CLICK-FRAUD PROTECTOR

• US 20080114624A1
• Filed: 11/13/2006
• Published: 05/15/2008
• Est. Priority Date: 11/13/2006
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Kitts, Brendan James
• Application Number:
  US11/559,291
• Publication Number:
  US 20080114624A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0248 Avoiding fraud

Abstract

Determining the probability that a user or program fraudulently initiated a web-page request is described herein. A data-mining component is configured to determine attributes associated with the web-page request. A computation component is configured to calculate a probability that the web-page request was fraudulently initiated. To calculate this probability, the attributes and other parameters are fed into a statistical model. An auction component is configured to locate one or more advertisements to display on the web page based on the probability. The auction component may also be configured to restrict the advertisements for display based on advertiser-specified target criteria.

Claims

1. One or more computer-readable media having computer-executable instructions for performing a method to present one or more advertisements on a web page, the method comprising:

receiving information associated with a web page request;

determining one or more attributes from the information;

based on the one or more attributes, determining a quality score indicative of whether the web page request was fraudulently initiated; and

based on the quality score, selecting the one or more advertisements to display on the web page.

2. The one or more computer-readable media of claim 1, wherein the one or more attributes include one of a date, time, geographic location, gender, ethnicity, or metadata.

3. The one or more computer-readable media of claim 1, wherein the web page request comprises a search-engine query.

4. The one or more computer-readable media of claim 1, wherein determining the one or more attributes from the information further comprises at least one of:

parsing the web page request for the one or more attributes;

or receiving one or more cookies.

5. The one or more computer-readable media of claim 1, wherein a statistical model is used to determine the probability that the request was fraudulently initiated.

6. The one or more computer-readable media of claim 1, further comprising displaying the one or more advertisements on a client-computing device.

7. The one or more computer-readable media of claim 1, further comprising determining a charge-per-click rate to charge an advertiser based on the quality score.

8. The one or more computer-readable media of claim 1, further comprising:

receiving one or more target criteria from an advertiser; and

based on the one or target criteria, locating the one or more advertisements to display on the web page.

9. One or more computer-readable media having computer-executable components to present one or more advertisements on a web page, comprising:

a data-mining component configured to identify one or more attributes associated with a web-page request;

a computation component configured to calculate a probability that the web-page request was fraudulently initiated; and

an auction component configured to locate the one or more advertisements to display on the web page based on the probability.

10. The one or more computer-readable media of claim 9, further comprising a database to store the one or more advertisements.

11. The one or more computer-readable media of claim 9, wherein the computation component utilizes one of a Bayesian model, logistic-regression model, if-then model, or neural network to calculate the probability that the web-page request was initiated fraudulently from the computing device.

12. The one or more computer-readable media of claim 9, wherein the auction component is further configured to receive one or more target criteria from a user.

13. The one or more computer-readable media of claim 12, wherein the one or more target criteria includes a threshold probability that the web page was fraudulently requested.

14. The one or more computer-readable media of claim 12, wherein the auction component is further configured to locate the one or more advertisements based on the one or more target criteria.

15. The one or more computer-readable media of claim 12, wherein the auction component is configured to locate the one or more advertisements to display on the web page based on the probability by comparing the probability with one or more threshold probabilities associated with the one or more advertisements.

16. In a computer system having a graphical user interface including a display and a user interface selection device, a method of designating one or more target criteria to reduce click fraud by restricting the web pages that an online advertisement is displayed on, comprising:

presenting one or more web-page criteria on the display;

allowing a user to designate the one or more target criteria with the user-interface selection, wherein the one or more target criteria are associated with the characteristics of a web-page request and comprise at least an indication of a quality score; and

transmitting the one or more target criteria.

17. The computer system of claim 16, wherein the one or more target criteria includes a threshold probability that a web page request was fraudulently initiated.

18. The computer system of claim 16, wherein the display further comprises a display area for the user to elect between two or more advertisement pricing schemes based on a probability that a web-page request was fraudulently initiated.

19. The computer system of claim 16, wherein the one or more web-page criteria are presented on the display in a display area that includes one of a geographic location, day of the week, age, or gender.

20. The computer system of claim 16, wherein the one or more target criteria further comprise a threshold probability to be compared with a quality score associated with one of a web page request or a search engine query.