MUSIC RECOMMENDATION SYSTEM FOR VEHICLE AND METHOD THEREOF

• US 20160125076A1
• Filed: 07/09/2015
• Published: 05/05/2016
• Est. Priority Date: 10/30/2014
• Status: Abandoned Application

• Current Assignee: Hyundai Motor Company (Hyundai Motor Group)
• Original Assignee: Hyundai Motor Company (Hyundai Motor Group)
• Inventors: JUNG, Woochul, CHOI, Young Woo
• Application Number:
  US14/795,705
• Publication Number:
  US 20160125076A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/686 using information manually generated, e.g. tags, keywords, comments, title or artist information, time, location or usage information, user ratings

Abstract

An automotive music recommendation system may include: a data collector that collects a plurality of musical data from social data kept in a social network; a data analyzer that checks keywords and music metadata by analyzing the musical data; a music manager that creates a matching table by matching the music metadata with the keywords; and a music recommender that checks a keyword according to driving state data by a driver from the matching table and creates a recommendation list using at least one piece of music metadata matched with the keyword. The automotive music recommendation system may recommend music to the driver in accordance with a driving condition on the basis of social data.

Claims

1. An automotive music recommendation system comprising:

a data collector that collects a plurality of musical data from social data kept in a social network;

a data analyzer that checks keywords and music metadata by analyzing the musical data;

a music manager that creates a matching table by matching the music metadata with the keywords; and

a music recommender that checks a keyword according to driving state data by a driver from the matching table and creates a recommendation list using at least one piece of music metadata matched with the keyword.

2. The system of claim 1, wherein the music manager determines whether there is music metadata analyzed by the data analyzer in a matching table kept in advance, determines whether a keyword matched with the music meta data in the matching table is the same as a keyword analyzed by the data analyzer, when there is music metadata in the matching table, and gives a weight value to the music metadata when the keywords are the same.

3. The system of claim 1, wherein the data analyzer extracts situational texts or emotional texts by analyzing texts of the musical data and checks keywords on the basis of the situational texts or the emotional texts.

4. The system of claim 1, further comprising a data detector that detects the driving state data, upon a request for recommending music from the driver.

5. The system of claim 1, wherein the driving state data includes at least one of weather data, time data, traffic situation data, road type data, season data, driver'"'"'s emotion data, and vehicle mode data.

6. The system of claim 1, wherein the music metadata includes at least one of music identification data, location data, title, genre, singer'"'"'s name, album data, and lyric data.

7. An automotive music recommendation method comprising:

(a) collecting a plurality of musical data from social data kept in a social network;

(b) checking keywords and music metadata by analyzing the musical data;

(c) creating a matching table by matching the music metadata with the keywords;

(d) checking keywords according to driving state data by a driver from the matching table; and

(e) creating a recommendation list using at least one piece of music metadata matched with the keywords from the matching table.

8. The method of claim 7, wherein the step (c) includes:

determining whether there is the music metadata analyzed in the step (b) in a matching table kept in advance;

when there is music metadata analyzed in the step (b) in the matching table kept in advance, determining whether a keyword matched with the music meta data in the matching table kept in advance is the same as a keyword analyzed in the step (b); and

when the keyword matched with the music meta data in the matching table kept in advance is the same as the keyword analyzed in the step (b), giving a weight value to music metadata in the matching table kept in advance.

9. The method of claim 8, wherein the step (c) further includes:

when the music metadata analyzed in the step (b) is not in the matching table kept in advance, creating a matching table by matching the music metadata with the keywords.

10. The method of claim 8, wherein the step (c) further includes:

when the keyword matched with the music metadata in the matching table kept in advance is not the same as the keyword analyzed in the step (b), adding a keyword obtained by analyzing the musical data to the music metadata.

11. The method of claim 7, wherein the step (b) includes:

extracting at least one of situational texts and emotional texts by analyzing texts of the musical data; and

checking the keywords on the basis of at least one of the situational texts and the emotional texts.

12. The method of claim 7, wherein the driving state data includes at least one of weather data, time data, traffic situation data, road type data, season data, driver'"'"'s emotion data, and vehicle mode data.

13. The method of claim 7, wherein the music metadata includes at least one of music identification data, location data, title, genre, singer'"'"'s name, album data, and lyric data.

14. An automotive music recommendation method comprising:

collecting a plurality of musical data from social data kept in a social network;

checking keywords and music metadata by analyzing the musical data;

creating a matching table by matching the music metadata with the keywords;

receiving a request for recommending music from a driver;

detecting driving state data when receiving the request for recommending music;

checking keywords according to the driving state data from the matching table;

creating a recommendation list using at least one piece of music metadata matched with the keywords from the matching table; and

playing the recommendation list.

15. The method of claim 14, wherein the checking of keywords and music metadata by analyzing the musical data includes:

extracting at least one of situational texts and emotional texts by analyzing texts of the musical data; and

checking the keywords on the basis of at least one of the situational texts and the emotional texts.

16. The method of claim 14, wherein the creating a matching table comprises:

determining whether there is music metadata obtained by analyzing the musical data in a matching table kept in advance;

when there is music metadata obtained by analyzing the musical data is in the matching table kept in advance, determining whether a keyword matched with the music meta data in the matching table is the same as a keyword obtained by analyzing the musical data ; and

when the keyword matched with the music meta data in the matching table kept in advance is the same as the keyword obtained by analyzing the musical data, giving a weight value to music metadata in the matching table kept in advance.

17. The method of claim 16, wherein the creating a recommendation list includes:

extracting at least one piece of music metadata matched with the keywords from the matching table;

checking a weight value of at least one piece of music metadata extracted from the matching table; and

creating a recommendation list including music metadata of a set number of pieces in order of weight values from greatest to smallest.