Aircraft cabin artificial intelligence crew assistance

Patent metadata

Field	Value
Publication number	US-10691955-B1
Application number	US-201916372845-A
Country	US
Kind code	B1
Filing date	Apr 2, 2019
Priority date	Apr 2, 2019
Publication date	Jun 23, 2020
Grant date	Jun 23, 2020

Abstract

A method may include receiving an image from at least one camera positioned within an aircraft cabin. The method may further include associating the image with a category and a corresponding probability value based on a predictive model. The method may include displaying an alert at a cabin crew station within the aircraft cabin when the probability value is greater than a threshold probability value. A system may include at least one camera positioned within an aircraft cabin, a cabin crew station, and a processor positioned within an aircraft avionics bay. The processor may be configured to receive an image from the at least one camera, associate the image with a category and a probability value based on a predictive model, and generate an alert at the cabin crew station when the probability value is greater than a threshold probability value.

First claim

What is claimed is:

1. A method comprising:
receiving an image from at least one camera positioned within an aircraft cabin;
associating the image with a category and a corresponding probability value based on a predictive model; and
displaying an alert at a cabin crew station within the aircraft cabin when the corresponding probability value is greater than a threshold probability value.

2. The method of claim 1 , wherein the category corresponds to a seatbelt state, a tray table state, a seat occupancy state, or a passenger assistance matter.

3. The method of claim 1 , further comprising:
training the predictive model using a set of training images associated with the category.

4. The method of claim 1 , wherein the alert includes an identification of the category, a seat location, the image, a video, or any combination thereof.

5. The method of claim 1 , further comprising:
receiving user input at the cabin crew station in response to the alert, the user input indicating that the image corresponds to the category or that the image does not correspond to the category; and
updating the predictive model based on the image and the user input.

6. The method of claim 1 , further comprising:
receiving user input at the cabin crew station in response to the alert, the user input including a communication; and
sending the communication to a passenger information system.

7. The method of claim 6 , wherein the passenger information system is an in-flight entertainment system.

8. The method of claim 1 , wherein the at least one camera detects infrared light, visual light, or a combination thereof.

9. The method of claim 1 , wherein the at least one camera is part of a camera array installed in a ceiling of the aircraft cabin.

10. The method of claim 9 , wherein the aircraft cabin includes between one and three rows of seats per camera within the camera array.

11. The method of claim 9 , further comprising:
generating the image by fusing multiple images from multiple cameras of the camera array.

12. The method of claim 9 , further comprising:
providing an interactive virtual walkthrough of the aircraft cabin at the cabin crew station using the camera array.

13. The method of claim 9 , wherein the camera array is wirelessly networked together to send the image to a processor positioned within an avionics bay.

14. A system comprising:
at least one camera positioned within an aircraft cabin;
a cabin crew station;
a processor positioned within an aircraft avionics bay, the processor configured to:
receive an image from the at least one camera;
associate the image with a category and a probability value based on a predictive model; and
generate an alert at the cabin crew station when the probability value is greater than a threshold probability value.

15. The system of claim 14 , wherein the cabin crew station is configured to receive user input in response to the alert, the user input indicating that the image corresponds to the category or that the image does not correspond to the category, and wherein the processor is configured to update the predictive model based on the image and the user input.

16. The system of claim 14 , further comprising:
a passenger information system, wherein the cabin crew station is configured to receive user input in response to the alert, the user input including a communication, and to send the communication to the passenger information system.

17. The system of claim 14 , further comprising:
a camera array installed in a ceiling of the aircraft cabin, wherein the at least one camera is part of the camera array.

18. The system of claim 17 , wherein the camera array is wirelessly networked together to send the image to the processor.

19. A method comprising:
receiving multiple images during a flight from at least one camera positioned within an aircraft cabin;
associating the multiple images with categories and corresponding probability values based on a predictive model; and
displaying alerts at a cabin crew station within the aircraft cabin when one or more of the multiple images is associated with a category having a corresponding probability value that is greater than a threshold probability value.

20. The method of claim 19 , further comprising:
generating data that associates the categories with the flight;
storing the data in a database; and
adjusting marketing parameters, security parameters, service parameters, or a combination thereof based on the data.

Assignees

• Boeing Co

Inventors

• Correia Gracio Bruno J
• Sweeney Stephen

Classifications

• B64D11/0015 Primary

  Arrangements for entertainment or communications, e.g. radio, television · CPC title

• G06V20/59

  inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions · CPC title

• G06V20/52

  Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) · CPC title

• H04N23/90

  Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title