Neural network system and method for controlling information output based on user feedback

• US 6,792,412 B1
• Filed: 02/02/1999
• Issued: 09/14/2004
• Est. Priority Date: 02/02/1999
• Status: Expired due to Term

• Current Assignee: Hyper Search LLC (IPValuation Partners, LLC)
• Original Assignee: Alan Sullivan, Ivan Pope
• Inventors: Pope, Ivan, Sullivan, Alan
• Primary Examiner(s): Davis, George B.
• Application Number:
  US09/241,440
• Time in Patent Office: 2,051 Days
• Field of Search: 706/25, 706/39, 709/203
• US Class Current: 706/25
• CPC Class Codes: G06F 16/3326 using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/063 using electronic means
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: H04L 51/212 using filtering or selective blocking
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/1001 for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]

Abstract

A system and method for controlling information output based on user feedback about the information that includes a plurality of information sources. At least one neural network module selects one or more of a plurality of objects to receive information from the plurality of information sources based on a plurality of inputs and a plurality of weight values during that epoch. At least one server, associated with the neural network module, provides one or more of the objects to a plurality of recipients. The recipients provide feedback during an epoch. At the conclusion of an epoch, the neural network takes the feedback that has been provided from the recipients and generates a rating value for each of the objects. Based on the rating value and selections made, the neural network redetermines the weight values. The neural network then selects the objects to receive information during a subsequent epoch.

Claims

1. A system for controlling information output based on user feedback about the information comprising:

a plurality of information sources providing information;

at least one neural network module that selects one or more of a plurality of objects to receive information from the plurality of information sources based at least in part on a plurality of inputs and a plurality of weight values;

at least one server, associated with the neural network module, that provides one or more of the objects to one or more recipients;

the recipients enabling for one or more users to generate feedback about the information; and

wherein the neural network module generates a rating value for a plurality of the objects at the end of an epoch, redetermines the weight values using the rating values, and selects which objects to receive information during a subsequent epoch using the redetermined weight values and the inputs for that subsequent epoch.

2. The system of claim 1 wherein the server provides one object to each recipient.

3. The system of claim 1 wherein the server provides every object selected to receive information to each recipient.

4. The system of claim 1 wherein the neural network further uses thresholds to select the one or more of a plurality of objects to receive information;

and

comprising a threshold modification module for recipients to modify the one or more thresholds used by the neural network.

5. The system of claim 1 wherein each input and object has a unique identifier associated therewith;

further comprising a plurality of lower tier systems, each lower tier system comprising at least one neural network module, at least one server and a plurality of recipients; and

a hierarchical neural network system connected to the plurality of lower tier systems.

6. The system of claim 1 wherein the information sources comprise electronic mail providers, the inputs comprise one or more attributes about the electronic mail providers, and the objects comprise recipient addressed electronic mail messages;

and

wherein the neural network determines which recipients to receive a particular electronic mail message based on attributes about the electronic mail providers that originated the information to be contained in the electronic mail message.

7. The system of claim 6 wherein the attributes comprises the unique identifier assigned for the electronic mail provider.

8. The system of claim 1 wherein the information sources comprise chat participants, the inputs comprise one or more attributes about the chat participants, and the objects comprise chat participant viewers;

and

wherein the neural network selects chat participant viewers to receive information provided from the chat participants based on one or more attributes of the chat participants that provide the information.

9. The system of claim 8 wherein the attributes comprises the unique identifier assigned for the chat participant.

10. The system of claim 1 wherein the information sources comprise page links and the objects comprise slots within a link collection page.

11. The system of claim 10 wherein the inputs comprise attributes about the page links.

12. The system of claim 10 wherein the inputs comprise attributes of users of the link collection page.

13. The system of claim 10 wherein the inputs comprise key words associated with the page links.

14. The system of claim 1 wherein the feedback regards the usefulness of the information provided.

15. The system of claim 1 wherein the neural network uses thresholds to select which objects to receive information and further comprising a mechanism for enabling recipients to modify the threshold to thereby modify the frequency that the particular recipient is selected to receive information.

16. A method for controlling information output based on user feedback about the information comprising:

receiving information from one or more of a plurality of information sources;

selecting one or more of a plurality of objects utilizing a neural network with using a plurality of inputs and a plurality of weight values and transmitting information to the selected objects;

providing one or more objects to one or more recipients;

generating a rating value for the plurality of the objects at the end of the epoch;

redetermining the weight values using the rating values; and

selecting one or more of the plurality of objects to receive information during a subsequent epoch using the redetermined weight values and the inputs for that subsequent epoch.

17. The method of claim 16 further comprising the step of providing one object to each recipient.

18. The method of claim 16 further comprising the step of providing every object to every recipient.

19. The method of claim 16 wherein the steps of selecting comprise using thresholds;

and

further comprising the step of enabling one or more recipients to modify one or more thresholds used by the neural network.

20. The method of claim 16 wherein the information sources comprise electronic mail providers, the inputs comprise one or more attributes about the electronic mail providers, and the objects comprise recipient addressed electronic mail messages;

and

wherein the neural network selects recipient-designated electronic mail messages based on attributes about the electronic mail providers that originated the information to be contained in the electronic mail message.

21. The method of claim 20 wherein the attributes comprise unique identifiers assigned for the electronic mail providers.

22. The method of claim 16 wherein the information sources comprise chat participants, the inputs comprise one or more attributes about the chat participants, and the objects comprise chat participant viewers;

and

wherein the neural network selects chat participant viewers to receive information provided from the chat participants based on one or more attributes of the chat participants that provide the information.

23. The method of claim 22 wherein the attributes comprise the unique identifiers assigned for the chat participants.

24. The method of claim 16 wherein the information sources comprise page links and the objects comprise slots within a link collection page.

25. The method of claim 24 wherein the inputs comprise attributes about the page links.

26. The method of claim 24 wherein the inputs comprise attributes of users of the link collection page.

27. The method of claim 24 wherein the inputs comprise key words associated with the page links.

28. The method of claim 16 wherein the feedback regards the usefulness of the information provided.

29. The method of claim 16 wherein the neural network uses thresholds to select which objects to receive information;

and

further comprising the steps of enabling a user to modify one or more thresholds; and

transmitting information to the selected to receive information from the plurality of information sources.

30. A computer usable medium having computer readable program code means embodied therein for controlling information output based on user feedback about the information, comprising:

computer readable program code means for causing a computer to receive information from one or more of a plurality of information sources;

computer readable program code means for causing a computer to select one or more of a plurality of objects utilizing a neural network with using a plurality of inputs and a plurality of weight values and transmitting information to the selected objects;

computer readable program code means for causing a computer to provide one or more objects to one or more recipients;

computer readable program code means for causing a computer to generate a rating value for the plurality of the objects at the end of the epoch;

computer readable program code means for causing a computer to redetermine the weight values using the rating values; and

computer readable program code means for causing a computer to select one or more of the plurality of objects to receive information during a subsequent epoch using the redetermined weight values and the inputs for that subsequent epoch.

31. The computer readable medium of claim 30 further comprising computer readable program code means for causing a computer to provide one object to each recipient.

32. The computer readable medium of claim 30 computer readable program code means for causing a computer to provide every object to every recipient.

33. The computer readable medium of claim 30 wherein the computer readable code for causing a compute to select comprises thresholds;

and

further comprising computer readable program code means for causing a computer to receive threshold modification commands from recipients and modifying the thresholds used by the neural network in response.

34. The computer readable medium of claim 30 wherein the information sources comprise electronic mail providers, the inputs comprise one or more attributes about the electronic mail providers, and the objects comprise recipient addressed electronic mail messages;

and

further comprising computer readable program code means for causing a computer to select recipient-designated electronic mail messages based on attributes about the electronic mail providers that originated the information to be contained in the electronic mail message.

35. The computer readable medium of claim 30 wherein the information sources comprise chat participants, the inputs comprise one or more attributes about the chat participants, and the objects comprise chat participant viewers;

and

further comprising computer readable program code means for causing a computer to select chat participant viewers to receive information provided from the chat participants based on one or more attributes of the chat participants that provide the information.

36. The computer readable medium of claim 30 wherein the information sources comprise page links and the objects comprise slots within a link collection page.

37. The computer readable medium of claim 30 wherein the feedback regards the usefulness of the information provided.

38. The computer readable medium of claim 30 wherein the neural network uses thresholds to select which objects to receive information and further comprising computer readable program code means for causing a computer to receive threshold modifications from one or more recipients.