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. 27, 2023

Filed:

Dec. 22, 2020
Applicant:

Hughes Network Systems, Llc, Germantown, MD (US);

Inventors:

Sriram Vasudevan, Germantown, MD (US);

Kaustubh Jain, Germantown, MD (US);

Chi-Jiun Su, Germantown, MD (US);

Assignee:

Hughes Network Systems, LLC, Germantown, MD (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04W 24/00 (2009.01); H04W 24/08 (2009.01); G06N 20/00 (2019.01); H04W 24/10 (2009.01); H04W 28/24 (2009.01);
U.S. Cl.
CPC ...
H04W 24/08 (2013.01); G06N 20/00 (2019.01); H04W 24/10 (2013.01); H04W 28/24 (2013.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for traffic flow classification using machine learning. In some implementations, a communication device includes an anomaly detector comprising a machine learning model trained to predict whether data traffic patterns differ from a set of observed traffic patterns present in a set of training data. The communication device includes a traffic classifier comprising a machine learning model trained to predict a quality of service (QoS) class for network connections or data flows. The communication device is configured to evaluate network connections or data flows using the anomaly detector. The communication device may (i) use the traffic classifier to predict QoS classes for traffic that the anomaly detector predicts to be similar to the observed traffic patterns, and (ii) store data traffic that the anomaly detector predicts to be different from the observed traffic patterns.


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