IP Library › Granted Patent US 11,597,519
Granted Patent B2
US 11,597,519 · App. 16/001,639 · Granted Mar 7, 2023

Artificially intelligent flight crew systems and methods

Inventors: Yakentim M. Ibrahim (Brier, WA); Daniel K. Bittner (Everett, WA)
B64D11/0015B64D43/00G06F9/453G06F9/5038G06K9/62G06N20/00
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Quick Facts
Patent No.
US 11,597,519
App. No.
16/001,639
Granted
Mar 7, 2023
Kind
B2
Abstract

A passenger assistant system includes an artificial intelligence processing unit, a sensor interface, a flight crew interface, a passenger interface, and at least one end effector. The sensor interface couples the artificial intelligence processing unit with at least one sensor configured to provide environmental information regarding a cabin of an aircraft. The flight crew interface couples the artificial intelligence processing unit with at least one flight crew input. The passenger interface couples the artificial intelligence processing unit with at least one passenger input. The at least one end effector is coupled to the artificial intelligence processing unit. The artificial intelligence processing unit is configured to direct the at least one end effector to perform at least one task responsive to information acquired from one or more of the sensor interface, flight crew interface, or passenger interface.

Claims (50)

1. A passenger assistant system comprising:

an artificial intelligence processing unit configured for one or both of machine learning or being trained based on received information;

a sensor interface coupling the artificial intelligence processing unit with at least one sensor configured to provide environmental information regarding a cabin of an aircraft;

a flight crew interface coupling the artificial intelligence processing unit with at least one flight crew input;

a passenger interface coupling the artificial intelligence processing unit with at least one passenger input; and at least one end effector coupled to the artificial intelligence processing unit;

wherein the artificial intelligence processing unit is configured to direct the at least one end effector to perform at least one task responsive to information acquired from one or more of the sensor interface, flight crew interface, or passenger interface, wherein the at least one task comprises a localized task tailored for a portion of the cabin.

2. The passenger assistant system of claim 1 , wherein the at least one task is a predictive task determined by the artificial intelligence processing unit based on the information acquired and historical information.

3. The passenger assistant system of claim 1 , wherein the artificial intelligence processing unit is configured to prioritize a series of tasks, and to direct the at least one end effector to perform the prioritized series of tasks.

4. The passenger assistant system of claim 1 , wherein the artificial intelligence processing unit is configured to direct the at least one end effector to perform a universal task that affects an entirety of the cabin.

5. The passenger assistant system of claim 1 , wherein the artificial intelligence processing unit is configured to receive an initial command from at least one of the flight crew interface or the passenger interface, modify the command based on historical information, and direct the at least one end effector to perform the modified command.

6. The passenger assistant system of claim 5 , wherein the artificial intelligence processing unit is configured to modify the command using sensor information received from the sensor interface.

7. The passenger assistant of claim 1 , wherein the artificial intelligence processing unit is configured to direct the at least one end effector to perform tasks responsive to the sensor interface, the flight crew interface, and the passenger interface.

8. A method comprising:

receiving, via a sensor interface coupling an artificial intelligence processing unit with at least one sensor, environmental information regarding a cabin of an aircraft;

the artificial intelligence processing unit configured for one or both of machine learning or being trained based on received information;

receiving, via a flight crew interface, at least one flight crew input;

receiving, via a passenger interface, at least one passenger input; and

directing, with the artificial intelligence processing unit, at least one end effector to perform at least one task responsive to one or more of the environmental information, flight crew input, or passenger input, wherein the at least one task comprises a localized task tailored for a portion of the cabin.

9. The method of claim 8 , wherein the at least one task is a predictive task determined by the artificial intelligence processing unit based on historical information and the one or more of the environmental information, flight crew input, or passenger input.

10. The method of claim 8 , wherein the artificial intelligence processing unit is configured to prioritize a series of tasks, the method comprising directing the at least one end effector to perform the prioritized series of tasks.

11. The method of claim 8 , wherein the at least one task comprises a universal task that affects an entirety of the cabin.

12. The method of claim 8 , comprising receiving an initial command via at least one of the flight crew interface or the passenger interface, modifying the command based on historical information, and directing the at least one end effector to perform the modified command.

13. The method of claim 12 , further comprising modifying the command using the environmental information received from the sensor interface.

14. A tangible and non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by a computer having an artificial intelligence processing unit, the artificial intelligence processing unit configured for one or both of machine learning or being trained based on received information;

causing the computer to:

receive, via a sensor interface with at least one sensor, environmental information regarding a cabin of an aircraft;

receive, via a flight crew interface, at least one flight crew input;

receive, via a passenger interface, at least one passenger input; and

direct at least one end effector to perform at least one task responsive to one or more of the environmental information, flight crew input, or passenger input, wherein the at least one task comprises a localized task tailored for a portion of the cabin.

15. The tangible and non-transitory computer-readable medium of claim 14 , wherein the at least one task is a predictive task determined based on historical information and the one or more of the environmental information, flight crew input, or passenger input.

16. The tangible and non-transitory computer-readable medium of claim 14 , wherein the instructions cause the computer to prioritize a series of tasks, and direct the at least one end effector to perform the prioritized series of tasks.

17. The tangible and non-transitory computer-readable medium of claim 14 , wherein the at least one task comprises a universal task that affects an entirety of the cabin.

18. The tangible and non-transitory computer-readable medium of claim 14 , wherein the instructions cause the computer to receive an initial command via at least one of the flight crew interface or the passenger interface, modify the command based on historical information, and direct the at least one end effector to perform the modified command.

19. A passenger assistant system comprising:

an artificial intelligence processing unit configured for one or both of machine learning or being trained based on received information;

a sensor interface coupling the artificial intelligence processing unit with at least one sensor configured to provide environmental information regarding a cabin of an aircraft;

a flight crew interface coupling the artificial intelligence processing unit with at least one flight crew input;

a passenger interface coupling the artificial intelligence processing unit with at least one passenger input; and

at least one end effector coupled to the artificial intelligence processing unit;

wherein the artificial intelligence processing unit is configured to direct the at least one end effector to perform at least one task responsive to information acquired from one or more of the sensor interface, flight crew interface, or passenger interface, wherein the artificial intelligence processing unit is configured to receive an initial command from at least one of the flight crew interface or the passenger interface, modify the initial command based on historical information, and direct the at least one end effector to perform the modified command, and

wherein the artificial intelligence processing unit is configured to modify the command using sensor information received from the sensor interface.

20. A method comprising:

receiving, via a sensor interface coupling an artificial intelligence processing unit with at least one sensor, environmental information regarding a cabin of an aircraft;

the artificial intelligence processing unit configured for one or both of machine learning or being trained based on received information;

receiving, via a flight crew interface, at least one flight crew input;

receiving, via a passenger interface, at least one passenger input;

directing, with the artificial intelligence processing unit, at least one end effector to perform at least one task responsive to one or more of the environmental information, flight crew input, or passenger input;

receiving an initial command via at least one of the flight crew interface or the passenger interface;

modifying the initial command based on historical information and using the environmental information received from the sensor interface; and

directing the at least one end effector to perform the modified command.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 6, 2018
From: IBRAHIM, YAKENTIM M.; BITTNER, DANIEL K.
To: THE BOEING COMPANY
Reel/Frame 046005/0704 →
Continuity (2)
Provisional Application 62573509 · Oct 17, 2017
Related Publication 20190112050A1 · Apr 18, 2019