Digital assistant and associated methods for a transportation vehicle

1. A method comprising:

receiving a user input for a valid communication session by a processor executable, digital assistant on a device at a transportation vehicle;

tagging by the digital assistant, the user input words with a grammatical connotation;

generating an action context that maps to an action application programming interface (API), a filter context and a response context by a neural network, based on the tagged user input, the neural network trained in a plurality of categories including an entertainment data category for entertainment related actions on the transportation vehicle, a shopping category for shopping related actions on the transportation vehicle, a crew instructions category, and a security category associated with transportation vehicle security;

wherein the action context identifies a type of action associated with one of the plurality of categories;

wherein the filter context defines one or more filter parameters that define a query parameter for the action API;

wherein the response context provides information to extract from the action context to execute an action;

determining by the digital assistant that the action context is a most current action context based on a user session;

retrieving by the digital assistant a missing parameter for the action API;

storing by the digital assistant, a key-value pair for a parameter of the filter context at a short term memory, based on an output from the neural network and the retrieved missing parameter; wherein the short term memory stores key-value pairs for each user session with the digital assistant;

updating by the digital assistant, the key-value pair at the short term memory after receiving a reply to a follow-up request from the digital assistant and another output from the neural network; and

providing a response to the reply by the digital assistant.

2. The method of claim 1 , wherein a natural language parser of the digital assistant tags the words of the user input.

3. The method of claim 1 , wherein the missing parameter is retrieved from a memory device different from the short term memory.

4. The method of claim 1 , wherein the device provides entertainment on the transportation vehicle.

5. The method of claim 1 , wherein the device is a seat function controller for controlling functions associated with a seat on the transportation vehicle.

6. The method of claim 1 , wherein the short term memory is configured as a least recently used memory.

7. The method of claim 1 , wherein the transportation vehicle is one of an aircraft, a train, a bus, a ship and a recreation vehicle.

8. A system comprising:

a memory including a machine readable medium comprising machine executable code; and a processor module coupled to the memory, the processor module executing the machine executable code for a digital assistant at a device on a transportation vehicle to:

receive a user input for a valid communication session by the digital assistant;

tag by the digital assistant, the user input words with a grammatical connotation;

generate an action context that maps to an action application programming interface (API), a filter context and a response context by a neural network, based on the tagged user input, the neural network trained in a plurality of categories including an entertainment data category for entertainment related actions on the transportation vehicle, a shopping category for shopping related actions on the transportation vehicle, a crew instructions category, and a security category associated with transportation vehicle security;

wherein the action context identifies a type of action associated with one of the plurality of categories;

wherein the filter context defines one or more filter parameters that define a query parameter for the action API;

wherein the response context provides information to extract from the action context to execute an action;

determining by the digital assistant that the action context is a most current action context based on a user session;

retrieving by the digital assistant a missing parameter for the action API

store by the digital assistant, a key-value pair for a parameter of the filter context at a short term memory, based on an output from the neural network and the retrieved missing parameter; wherein the short term memory stores key-value pairs for each user session with the digital assistant

update by the digital assistant, the key-value pair at the short term memory after receiving a reply to a follow-up request from the digital assistant and another output from the neural network; and

provide a response to the reply by the digital assistant.

9. The system of claim 8 , wherein a natural language parser of the digital assistant tags the words of the user input.

10. The system of claim 8 , wherein the missing parameter is retrieved from a memory device different from the short term memory.

11. The system of claim 8 , wherein the device provides entertainment on the transportation vehicle.

12. The system of claim 8 , wherein the device is a seat function controller for controlling functions associated with a seat on the transportation vehicle.

13. The system of claim 8 , wherein the short term memory is configured as a least recently used memory.

14. The system of claim 8 , wherein the transportation vehicle is one of an aircraft, a train, a bus, a ship and a recreation vehicle.

15. A non-transitory machine-readable storage medium having stored thereon instructions for performing a method, comprising machine executable code which when executed by at least one machine, causes the machine to:

receive a user input for a valid communication session by a processor executable, digital assistant on a device at a transportation vehicle;

tag by the digital assistant, the user input words with a grammatical connotation;

generate an action context that maps to an action application programming interface (API), a filter context and a response context by a neural network, based on the tagged user input, the neural network trained in a plurality of categories including an entertainment data category for entertainment related actions on the transportation vehicle, a shopping category for shopping related actions on the transportation vehicle, a crew instructions category, and a security category associated with transportation vehicle security;

wherein the action context identifies a type of action associated with one of the plurality of categories;

wherein the filter context defines one or more filter parameters that define a query parameter for the action API;

wherein the response context provides information to extract from the action context to execute an action;

determine by the digital assistant that the action context is a most current action context based on a user session;

retrieve by the digital assistant a missing parameter for the action API;

store by the digital assistant, a key-value pair for a parameter of the filter context at a short term memory, based on an output from the neural network and the retrieved missing parameter; wherein the short term memory stores key-value pairs for each user session with the digital assistant;

update by the digital assistant, the key-value pair at the short term memory after receiving a reply to a follow-up request from the digital assistant and another output from the neural network; and

providing a response to the reply by the digital assistant.

16. The non-transitory machine-readable storage medium of claim 15 , wherein a natural language parser of the digital assistant tags the words of the user input.

17. The non-transitory machine-readable storage medium of claim 15 , wherein the missing parameter is retrieved from a memory device different from the short term memory.

18. The non-transitory machine-readable storage medium of claim 15 , wherein the device provides entertainment on the transportation vehicle.

19. The non-transitory machine-readable storage medium of claim 15 , wherein the device is a seat function controller for controlling functions associated with a seat on the transportation vehicle.

20. The non-transitory machine-readable storage medium of claim 15 , wherein the short term memory is configured as a least recently used memory.