Method of delivery, targeting, and measuring advertising over networks

• US 7,930,207 B2
• Filed: 03/12/2004
• Issued: 04/19/2011
• Est. Priority Date: 10/29/1996
• Status: Expired due to Fees

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: O'Connor, Kevin Joseph, Merriman, Dwight Allen
• Primary Examiner(s): Laneau; Ronald
• Application Number:
  US10/798,340
• Publication Number:
  US 20040172331A1
• Time in Patent Office: 2,594 Days
• Field of Search: 705/14, 705/26, 705/27, 709/224, 709/245, 709/218
• US Class Current: 705/14.66
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0225 Avoiding frauds
• CPC Class Codes: G06Q 30/0243 Comparative campaigns
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0257 User requested
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0264 based upon schedule
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0272 Period of advertisement exposure
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

Methods and apparatuses for targeting the delivery of advertisements over a network such as the Internet are disclosed. Statistics are compiled on individual users and networks and the use of the advertisements is tracked to permit targeting of the advertisements of individual users. In response to requests from affiliated sites, an advertising server transmits to people accessing the page of a site an appropriate one of the advertisement based upon profiling of users and networks.

Claims

1. A method for advertising, comprising:

contracting with a first affiliate web site to embed a first link to an advertisement server within content of said first affiliate web site, wherein said first link is sent to a user node in response to a request for said content from said user node to said first affiliate web site;

receiving, by an advertisement server computer, a first advertising request from said user node based upon said first link; and

selecting, by the advertisement server computer, an advertisement from among candidate advertisements based upon stored information about said user node and based upon stored information about the candidate advertisements, to send to said user node in response to said first advertising request,

wherein at least one of the stored information about the user node and the stored information about the candidate advertisements includes click-through rate information, the click-through rate information being based on;

(1) empirically-gathered information regarding at least one of;

the candidate advertisements, and

types of advertisements; and

(2) compiled information regarding at least one of;

the user node,

a network associated with the user node, and

the content within which the first link is embedded; and

wherein the advertisement is selected based on a comparison of a predicted click-through rate of the advertisement and predicted click-through rates of other ones of the candidate advertisements.

2. The method of claim 1, further comprising:

storing information associated with said first advertising request from said user node;

contracting with a second affiliate web site to embed a second link to an advertisement server within content of said second affiliate web site, wherein said second link is sent to said user node in response to a request for said content from said user node to said second affiliate web site;

receiving a second advertising request from said user node based upon said second link; and

selecting an advertisement, based upon said stored information associated with said first advertising request from said user node, to send to said user node in response to said second advertising request.

3. The method of claim 1, wherein the information about the user node comprises information regarding a type of advertisement previously selected by the user, the type of advertisement indicating an interest of the user.

4. The method of claim 1, wherein the information regarding the candidate advertisements comprises information regarding a click-through rate.

5. The method of claim 1, further comprising compiling information regarding users belonging to a group of users and attributing the compiled information to a user, wherein selecting the advertisement from among the candidate advertisements comprises selecting the advertisement based on the attribution.

6. The method of claim 1, wherein selecting the advertisement from among the candidate advertisements comprises targeting the user based on the historical information regarding the candidate advertisements and information regarding at least one of the user and a group including the user.

7. The method of claim 1, wherein the predicted click-through rate of the advertisement is greater than the predicted click-through rates of the other ones of the candidate advertisements.

8. A method for advertising, comprising:

receiving, by an advertisement server computer, a first advertising request requesting an advertisement for delivery to a user node; and

selecting, by the advertisement server computer, an advertisement from among candidate advertisements to provide to the user in response to the first advertising request based on a prediction of a response by the user to each of the candidate advertisements, the prediction of the user response to the candidate advertisements being based on click-through rate information compiled from received messages regarding historical user responses to historical advertisement selections, the click-through rate information being based on;

(1) empirically-gathered information regarding at least one of;

the candidate advertisements, and

types of advertisements; and

(2) compiled information regarding at least one of;

the user node,

a network associated with the user node, and

content with which the selected advertisement will be displayed; and

wherein the advertisement is selected based on a comparison of a predicted click-through rate of the advertisement and predicted click-through rates of other ones of the candidate advertisements.

9. The method of claim 8, wherein identifying a user comprises resolving identification of an unknown user based on an IP address of the user.

10. The method of claim 8, further comprising reporting, to an advertiser, information based on the historical user responses to historical advertisement selections involving an advertisement associated with the advertiser.

11. The method of claim 8, further comprising receiving messages regarding historical user responses to historical advertisement selections.

12. The method of claim 8, wherein the predicted click-through rate of the advertisement is greater than the predicted click-through rates of the other ones of the candidate advertisements.

13. A method for distributing advertisements comprising:

receiving, by an advertisement server computer, a first advertising request for an advertisement for delivery to a user node, the first advertising request being based on a link included in a response to a request for a web page, the request for the web page originating from the user node;

selecting, by the advertisement server computer, an advertisement to send to the user node in response to the first advertising request from among candidate advertisements based on;

targeting information for candidate advertisements,

information regarding a likelihood of click through for the candidate advertisements, and

at least one of information contained in the request for an advertisement and information about the web page requested by the user; and

storing information regarding the selection of the advertisement,

wherein the selection of the advertisement based on information regarding a likelihood of click through for the candidate advertisements is based on a comparison of a predicted click-through rate of the advertisement and predicted click-through rates of other ones of the candidate advertisements, and

wherein the information regarding the likelihood of click through for the candidate advertisements is based on;

(1) empirically-gathered information regarding at least one of;

the candidate advertisements, and

types of advertisements; and

(2) compiled information regarding at least one of;

the user node,

a network associated with the user node, and

the web page requested by the user node.

14. The method of claim 13, wherein the targeting information includes interest information and wherein selecting comprises identifying a match between an interest of the user and the interest information for the candidate advertisements, wherein the interest of the user is determined based on at least one of the information contained in the request for an advertisement and the information about the web page requested by the user.

15. The method of claim 13, wherein the targeting information includes information regarding groups to which the advertisement should be distributed, and wherein selecting comprises determining a group membership of the user based on at least one of information contained in the request for an advertisement and information about the web page requested by the user.

16. The method of claim 13, wherein the predicted click-through rate of the advertisement is greater than the predicted click-through rates of the other ones of the candidate advertisements.