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. 11, 2025

Filed:

Nov. 24, 2023
Applicant:

Centurylink Intellectual Property Llc, Broomfield, CO (US);

Inventors:

Steven M. Casey, Littleton, CO (US);

Felipe Castro, Erie, CO (US);

Stephen Opferman, Denver, CO (US);

Kevin M. McBride, Littleton, CO (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/50 (2006.01); G06F 11/34 (2006.01); G06N 20/00 (2019.01); H04L 43/0882 (2022.01); H04L 43/12 (2022.01); H04L 43/16 (2022.01); H04L 47/83 (2022.01); H04L 61/50 (2022.01); H04L 67/10 (2022.01); H04L 67/1008 (2022.01); H04L 67/1021 (2022.01); H04L 67/146 (2022.01); H04L 67/63 (2022.01); H04L 101/622 (2022.01);
U.S. Cl.
CPC ...
G06F 9/5072 (2013.01); G06F 9/505 (2013.01); G06F 9/5077 (2013.01); G06F 11/3409 (2013.01); G06N 20/00 (2019.01); H04L 43/0882 (2013.01); H04L 43/12 (2013.01); H04L 43/16 (2013.01); H04L 47/83 (2022.05); H04L 61/50 (2022.05); H04L 67/10 (2013.01); H04L 67/1008 (2013.01); H04L 67/1021 (2013.01); H04L 67/146 (2013.01); H04L 67/63 (2022.05); G06F 2209/5019 (2013.01); H04L 2101/622 (2022.05);
Abstract

The present technology relates to improving computing services in a distributed network of remote computing resources, such as edge nodes in an edge compute network. In an aspect, the technology relates to a method that includes aggregating historical request data for a plurality of requests, wherein the aggregated historical request data a time of the request, a location of a device from which the request originated, and/or a type of service being requested. The method also incudes training a machine learning model based on the aggregated historical request data; generating, from the trained machine learning model, a prediction for a type of service to be request; identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node; and based on predicted service, allocating computing resources for the computing service on the identified edge node.


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