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:
Dec. 19, 2023

Filed:

Aug. 23, 2022
Applicants:

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

New Jersey Institute of Technology, Newark, NJ (US);

Inventors:

Manoop Talasila, Branchburg, NJ (US);

Anwar Syed Aftab, Budd Lake, NJ (US);

Wen-Ling Hsu, Bridgewater, NJ (US);

Cristian Borcea, Hillsborough, NJ (US);

Yi Chen, Short Hills, NJ (US);

Xiaopeng Jiang, Kearny, NJ (US);

Shuai Zhao, Bellevue, WA (US);

Guy Jacobson, Bridgewater, NJ (US);

Rittwik Jana, Montville, NJ (US);

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 41/16 (2022.01); G06N 20/00 (2019.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
H04L 41/16 (2013.01); G06N 3/045 (2023.01); G06N 20/00 (2019.01);
Abstract

An artificial intelligence (AI) automation to improve network quality based on predicted locations is provided. A method can include training, by a first device comprising a processor and according to model configuration parameters received from a second device that is not the first device, a local machine learning model with training data derived from first location data collected by the first device; transmitting, by the first device to the second device, anonymized model features associated with the local machine learning model; in response to the transmitting of the anonymized model features, receiving, by the first device from the second device, an aggregated machine learning model; and estimating, by the first device, a future position of the first device by applying the aggregated machine learning model to second location data collected by the first device.


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