US 7,024,424 B1Granted

Auto playlist generator

AssigneeMicrosoft Corporation

InventorsJohn C. Platt, Christopher Burges, Alice Zheng, Christopher B. Weare, Steven E. Swnson

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Abstract

A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Claims14

Dependent claims

A computer implemented method for generating a list, the computer implemented method comprising the following computer executable acts:

associating descriptive metadata with one or more candidate user items;
producing similarity data that characterizes the similarity between a candidate user item and at least one seed item;
producing a list of one or more user items related to the at least one seed item;
performing inexact matching between identifying metadata associated with a candidate user item to be added to a media library and identifying metadata associated with items in a reference metadata database;
and
comparing descriptive metadata associated with the at least one seed item to descriptive metadata associated with the candidate user item, where comparing the descriptive metadata comprises:
comparing at least one feature vector associated with the at least one seed item to a feature vector associated with the candidate user item;
producing a difference vector related to the at least one feature vector associated with the at least one seed item and the feature vector associated with the candidate user item;
and
producing the similarity data by employing the difference vector to retrieve a similarity value stored in a data store, where the data store was created by a machine learning technique.

7 more elements

A computer readable medium containing computer executable instructions for performing a method for generating a list, the method comprising the following computer related acts:

producing as-added descriptive metadata associated with at least one candidate user item to be added to a user media library;
producing similarity data that characterizes the similarity between the at least one candidate user item and at least one seed item;
producing a list of the at least one candidate user item related to the at least one seed item;
and
performing inexact matching between identifying metadata associated with the at least one candidate user item to be added to a media library and identifying metadata associated with items in a reference metadata database;
and
comparing descriptive metadata associated with the at least one seed item to as-added descriptive metadata associated with the at least one candidate user item, where comparing the descriptive metadata comprises:
comparing at least one feature vector associated with the at least one seed item to a feature vector associated with the at least one candidate user item;
producing a difference vector related to the at least one feature vector associated with the at least one seed item and the feature vector associated with the at least one candidate user item;
and
producing the similarity data by employing the difference vector to retrieve a similarity value stored in a data store, where the data store was created by a machine learning technique.

8 more elements

A computer implemented method for generating a list comprising the following computer executable acts:

associating descriptive metadata with at least one candidate user item;
producing similarity data that characterizes a similarity between the at least one candidate user item and at least one seed item;
and
producing a list of the at least one candidate user item related to the at least one seed item;
and
performing inexact matching between identifying metadata associated with the at least one candidate user item to be added to a media library and identifying metadata associated with items in a reference metadata database;
and
comparing descriptive metadata associated with the at least one seed item to descriptive metadata associated with the at least one candidate user item, where comparing the descriptive metadata comprises:
comparing at least one feature vector associated with the at least one seed item to a feature vector associated with the at least one candidate user item;
producing a difference vector related to the at least one feature vector associated with the at least one seed item and the feature vector associated with the at least one candidate user item;
and
producing the similarity data by employing the difference vector to retrieve a similarity value stored in a data store, where the data store was created by a machine learning technique.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application: 11/077,646Filed 11 Mar 2005 · division
Priority: 30 May 2001
Granted: 4 Apr 2006
Term ends: Not estimatedThe source records don’t support an estimate.

Classification13 codes

G06F 16/683
Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually: using metadata automatically…
G06F 16/634
Query formulation: query by example, e.g. query by humming
G06F 16/639
Presentation of query results: using playlists
G06F 16/68
Of audio data: retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually