Music intelligence universe server

• US 20080021851A1
• Filed: 07/06/2007
• Published: 01/24/2008
• Est. Priority Date: 10/03/2002
• Status: Active Grant

• Current Assignee: Music Intelligence Solutions, Inc. (Akazoo S.A.)
• Original Assignee: Music Intelligence Solutions
• Inventors: Alcalde, Vicenc, Llopis, Antonio, Bonet, Antonio, Marcos, Jesus, Ricard, Julien
• Granted Patent: US 8,053,659 B2
• Time in Patent Office: Days
• US Class Current: 706/21
• CPC Class Codes: G06F 16/637 Administration of user profiles, e.g. generation, initialization, adaptation or distribution
• CPC Class Codes: G06F 16/64 Browsing; Visualisation therefor generation of a list or set of audio data G06F16/638
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• CPC Class Codes: G06Q 30/0603 Catalogue ordering
• CPC Class Codes: G10H 1/0058 Transmission between separate instruments or between individual components of a musical system G10H1/0083 takes precedence
• CPC Class Codes: G10H 2210/036 of musical genre, i.e. analysing the style of musical pieces, usually for selection, filtering or classification
• CPC Class Codes: G10H 2240/105 User profile, i.e. data about the user, e.g. for user settings or user preferences
• CPC Class Codes: G10H 2250/235 Fourier transform; Discrete Fourier Transform [DFT]; Fast Fourier Transform [FFT]

Abstract

An artificial intelligence song/music recommendation system and method is provided that allows music shoppers to discover new music. The system and method accomplish these tasks by analyzing a database of music in order to identify key similarities between different pieces of music, and then recommends pieces of music to a user depending upon their music preferences.

Claims

1. A method of determining a user'"'"'s preference of music, said method comprising the steps of:

a) providing a digital database comprising a plurality of digital song files;

b) mathematically analyzing each said digital song file to determine a numerical value for a plurality of selected quantifiable characteristics;

c) compiling a song vector comprising a list of said numerical values for each of said plurality of selected characteristic for each said song file;

d) selecting and storing a representative portion of each said song file wherein said representative portion substantially mathematically matches the song vector of said song file;

f) choosing a pair of two dissimilar representative portions and enabling said user to evaluate both representative portions;

g) permitting said user to indicate which of said two dissimilar representative portions said user prefers;

h) based on the indication from said user of which of said two dissimilar representative portions said user prefers, finding another pair of dissimilar representative portions to maximize the probability to learn something about the user'"'"'s preference; and

i) repeating steps f) through h), as necessary, to establish a taste vector for said user comprising song characteristics that said user prefers.

2. The method according to claim 1, wherein said method is performed via a computer website.

3. The method according to claim 1, wherein none of said pairs of two dissimilar representative portions are repeated between consecutive steps.

4. The method according to claim 1, wherein each of said pairs of two dissimilar representative portions are selected to maximize Euclidian distance between each song represented by said representative portion.

5. The method according to claim 1, wherein each of said pairs of two dissimilar representative portions are selected to maximize orthogonality with respect to previous pairs of representative portions.

6. A method of recommending music comprising:

a) establishing a digital database comprising a plurality of digital song files;

b) mathematically analyzing each said digital song file to determine a numerical value for a plurality of selected quantifiable characteristics;

c) compiling a song vector comprising a list of said numerical values for each of said plurality of selected characteristic for each said song file;

d) establishing an affinity value for each song in the database as compared to every other song in the database; and

j) displaying a representation of each song vector on a two-dimensional array wherein the distance between each song vector representation corresponds to the affinity value.

7. The method according to claim 6, wherein said representation of the song vector uses color codes or other symbols to identify different genres of songs.

8. The method according to claim 6, wherein at least some of the characteristics are based on global characteristics.

9. The method according to claim 8, wherein said global characteristics are selected from the group consisting of:

happy;

sad;

calm; and

energetic.

10. The method according to claim 6, wherein said two-dimensional array comprises a computerized graphic user interface.

11. The method according to claim 10, wherein said computerized graphic user interface is displayed in a location selected from the group consisting of:

in-store retail terminals;

retail internet websites;

personal computers

personal music player devices; and

mobile phone handsets.

12. The method according to claim 6, wherein said digital database comprising a plurality of digital song files is selected from the group consisting of:

private music collection;

radio station music library;

recording label music library; and

music store song library.