US 12,002,068 B2
Data processing system with machine learning engine to provide output generation functions
Sunil Chintakindi, Menlo Park, CA (US); Timothy W. Gibson, Barrington, IL (US); Howard Hayes, Glencoe, IL (US); Regina Madigan, Mountain View, CA (US); Soton Ayodele Rosanwo, Chicago, IL (US); and Juan Ortiz-Zabala, Buffalo Grove, IL (US)
Assigned to ALLSTATE INSURANCE COMPANY, Northbrook, IL (US)
Filed by Allstate Insurance Company, Northbrook, IL (US)
Filed on Sep. 25, 2019, as Appl. No. 16/582,611.
Claims priority of provisional application 62/845,560, filed on May 9, 2019.
Claims priority of provisional application 62/836,114, filed on Apr. 19, 2019.
Claims priority of provisional application 62/738,460, filed on Sep. 28, 2018.
Claims priority of provisional application 62/738,422, filed on Sep. 28, 2018.
Prior Publication US 2020/0104744 A1, Apr. 2, 2020
Int. Cl. G06Q 30/0207 (2023.01); A61B 5/024 (2006.01); G06F 16/335 (2019.01); G06F 21/31 (2013.01); G06F 21/32 (2013.01); G06N 20/00 (2019.01); G06Q 30/0251 (2023.01); G16H 10/60 (2018.01); H04W 4/029 (2018.01)
CPC G06Q 30/0239 (2013.01) [A61B 5/024 (2013.01); G06F 16/337 (2019.01); G06F 21/31 (2013.01); G06F 21/32 (2013.01); G06N 20/00 (2019.01); G06Q 30/0222 (2013.01); G06Q 30/0236 (2013.01); G06Q 30/0269 (2013.01); G16H 10/60 (2018.01); H04W 4/029 (2018.02); G06Q 30/0255 (2013.01); G06Q 30/0261 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computing platform, comprising:
a processing unit comprising a processor;
a communication interface; and
a memory unit storing computer-executable instructions that, when executed by the processing unit, cause the computing platform to:
receive a request to generate a customized output;
generate an instruction to capture physical trait data of a user;
transmit the instruction to a mobile device of the user;
receive, from the mobile device, the captured physical trait data, wherein the physical trait data is captured via one or more sensors of the mobile device;
receive, from the mobile device, one or more mobility patterns of the user based on one or more patterns of driving location;
receive, from one or more sensors of a vehicle associated with the user, vehicle performance data corresponding to maneuvers performed by the vehicle while being driven by the user;
apply machine learning to the received captured physical trait data, the one or more mobility patterns of the user, and the received vehicle performance data to determine a pattern of decision making behavior made by the user while interacting with the vehicle at specific locations;
determine a risk profile based on the pattern of decision making behavior;
generate the customized output to include a set of options that correspond to the risk profile of the user;
generate a user interface including the customized output; and
transmit the user interface to the mobile device for display on a display of the mobile device.