SYSTEM, METHOD AND COMPUTER READABLE MEDIUM FOR PLACING ADVERTISEMENTS INTO WEB PAGES

• US 20100287134A1
• Filed: 05/05/2009
• Published: 11/11/2010
• Est. Priority Date: 05/05/2009
• Status: Active Grant

• Current Assignee: Oracle America, Inc. (Oracle Corporation)
• Original Assignee: Paul A. Lipari, Suboti, LLC
• Inventors: Hauser, Robert R.
• Granted Patent: US 10,699,235 B2
• Time in Patent Office: Days
• US Class Current: 706/54
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0263 based upon Internet or website rating
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

Selection of web page advertisements for placement into a web page may use attention data that ranks the elements of the web page according to the attention the elements receive from users during interactions with the web page. The content of ads can be matched to the content of the most highly ranked elements of the web page rather than the web page as a whole. Alternatively, ads can be selected that historically produce a preferred pattern of behavior such as an attention shift from a highly ranked element of the web page to the ad.

Claims

1. A method for placing an advertisement into a web page comprising a plurality of content elements, the method comprising:

determining a plurality of candidate advertisements;

retrieving attention data in respect of the content elements of the web page;

using the attention data to select an advertisement from the candidate advertisements; and

placing the selected advertisement into the web page.

2. The method according to claim 1 wherein the attention data comprises a rank of the plurality of content elements, wherein the rank is dependent on the relative attention received by the respective content elements for a plurality of interactions between a plurality of clients and the web page.

3. The method according to claim 2 comprising:

determining a relevance score for at least one candidate advertisement, the relevance score being dependent on the relevance of the content of the at least one candidate advertisement to the ranked plurality of content elements; and

selecting an advertisement dependent on the relevance scores for the plurality of candidate advertisements.

4. The method according to claim 3 wherein the relevance score is dependent on the relevance of the content of the candidate advertisement to the highest ranked content element.

5. The method according to claim 2 comprising deriving the attention data, wherein deriving the attention data comprises:

recording event data for a plurality of user interactions with the web page;

processing the event data for at least one interaction to create an attention data set for that interaction; and

processing a plurality of the attention data sets to determine the attention data for the web page.

6. The method according to claim 5 wherein processing a plurality of the attention data sets comprises:

selecting a content element;

selecting an attention data set; and

determining a ratio of interaction time attributable to the selected content element relative to the total interaction time of the selected attention data set;

wherein the attention data comprises a list of content elements for the web page ranked at least partially dependent on said ratio.

7. The method according to claim 5 wherein processing the event data comprises determining at least one attention event in respect of at least one content element, and wherein processing a plurality of the attention data sets comprises:

selecting a content element;

selecting an attention data set; and

determining a ratio of attention events attributable to the selected content element relative to the total interaction time of the selected attention data set;

wherein the attention data comprises a list of content elements for the web page ranked at least partially dependent on said ratio.

8. The method according to claim 5 wherein the content elements comprise document object model elements of a document object model of the web page.

9. The method according to claim 5 wherein the event data comprises a plurality of mouse move events.

10. The method according to claim 1 wherein the attention data comprises a plurality of recorded attention events;

wherein at least one attention event is derived from an interaction between a user and the web page comprising an embedded candidate advertisement within the web page; and

wherein the at least one attention event indicates a shift in user attention from a source element of the web page to the embedded candidate advertisement.

11. The method according to claim 10 comprising determining a relevance score for at least one candidate advertisement dependent on the attention events for the respective candidate advertisement.

12. The method according to claim 11 wherein determining a relevance score for a candidate advertisement comprises:

determining a set of attention events for the web page, the set of attention events comprising the candidate advertisement as a destination element;

for each attention event of the set, determining the web page source element of the attention event; and

determining the relevance score dependent on the source elements of the attention events.

15. The method according to claim 10 comprising:

recording event stream data from the interaction; and

processing the event stream data to derive one or more of the attention events;

wherein the event stream data comprises a plurality of mouse move events.

13. The method according 12 wherein the attention data comprises an attention rank of the source elements and wherein the relevance score is dependent on the attention rank.

14. The method according to claim 13 wherein selecting an advertisement comprises selecting an advertisement for which the attention data comprises at least one attention event comprising a highest ranked element as the source element and the selected candidate advertisement as the destination element.

16. An ad server for placing advertisements into a web page, the ad server comprising:

an ad data store that stores a plurality of ads; and

an ad server processing module configured to;

receive an ad request in respect of a URL;

determine a set of candidate advertisements from the ad store;

retrieve ad performance statistics from the ad data store for the candidate advertisements; and

select an advertisement for the ad request using the ad performance statistics;

wherein the ad performance statistics comprise a plurality of attention events, each attention event indicating an element of a web page corresponding to the URL as a source element and a candidate advertisement as a destination element.

17. The ad server according to claim 16 further comprising:

a content interest data store that stores a list of document object elements of the web page, wherein the list is ranked dependent on the user interest attributable to the respective document object model elements for a plurality of interactions between a plurality of users and the web page;

wherein the ad server processing module is configured to select an ad dependent on the ranks of source document object model elements of the respective attention events for the candidate ad.

18. The ad server according to claim 17 wherein the ad server processing module is configured to select an ad for which the ad performance statistics comprise at least one attention event comprising a highest ranked document object model as a source element and the selected ad as a destination element.

19. A computer-readable medium comprising computer-executable instructions for execution by a processor, that, when executed, cause the processor to:

receive an advertisement placement request in respect of a URL;

determine a document object model of a web page corresponding to the URL;

determine a plurality of document object model elements of the URL;

retrieve an attention ranking of the document object model elements;

determine a plurality of candidate advertisements;

determine a relevance score for each candidate advertisement;

select an advertisement from the plurality of candidate advertisements dependent on the relevance scores;

wherein determining the relevance score comprises;

determining a content matching factor dependent on a match of the content of the advertisement to the content of a document object model element; and

weighting the content matching factor dependent on the rank of the respective document object model element.

20. The computer-readable according to claim 19 wherein determining the relevance score comprises:

determining a set of attention events for the web page that comprise the respective candidate advertisement as a destination element;

for each attention event of the set, determining the document object model element that is a source of the attention event; and

weighting the relevance score dependent on the rank of the source element of the attention events.