METHOD AND SYSTEM TO ENABLE USER RELATED CONTENT PREFERENCES INTELLIGENTLY ON A HEADPHONE

• US 20160070702A1
• Filed: 09/08/2015
• Published: 03/10/2016
• Est. Priority Date: 09/09/2014
• Status: Abandoned Application

• Current Assignee: AIVVY Inc.
• Original Assignee: AIVVY Inc.
• Inventors: MAO, Xiaodong, MAO, Xianghui
• Application Number:
  US14/848,327
• Publication Number:
  US 20160070702A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 1/163 Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 16/48 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/06 specially adapted for file transfer, e.g. file transfer protocol [FTP]
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A Method and System to enable user related content preferences intelligently on a headphone is disclosed. The embodiments relate generally to the field of user related content preferences. The embodiments are directed at controlling the content preference data so that a music cortex functions as a data agency to provide the music devices, music applications and streaming services smarter choices and intelligent recommendations.

Claims

1. A computer-implemented system for determining user preference for units of multimedia information, from within a library of units of multimedia information;

the system comprising;

a user interface device for allowing a user to access the units of multimedia information, the device having a user reaction input for receiving indicia of favorable user reaction to the units of multimedia information;

a taxonomy module for classifying the units of multimedia information according to a predetermined set of classification parameters;

a user reaction correlation module for determining user preference of units of multimedia information based on the taxonomy module'"'"'s classification of the units of multimedia information for which the indicia of favorable user reaction were received by the user reaction input;

wherein the user interface device also has an information selector, for selecting units of multimedia information based on correlation between the classification parameters of the units of multimedia information, and the classification parameters for which indicia of favorable user reactions have been received.

2. A computer-implemented system as recited in claim 1, further comprising an anti-theft feature module, said module configured to disable and locate the device, wherein a mobile application installed in the device triggers a change in the state of the device to at least one of lost or stolen.

3. A computer-implemented system as recited in claim 1, further comprising a communication network interface for communicating with a remote repository of units of multimedia information;

whereby the system establishes communication with the remote repository for downloading selected ones of the units of multimedia information.

4. A computer-implemented system as recited in claim 3, wherein the user reaction correlation module employs the received indicia of favorable user reaction to infer, based on the taxonomy module'"'"'s classification of the units of multimedia information, classifications of the units of multimedia information which are preferred by the user, whereby the downloading selected ones of the units of multimedia information is performed for ones of the units of multimedia information that are selected from the preferred classifications thereof.

5. A computer-implemented system as recited in claim 1, wherein the classification parameters employed by the taxonomy module to classify the units of multimedia information are based on feature vectors.

6. A computer-implemented system as recited in claim 5, wherein the feature vectors include elements which are one of (i) numeric values, (ii) mean/standard deviation measures of statistic distance between social clusters, and (iii) non-numeric values selected from a set of possibilities.

7. A computer-implemented system as recited in claim 6, wherein the feature vectors include one of (i) statistical measures of short time, frame based features across long term windows, (ii) measures describing the rhythmic properties of the excerpt, and (iii) social feature data from other users which may be compared and correlated with the user'"'"'s preferences.

8. A computer-implemented system as recited in claim 1, wherein:

the units of multimedia information include (i) audio information including one of music, speech, and sound effects; and

(ii) visual information including one of video, still images, graphics, and displayed text or other symbols; and

the classification parameters for the audio information include one of (i) categories of music genre, (ii) musicians including one of instrumental or vocal artists;

(iii) music theory concepts, and (iv) social similarity clustering from analysis of preference data from other users.

9. A computer-implemented system as recited in claim 3, wherein the system establishes communication with the remote repository through the communication network interface for downloading selected ones of the units of multimedia information from the remote repository of units of multimedia information, based on correlation of the units of multimedia information to be downloaded with the user preference.

10. A computer-implemented system as recited in claim 3, wherein the system establishes communication with the remote repository through the communication network interface at times when (i) communication over the communication network is available, and (ii) the system is not otherwise occupied providing downloaded units of multimedia information to the user.

11. A computer-implemented method for determining user preference for units of multimedia information, from within a library of units of multimedia information;

the method comprising;

accessing the units of multimedia information for playback to the user by an audio output;

receiving indicia of favorable user reaction to the units of multimedia information through a user input interface;

classifying the units of multimedia information according to a predetermined set of classification parameters;

determining user preference of units of multimedia information based on the classification of the units of multimedia information for which the indicia of favorable user reaction were received; and

selecting units of multimedia information based on correlation between the classification parameters of the units of multimedia information, and the classification parameters for which indicia of favorable user reactions have been received.

12. A computer-implemented method as recited in claim 11, further comprising communicating with a remote repository of units of multimedia information;

and establishing communication with the remote repository for downloading selected ones of the units of multimedia information, by employs the received indicia of favorable user reaction to infer, based on the classification of the units of multimedia information, classifications of the units of multimedia information which are preferred by the user, whereby the downloading selected ones of the units of multimedia information is performed for ones of the units of multimedia information that are selected from the preferred classifications thereof.

13. A computer-implemented method as recited in claim 11, wherein the classification parameters employed to classify the units of multimedia information are based on feature vectors including elements which are one of (i) numeric values, (ii) mean/standard deviation measures of statistic distance between social clusters, and (iii) non-numeric values selected from a set of possibilities.

14. A computer-implemented method as recited in claim 13, wherein the feature vectors include one of (i) statistical measures of short time, frame based features across long term windows, (ii) measures describing the rhythmic properties of the excerpt, and (iii) social feature data from other users which may be compared and correlated with the user'"'"'s preferences.

15. A computer-implemented method as recited in claim 11, wherein:

the units of multimedia information include (i) audio information including one of music, speech, and sound effects; and

(ii) visual information including one of video, still images, graphics, and displayed text or other symbols; and

the classification parameters for the audio information include one of (i) categories of music genre, (ii) musicians including one of instrumental or vocal artists;

(iii) music theory concepts, and (iv) social similarity clustering from analysis of preference data from other users.

16. A computer program product for determining user preference for units of multimedia information, from within a library of units of multimedia information;

the computer program product comprising;

a non-transitory computer-readable medium; and

computer software program code, provided on the non-transitory computer-readable medium, for directing a computer-implemented user playback, input and network communication apparatus to perform the actions of;

accessing the units of multimedia information for playback to the user by an audio output;

receiving indicia of favorable user reaction to the units of multimedia information through a user input interface;

classifying the units of multimedia information according to a predetermined set of classification parameters;

determining user preference of units of multimedia information based on the classification of the units of multimedia information for which the indicia of favorable user reaction were received; and

selecting units of multimedia information based on correlation between the classification parameters of the units of multimedia information, and the classification parameters for which indicia of favorable user reactions have been received.

17. A computer program product as recited in claim 16, further comprising communicating with a remote repository of units of multimedia information;

and establishing communication with the remote repository for downloading selected ones of the units of multimedia information, by employs the received indicia of favorable user reaction to infer, based on the classification of the units of multimedia information, classifications of the units of multimedia information which are preferred by the user, whereby the downloading selected ones of the units of multimedia information is performed for ones of the units of multimedia information that are selected from the preferred classifications thereof.

18. A computer program product as recited in claim 16, wherein the classification parameters employed to classify the units of multimedia information are based on feature vectors including elements which are one of (i) numeric values, (ii) mean/standard deviation measures of statistic distance between social clusters, and (iii) non-numeric values selected from a set of possibilities.

19. A computer program product as recited in claim 18, wherein the feature vectors include one of (i) statistical measures of short time, frame based features across long term windows, (ii) measures describing the rhythmic properties of the excerpt, and (iii) social feature data from other users which may be compared and correlated with the user'"'"'s preferences.

20. A computer program product as recited in claim 16, wherein:

the units of multimedia information include (i) audio information including one of music, speech, and sound effects; and

(ii) visual information including one of video, still images, graphics, and displayed text or other symbols; and

the classification parameters for the audio information include one of (i) categories of music genre, (ii) musicians including one of instrumental or vocal artists;

(iii) music theory concepts, and (iv) social similarity clustering from analysis of preference data from other users.