Processes and Systems for Automated Collective Intelligence

• US 20080222064A1
• Filed: 03/08/2007
• Published: 09/11/2008
• Est. Priority Date: 03/08/2007
• Status: Abandoned Application

• Current Assignee: Daniel J. Larimer
• Original Assignee: Daniel J. Larimer
• Inventors: Larimer, Daniel J.
• Application Number:
  US11/683,869
• Publication Number:
  US 20080222064A1
• Time in Patent Office: Days
• US Class Current: 706/16
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

The present invention relates to the field of collective intelligence. More specifically, to the collaborative acquisition of knowledge and the relationships among said knowledge and the application of acquired knowledge and relationships to solving problems. The present invention presents an interface to a community of users that will create nodes and relationships in an artificial neural network and then weight each node and relationship through votes from one or more users.

Claims

1. An artificial neural network comprising:

a first node and at least one second node, wherein

each of said nodes is associated with at least one media;

each of said nodes is created by a user;

at least one relationship between said nodes is created by a user; and

output from said first node is calculated from a numerical weight of said relationship and is optionally an input to said second node.

2. The artificial neural network according to claim 1, wherein said at least one media is chosen from text, audio, video, HTML, pictures, numbers, logic, programs, or raw data.

3. The artificial neural network according to claim 1, wherein said at least one media comprises dynamic content.

4. The artificial neural network according to claim 3, wherein said dynamic content is associated with output from an external source.

5. The artificial neural network according to claim 4, wherein said output is a data feed or is data from a sensor or instrument.

6. The artificial neural network according to claim 1, wherein said output corresponds to a quality of said media.

7. The artificial neural network according to claim 1, wherein a total number of said nodes is dynamic.

8. The artificial neural network according to claim 1, wherein a total number of said relationships is dynamic.

9. The artificial neural network according to claim 1, wherein said weight is user specified.

10. A method for generating output from an artificial neural network comprising:

creating at least one node in an artificial neural network by user interfacing, wherein each of said nodes is associated with at least one media;

linking said node with at least one other node by user interfacing;

voting, by user interfacing, on a numerical weight of said linking; and

calculating, with at least one algorithm, a numerical output for each of said nodes based upon said numerical weight of said linking and optionally based upon input from at least one other node.

11. The method according to claim 10, wherein said user interfacing is performed by a plurality of users.

12. The method according to claim 10, wherein said user interfacing is performed by way of at least one web page.

13. The method according to claim 10, wherein said user interfacing is performed by way of at least one Desktop Graphical User Interface.

14. The method according to claim 10, wherein said user interfacing is performed by way of at least one mobile device.

15. The method according to claim 10, wherein said at least one media is chosen from text, audio, video, HTML, pictures, numbers, logic, programs, or raw data.

16. The method according to claim 10, wherein said at least one media comprises dynamic content.

17. The method according to claim 16, wherein said dynamic content is associated with output from an external source.

18. The artificial neural network according to claim 17, wherein said output is a data feed or is data from a sensor or instrument.

19. The method according to claim 10, wherein said voting is weighted by a comparison of historic user votes with the historic weighted average of all votes.

20. The method according to claim 10, wherein said calculating is solely based upon said voting when there is no input from at least one other node.

21. The method according to claim 10, wherein a history of said output for each of said nodes is maintained and referenced as input to other nodes.

22. The method according to claim 10, wherein said at least one media is an algorithm description corresponding to logical operations for calculating based upon input from at least one other node.

23. The method according to claim 10, wherein the weight of said input to at least one other node is determined by an output of another node.

24. The method according to claim 10, wherein said output may be used to cause a direct action.