IP Library Granted Patent US 12,347,211
Granted Patent B2
US 12,347,211 · App. 18/422,830 · Granted Jul 1, 2025

Apparatuses, systems and methods for generation and transmission of vehicle operation mode data

Inventors: Kenneth J. Sanchez (San Francisco, CA); Aaron Scott Chan (San Jose, CA)
Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
G06V20/597G06V20/64G06V40/103G06V40/107G06V40/20G06V40/10
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,347,211
App. No.
18/422,830
Granted
Jul 1, 2025
Kind
B2
Abstract

Apparatuses, systems and methods are provided for generating and transmitting data representative of a vehicle operation mode. More particularly, apparatuses, systems and methods are provided for generating data representative of a vehicle operation mode based on vehicle interior image data.

Claims (33)

1. A device comprising one or more processors, and a memory storing computer-readable instructions that, when executed by the one or more processors, cause the one or more processors to:

extract image features from vehicle interior data from at least one vehicle interior sensor, wherein at least a portion of the extracted image features include data representative of one or more postures of a vehicle operator; and

generate data representative of a pattern of behaviors of the vehicle operator based on comparing the extracted image features with previously classified vehicle interior data, wherein the previously classified vehicle interior data is representative of known postures of vehicle operators during known patterns of behavior of vehicle operators, including by comparing the data representative of the one or more postures of the vehicle operator to data representative of rotated and scaled versions of the known postures that are normalized for a range of different drivers.

2. The device as in claim 1 , wherein the at least one vehicle interior sensor is selected from: at least one digital image sensor, at least one ultra-sonic sensor, or at least one infrared light sensor.

3. The device as in claim 1 , wherein the computer-readable instructions, when executed by the one or more processors, further cause the one or more processors to:

to extract image features from the previously classified vehicle interior data, wherein the extracted image features from the previously classified vehicle interior data are representative of known patterns of behavior of vehicle operators; and

determine a pattern of behaviors of the vehicle operator based on a comparison of the vehicle interior data with the image features extracted from the previously classified vehicle interior data.

4. The device as in claim 1 , wherein the vehicle interior data is representative of a three-dimensional representation of at least one occupant within a vehicle interior.

5. The device as in claim 1 , wherein the image features extracted from the vehicle interior data are representative of a pattern of behaviors of the vehicle operator, wherein the computer-readable instructions, when executed by the one or more processors, further cause the one or more processors to determine the pattern of behaviors of the vehicle operator based on a comparison of the image features extracted from the vehicle interior data with the previously classified vehicle interior data.

6. The device as in claim 1 , wherein the vehicle interior data includes images and/or extracted image features that are representative of one or more of: a vehicle occupant using a cellular telephone, a vehicle occupant looking out a vehicle side window, a vehicle occupant adjusting a vehicle radio, a vehicle occupant adjusting a vehicle heating, ventilation and air conditioning system, two vehicle occupants talking with one-another, a vehicle occupant reading a book or magazine, a vehicle occupant putting on makeup, or a vehicle occupant looking at themselves in a mirror.

7. The device as in claim 1 , wherein the previously classified vehicle interior data includes images and/or extracted image features that have previously been classified as being representative of a vehicle occupant using a cellular telephone, a vehicle occupant looking out a vehicle side window, a vehicle occupant adjusting a vehicle radio, a vehicle occupant adjusting a vehicle heating, ventilation and air conditioning system, two vehicle occupants talking with one-another, a vehicle occupant reading a book or magazine, a vehicle occupant putting on makeup, or a vehicle occupant looking at themselves in a mirror.

8. A computer-implemented method, comprising:

extracting, by one or more processors, image features from vehicle interior data from at least one vehicle interior sensor, wherein at least a portion of the extracted image features include data representative of one or more postures of a vehicle operator; and

generating, by one or more processors, data representative of a pattern of behaviors based on comparing the extracted features with previously classified vehicle interior data wherein the previously classified vehicle interior data is further representative of known postures of vehicle operators during known patterns of behavior of vehicle operators, including by comparing data representative of the one or more postures of the vehicle operator to data representative of rotated and scaled versions of the known postures that are normalized for a range of different drivers.

9. The computer-implemented method as in claim 8 ,

wherein the at least one vehicle interior sensor is selected from: at least one digital image sensor, at least one ultra-sonic sensor, or least one infrared light sensor.

10. The computer-implemented method as in claim 8 , further comprising:

extracting, by one or more processors, image features from previously classified vehicle interior data, wherein the extracted image features from the previously classified vehicle interior data are representative of known patterns of behavior of vehicle operators; and

determining, by one or more processors, a pattern of behaviors of the vehicle operator based on a comparison of the vehicle interior data with the image features extracted from the previously classified vehicle interior data.

11. The computer-implemented method as in claim 8 , wherein the vehicle interior data is representative of a three-dimensional representation of at least one occupant within a vehicle interior.

12. The computer-implemented method as in claim 8 , wherein the image features extracted from the vehicle interior data are representative of a vehicle operation mode, the computer-implemented method further comprising determining, by one or more processors, a pattern of behaviors of a vehicle operator based on a comparison of the image features extracted from the vehicle interior data with the previously classified vehicle interior data.

13. The computer-implemented method as in claim 8 , wherein the vehicle interior data includes images and/or extracted image features that are representative of vehicle occupant locations/orientations, cellular telephone locations/orientations, vehicle occupant eye locations/orientations, vehicle occupant head location/orientation, vehicle occupant hand location/orientation, a vehicle occupant torso location/orientation, a seat belt location, or a vehicle seat location/orientation.

14. The computer-implemented method as in claim 8 , wherein the previously classified vehicle interior data includes images and/or extracted image features that have previously been classified as being representative of known vehicle occupant locations/orientations, known cellular telephone locations/orientations, known vehicle occupant eye locations/orientations, known vehicle occupant head location/orientation, known vehicle occupant hand location/orientation, a known vehicle occupant torso location/orientation, a known seat belt location, or a known vehicle seat location/orientation.

15. A non-transitory computer-readable medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to:

extract image features from vehicle interior data from at least one vehicle interior sensor, wherein at least a portion of the extracted image features include data representative of one or more postures of a vehicle operator; and

generate data representative of a pattern of behaviors based on comparing the extracted image features with previously classified vehicle interior data, wherein the previously classified vehicle interior data is representative of known postures of vehicle operators during known patterns of behavior of vehicle operators, including by comparing data representative of the one or more postures of the vehicle operator to data representative of rotated and scaled versions of the known postures that are normalized for a range of different drivers.

16. The non-transitory computer-readable medium as in claim 15 , wherein the computer-readable instructions, when executed by the one or more processors, further cause the one or more processors to:

extract image features from the previously classified vehicle interior data, wherein the extracted image features from the previously classified vehicle interior data are representative of known patterns of behavior of vehicle operators; and

determine at least one vehicle occupant posture based on a comparison of the vehicle interior data with the image features extracted from the previously classified vehicle interior data.

17. The non-transitory computer-readable medium as in claim 15 , wherein the image features extracted from the vehicle interior data are representative of a pattern of behaviors of the vehicle operator, and wherein the computer-readable instructions, when executed by the one or more processors, further cause the one or more processors to: to determine a pattern of behaviors of the vehicle operator based on a comparison of the image features extracted from the vehicle interior data with the previously classified vehicle interior data.

18. The non-transitory computer-readable medium as in claim 15 , wherein the vehicle interior data includes images and/or extracted image features that are representative of a vehicle occupant using a cellular telephone, a vehicle occupant looking out a vehicle side window, a vehicle occupant adjusting a vehicle radio, a vehicle occupant adjusting a vehicle heating, ventilation and air conditioning system, two vehicle occupants talking with one-another, a vehicle occupant reading a book or magazine, a vehicle occupant putting on makeup, or a vehicle occupant looking at themselves in a mirror, vehicle occupant locations/orientations, cellular telephone locations/orientations, vehicle occupant eye locations/orientations, vehicle occupant head location/orientation, vehicle occupant hand location/orientation, a vehicle occupant torso location/orientation, a seat belt location, or a vehicle seat location/orientation.

19. The non-transitory computer-readable medium as in claim 15 , wherein the previously classified vehicle interior data includes images and/or extracted image features that have previously been classified as being representative of a vehicle occupant using a cellular telephone, a vehicle occupant looking out a vehicle side window, a vehicle occupant adjusting a vehicle radio, a vehicle occupant adjusting a vehicle heating, ventilation and air conditioning system, two vehicle occupants talking with one-another, a vehicle occupant reading a book or magazine, a vehicle occupant putting on makeup, a vehicle occupant looking at themselves in a mirror, known vehicle occupant locations/orientations, known cellular telephone locations/orientations, known vehicle occupant eye locations/orientations, known vehicle occupant head location/orientation, known vehicle occupant hand location/orientation, a known vehicle occupant torso location/orientation, a known seat belt location, or a known vehicle seat location/orientation.

20. The non-transitory computer-readable medium as in claim 15 , wherein the previously classified vehicle interior data is representative of a three-dimensional representation of at least one occupant within a vehicle interior.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 16, 2024
From: SANCHEZ, KENNETH J.; CHAN, AARON SCOTT
To: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Reel/Frame 067117/0592 →
Continuity (5)
Continuation 17554776 · Dec 17, 2021
Continuation 16727011 · Dec 26, 2019
Continuation 15717312 · Sep 27, 2017
Provisional Application 62448043 · Jan 19, 2017
Related Publication 20240169744A1 · May 23, 2024
References Cited (55)
US 7511833B2 · Breed · 2009 [cited by applicant]
US 9690292B1 · Chan et al. · 2017 [cited by applicant]
US 9710717B1 · Sanchez et al. · 2017 [cited by applicant]
US 9764742B1 · Goldfarb et al. · 2017 [cited by applicant]
US 9830748B2 · Rosenbaum · 2017 [cited by applicant]
US 9944296B1 · Sanchez et al. · 2018 [cited by applicant]
US 9990554B1 · Sanchez et al. · 2018 [cited by applicant]
US 9990782B2 · Rosenbaum · 2018 [cited by applicant]
US 10140533B1 · Chan et al. · 2018 [cited by applicant]
US 10147007B1 · Chan et al. · 2018 [cited by applicant]
US 10147008B1 · Chan et al. · 2018 [cited by applicant]
US 10174007B2 · Dowling et al. · 2019 [cited by applicant]
US 10189480B1 · Sanchez et al. · 2019 [cited by applicant]
US 10241512B1 · Chan et al. · 2019 [cited by applicant]
US 10269190B2 · Rosenbaum · 2019 [cited by applicant]
US 10325167B1 · Chan et al. · 2019 [cited by applicant]
US 10467824B2 · Rosenbaum · 2019 [cited by applicant]
US 10558874B1 · Chan et al. · 2020 [cited by applicant]
US 10562536B1 · Sanchez et al. · 2020 [cited by applicant]
US 10676097B1 · Goldfarb et al. · 2020 [cited by applicant]
US 11227452B2 · Rosenbaum · 2022 [cited by applicant]
US 11238293B1 · Sanchez et al. · 2022 [cited by applicant]
US 11407410B2 · Rosenbaum · 2022 [cited by applicant]
US 11524707B2 · Rosenbaum · 2022 [cited by applicant]
US 11594083B1 · Rosenbaum · 2023 [cited by applicant]
US 11922705B2 · Sanchez · 2024 [cited by examiner]
US 20110030642A1 · Nishida et al. · 2011 [cited by applicant]
US 20110306420A1 · Nishimoto et al. · 2011 [cited by applicant]
US 20140019167A1 · Cheng et al. · 2014 [cited by applicant]
US 20150186714A1 · Ren et al. · 2015 [cited by applicant]
US 20170210357A1 · Nagai · 2017 [cited by examiner]
US 20190318181A1 · Katz et al. · 2019 [cited by applicant]
US 20190392230A1 · Welland et al. · 2019 [cited by applicant]
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]
Dong et al. “Driver Inattention Monitoring System for Intelligent Vehicles: A Review.” IEEE Transactions on Intelligent Transportation Systems, vol. 12, No. 2, Jun. 2011, pp. 596-614 (Year: 2011). [cited by examiner]
Zhao et al. “Classification of Driving Postures by Support Vector Machines.” Sixth International Conference on Image and Graphics, DOI: 10.1109/ICIG.2011.184, Aug. 12, 2011, pp. 926-930 (Year: 2011). [cited by examiner]
Final Office Action for U.S. Appl. No. 16/727,011 dated Mar. 24, 2021. [cited by applicant]
Final Office Action for U.S. Appl. No. 15/717,312 dated Jul. 17, 2019. [cited by applicant]
Final Office Action for U.S. Appl. No. 17/554,776 dated Jul. 12, 2023. [cited by applicant]
Gallahan et al. “Detecting and Mitigating Driver Distraction with Motion Capture Technology: Distracted Driving Warning System.” IEEE Systems and Information Engineering Design Symposium, Apr. 26, 2013, pp. 76-81 (Year:… [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 16/727,011 dated Jul. 8, 2021. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 16/727,011 dated Oct. 6, 2020. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 15/717,312 dated Apr. 4, 2019. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 17/554,776 dated Feb. 2, 2023. [cited by applicant]
Notice of Allowance for U.S. Appl. No. 17/554,776 dated Oct. 25, 2023. [cited by applicant]
Notice of Allowance for U.S. Appl. No. 15/717,312 dated Sep. 25, 2019. [cited by applicant]
Notice of Allowance for U.S. Appl. No. 16/727,011 dated Sep. 27, 2021. [cited by applicant]