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:
Mar. 10, 2026

Filed:

Apr. 12, 2022
Applicant:

Lodestar Licensing Group Llc, Evanston, IL (US);

Inventors:

Antonino Mondello, Messina, IT;

Alberto Troia, Munich, DE;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 30/00 (2006.01); G05D 1/00 (2024.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06V 10/764 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); B60W 30/00 (2013.01); G05D 1/0088 (2013.01); G05D 1/0221 (2013.01); G05D 1/0223 (2013.01); G05D 1/0287 (2013.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06V 10/764 (2022.01); G06V 10/7788 (2022.01); G06V 20/58 (2022.01); G05D 1/024 (2013.01); G05D 1/0242 (2013.01); G05D 1/0246 (2013.01); G05D 1/0255 (2013.01); G05D 1/0257 (2013.01);
Abstract

A vehicle having the first ANN model initially installed therein to generate outputs from inputs generated by one or more sensors of the vehicle. The vehicle selects an input based on an output generated from the input using the first ANN model. The vehicle has a module to incrementally train the first ANN model through unsupervised machine learning from sensor data that includes the input selected by the vehicle. Optionally, the sensor data used for the unsupervised learning may further include inputs selected by other vehicles in a population. Sensor inputs selected by vehicles are transmitted to a centralized computer server, which trains the first ANN model through supervised machine learning from sensor received inputs from the vehicles in the population and generates a second ANN model as replacement of the first ANN model previously incrementally improved via unsupervised machine learning in the population.


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