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. 08, 2021

Filed:

Feb. 25, 2020
Applicant:

At&t Intellectual Property I, L.p., Atlanta, GA (US);

Inventors:

Abraham George, Litchfield, CT (US);

Ye Ge, Holmdel, NJ (US);

Jie Chen, Watchung, NJ (US);

Wenjie Zhao, Princeton, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04W 4/021 (2018.01); H04W 40/20 (2009.01); H04W 28/02 (2009.01); H04W 36/00 (2009.01); G06N 3/04 (2006.01); H04W 36/32 (2009.01); H04B 17/318 (2015.01); H04W 64/00 (2009.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
H04W 4/021 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); H04B 17/318 (2015.01); H04W 28/0226 (2013.01); H04W 36/0061 (2013.01); H04W 36/00835 (2018.08); H04W 36/32 (2013.01); H04W 40/205 (2013.01); H04W 64/003 (2013.01);
Abstract

The described technology is generally directed towards user equipment (UE) geolocation. A machine learning model can be trained to estimate UE locations based on historical network communication data associated with the UEs. In order to train the machine learning model, known previous UE locations and corresponding historical network communication data can be provided to the machine learning model. A variety of other information, such as topographical information, can also be provided to the machine learning model. The machine learning model can be trained to predict the known previous UE locations based on the corresponding historical network communication data and any other provided information. Once it is trained, the machine learning model can be deployed to estimate real-time UE locations based on historical network communication data associated with the UEs.


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