The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 21, 2023

Filed:

Mar. 30, 2020
Applicant:

Affectiva, Inc., Boston, MA (US);

Inventors:

Rana el Kaliouby, Milton, MA (US);

Abdelrahman N. Mahmoud, Somerville, MA (US);

Mohamed Ezzeldin Abdelmonem Ahmed Mohamed, Cairo, EG;

Panu James Turcot, Pacifica, CA (US);

Andrew Todd Zeilman, Beverly, MA (US);

Gabriele Zijderveld, Somerville, MA (US);

Assignee:

Affectiva, Inc., Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06N 3/084 (2023.01); G06V 20/59 (2022.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01); G06F 18/214 (2023.01); G06F 18/25 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06F 18/214 (2023.01); G06F 18/25 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/59 (2022.01); G06V 20/593 (2022.01); G06V 40/10 (2022.01); G06V 40/174 (2022.01); G06V 40/20 (2022.01); G06V 40/23 (2022.01);
Abstract

Vehicular in-cabin sensing is performed using machine learning. In-cabin sensor data of a vehicle interior is collected. The in-cabin sensor data includes images of the vehicle interior. An occupant is detected within the vehicle interior. The detecting is based on identifying an upper torso of the occupant, using the in-cabin sensor data. The imaging is accomplished using a plurality of imaging devices within a vehicle interior. The occupant is located within the vehicle interior, based on the in-cabin sensor data. An additional occupant within the vehicle interior is detected. A human perception metric for the occupant is analyzed, based on the in-cabin sensor data. The detecting, the locating, and/or the analyzing are performed using machine learning. The human perception metric is promoted to a using application. The human perception metric includes a mood for the occupant and a mood for the vehicle. The promoting includes input to an autonomous vehicle.


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