METHODS AND SYSTEMS FOR PREDICTING FUTURE DATA

• US 20090144130A1
• Filed: 07/14/2008
• Published: 06/04/2009
• Est. Priority Date: 07/13/2007
• Status: Abandoned Application

• Current Assignee: HBC Solutions Incorporated (AEG Holdings, Inc.)
• Original Assignee: HBC Solutions Incorporated (AEG Holdings, Inc.)
• Inventors: Waxman, David, Lichstein, Alexander, DeMello, Marco, Tadlock, Craig, Grouf, Nicholas A.
• Application Number:
  US12/172,987
• Publication Number:
  US 20090144130A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 16/438 Presentation of query results
• CPC Class Codes: G06F 21/10 Protecting distributed programs or content, e.g. vending or licensing of copyrighted material protection in video systems or pay television H04N7/16 ; Digital rights management [DRM]
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0243 Comparative campaigns
• CPC Class Codes: G06Q 30/0247 Calculate past, present or future revenues
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0603 Catalogue ordering

Abstract

Described herein are methods and systems for predicting data, such as values. A first interface is configured to receive an indication as to how many search queries have been submit that are related to a first item of content. A second interface is configured to receive information related to web traffic associated with the first item of content. A third interface is configured to receive news information correlated with the first item of content. An engine is configured to estimate a value for the first item content at least partly based on information received from the first, second, and/or third interface.

Claims

1. A method for predicting advertisement content prices, the method comprising:

accessing information regarding a plurality of users'"'"' interest in a first advertising content;

accessing information regarding historical pricing trends;

accessing information indicating a correlation with respect to a first event-type and interest in the first content; and

providing a pricing estimate for the use of the first content with respect to an advertisement using at least a portion of the accessed information regarding users'"'"' interest, pricing trends, and correlations.

2. The method as defined in claim 1, wherein the pricing estimate is based at least in part on a quantity of page views of a Web page associated with the first advertising content.

3. The method as defined in claim 1, wherein the pricing estimate is based at least in part on a quantity of search queries related to the content.

4. The method as defined in claim 1, wherein the pricing estimate is based at least in part on website traffic.

5. The method as defined in claim 1, wherein the pricing estimate is based at least in part on a content of a user on a website hosting the content.

6. The method as defined in claim 1, wherein the pricing estimate is based at least in part on references to the content on one or more user web logs.

7. The method as defined in claim 1, wherein the pricing estimate is based at least in part on content of a first news feed.

8. The method as defined in claim 1, wherein the pricing estimate is based at least in part on IP addresses of content viewers.

9. The method as defined in claim 1, wherein the pricing estimate is based at least in part on one or more user purchase histories.

10. The method as defined in claim 1, wherein the pricing estimate is based at least in part on locations of users viewing the content.

11. The method as defined in claim 1, wherein the pricing estimate is based at least in part on a Really Simple Syndication feed.

12. The method as defined in claim 1, wherein the pricing estimate is based at least in part on an ATOM feed.

13. The method as defined in claim 1, the method further comprising calculating a local maxima in revenue.

14. Programmatic code stored on a computer readable medium, that when executed is configured to:

access information regarding a plurality of users'"'"' interest in a first advertising content;

access information regarding historical pricing trends;

access information indicating a correlation with respect to a first event-type and interest in the first content; and

provide a pricing estimate for the use of the first content with respect to an advertisement.

15. The code as defined in claim 14, wherein the pricing estimate is based at least in part on a quantity of page views of a Web page associated with the first advertising content.

16. The code as defined in claim 14, wherein the pricing estimate is based at least in part on a quantity of search queries related to the content.

17. The code as defined in claim 14, wherein the pricing estimate is based at least in part on website traffic.

18. The code as defined in claim 14, wherein the pricing estimate is based at least in part on a content of a user on a website hosting the content.

19. The code as defined in claim 14, wherein the pricing estimate is based at least in part on references to the content on one or more user web logs.

20. The code as defined in claim 14, wherein the pricing estimate is based at least in part on content of a first news feed.

21. The code as defined in claim 14, wherein the pricing estimate is based at least in part on IP addresses of content viewers.

22. The code as defined in claim 14, wherein the pricing estimate is based at least in part on one or more user purchase histories.

23. The code as defined in claim 14, wherein the pricing estimate is based at least in part on locations of users viewing the content.

24. The code as defined in claim 14, wherein the pricing estimate is based at least in part on a Really Simple Syndication feed.

25. The code as defined in claim 14, wherein the pricing estimate is based at least in part on an ATOM feed.

26. The code as defined in claim 14, further configured to comprise a local maxima in revenue.

27. A computer-based content pricing estimator system, comprising:

a first interface configured to receive an indication as to how many search queries have been submit that are related to a first item of content;

a second interface configured to receive information related to web traffic associated with the first item of content;

a third interface configured to receive news information correlated with the first item of content; and

a pricing engine configured to estimate a price for the first item content at least partly based on information received from the first, second, and/or third interface.

28. The system as defined in claim 27, wherein the pricing engine is further configured to generate the estimate at least partly based on blog references related to the content.

29. The system as defined in claim 27, wherein the pricing engine is further configured to generate the estimate at least partly based on page views of a page related to the content.

30. The system as defined in claim 27, wherein the pricing engine is further configured to generate the estimate at least partly based on a purchase history.

31. The system as defined in claim 27, wherein the pricing engine is further configured to generate the estimate based at least in part on locations of users viewing the content.

32. The system as defined in claim 27, wherein the pricing engine is further configured to generate the estimate based at least in part on a Really Simple Syndication feed.

33. The system as defined in claim 27, wherein the pricing engine is further configured to generate the estimate based at least in part on an ATOM feed.

34. The system as defined in claim 27, wherein the pricing engine is further configured to identify a local maxima in revenue.