US 12,293,418 B2
Method and system for vehicular collision reconstruction
Nitin Dileep Salodkar, San Francisco, CA (US); Nikhil Mudaliar, San Francisco, CA (US); Jayanta Kumar Pal, San Francisco, CA (US); Pankaj Risbood, San Francisco, CA (US); and Jonathan Matus, San Francisco, CA (US)
Assigned to Credit Karma, LLC, Oakland, CA (US)
Filed by Credit Karma, LLC, Oakland, CA (US)
Filed on Nov. 27, 2023, as Appl. No. 18/519,504.
Application 18/519,504 is a continuation of application No. 17/222,406, filed on Apr. 5, 2021, granted, now 11,928,739.
Application 17/222,406 is a continuation of application No. 17/155,939, filed on Jan. 22, 2021, granted, now 10,997,800, issued on May 4, 2021.
Claims priority of provisional application 62/964,559, filed on Jan. 22, 2020.
Prior Publication US 2024/0095844 A1, Mar. 21, 2024
Int. Cl. G06Q 40/08 (2012.01); G06N 20/00 (2019.01); G06V 10/80 (2022.01); G06V 20/56 (2022.01); G07C 5/00 (2006.01); G07C 5/08 (2006.01); G06F 18/214 (2023.01)
CPC G06Q 40/08 (2013.01) [G06N 20/00 (2019.01); G06V 10/809 (2022.01); G06V 20/56 (2022.01); G07C 5/008 (2013.01); G07C 5/0808 (2013.01); G07C 5/0841 (2013.01); G06F 18/2148 (2023.01)] 18 Claims
OG exemplary drawing
 
1. A method for detecting and characterizing a collision based on a set of mobile device sensor data, the method comprising:
training a set of multiple models to produce a set of trained models;
with a Software Development Kit (SDK) operating at a mobile device, collecting the set of mobile device sensor data from a set of sensors onboard the mobile device;
evaluating the set of trained models, comprising:
checking for a detected collision based on a set of outputs of a first portion of the set of trained models;
producing a first confidence level associated with a detected collision;
in response to the first confidence level falling below a predetermined threshold, iteratively repeating evaluation of the first portion of the set of trained models to produce a set of additional confidence levels associated with the detected collision;
in response to at least one of the set of additional confidence levels exceeding the predetermined threshold, characterizing a set of features of the detected collision with a second portion of the set of trained models, the set of features comprising an identification of a collision type, the collision type comprising one of: a frontal impact, a rear impact, a side impact, or a rollover impact;
in response to detecting the collision and characterizing the set of features, retraining each of the set of trained models;
wherein the set of trained models comprises a set of gradient boosting machines.