06 (2023.01); G06Q 10/0631 (2023.01); G06Q 10/1093 (2023.01); G06Q 30/0202 (2023.01); G06T 11/00 (2006.01); G06T 11/20 (2006.01); H04L 41/0896 (2022.01);
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 automatically detecting and accommodating state changes in a computer-generated forecast. In one or more embodiments, a representation of a time-series signal is generated within volatile and/or non-volatile storage of a computing device. The representation may be generated in such a way as to approximate the behavior of the time-series signal across one or more seasonal periods. Once generated, a set of one or more state changes within the representation of the time-series signal is identified. Based at least in part on at least one state change in the set of one or more state changes, a subset of values from the sequence of values is selected to train a model. An analytical output is then generated, within volatile and/or non-volatile storage of the computing device, using the trained model.


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