Digital assistant and associated methods for a transportation vehicle

• US 10,573,299 B2
• Filed: 04/16/2018
• Issued: 02/25/2020
• Est. Priority Date: 08/19/2016
• Status: Active Grant

• Current Assignee: Panasonic Avionics Corporation (Panasonic Holdings Corporation)
• Original Assignee: Panasonic Avionics Corporation (Panasonic Holdings Corporation)
• Inventors: Hilal, Rawad, Khabrani, Gurmukh, Perng, Chin
• Primary Examiner(s): Zhu, Richard Z
• Application Number:
  US15/954,271
• Publication Number:
  US 20180233133A1
• Time in Patent Office: 680 Days
• Field of Search: None
• CPC Class Codes: B60R 11/0235 of flat type, e.g. LCD
• CPC Class Codes: B60R 11/0264 for control means
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/16 using artificial neural networks
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 2015/223 Execution procedure of a spoken command

Abstract

Methods and systems for a transportation vehicle are provided. One method includes receiving a user input for a valid communication session by a processor executable, digital assistant at a device on a transportation vehicle; tagging by the digital assistant, the user input words with a grammatical connotation; generating an action context, a filter context and a response context by a neural network, based on the tagged user input; 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; updating by the digital assistant, the key-value pair at the short term memory after receiving a reply to a follow-up request and another output from the trained neural network; and providing a response to the reply by the digital assistant.

Claims

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.