Method of determining influence of a member within a dataset

• US 8,407,179 B2
• Filed: 01/06/2010
• Issued: 03/26/2013
• Est. Priority Date: 01/06/2009
• Status: Expired due to Fees

• Current Assignee: Trueanthem Corporation
• Original Assignee: Trueanthem Corporation
• Inventors: Hart, Christopher, Wilkerson, Joseph Chandra
• Primary Examiner(s): Mahmoudi, Tony
• Assistant Examiner(s): Le, Michael
• Application Number:
  US12/652,962
• Publication Number:
  US 20100205220A1
• Time in Patent Office: 1,175 Days
• Field of Search: 707/603
• US Class Current: 707/603
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence

Abstract

A method of determining influence of a member within an overall dataset may include functions which capture, aggregate, analyze and visually present the relationships and influence that members of a dataset or collection, such as individual persons, websites, applications and/or machines, exercise over one another based on interaction, behavioral and demographic factors, etc. The calculated results of influence may be employed for the tracking of content and/or marketing, for example.

Claims

1. A method of determining influence of a member within an overall dataset in which a plurality of nodes exist, the member represented by one of the nodes in the dataset, comprising:

providing a root originator node to a location directly or indirectly accessible to a central server, the root originator node embodied by content and one or more tracking tags that identify the root node, the root node having an ability to invoke a movement action resulting in the publishing of a new successor node to a new location directly or indirectly accessible to the central server, the new successor node receiving a new instance of the content and tracking tags referencing a ne‘

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instance of the content, tracking tags that identify the successor node, and an ability to invoke the movement action to generate additional successor nodes,

wherein the tracking tags further include;

primary keys to identify the respective node, the primary keys including an instance identifier associated with the tracking tags that are generated by the central server at the root originator node and passed on to linked successor nodes at node generation, and

alternate keys with additional data associated with the respective node, the alternate keys including a person identifier of the person who caused the respective node to be created or a machine identifier of the originating machine which caused the respective node to be created,

generating the additional successor nodes by invoking the movement action to pass on a new instance of the content and tracking tags identifying the successor nodes, each additional successor node configured to serve as a distinct originator node for subsequently generated nodes, each subsequently generated node with content and tracking tags identifying the subsequently generated node,

capturing, by the central server, raw data from one or more events invoked at each node after a node has been generated,

assembling, by the central server, originator and successor nodes into a tree data structure, wherein assembling nodes further includes;

assembling the tree structure based on one or more common instance identifiers contained in the primary keys of linked nodes which form the tree, and

aggregating the captured raw event data for the events at each node, as well as aggregating by rolling up sibling or successor event data for each linked successor node of the tree,

transforming the tree structure into a new graph data structure from alternate keys present in originator nodes of the tree structure, the alternate keys including at least one of a common person identifier or a common machine identifier therein to provide the new graph data structure with a set of nodes comprising a complete dataset with established connections between all members thereof, each member possessing supporting metadata and aggregated event data associated with the transformation, and

calculating an influence factor from the graph data structure that reflects the member'"'"'s influence within the dataset by dividing the total occurrences of an event at a specific node by the total occurrences of the event in the complete dataset.

2. The method of claim 1, wherein each node in the graph structure represents a member in the dataset, the member selected from a group comprising an individual person, a machine, a website and an application.

3. The method of claim 1, further comprising:

displaying a plurality of nodes in the dataset on a display medium for review of the influence thereof by a viewer.

4. The method of claim 3, wherein the visual size of the node indicates a member'"'"'s influence within the dataset.

5. The method of claim 3, wherein the display medium includes boundaries between nodes to reflect a grouping or other common characteristic responsible for the influence.

6. The method of claim 3, wherein color of the node represents a demographic or other characteristic of the dataset.

7. The method of claim 1, wherein the movement action to generate a successor node includes one of a sharing, forwarding, posting, installing and passing along of a new instance of the content and tracking tags in order to track the successor node.

8. The method of claim 1, wherein the content is stored within a content holder selected from a group comprising a web widget, electronic mail message, mobile application, rich internet application (RIA), SMS message, MMS message, web application, web page, block of web or video content, block of mobile content, web advertisement, mobile advertisement and desktop application.

9. The method of claim 1, wherein capturing raw data further includes storing raw data as an event at a node and reporting to the central server there from, the event representative of direct or indirect human or automated interaction with the node.

10. The method of claim 1, wherein an event is selected from a group comprising a load action, an open action, a user interaction, a machine interaction, a post of the content, a send of the content, a publish of the content, a share of the content, a listen, a stream, a view, a download, an advertisement impression, an advertisement click, a poll question answered, direct resulted e-commerce, and indirect resulted e-commerce.

11. A method of determining influence of a member within an overall dataset in which a plurality of nodes exist, the member represented by one of the nodes in the dataset, comprising:

publishing a root originator node to a location directly or indirectly accessible to a central server, the root node having content and a plurality of tracking tags comprising primary and alternate keys,

creating one or more successor nodes at locations accessible to the central server by the root node performing one of a sharing, forwarding, posting, installing or passing along action of the content and tracking tags identifying the successor nodes in order to publish a successor node to a new location, each of the successor nodes configured to serve as a distinct originator node in generating additional successor nodes with content and tracking tags identifying the additional successor nodes that are linked thereto,

wherein the tracking tags further include;

primary keys to identify the respective node, the primary keys including an instance identifier associated with the tracking tags that are generated by the central server at the root originator node and passed on to linked successor nodes at node generation, and

alternate keys with additional data associated with the respective node, the alternate keys including a person identifier of the person who caused the respective node to be created or a machine identifier of the originating machine which caused the respective node to be created,

generating the additional successor nodes by invoking the movement action to pass on a new instance of the content and tracking tags identifying the successor nodes, each additional successor node configured to serve as a distinct originator node for subsequently generated nodes, each subsequently generated node with content tracking tags identifying the subsequently generated node,

capturing, by the central server, raw data from one or more events invoked at each node after a node has been created,

assembling, by the central server, the originator and successor nodes into a tree data structure, wherein assembling nodes into the tree structure further includes;

assembling the tree structure based on one or more instance identifiers in the primary keys of linked nodes which form the tree, and

aggregating, for each node of the tree structure, the raw data captured from an event at each originator node and rollup event occurrences transpiring at linked successor nodes to form the tree structure with aggregated data,

transforming the tree structure into a new graph data structure by aggregating event data based on alternate keys to provide a complete dataset with established connections between all members thereof, each member possessing supporting metadata and aggregated event data associated with the transformation, and

calculating an influence factor from the graph data structure reflecting the member'"'"'s influence within the dataset by dividing the total occurrences of an event at a specific node by the total occurrences of the event in the complete dataset.

12. The method of claim 11, wherein each node of the graph structure represents a member in the dataset, the member selected from a group comprising an individual person, a machine, a website and an application.

13. The method of claim 11, further comprising:

displaying a plurality of nodes in the dataset on a display medium for review of the influence thereof by a viewer.

14. A method of tracking viral spread of content across a plurality of nodes of a dataset to determine influence of a member within the overall dataset, the member represented by one of the nodes in the dataset, comprising:

publishing a root originator node to a location accessible to a central server, the root node having content and a plurality of tracking tags composed of primary and alternate keys identifying the root node,

creating one or more successor nodes at locations accessible to the central server by the root node performing a movement action resulting in the publishing of a new successor node with content and tracking tags identifying successor nodes to a new location accessible to the central server, each of the successor nodes configured to serve as a distinct originator node in generating additional successor nodes with content and tracking tags identifying additional successor nodes that are linked thereto,

wherein the tracking tags further include;

primary keys to identify the respective node, the primary keys including an instance identifier associated with the tracking tags that are generated by the central server at the root originator node and passed on to linked successor nodes at node generation, and

alternate keys with additional data associated with the respective node including a person identifier of the person who caused the respective node to be created or a machine identifier of the originating machine which caused the respective node to be created,

generating the additional successor nodes by invoking the movement action to pass on a new instance of the content and tracking tags identifying the successor nodes, each additional successor node configured to serve as a distinct originator node for subsequently generated nodes, each subsequently generated node with content and tracking tags identifying the subsequently generated node,

capturing, by the central server, raw data from one or more events invoked at each node after a node has been created,

assembling, by the central server, the originator and successor nodes into a tree data structure, wherein assembling nodes further includes;

assembling the tree structure based on instance identifiers contained in the primary keys of linked nodes which form the tree, and

aggregating the captured raw event data for the events at each node, as well as aggregating by rolling up sibling or successor event data for each linked successor node of the tree,

transforming the tree structure into a new graph data structure by aggregating event data based on alternate keys to provide a new graph data structure with a set of nodes comprising a complete dataset with established connections between all members thereof, each member possessing supporting metadata and aggregated event data associated with the transformation, and

calculating an influence factor from the graph data structure reflecting a member'"'"'s influence within the dataset by dividing the total occurrences of an event at a specific node by the total occurrences of the event in the complete dataset.

15. The method of claim 14, wherein the movement action to generate a successor node includes one of a sharing, forwarding, posting, installing and passing along of a new instance of the content and tracking tags in order to track the successor node.

16. The method of claim 14, wherein the content is stored within a content holder selected from a group comprising a web widget, electronic mail message, mobile application, rich internet application (RIA), SMS message, MMS message, web application, web page, block of web or video content, block of mobile content, web advertisement, mobile advertisement and desktop application.

17. The method of claim 14, wherein an event is selected from a group comprising a load action, an open action, a user interaction, a machine interaction, a post of the content, a send of the content, a publish of the content, a share of the content, a listen, a stream, a view, a download, an advertisement impression, an advertisement click, a poll question answered, direct resulted e-commerce, and indirect resulted e-commerce.