Computer-Implemented System And Method For Providing Contextual Media Tagging For Selective Media Exposure

Patent metadata

Field	Value
Publication number	US-2016179864-A1
Application number	US-201414582109-A
Country	US
Kind code	A1
Filing date	Dec 23, 2014
Priority date	Dec 23, 2014
Publication date	Jun 23, 2016
Grant date	—

Abstract

A computer-implemented system and method for providing contextual media tagging for selective media exposure is provided. A media file is maintained in a database. Contextual information is generated for a user. The media file is associated with the user contextual information, and the media file is shared to individuals in a social network of the user based on the user contextual information.

First claim

What is claimed is:

1 . A computer-implemented system for providing contextual media tagging for selective media exposure, comprising:
a database configured to maintain a media file; a contextual information module configured to generate contextual information for a user; a tagging module configured to associate the media file with the user contextual information; and a contextual analysis module configured to share the media file to individuals in a social network of the user based on the user contextual information, wherein a non-transitory computer readable storage medium storing code for executing on a computer system to perform the method steps.

2 . A system according to claim 1 , further comprising:
a contextual data module configured to collect contextual data regarding the user; an insight identification module configured to identify insights of the contextual data of the user; and a semantic graph module configured to generate a semantic graph for the user as the user contextual information comprising a plurality of nodes and edges that create graph structures.

3 . A system according to claim 2 , further comprising:
a transformation rule module configured to define a set of transformation rules for the user semantic graph; a transformation matching module configured to match each transformation rule with each graph structure of the user semantic graph; and a transformation module configured to transform the matched graph structure with a single node in the user semantic graph.

4 . A system according to claim 3 , further comprising:
the semantic graph module configured to generate a semantic graph for each individual in the social network; a graph production rule module configured to define a set of graph production rules for the user semantic graph, each graph production rule applying for a relationship between the user and one individual in the social network; a graph production matching module configured to match each graph production rule to each graph structure of the user semantic graph; and a graph production module configured to copy the matched graph structure of the user semantic graph to the semantic graph for the individual.

5 . A system according to claim 3 , further comprising:
a hierarchy of node categories maintained in the database comprising high level node categories and low level node categories corresponding to each high level node category as sub node categories; the transformation rule module configured to define the transformation rules as replacing the low level node categories with the high level node category corresponding to the low level node categories; a graph transformation module configured to apply the transformation rules to each graph structure of the user semantic graph; and a replacement module configured to replace the graph structure as the low level node categories to the single node which is a high level node category corresponding to the low level node categories.

6 . A system according to claim 2 , further comprising:
a serial module configured to serialize the nodes of the semantic graph; and an embedding module configured to embed the serialized nodes of the semantic graph into a metadata of the media file.

7 . A system according to claim 2 , further comprising:
a fingerprint module configured to compute a fingerprint of the semantic graph; and an embedding module configured to embed the fingerprint of the semantic graph into the media file.

8 . A system according to claim 2 , further comprising:
a link module configured to identify a link of the database to the user contextual information; and an embedding module configured to embed the link to the media file into the media file.

9 . A system according to claim 2 , further comprising:
an association module configured to determine an association of the media file with the contextual information and storing the association in the database; and an embedding module configured to embed the association into the media file.

10 . A system according to claim 2 , further comprising:
an incoming context module configured to recognize incoming new contextual data regarding the user; and an update module configured to update the user contextual information based on the incoming new contextual data.

11 . A computer-implemented method for providing contextual media tagging for selective media exposure, comprising:
maintaining a media file in a database; generating contextual information for a user; associating the media file with the user contextual information; and sharing the media file to individuals in a social network of the user based on the user contextual information, wherein a non-transitory computer readable storage medium storing code for executing on a computer system to perform the method steps.

12 . A method according to claim 11 , further comprising:
collecting contextual data regarding the user; identifying insights of the contextual data of the user; and generating a semantic graph for the user as the user contextual information comprising a plurality of nodes and edges that create graph structures.

13 . A method according to claim 12 , further comprising:
defining a set of transformation rules for the user semantic graph; matching each transformation rule with each graph structure of the user semantic graph; and transforming the matched graph structure with a single node in the user semantic graph.

14 . A method according to claim 13 , further comprising:
generating a semantic graph for each individual in the social network; defining a set of graph production rules for the user semantic graph, each graph production rule applying for a relationship between the user and one individual in the social network; matching each graph production rule to each graph structure of the user semantic graph; and copying the matched graph structure of the user semantic graph to the semantic graph for the individual.

15 . A method according to claim 13 , further comprising:
maintaining a hierarchy of node categories in the database comprising high level node categories and low level node categories corresponding to each high level node category as sub node categories; defining the transformation rules as replacing the low level node categories with the high level node category corresponding to the low level node categories; applying the transformation rules to each graph structure of the user semantic graph; and replacing the graph structure as the low level node categories to the single node which is a high level node category corresponding to the low level node categories.

16 . A method according to claim 12 , further comprising:
serializing the nodes of the semantic graph; and embedding the serialized nodes of the semantic graph into a metadata of the media file.

17 . A method according to claim 12 , further comprising:
computing a fingerprint of the semantic graph; and embedding the fingerprint of the semantic graph into the media file.

18 . A method according to claim 12 , further comprising:
identifying a link of the database to the user contextual information; and embedding the link to the media file into the media file.

19 . A method according to claim 12 , further comprising:
determining an association of the media file with the contextual information and storing the association in the database; and embedding the association into the media file.

20 . A method according to claim 1

Assignees

• Palo Alto Res Ct Inc

Inventors

• Roberts Michael

Classifications

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• G06F17/30528

  Physics · mapped topic

• G06F17/30345 Primary

  Physics · mapped topic

• G06F17/30038

  Physics · mapped topic

• G06F17/30958

  Physics · mapped topic