IP Library Granted Patent US 12,610,210
Granted Patent B2
US 12,610,210 · App. 17/827,045 · Granted Apr 21, 2026

Method for smartphone-based accident detection

Inventors: Jayanta Pal (San Francisco, CA); Bipul Islam (San Francisco, CA); Romit Roy Choudhury (San Francisco, CA); Pankaj Risbood (San Francisco, CA); Jonathan Matus (San Francisco, CA); Vishal Verma (San Francisco, CA)
Assignee: Credit Karma LLC
H04W4/027B60R25/24B60W40/10G06N7/01G06Q40/08G06V20/10G06V20/56G06V20/597G07C5/008G07C5/02G07C5/0808G07C5/0866G08B25/00G08B25/001G08B25/006G08B25/016G08G1/012G08G1/0129H04M1/72421H04W4/029H04W4/40B60R2325/205G06V40/15H04M2250/12H04W88/00
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Quick Facts
Patent No.
US 12,610,210
App. No.
17/827,045
Granted
Apr 21, 2026
Kind
B2
Abstract

A method and system for detecting an accident of a vehicle, the method including: receiving a movement dataset collected at least at one of a location sensor and a motion sensor arranged within the vehicle, during a time period of movement of the vehicle, extracting a set of movement features associated with at least one of a position, a velocity, and an acceleration characterizing the movement of the vehicle during the time period, detecting a vehicular accident event from processing the set of movement features with an accident detection model, and in response to detecting the vehicular accident event, automatically initiating an accident response action.

Claims (44)

1 . A method comprising:

determining a vehicle characteristic based on a first dataset sampled from a set of sensors of a user device during a first time period;

in response to the vehicle characteristic satisfying a first condition, executing an accident detection routine comprising:

determining a second dataset sampled from a sensor associated with a user of the user device during a second time period, wherein the second dataset comprises biosignal sensor data;

retrieving an accident detection model from a set of accident detection models, each accident detection model of the set of accident detection models associated with a portion of a user's body, wherein the accident detection model is retrieved based on a position of the user device with respect to the user's body;

with the accident detection model, determining a vehicular accident event during the second time period based on the second dataset, wherein the accident detection model is trained to detect vehicular accident events with biosignal data; and

with an accident characteristics model, determining a set of accident characteristics associated with the vehicular accident event based on the first dataset and the second dataset; and

automatically triggering an insurance-related action based on the set of accident characteristics.

2 . The method of claim 1 , wherein the insurance-related action comprises: at least partially pre-filling an insurance claim based on the set of accident characteristics.

3 . The method of claim 2 , wherein the set of accident characteristics comprises a vehicular accident type.

4 . The method of claim 1 , wherein the insurance-related action comprises:

receiving a driver selection permitting information release to an insurance entity; and

transmitting an insurance claim to the insurance entity based on the driver selection.

5 . The method of claim 1 , wherein the set of accident characteristics comprises a damage location and a damage extent.

6 . The method of claim 1 , wherein the insurance-related action comprises:

determining an adjusted vehicle insurance pricing metric for the user; and

transmitting the adjusted vehicle insurance pricing metric to an insurance entity.

7 . The method of claim 1 , wherein the vehicle characteristic comprises a vehicle motion characteristic associated with vehicle motion during the first time period, wherein the first condition comprises a vehicle motion threshold.

8 . The method of claim 7 , wherein the set of sensors comprises at least one of a motion sensor or a location sensor.

9 . The method of claim 8 , wherein the second dataset further comprises at least one of a motion dataset or a location dataset.

10 . The method of claim 1 , wherein detecting the vehicular accident event based on the second dataset, further comprises detecting a blood pressure of the user is higher than an average blood pressure associated with the user.

11 . The method of claim 1 , further comprising receiving a weather dataset associated with a vehicle location, wherein detecting the vehicular accident event further comprises processing the weather dataset with the accident detection model.

12 . The method of claim 1 , further comprising receiving a traffic dataset proximate to a vehicle location, wherein detecting the vehicular accident event further comprises processing the traffic dataset with the accident detection model.

13 . A system for vehicular accident detection, comprising: a memory comprising computer-executable instructions, and a processor configured to execute the computer-executable instructions and cause the system to:

determine a vehicle characteristic based on a first dataset sampled from at least one sensor of a user device during a first time period; and

in response to the vehicle characteristic satisfying a first condition, execute an accident detection routine, comprising:

determining a second dataset sampled from a sensor of a user of the user device during a second time period, wherein the second dataset comprises blood pressure data;

retrieving an accident detection model from a set of accident detection models, each accident detection model of the set of accident detection models associated with a portion of a user's body, wherein the accident detection model is retrieved based on a position of the user device;

with the accident detection model, detecting a vehicular accident event during the second time period based on the second dataset, wherein the accident detection model is trained to detect vehicular accident events with the blood pressure data; and

with an accident characteristics model, determining a set of accident characteristics associated with the vehicular accident event based on the first dataset and the second dataset; and

in response to detection of an accident, automatically trigger an insurance-related action based on the set of accident characteristics.

14 . The system of claim 13 , wherein the insurance-related action comprises: at least partially pre-filling an insurance claim based on the set of accident characteristics.

15 . The system of claim 14 , wherein the set of accident characteristics comprises a vehicular accident type.

16 . The system of claim 13 , wherein the insurance-related action comprises:

receiving a driver selection permitting information release to an insurance entity; and

in response to the driver selection, transmitting, to an insurance entity, an insurance claim.

17 . The system of claim 13 , wherein the set of accident characteristics comprises a damage location and a damage extent.

18 . The system of claim 13 , wherein the insurance-related action comprises:

determining an adjusted vehicle insurance pricing metric for the user; and

transmitting the adjusted vehicle insurance pricing metric to an insurance entity.

19 . The system of claim 13 , wherein:

the vehicle characteristic comprises a vehicle motion characteristic is associated vehicle motion during the first time period; and

the first condition comprises a vehicle motion threshold.

20 . The system of claim 13 , wherein the accident detection routine further comprises receiving a weather dataset associated with a vehicle location, wherein detecting the vehicular accident event further comprises processing the weather dataset with the accident detection model.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 16, 2024
From: ZENDRIVE, INC.
To: CREDIT KARMA, LLC
Reel/Frame 068584/0017 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 27, 2022
From: PAL, JAYANTA; ISLAM, BIPUL; CHOUDHURY, ROMIT ROY; RISBOOD, PANKAJ; MATUS, JONATHAN; VERMA, VISHAL
To: ZENDRIVE, INC.
Reel/Frame 060041/0145 →
Continuity (6)
Continuation 17071905 · Oct 15, 2020
Division 16297178 · Mar 8, 2019
Continuation 15492287 · Apr 20, 2017
Continuation 15243565 · Aug 22, 2016
Provisional Application 62207468 · Aug 20, 2015
Related Publication 20220286811A1 · Sep 8, 2022
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