Selecting and/or scoring content-relevant advertisements

• US 20070016473A1
• Filed: 07/18/2005
• Published: 01/18/2007
• Est. Priority Date: 07/18/2005
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Shivakumar, Narayanan, Nguyen, Giao, Anderson, Darrell, Carobus, Alexander
• Granted Patent: US 8,706,546 B2
• Time in Patent Office: Days
• US Class Current: 705/14.410
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising

Abstract

Ads eligible to be served with a document (for example, because they are relevant to the document) may each be scored using a price parameter associated with the ad and an indication of relevancy of the ad to the document. The indication of relevancy of the ad to the document may be based on an ordinal ranking of a relevancy criteria of the document used to select the ad, and/or a value of a relevancy criteria of the document used to select the ad. The eligible ads may be determined by obtaining relevancy criteria for the document and selecting ads using at least some of the obtained relevancy criteria. The ads may be selected, and perhaps filtered, in a distributed manner.

Claims

1. A computer-implemented method comprising:

a) accepting ads relevant to a document; and

b) scoring each of the accepted ads using a price parameter associated with the ad and an indication of relevancy of the ad to the document.

2. The computer-implemented method of claim 1 wherein the indication of relevancy of the ad to the document is based on an ordinal ranking of a relevancy criteria of the document used to select the ad.

3. The computer-implemented method of claim 1 wherein the indication of relevancy of the ad to the document is based on a value of a relevancy criteria of the document used to select the ad.

4. The computer-implemented method of claim 1 wherein the indication of relevancy of the ad to the document is based on both an ordinal ranking of a relevancy criteria of the document used to select the ad, and a value of the relevancy criteria.

5. The computer-implemented method of claim 1 wherein the act of accepting the ads relevant to the document includes

i) obtaining relevancy criteria for the document, and

ii) selecting ads using at least some of the obtained relevancy criteria.

6. The computer-implemented method of claim 1 wherein the act of accepting the ads relevant to the document includes

i) obtaining relevancy criteria for the document,

ii) broadcasting a query including at least some of the obtained relevancy criteria to a plurality of ad determination facilities, and

ii) receiving, from the ad determination facilities, ads using at least some of the obtained relevancy criteria.

7. The computer-implemented method of claim 1 wherein the act of accepting the ads relevant to the document includes

i) obtaining relevancy criteria for the document,

ii) broadcasting a query including at least some of the obtained relevancy criteria to a plurality of ad determination facilities,

iii) determining, at each of the ad determination facilities, ads using at least some of the obtained relevancy criteria,

iv) filtering the determined ads to generate a second set of ads, and

v) sending, from each of the ad determination facilities, the second set of ads.

8. The computer-implemented method of claim 1 wherein the price parameter associated with the ad is one of an offer per impression, an offer per selection, an offer per conversion, a maximum offer per impression, a maximum offer per selection and a maximum offer per conversion.

9. The computer-implemented method of claim 1 wherein the indication of relevancy of the ad to the document was determined by:

determining concepts of the document,

determining a set of one or more ads with the determined concepts, and

obtaining targeting information from at least some ads belonging to the set of one or more ads.

10. The computer-implemented method of claim 9 wherein the targeting information includes one or more targeting keywords.

11. The computer-implemented method of claim 9 wherein the concepts are probabilistic hierarchical inferential learned clusters.

12. The computer-implemented method of claim 1 wherein the act of scoring each of the accepted ads further uses a performance parameter associated with the ad.

13. The computer-implemented method of claim 12 wherein the performance parameter associated with the ads is one of a selection rate and a conversion rate.

14. The computer-implemented method of claim 12 wherein the indication of relevancy of the ad to the document is based on an ordinal ranking of a relevancy criteria of the document used to select the ad.

15. The computer-implemented method of claim 12 wherein the indication of relevancy of the ad to the document is based on a value of a relevancy criteria of the document used to select the ad.

16. The computer-implemented method of claim 12 wherein the indication of relevancy of the ad to the document is based on both an ordinal ranking of a relevancy criteria of the document used to select the ad, and a value of the relevancy criteria.

17. The computer-implemented method of claim 12 wherein the act of accepting the ads relevant to the document includes

i) obtaining relevancy criteria for the document, and

ii) selecting ads using at least some of the obtained relevancy criteria.

18. The computer-implemented method of claim 12 wherein the act of accepting the ads relevant to the document includes

i) obtaining relevancy criteria for the document,

ii) broadcasting a query including at least some of the obtained relevancy criteria to a plurality of ad determination facilities, and

ii) receiving, from the ad determination facilities, ads using at least some of the obtained relevancy criteria.

19. The computer-implemented method of claim 12 wherein the act of accepting the ads relevant to the document includes

i) obtaining relevancy criteria for the document,

ii) broadcasting a query including at least some of the obtained relevancy criteria to a plurality of ad determination facilities,

iii) determining, at each of the ad determination facilities, ads using at least some of the obtained relevancy criteria,

iv) filtering the determined ads to generate a second set of ads, and

v) sending, from each of the ad determination facilities, the second set of ads.

20. The computer-implemented method of claim 19 wherein the act of filtering the determined ads includes scoring each of the accepted ads using one or more of a performance parameter associated with the ad, a price parameter associated with the ad, and an indication of relevancy of the ad to the document.

21. The computer-implemented method of claim 12 wherein the price parameter associated with the ad is one of an offer per impression, an offer per selection, an offer per conversion, a maximum offer per impression, a maximum offer per selection and a maximum offer per conversion.

22. The computer-implemented method of claim 12 wherein the indication of relevancy of the ad to the document was determined by:

determining concepts of the document,

determining a set of one or more ads with the determined concepts, and

obtaining targeting information from at least some ads belonging to the set of one or more ads.

23. The computer-implemented method of claim 22 wherein the targeting information includes one or more targeting keywords.

24. The computer-implemented method of claim 22 wherein the concepts are probabilistic hierarchical inferential learned clusters.

25. Apparatus comprising:

a) means for accepting ads relevant to a document; and

b) means for scoring each of the accepted ads using a price parameter associated with the ad and an indication of relevancy of the ad to the document.