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. 08, 2022

Filed:

Aug. 19, 2020
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Abhinav Maurya, Seattle, WA (US);

Pawel Cholewinski, Kirkland, WA (US);

Kerem Bulbul, Seattle, WA (US);

John David Dunagan, Redmond, WA (US);

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 12/911 (2013.01); G06F 9/455 (2018.01); G06F 17/18 (2006.01); G06N 3/04 (2006.01); G06N 20/20 (2019.01); G06N 5/02 (2006.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
H04L 47/781 (2013.01); G06F 9/45558 (2013.01); G06F 17/18 (2013.01); G06K 9/6257 (2013.01); G06N 3/0454 (2013.01); G06N 5/025 (2013.01); G06N 20/20 (2019.01); H04L 47/826 (2013.01); H04L 47/827 (2013.01); G06F 2009/45583 (2013.01); G06F 2009/45595 (2013.01);
Abstract

Techniques for predicting the availability of a resource are described. An exemplary method includes obtaining capacity data indicating an amount of capacity available in a cloud provider network to satisfy the request; generating, using a machine learning model that has been trained based at least in part on an output of an automated historical hindsight learner that is an integer linear program, an approval prediction, wherein the approval prediction indicates that the request is to be approved based on one or more launch parameters of the request and the capacity data; receiving information from a downstream component that controls the resource that the approval prediction is incorrect; and evaluating the incorrect approval prediction using a hindsight learner and predictor explainer.


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