Systems and methods for global adaptation of an implicit gesture control system

Patent metadata

Field	Value
Publication number	US-10466657-B2
Application number	US-201414490591-A
Country	US
Kind code	B2
Filing date	Sep 18, 2014
Priority date	Apr 3, 2014
Publication date	Nov 5, 2019
Grant date	Nov 5, 2019

Abstract

A method and system for globally updating a plurality of learning implicit gesture control systems. Embodiments can comprise receiving, by a global server and from a plurality of learning implicit gesture control systems, a user data. The global server configured to analyze the user data and determine an applicable integration level, and the global server further able to communicate with the plurality of learning implicit gesture control systems. Modifying a global parameter when the global server determines the applicable integration level is a global integration level; and transmitting the modified global parameter to the plurality of learning implicit gesture control systems.

First claim

What is claimed is:

1. A method for globally updating a plurality of learning implicit gesture control systems, the method comprising the steps of:
receiving, by a global server and from a plurality of learning implicit gesture control systems, a user data,
the global server configured to analyze the user data and determine an applicable integration level, wherein the global server determines the applicable integration level to be a local integration level when the user data is related to a vehicular application,
the global server further able to communicate with the plurality of learning implicit gesture control systems;
modifying a global parameter for globally updating the plurality of learning implicit gesture control systems, when the global server determines the applicable integration level is a global integration level; or
modifying the global parameter when the global server determines the applicable integration level is associated with individual users, wherein the modifying the global parameter is based on a type of environment in which the user data is received, wherein the type of environment includes one or more of a vehicular local environment or a centralized environment; and

transmitting the modified global parameter to the plurality of learning implicit gesture control systems.

2. The method of claim 1 , wherein the user data is an antecedent knowledge.

3. The method of claim 1 , wherein the user data is a precedential knowledge.

4. The method of claim 1 , further comprising the steps of:
modifying a local parameter when the global server determines the applicable integration level is the local integration level; and
transmitting the modified local parameter to the plurality of learning implicit gesture control systems associated with the local parameter.

5. The method of claim 4 , wherein the local parameter is associated with a plurality of learning implicit gesture control systems for vehicles.

6. The method of claim 1 , wherein the global server is configured to analyze the user data using a Markovian process.

7. The method of claim 1 , wherein the plurality of learning implicit gesture control systems include a plurality of sensing devices.

8. The method of claim 7 , wherein the plurality of sensing devices includes a visual sensor.

9. A method for updating a plurality of learning implicit gesture control systems, the method comprising the steps of:
receiving a user data, the user data received by a global server from the one of the plurality of learning implicit gesture control systems when the one of the plurality of learning implicit gesture control systems has a permission level to transmit the user data to the global server,
the global server configured to analyze the user data;
determining an applicable integration level, wherein the applicable integration level is determined to be a local integration level when the user data is related to a vehicular application;
modifying a global parameter for globally updating the plurality of learning implicit gesture control systems, when the global server determines the applicable integration level is a global integration level; or
modifying the global parameter when the global server determines the applicable integration level is associated with individual users, wherein the modifying the global parameter is based on a type of environment in which the user data is received, wherein the type of environment includes one or more of a vehicular local environment or a centralized environment; and

transmitting the modified parameter to the plurality of learning implicit gesture control systems based on the determined applicable integration level.

10. The method of claim 9 , wherein the applicable integration level is the global integration level.

11. The method of claim 9 , wherein the applicable integration level is the local integration level.

12. The method of claim 9 , wherein the applicable integration level is an individual integration level.

13. The method of claim 9 , wherein the permission level can be set by a user of the one of the plurality of learning implicit gesture control systems.

14. A global implicit gesture learning system, the system comprising:
a global server, the global server being configured to communicate with a plurality of local implicit gesture learning systems, the plurality of local implicit gesture learning systems being in communication with a plurality of sensors; and
the global server including a processor, the processor being configured to perform the steps of:
receiving a user data;
analyzing the user data to determine an applicable integration level, wherein the global server determines the applicable integration level to be a local integration level when the user data is related to a vehicular application;
modifying a parameter when the global server determines the applicable integration level;
modifying a global parameter for globally updating the plurality of local implicit gesture learning systems, when the global server determines the applicable integration level is a global integration level; or

modifying the global parameter when the global server determines the applicable integration level is associated with individual users, wherein the modifying the global parameter is based on whether a type of environment in which the user data is received is a vehicular local environment or a centralized environment; and
transmitting the modified parameter to the plurality of local learning implicit gesture control systems based on the determined applicable integration level.

15. The system of claim 14 , wherein the plurality of local implicit gesture learning systems communicate with the global server using a cellular connection.

16. The system of claim 14 , wherein the plurality of local implicit gesture learning systems communicate with the global server using a wireless internet connection.

17. The system of claim 14 , wherein the applicable integration level is one of the global integration level, the local integration level, and an individual integration level.

18. The system of claim 14 , wherein the plurality of sensors include visual sensors.

19. The system of claim 14 , wherein the plurality of sensors include audio sensors.

20. The system of claim 14 , wherein the plurality of local implicit gesture learning systems are located in vehicles.

Assignees

• Honda Motor Co Ltd
• Edge 3 Tech Inc

Inventors

• El Dokor Tarek A
• King Joshua
• Cluster Jordan
• Holmes James E

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G05B13/0265 Primary

  the criterion being a learning criterion · CPC title

• G06F3/017

  Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883) · CPC title

• G06N7/005

  Physics · mapped topic

• G06N20/00

  Machine learning · CPC title