IP Library Granted Patent US 12,651,430
Granted Patent B2
US 12,651,430 · App. 18/416,840 · Granted Jun 9, 2026

Methods and systems for automated machine vision monitoring of vehicle seats

Inventors: Catherine Espel-Logan (Plano, TX); Brian Mark Fields (Phoenix, AZ); Joshua John Freitas (Peoria, AZ); Melissa Collette Miles (Normal, IL); Jeanne Koehler (Bloomington, IL)
Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
G06V10/70B60N2/266B60N2/268B60N2/272B60W30/08G06F21/6218G06Q30/0643G06V10/764G06V20/41G06V20/59B60W2756/10G06T2207/30268
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Quick Facts
Patent No.
US 12,651,430
App. No.
18/416,840
Filed
Jan 18, 2024
Granted
Jun 9, 2026
Kind
B2
Art Unit
3669
USPC
701/34.4
Abstract

Methods and systems for monitoring and analyzing vehicle seats, vehicle seat installation, and/or vehicle seat operation. The systems and methods may include (1) obtaining, by one or more processors, image data of a vehicle seat located within a vehicle, wherein the image data may include one or more connecting points of the vehicle seat to the vehicle; (2) inputting, by the one or more processors, the image data into a machine vision model that is trained: (a) using historical image data of vehicle seats within vehicles, (b) to learn a relationship between extracted features of the historical image data and a properness of an installation of a vehicle seat, and/or (c) to output a determination of a properness of an installation of a vehicle seat in response to detecting input image data; and/or (3) presenting, by the one or more processors, an indication of the output of the machine vision model.

Claims (55)

1 . A computer-implemented method for automated machine vision monitoring of vehicle seats, the computer-implemented method comprising:

obtaining, by one or more processors, an initial frame of image data, wherein the initial frame of image data is captured via an image sensor having a field of view (FOV) directed at an interior location within a vehicle;

analyzing, by the one or more processors, the initial frame of image data to detect a safety seat configured to be installed in the vehicle;

obtaining, by the one or more processors, one or more subsequent frames of image data captured via the image sensor;

tracking, by the one or more processors, the safety seat across the one or more subsequent frames of image data;

based upon the tracking, determining, by the one or more processors, that the safety seat is not installed in the vehicle; and

presenting, by the one or more processors, a notification that the safety seat is not installed in the vehicle.

2 . The computer-implemented method of claim 1 , further comprising:

analyzing, by the one or more processors, the initial frame of image data to identify one or more reference objects; and

tracking, by the one or more processors, the one or more reference objects across the one or more subsequent frames of image data.

3 . The computer-implemented method of claim 2 , wherein determining that the safety seat is not installed in the vehicle comprises:

determining, via the one or more processors, that a relative position between the safety seat and the one or more reference objects exceeds a threshold.

4 . The computer-implemented method of claim 1 , wherein obtaining a particular frame of the one or more subsequent frames of image data comprises:

detecting, by the one or more processors, a stimulus associated with the vehicle; and

causing, by the one or more processors, the image sensor to capture the particular frame of image data.

5 . The computer-implemented method of claim 4 , wherein detecting the stimulus comprises:

obtaining, by the one or more processors, sensor data generated by one or more sensors of the vehicle; and

analyzing, by the one or more processors, the sensor data to detect the stimulus.

6 . The computer-implemented method of claim 4 , wherein the stimulus is indicative of abnormal motion of the vehicle.

7 . The computer-implemented method of claim 1 , wherein the one or more subsequent frames of image data are periodically obtained.

8 . The computer-implemented method of claim 1 , wherein the notification includes one or more of (i) an auditory alert, (2) a visual alert, or (3) a haptic alert.

9 . The computer-implemented method of claim 1 , wherein the image sensor is communicatively coupled to the vehicle.

10 . The computer-implemented method of claim 1 , further comprising:

confirming, by the one or more processors, that the safety seat is not installed in the vehicle by inputting a subsequent frame of image data into a machine vision model.

11 . A computer system for automated machine vision monitoring of vehicle seats, the computer system comprising:

one or more processors; and

a non-transitory program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:

obtain an initial frame of image data, wherein the initial frame of image data is captured via an image sensor having a field of view (FOV) directed at an interior location within a vehicle;

analyze the initial frame of image data to detect a safety seat configured to be installed in the vehicle;

obtain one or more subsequent frames of image data captured via the image sensor;

track the safety seat across the one or more subsequent frames of image data;

based upon the tracking, determine that the safety seat is not installed in the vehicle; and

present a notification that the safety seat is not installed in the vehicle.

12 . The computer system of claim 11 , wherein the executable instructions, when executed by the one or more processors, further cause the computer system to:

analyze the initial frame of image data to identify one or more reference objects; and

track the one or more reference objects across the one or more subsequent frames of image data.

13 . The computer system of claim 12 , wherein determining that the safety seat is not installed in the vehicle causes the computer system to:

determine that a relative position between the safety seat and the one or more reference objects exceeds a threshold.

14 . The computer system of claim 11 , wherein obtaining a particular frame of the one or more subsequent frames of image data causes the computer system to:

detect a stimulus associated with the vehicle; and

cause the image sensor to capture the particular frame of image data.

15 . The computer system of claim 14 , wherein detecting the stimulus causes the computer system to:

obtain sensor data generated by one or more sensors of the vehicle; and

analyze the sensor data to detect the stimulus.

16 . The computer system of claim 14 , wherein the stimulus is indicative of abnormal motion of the vehicle.

17 . The computer system of claim 11 , wherein the one or more subsequent frames of image data are periodically obtained.

18 . The computer system of claim 11 , wherein the notification includes one or more of (i) an auditory alert, (2) a visual alert, or (3) a haptic alert.

19 . The computer system of claim 11 , wherein the image sensor is communicatively coupled to the vehicle.

20 . A tangible, non-transitory computer-readable medium storing executable instructions for automated machine vision monitoring of vehicle seats, wherein the executable instructions, when executed by one or more processors of a computer system, cause the computer system to:

obtain an initial frame of image data, wherein the initial frame of image data is captured via an image sensor having a field of view (FOV) directed at an interior location within a vehicle;

analyze the initial frame of image data to detect a safety seat configured to be installed in the vehicle;

obtain one or more subsequent frames of image data captured via the image sensor;

track the safety seat across the one or more subsequent frames of image data;

based upon the tracking, determine that the safety seat is not installed in the vehicle; and

present a notification that the safety seat is not installed in the vehicle.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 26, 2024
From: ESPEL-LOGAN, CATHERINE; FIELDS, BRIAN MARK; FREITAS, JOSHUA JOHN; MILES, MELISSA COLLETTE; KOEHLER, JEANNE
To: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Reel/Frame 066252/0500 →
Continuity (6)
Provisional Application 63541659 · Sep 29, 2023
Provisional Application 63530418 · Aug 2, 2023
Provisional Application 63524035 · Jun 29, 2023
Provisional Application 63488042 · Mar 2, 2023
Provisional Application 63445879 · Feb 15, 2023
Related Publication 20240273922A1 · Aug 15, 2024
References Cited (20)
US 9830748B2 · Rosenbaum · 2017 [cited by applicant]
US 9990782B2 · Rosenbaum · 2018 [cited by applicant]
US 10269190B2 · Rosenbaum · 2019 [cited by applicant]
US 10467824B2 · Rosenbaum · 2019 [cited by applicant]
US 11227452B2 · Rosenbaum · 2022 [cited by applicant]
US 11273779B2 · Murata · 2022 [cited by examiner]
US 11407410B2 · Rosenbaum · 2022 [cited by applicant]
US 11524707B2 · Rosenbaum · 2022 [cited by applicant]
US 11594083B1 · Rosenbaum · 2023 [cited by applicant]
US 12157485B2 · Hawley · 2024 [cited by examiner]
US 20200410790A1 · Thompson · 2020 [cited by examiner]
US 20220092893A1 · Rosenbaum · 2022 [cited by applicant]
US 20220340148A1 · Rosenbaum · 2022 [cited by applicant]
US 20230060300A1 · Rosenbaum · 2023 [cited by applicant]
EP 3239686A1 · 2017 [cited by applicant]
EP 3578433B1 · 2020 [cited by applicant]
EP 3730375B1 · 2021 [cited by applicant]
EP 3960576A1 · 2022 [cited by applicant]
EP 4190659A1 · 2023 [cited by applicant]
EP 4190660A1 · 2023 [cited by applicant]