Method and system for psychological evaluation based on music preferences

• US 10,346,754 B2
• Filed: 09/15/2015
• Issued: 07/09/2019
• Est. Priority Date: 09/18/2014
• Status: Active Grant

• Current Assignee: DICE Trust
• Original Assignee: Sounds Like Me Limited
• Inventors: Ritchie, David I., North, Adrian
• Primary Examiner(s): Vincent, David R
• Application Number:
  US14/854,091
• Publication Number:
  US 20160086089A1
• Time in Patent Office: 1,393 Days
• Field of Search: 706 15, 706 45
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/00 Computing arrangements based on specific mathematical models
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A system and method for determining users personality types, profiles, and infer the likely intent based on the users'"'"' musical taste and listening habits. The process involves identifying the listener'"'"'s musical preferences and linking this to a predetermined scale within the psychological Big 5 that in turn links to a dictionary of descriptors that defines the personality type. This can be further fine tuned by knowing the persons age, gender, and many other lifestyle variables, as well as how a user may use the music.

Claims

1. A method comprising:

providing a database comprising digital song files;

mathematically analyzing said digital song files, using a computerized device, said mathematically analyzing determining a numerical value for each of a plurality of selected quantifiable characteristics, each of said quantifiable characteristics comprising a physical parameter based on human perception;

creating a descriptive profile for the quantifiable characteristics of each song in the database of digital song files, using said computerized device;

linking each song in the database to a predetermined psychological scale based on said descriptive profile for the quantifiable characteristics, using said computerized device;

providing pairs of audible samples of songs to a user, using said computerized device, each sample of said pairs audible samples being selected from said database of digital song files;

permitting said user to listen to each sample of said pair of audible samples of songs and subjectively indicate, using said computerized device, likes and dislikes for each song of said samples of songs;

said computerized device determining a music taste profile for said user based on said likes and dislikes indicated by said user, wherein said music taste profile identifies relative preferences for genre, theme, and style of music for said user;

correlating said relative preferences to known dimensions of said predetermined psychological scale for said user by associating said relative preferences with a plurality of lifestyle factors for said user, using said computerized device; and

identifying distinct human psychological traits, based on said music taste profile to classify said user into a known personality type.

2. The method according to claim 1, said mathematically analyzing said digital song files further comprising:

compiling a song vector comprising a list of numerical values for each of said plurality of selected quantifiable characteristics for each said digital song file; and

extracting a plurality of music descriptors for each song vector.

3. The method according to claim 2, said mathematically analyzing said digital song files further comprising:

using fast Fourier Transform techniques to establish a plurality of sonic coefficients, said sonic coefficients being representative of said characteristics of said song.

4. The method according to claim 1, further comprising:

choosing two dissimilar samples of songs from said database;

enabling said user to listen to both of said two dissimilar samples of songs;

receiving an indication of which of said two dissimilar samples of songs said user prefers; and

choosing additional dissimilar samples of songs, as necessary, to establish a taste vector for said user comprising song characteristics that said user prefers.

5. The method according to claim 1, said predetermined psychological scale comprising:

openness;

conscientiousness;

extraversion;

agreeableness; and

neuroticism.

6. The method according to claim 1, said identifying human psychological traits further comprising:

identifying personality triggers from said music taste profile;

linking said personality triggers to a dictionary of descriptors defining a personality type according to musical tastes; and

producing a personal profile based on a determined personality type.

7. The method according to claim 1, further comprising:

providing recommendations based on said music taste profile and said personality type, said recommendations being related to one of music, films, television, and books.

8. The method according to claim 1, said method being performed using a real-time process based on dynamic interaction with said user.

9. A computer implemented method of determining psychological evaluation based on music preferences, said method comprising:

providing a database comprising digital song files;

mathematically analyzing said digital song files, using a computerized device, said mathematically analyzing determining a numerical value for each of a plurality of selected quantifiable characteristics, said quantifiable characteristics comprising physical parameters based on human perception;

creating a descriptive profile for the quantifiable characteristics of each song in the database of digital song files, using said computerized device;

linking each song in the database to a predetermined psychological scale based on said descriptive profile for the quantifiable characteristics, using said computerized device;

choosing samples of two songs from said database, using said computerized device;

enabling a user to listen to both of said samples of two songs, using said computerized device;

receiving an indication of which of said samples of two songs said user prefers, using said computerized device;

choosing additional samples of songs, as necessary, to establish a taste vector for said user comprising song characteristics that said user prefers, using said computerized device;

determining a music taste profile for said user based on likes and dislikes indicated by said user, wherein said music taste profile identifies relative preferences for genre, theme, and style of music for said user, using said computerized device;

correlating said relative preferences to known dimensions of said predetermined psychological scale for said user by associating said relative preferences with a plurality of lifestyle factors for said user, using said computerized device;

identifying distinct human psychological traits, based on said music taste profile, using said computerized device; and

classifying said user into a known personality type, using said computerized device.

10. The computer implemented method according to claim 9, further comprising:

providing recommendations to said user based on said music taste profile and said personality type, using said computerized device, said recommendations being related to one of music, films, television, and books.

11. The computer implemented method according to claim 9, said mathematically analyzing said digital song files further comprising:

compiling a song vector comprising a list of numerical values for each of said plurality of selected quantifiable characteristics for each said digital song file; and

extracting a plurality of music descriptors for each song vector.

12. The computer implemented method according to claim 11, said mathematically analyzing said digital song files further comprising:

using fast Fourier Transform techniques to establish a plurality of sonic coefficients, said sonic coefficients being representative of said characteristics of said song.

13. The computer implemented method according to claim 9, said identifying human psychological traits further comprising:

identifying personality triggers from said music taste profile;

linking said personality triggers to a dictionary of descriptors defining a personality type according to musical tastes; and

producing a personal profile based on a determined personality type,

said human psychological traits being based on said music taste profile.

14. The computer implemented method according to claim 9, said method being performed using a real-time process based on dynamic interaction with said user.

15. A system comprising:

a digital database comprising digital song files;

a processor operatively connected to said digital database; and

a user interface operatively connected to said processor;

said processor mathematically analyzing said digital song files and determining characteristic vectors for songs in said digital song files, said characteristic vectors comprising a numerical value for each of a plurality of selected quantifiable characteristics, each of said quantifiable characteristics comprising a physical parameter based on human perception,

said processor creating a descriptive profile for the quantifiable characteristics of each song in the digital database of digital song files,

said processor linking each song in the digital database to a predetermined psychological scale based on said descriptive profile for the quantifiable characteristics,

said processor selecting samples of two songs from said database and enabling a user to listen to both of said samples of two songs using said user interface,

said processor receiving an indication of which of said samples of two songs said user prefers, using said user interface,

said processor selecting additional samples of songs, as necessary, to establish a taste vector for said user comprising song characteristics that said user prefers,

said processor determining a music taste profile for said user based on likes and dislikes for said samples of songs, and said user'"'"'s age, sex, and location, wherein said music taste profile identifies relative preferences for genre, theme, and style of music for said user,

said processor correlating said relative preferences to known dimensions of said predetermined psychological scale for said user by associating said relative preferences with a plurality of lifestyle factors for said user, and

said processor identifying distinct human psychological traits, based on said music taste profile to classify said user into a known personality type.

16. The system according to claim 15, further comprising:

said processor providing recommendations to said user on said user interface, said recommendations being based on said music taste profile and said personality type.

17. The system according to claim 16, said recommendations being related to one of music, films, television, books, leisure time interests, means of advertising, and otherwise communicating with consumers.

18. The system according to claim 15, said processor mathematically analyzing said digital song files further comprising:

said processor compiling a song vector comprising a list of numerical values for each of said plurality of selected quantifiable characteristics for each said digital song file; and

said processor extracting a plurality of music descriptors for each song vector.

19. The system according to claim 18, said processor mathematically analyzing said digital song files further comprising:

said processor using fast Fourier Transform techniques to establish a plurality of sonic coefficients, said sonic coefficients being representative of said characteristics of said song.

20. The system according to claim 15, said processor identifying human psychological traits further comprising:

said processor identifying personality triggers from said music taste profile;

said processor linking said personality triggers to a dictionary of descriptors defining a personality type according to musical tastes; and

said processor producing a personal profile based on a determined personality type,

said human psychological traits being based on said music taste profile.