Optimization framework for association of advertisements with sequential media

• US 20080172293A1
• Filed: 12/28/2006
• Published: 07/17/2008
• Est. Priority Date: 12/28/2006
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Fixler, Eric M., Davis, Marc E., Martinez, Ronald G., Raskin, Oliver M.
• Application Number:
  US11/646,970
• Publication Number:
  US 20080172293A1
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0258 Registration
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]

Abstract

A method and apparatus are disclosed that are suitable for automatically identifying appropriate advertisements and locations for composting an advertisement with a media file for user consumption.

Claims

1. A method for providing a best offer with a sequential content file, the method comprising:

receiving an offer request to provide a best offer with a sequential content file wherein the sequential content file has associated metadata;

retrieving a plurality of offers from an offer store;

determining at least one opportunity event in the sequential content file;

optimizing the plurality of offers to determine the best offer;

customizing the best offer with the sequential content file; and

providing the best offer with the sequential content file.

2. The method of claim 1, wherein the offer request further comprises a user id.

3. The method of claim 1, wherein the determining at least one opportunity event in the sequential content file further comprises separating a content descriptor metadata of the sequential content file from other metadata of the sequential content file.

4. The method of claim 1, wherein the determining at least one opportunity event in the sequential content file further comprises analyzing the metadata using a canonical expert.

5. The method of claim 1, wherein the determining at least one opportunity event in the sequential content file further comprises analyzing the metadata using a disambiguation expert.

6. The method of claim 1, wherein the determining at least one opportunity event in the sequential content file further comprises analyzing the metadata using a concept expert.

7. The method of claim 1, wherein the determining at least one opportunity event in the sequential content file further comprises analyzing the metadata using an opportunity event expert to determine whether a marketing opportunity exists within the sequential content file.

8. The method of claim 7, wherein the opportunity event expert further comprises at least an interstitial advertisement event expert, a visual product placement event expert, an endorsement event expert, a visual sign insert event expert, an ambient audio event expert, a music placement event expert, or a textual insert event expert.

9. The method of claim 1, wherein the determining at least one opportunity event in the sequential content file further comprises analyzing the metadata using a probability expert.

12. The method of claim 1, wherein the optimizing the plurality of offers to determine the best offer further comprises:

computing a relevance score for each of the plurality of offers; and

selecting as the best offer the offer with a highest relevance score.

14. The method of claim 12, wherein the method further comprises screening the offer with the highest relevance score.

15. The method of claim 14, wherein the method further comprises selecting an offer with the next highest relevance score as the best offer if the offer with the highest relevance score fails the screen.

16. The method of claim 15, wherein the screening the offer with the highest relevance score further comprises using one or more constraints and relaxing the one or more constraints if an offer with the next highest relevance score does not exist.

17. The method of claim 1, wherein the customizing the best offer with the sequential content file further comprises varying an element of the best offer or sequential content file using a datum about an end user.

10. (canceled)

11. (canceled)

13. (canceled)

18. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor to provide a best offer with a sequential content file, the storage medium comprising instructions for:

receiving an offer request to provide a best offer with a sequential content file;

retrieving a plurality of offers from an offer store;

determining at least one opportunity event in the sequential content file;

optimizing the plurality of offers to determine the best offer; and

providing the best offer with the sequential content file.

19. (canceled)

20. A computer system comprising:

a semantic expert engine to analyze metadata of a sequential content file;

an offer optimization engine to select a best offer from a plurality of offers; and

an offer customization engine to customize the best offer and the sequential content file.

21. The system of claim 20, wherein the semantic expert engine further comprises a canonical expert to canonicalize annotations of the metadata.

22. The system of claim 20, wherein the semantic expert engine further comprises a concept expert for determining one or more concepts of the sequential content file.

23. The system of claim 20, wherein the semantic expert engine further comprises an opportunity event expert to identify offer opportunities of the sequential content file.

24. The system of claim 23, wherein the opportunity event expert further comprises at least an interstitial advertisement event expert, a visual product placement event expert, an endorsement event expert, a visual sign insert event expert, an ambient audio event expert, a music placement event expert, or a textual insert event expert.

25. (canceled)

26. (canceled)

27. A computer system comprising:

one or more computer programs configured to determine a best offer for association with a sequential content file from a plurality of offers by analyzing one or more pieces of metadata associated with the sequential content file.

28. The system of claim 27, wherein the system further comprises one or more computer programs that analyze an annotation of the metadata to identify one or more concepts of the sequential content file.

29. The system of claim 27, wherein the system further comprises one or more computer programs that varies an element of the best offer or sequential content file using data about an end user.

30. The system of claim 27, wherein the one or more computer programs;

compute a relevance score for each of the offers of the plurality of offers;

select the offer with the highest relevance score as the best offer;

screen the best offer against a prohibition set;

wherein if the screen of the offer yields no remaining offers, a constraint of the screen is relaxed.

32. The system of claim 27, wherein the best offer is provided with the sequential content file as an interstitial advertisement event, a visual product placement event, an endorsement event, a visual sign insert event, an ambient audio event, a music placement event, or a textual insert event.

31. (canceled)