Using a system for prediction of musical preferences for the distribution of musical content over cellular networks

• US 20030055516A1
• Filed: 09/27/2002
• Published: 03/20/2003
• Est. Priority Date: 06/29/2000
• Status: Active Grant

• Current Assignee: Pandora Media LLC (Liberty Media Corporation)
• Original Assignee: MusicGenome.Com Incorporated
• Inventors: Gang, Dan, Lehmann, Daniel
• Granted Patent: US 7,102,067 B2
• Time in Patent Office: Days
• US Class Current: 700/94
• CPC Class Codes: G06F 16/40 of multimedia data, e.g. slideshows comprising image and additional audio data retrieval of still image data G06F16/50; retrieval of audio data G06F16/60; retrieval of video data G06F16/70
• CPC Class Codes: G06F 16/637 Administration of user profiles, e.g. generation, initialization, adaptation or distribution
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• 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
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G10H 1/0008 Associated control or indicating means
• 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 2240/061 MP3, i.e. MPEG-1 or MPEG-2 Audio Layer III, lossy audio compression
• CPC Class Codes: G10H 2240/081 Genre classification, i.e. descriptive metadata for classification or selection of musical pieces according to style analysis genre G10H2210/036
• CPC Class Codes: G10H 2240/305 Internet or TCP/IP protocol use for any electrophonic musical instrument data or musical parameter transmission purposes
• CPC Class Codes: G10H 2250/151 Fuzzy logic
• CPC Class Codes: G10H 2250/311 Neural networks for electrophonic musical instruments or musical processing, e.g. for musical recognition or control, automatic composition or improvisation musical analysis G10H2210/031; neural networks G06N3/02

Abstract

A system and a methond for predicting the musical taste and/or preferences of the user and its integration into services provided by a wireless network provider Although the present application is directed toward implementations with wireless providers, the present invention can also be implemented on a regular, i.e., wireless, network. The core of the present invention is a system capable of predicting whether a given user, i.e., customer, likes or does not like a specific song from a pre-analyzed catalog Once such a prediction has been performed, those items that are predicted to be liked best by the user may be forwarded to the mobile device of the user on the cellular (or other wireless) network. The system maintains a database containing properiety information about the songs in the catalog and, most important, a description (profile) of the musical taste of each of its customers, identified by their cellular telephone number

Claims

1. A method for predicting a preference of a user for a musical selection, the method comprising:

analyzing a catalog of musical selections according to a plurality of characteristics;

rating at least one musical selection by the user;

matching said rating with said plurality of characteristics to predict the preference of the user for at least one of the musical selections of the catalog; and

recommending at least one predicted musical selection to the user;

wherein at least one of said rating and said recommending is performed through a mobile device connected to a wireless network.

2. The method of claim 1, wherein a plurality of matching processes are used to match said rating with said plurality of characteristics.

3. The method of claims 1 or 2, wherein a matching process is a neural network, such that said plurality of characteristics of said musical selection forms a binary vector and said neural network learns to match said binary vector to characteristics of each rated musical selection by the user.

4. The method of claims 1 or 2, wherein a matching process is a map method for creating a matrix of pseudo-distances between each pair of musical selections, for comparing each rated musical selection to at least one other musical selection in said catalog.

5. The method of claim 4, wherein said map method is a selection map method, for comparing all characteristics of each pair of musical selections.

6. The method of claim 4, wherein said map method is a feature map method, for comparing a selected group of features of each pair of musical selections.

7. The method of claim 4, wherein said map method is a fingerprint map method, for comparing a signature of each pair of musical selections.

8. The method of claims 1 or 2, wherein a matching process is a rating method for rating each musical selection of said catalog and directly comparing each rating of the user to said rating for said musical selection of said catalog.

9. The method of any of claims 1-8, wherein information about each musical selection in said catalog is stored in a feature base.

10. The method of any of claims 1-9, wherein each musical selection is also characterized by genre.

11. The method of any of claims 1-10, wherein said musical selection is analyzed along musical features by a trained musician.

12. The method of any of claims 1-10, wherein said musical selection is rated by a plurality of users.

13. The method of any of claims 1-10, wherein said musical selection is automatically analyzed according to a fingerprint of the audio signal.

14. The method of any of claims 1-13, further comprising:

purchasing a musical selection by the user.