Aircraft integrated cabin sentience neural network

1 . A method for optimizing assistance, the method comprising:

receiving from a sensor communicatively coupled to a computing device, data relating to a behavior of an occupant positioned in an area;

processing the data into a sequence with the computing device;

classifying, the sequence of data into time windows with the computing device;

performing predictions with the computing device based on the classification of the sequence of data into the time windows;

identifying, with the computing device, a requirement of the occupant in the area based on the predictions; and

outputting, with the computing device, a recommendation to optimize a workload to meet the requirement of the occupant in the area.

2 . The method of claim 1 , further comprising:

applying, with the computing device, machine learning algorithms to classify the sequence of data into the time windows.

3 . The method of claim 1 , further comprising:

updating, with the computing device, a schedule, electronic checklist or a flight plan in the area to optimize the workload of a crew member assisting the occupant in the area.

4 . The method of claim 1 , further comprising:

updating, with the computing device, an environmental setting in the area to meet the requirement of the occupant.

5 . The method of claim 1 , further comprising:

providing, by the computing device, the recommendation in multiple time intervals to optimize the workload of a crew member in the area.

6 . The method of claim 1 , further comprising:

identifying, with the computing device, time intervals in which the occupant requires privacy based on the predictions performed due to the classification of the sequence of data into the time windows and settings and states of environmental controls parsed from a cabin management system.

7 . The method of claim 1 , further comprising:

identifying, with the computing device, when the occupant requires the assistance based on the predictions performed due to the classification of the sequence of data into the time windows.

8 . The method of claim 1 , further comprising:

identifying, with the computing device, time intervals in which cabin settings in the area have to be adjusted based on the predictions that are performed.

9 . The method of claim 1 , further comprising:

identifying, with the computing device, intervals in which another occupant in the area will require medical assistance based on the performed predictions.

10 . A non-transitory machine-readable storage medium that provides instructions that, when executed by a processor, are configurable to cause the processor to perform an operation comprising:

receiving from a sensor communicatively coupled to the processor, data relating to a behavior of an occupant positioned in an area;

processing the data into a sequence;

classifying, the sequence of data into time windows;

performing predictions based on the classification of the sequence of data into the time windows;

identifying a requirement of the occupant in the area based on the predictions; and

outputting a recommendation to optimize a workload to meet the requirement of the occupant in the area.

11 . The non-transitory machine-readable storage medium of claim 10 , wherein the recommendation provides a series of prompts at various time intervals to crew members to optimize the workload.

12 . The non-transitory machine-readable storage medium of claim 10 , wherein the instructions are configurable to cause the processor to:

identify a sleeping interval of the occupant based on the predictions that are performed.

13 . The non-transitory machine-readable storage medium of claim 10 , wherein the instructions are configurable to cause the processor to:

identify when light, shades, or volume on a speaker in the area need to be adjusted to meet the requirement of the occupant.

14 . The non-transitory machine-readable storage medium of claim 10 , wherein the instructions are configurable to cause the processor to:

detect mood levels of the occupant based on the predictions that are performed.

15 . The non-transitory machine-readable storage medium of claim 10 , wherein the instructions are configurable to cause the processor to:

identify a potential communication pattern of the occupant with another occupant in the area based on the predictions that are performed.

16 . A computing system comprising:

a non-transitory machine-readable storage medium that stores software; and

a processor, coupled to the non-transitory machine-readable storage medium, the processor configured to execute the software that implements a large language model (LLM) grounding service and that is configured to perform operations comprising:

receiving from a sensor communicatively coupled to the processor, data relating to a behavior of an occupant positioned in an area;

processing the data into a sequence;

classifying, the sequence of data into time windows;

performing predictions based on the classification of the sequence of data into the time windows;

identifying a requirement of the occupant in the area based on the predictions; and

outputting a recommendation to optimize a workload to meet the requirement of the occupant in the area.

17 . The computing system of claim 16 , wherein the recommendation includes a prompt to provide a meal or beverage service to the occupant and to another occupant in multiple time intervals.

18 . The computing system of claim 16 , wherein the requirement includes increasing or addressing a comfort level of the occupant in multiple time intervals.

19 . The computing system of claim 16 , wherein the predictions include a prediction based on a time interval when the occupant will require medical assistance.

20 . The computing system of claim 16 , wherein the recommendation will be continuously provided in multiple time intervals during a time period.