Automatic, personalized online information and product services

• US 8,626,911 B2
• Filed: 09/14/2012
• Issued: 01/07/2014
• Est. Priority Date: 12/28/1999
• Status: Expired due to Term

• Current Assignee: Personalized User Model LLP
• Original Assignee: Personalized User Model LLP
• Inventors: Konig, Yochai, Twersky, Roy, Berthold, Michael R.
• Primary Examiner(s): Barot, Bharat N
• Application Number:
  US13/619,427
• Publication Number:
  US 20130073390A1
• Time in Patent Office: 480 Days
• Field of Search: 709217-226, 705 144- 1441, 705 1472- 1473
• US Class Current: 709/224
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04L 67/55 Push-based network services
• CPC Class Codes: H04L 67/75 Indicating network or usage conditions on the user display
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing

Abstract

A method for providing automatic, personalized information services to a computer user includes the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability. Personalized services include personalized searches that return only documents of interest to the user, personalized crawling for maintaining an index of documents of interest to the user; personalized navigation that recommends interesting documents that are hyperlinked to documents currently being viewed; and personalized news, in which a third party server customized its interaction with the user.

Claims

1. A computer-implemented method for providing personalized advertisements to a user, the method comprising selecting a plurality of advertisements for display to a user of a computer system, the advertisements selected according to an estimated probability that the advertisements are of interest to the user, wherein selecting the advertisements comprises:

monitoring user interactions with content while the user is engaged in use of a computer;

analyzing the monitored interactions to estimate parameters of a user specific learning machine;

identifying properties of candidate advertisements;

applying identified properties of the candidate advertisements to the user specific learning machine to estimate a probability that one or more of the candidate advertisements are of interest to the user;

providing, from a server to the computer, one or more of the candidate advertisements as the selected advertisements; and

presenting, via a browser running on said computer, the one or more selected advertisements in conjunction with a first web page.

2. The method of claim 1, further comprising analyzing the monitored interactions to determine advertisements not of interest to the user, and wherein estimating parameters of the user-specific learning machine comprises estimating parameters of the user-specific learning machine based at least in part on the advertisements not of interest to the user.

3. The method of claim 1, wherein monitoring user interactions with the content comprises monitoring at least one type of interaction selected from the group consisting of:

information about the user'"'"'s interaction with the content, context information, the user'"'"'s degree of interest in the content, time spent by the user viewing the content, whether the user followed at least one link contained in the content, and a number of links in the content followed by the user.

4. The method of claim 1, wherein, for each subject one of the plurality of advertisements, estimating the probability that the subject advertisement is of interest to the user comprises estimating the probability that the subject advertisement is of interest to the user according to a relevance of the subject advertisement to the web page displayed to the user.

5. The method of claim 1, wherein identifying properties of the subject advertisement comprises identifying properties selected from the group consisting of:

a topic associated with the subject advertisement, at least one product feature extracted from the subject advertisement, an author of the subject advertisement, and an age of the subject advertisement.

6. The method of claim 1, wherein the estimated probability that the advertisements are of interest to the user is determined according to information regarding the user.

7. The method of claim 6, wherein the information regarding the user is maintained remotely from the computer running the browser.

8. A computer-implemented method for providing personalized advertisements to a user, the method comprising:

monitoring user interactions with search results while the user is engaged in use of a browser program running on a computer;

receiving, at a server communicatively coupled to the computer via one or more networks, a search query from the user;

retrieving a plurality of advertisements based on the search query;

for each retrieved advertisement of said plurality of retrieved advertisements;

identifying properties of the retrieved advertisement and, based thereon, estimating a probability that the retrieved advertisement is of interest to the user; and

using the estimated probabilities for the respective plurality of retrieved advertisements to present at least some of the retrieved advertisements to the user.

9. The method of claim 8, further comprising presenting to said user a list of said retrieved advertisements ranked according to respective estimated probabilities of the plurality of retrieved advertisements being of interest to the user.

10. The method of claim 8, wherein estimating the probability that the retrieved advertisements are of interest to the user comprises estimating parameters of a user-specific learning machine based, at least in part, on previously retrieved advertisements not of interest to the user.

11. The method of claim 8, wherein monitoring user interactions with search results comprises one or more of:

monitoring whether the user viewed the search results, information about the user'"'"'s interaction with the search results, context information, and time spent by the user viewing individual ones of the search results.

12. The method of claim 8, wherein for each retrieved advertisement, the estimated probability that the retrieved advertisement is of interest to the user is determined according to information regarding the user.

13. The method of claim 12, wherein the information regarding the user is maintained remotely from the computer running the browser program.

14. A computer-implemented method, comprising:

responsive to a present search query submitted by a user via a client-based application, returning personalized advertisements for the user from a server-based application to the client-based application, the personalized advertisements selected for return to the client-based application according to information regarding the user'"'"'s interests, the personalized advertisements ranked in order of estimated user interest, the interest of the user estimated by applying identified properties of each respective one of the personalized advertisements to a user-specific learning machine to determine a probability of user interest for each respective personalized advertisement.

15. The method of claim 14, further comprising displaying, by the client-based application, the personalized advertisements in ranked order.

16. The method of claim 14, wherein the personalized advertisements comprise one or more of:

text, images, and non-text media.

17. The method of claim 14, wherein the information regarding the user'"'"'s interests is determined, in part, based on monitored user interactions with prior advertisements.

18. The method of claim 14, wherein the client-based application comprises a Web browser application.

19. The method of claim 18, wherein the personalized advertisements are determined according to information regarding monitored user interactions with prior search results from prior search queries.

20. The method of claim 14, wherein the personalized advertisements are selected according to monitored user interactions with prior search results from prior search queries.

21. The method of claim 14, wherein the personalized advertisements comprise advertisements estimated to be of likely interest to the user in the context of the present search query.

22. The method of claim 14, wherein the personalized advertisements are ranked for presentation to the user.

23. The method of claim 14, wherein the personalized advertisements are presented according to an estimated user interest therein.

24. The method of claim 14, wherein the estimated user interest is based, at least in part, on the present search query.

25. The method of claim 14, wherein the estimated user interest is further based, at least in part, on an assessment of potential advertisements in the context of the present search query.