INTELLIGENT MUSIC SELECTION IN VEHICLES

• US 20110040707A1
• Filed: 08/12/2009
• Published: 02/17/2011
• Est. Priority Date: 08/12/2009
• Status: Abandoned Application

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Gusikhin, Oleg Yurievitch, Theisen, Kacie Alane, Filev, Dimitar Petrov, MacNeille, Perry Robinson
• Application Number:
  US12/539,743
• Publication Number:
  US 20110040707A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G11B 27/105 of operating discs
• CPC Class Codes: G11B 27/11 by using information not detectable on the record carrier

Abstract

A method of intelligent music selection in a vehicle includes learning user preferences for music selection in the vehicle corresponding to a plurality of driving conditions of the vehicle. Input is received that is indicative of a current driving condition of the vehicle. And, music is selected and played based on the learned user preferences for music selection in the vehicle corresponding to the current driving condition.

Claims

1. A method of intelligent music selection in a vehicle, the method comprising:

learning user preferences for music selection in the vehicle corresponding to a plurality of driving conditions of the vehicle;

receiving input indicative of a current driving condition of the vehicle;

selecting music based on the learned user preferences for music selection in the vehicle corresponding to the current driving condition; and

playing the selected music.

2. The method of claim 1 wherein the vehicle includes a natural language interface, and wherein learning user preferences further comprises:

receiving input indicative of user preferences in the form of natural language received through the natural language interface.

3. The method of claim 2 wherein the vehicle includes an emotion recognition system, and wherein learning user preferences further comprises:

processing received natural language with the emotion recognition system to determine user preferences.

4. The method of claim 2 wherein the vehicle includes an emotive advisory system which includes the natural language interface and which interacts with the user by utilizing audible natural language and a visually displayed avatar, and wherein learning user preferences further comprises:

providing visual and audible output to the user by outputting data representing the avatar for visual display and data representing a statement for the avatar for audio play.

5. The method of claim 1 wherein selecting music further comprises:

selecting a music station based on the learned user preferences for music selection in the vehicle corresponding to the current driving condition; and

utilizing a recommender system to select music based on the selected music station.

6. The method of claim 1 wherein selecting music further comprises:

selecting music based on the learned user preferences for music selection in the vehicle corresponding to the current driving condition, and further based on an active collaborative filtering system that further refines the music selection based on affiliation group.

7. The method of claim 1 wherein selecting music further comprises:

selecting music based on the learned user preferences for music selection in the vehicle corresponding to the current driving condition, and further based on a context awareness system that further refines the music selection based on context.

8. A method of intelligent music selection in a vehicle, the method comprising:

receiving input indicative of a current driving condition of the vehicle;

establishing a discrete dynamic system having a state vector and receiving an input vector, the state vector representing a current music selection, the input vector representing the current driving condition of the vehicle, the discrete dynamic system operating to predict a next music selection according to a probabilistic state transition model representing user preferences for music selection in the vehicle corresponding to a plurality of driving conditions of the vehicle;

predicting the next music selection with the discrete dynamic system;

selecting music based on the predicted next music selection; and

playing the selected music.

9. The method of claim 8 further comprising:

learning user preferences for music selection in the vehicle corresponding to the plurality of driving conditions of the vehicle; and

establishing the probabilistic state transition model based on the learned user preferences.

10. The method of claim 9 wherein the vehicle includes a natural language interface, and wherein learning user preferences further comprises:

receiving input indicative of user preferences in the form of natural language received through the natural language interface.

11. The method of claim 10 wherein the vehicle includes an emotion recognition system, and wherein learning user preferences further comprises:

processing received natural language with the emotion recognition system to determine user preferences.

12. The method of claim 10 wherein the vehicle includes an emotive advisory system which includes the natural language interface and which interacts with the user by utilizing audible natural language and a visually displayed avatar, and wherein learning user preferences further comprises:

providing visual and audible output to the user by outputting data representing the avatar for visual display and data representing a statement for the avatar for audio play.

13. The method of claim 8 wherein selecting music further comprises:

selecting a music station based on the predicted next music selection; and

utilizing a recommender system to select music based on the selected music station.

14. The method of claim 8 wherein selecting music further comprises:

selecting music based on the predicted next music selection, and further based on an active collaborative filtering system that further refines the music selection based on affiliation group.

15. The method of claim 8 wherein selecting music further comprises:

selecting music based on the predicted next music selection, and further based on a context awareness system that further refines the music selection based on context.

16. The method of claim 8 wherein establishing the discrete dynamic system further comprises:

configuring the discrete dynamic system based on a maximum specified number of music selections, and further based on monitored driving conditions.

17. A system for intelligent music selection in a vehicle, the system comprising:

a music artificial intelligence module configured to learn specified user preferences for music selection in the vehicle corresponding to a plurality of driving conditions of the vehicle, to receive input indicative of a current driving condition of the vehicle, and to select music based on the learned user preferences for music selection in the vehicle corresponding to the current driving condition; and

a context aware music player configured to play the selected music.

18. The system of claim 17 wherein the context aware music player is further configured to play music in accordance with user commands, and wherein the music artificial intelligence module is operable in a learning mode in which the music artificial intelligence module learns user preferences for music selection in the vehicle corresponding to the plurality of driving conditions in accordance with the music played in response to the user commands.

19. The system of claim 18 wherein the music artificial intelligence module is operable in a prediction mode in which the music artificial intelligence module selects music based on the learned user preferences.

20. The system of claim 17 further comprising:

a natural language interface for receiving input indicative of user preferences in the form of natural language.