US 12,086,724 B2
Determining driver and vehicle characteristics based on an edge-computer device
Emad S. Isaac, Downers Grove, IL (US)
Assigned to Allstate Insurance Company, Northbrook, IL (US)
Filed by Allstate Insurance Company, Northbrook, IL (US)
Filed on Apr. 9, 2020, as Appl. No. 16/843,981.
Prior Publication US 2021/0319332 A1, Oct. 14, 2021
Int. Cl. G06N 5/04 (2023.01); B60W 30/095 (2012.01); B60W 40/04 (2006.01); B60W 40/06 (2012.01); B60W 40/09 (2012.01); B60W 50/14 (2020.01); G06N 20/00 (2019.01); G07C 5/00 (2006.01); G07C 5/08 (2006.01)
CPC G06N 5/04 (2013.01) [B60W 30/095 (2013.01); B60W 40/04 (2013.01); B60W 40/06 (2013.01); B60W 40/09 (2013.01); B60W 50/14 (2013.01); G06N 20/00 (2019.01); G07C 5/008 (2013.01); G07C 5/085 (2013.01); B60W 2552/00 (2020.02); B60W 2555/20 (2020.02)] 19 Claims
OG exemplary drawing
 
1. A method comprising:
collecting, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data comprising data indicative of driving characteristics associated with an operation of the vehicle;
analyzing, by the edge-computing device and in real-time and based on a machine learning model, one or more characteristics of the vehicle driving event data;
determining, by the edge-computing device and in real-time and based on the machine learning model, at least one of: a driving behavior, a driver rating, an occurrence of a collision, and vehicle diagnostics;
training, based on the determining of the at least one of: a driving behavior, a driver rating, an occurrence of a collision, and vehicle diagnostics, the machine learning model;
displaying, to a user in the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics;
determining, by the edge-computing device and in real-time, one or more geo-fenced zones associated with the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics;
determining, by the edge-computing device and in real-time, the edge-computing device has deviated from the one or more geo-fenced zones; and
dynamically configuring, based on determining the edge-computing device is has deviated from the one or more geo-fenced zones, one or more sensors of the vehicle to obtain additional data indicative of driving characteristics associated with an operation of the vehicle as determined by the trained machine learning model.