Method and system for utilizing user information to provide a network address

• US 7,945,545 B1
• Filed: 10/13/2005
• Issued: 05/17/2011
• Est. Priority Date: 10/13/2005
• Status: Expired due to Fees

• Current Assignee: Remy Incorporated (BorgWarner Incorporated), Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Tyler, Joshua R., Sorkin, Stephen P., Lukose, Rajan M.
• Primary Examiner(s): BOCCIO, VINCENT F
• Application Number:
  US11/250,136
• Time in Patent Office: 2,042 Days
• Field of Search: 708/5, 705/27, 705/14, 705/5, 705/26, 705/37, 707/3, 707/5, 707/10, 707/999.003, 707/705, 707/706, 707/732, 725/46, 715/206, 715744-745, 715810-811
• US Class Current: 707/705
• CPC Class Codes: G06F 16/9562 Bookmark management

Abstract

A method and system for utilizing user information to provide a network address. The method collects user information about a user from at least one electronic device that is associated with the user. In addition, a network address activity pattern is identified based on a combination of a network browsing activity and the user information. A list of network addresses that have been viewed in context of the activity pattern is then accessed. At least one network address on the list of network addresses is scored. Then, the at least one network address having a highest score is provided to the user.

Claims

1. A method for utilizing user information to provide a network address, said method comprising:

collecting user information from at least one electronic device associated with a user, said user information including previous user action information of web sites visited, software applications on said electronic device, web searches performed, and email usage;

identifying a network address activity pattern based on present network browsing activity of said user and said previous user action information, said activity pattern comprising an established routine of network browsing of said user;

generating an adaptive bookmark for said user based on said activity pattern, said adaptive bookmark including a list of network addresses routinely and previously accessed by said user in context of said activity pattern; and

providing said adaptive bookmark to said user,

wherein said activity pattern includes a network address recently accessed, applications recently used, applications in use, time of day, presence in bookmark list, predicted user on a computer that has multiple users, time of week, time of month and time of year.

2. The method of claim 1 wherein said collecting of user information further comprises:

monitoring the present network browsing activity of said user.

3. The method of claim 1 further comprising

dynamically sorting and ranking said list of network addresses based on previous user actions.

4. The method of claim 3 wherein dynamically sorting and ranking said list of network addresses includes listing a most commonly selected network address of said user first on said list of network addresses.

5. The method of claim 1 further comprising:

comparing said present network browsing activity of said user and said previous user action information; and

scoring at least one network address on said list of network addresses based on said comparison.

6. The method of claim 5 wherein said scoring comprises:

utilizing a machine learning model to update a score of at least one network address when said network address is selected in said context of the activity pattern.

7. The method of claim 5 wherein said scoring comprises:

utilizing a machine learning model to update a score of at least one network address based on historical click distribution, said distribution comprising;

generating a historical click distribution;

generating a probability that said user will select at least one network address on said list of network addresses during the context of the activity pattern; and

calculating the frequency of distribution in which the user clicks on the more frequently clicked network addresses and adjusting the network addresses score accordingly.

8. The method of claim 1 further comprising:

dynamically changing said adaptive bookmark based on time of day, user, day of week, time of year, repeatable actions of said user, and characteristics of said user.

9. The method of claim 1 further comprising:

utilizing a uniform resource locator (URL) to define said network address.

10. The method of claim 1 wherein providing said adaptive bookmark to said user includes providing said list of network addresses in an order based on previous selection order of said user.

11. The method of claim 1 wherein said list of network addresses includes a listing of most frequently selected network addresses of said user during said established routine of said activity pattern.

12. A method for utilizing user information to provide a network address, said method comprising:

collecting user information from at least one electronic device associated with a user, said user information including previous user action information of web sites visited, software applications on said electronic device, web searches performed, and email usage;

comparing present network browsing activity of said user and said previous user action information and identifying a network address activity pattern based on said present network browsing activity of said user and said previous user action information, said activity pattern comprising an established routine of network browsing of said user;

generating an adaptive bookmark for said user based on said comparing, said adaptive bookmark including a list of network addresses routinely and previously accessed by said user in context of said activity pattern; and

providing said adaptive bookmark to said user,

wherein said activity pattern includes a network address recently accessed, applications recently used, applications in use, time of day, presence in bookmark list, predicted user on a computer that has multiple users, time of week, time of month and time of year.

13. The method of claim 12 wherein said collecting of user information further comprises:

monitoring the present network browsing activity of said user.

14. The method of claim 12 further comprising:

scoring at least one network address on said list of network addresses based on said comparing.

15. The method of claim 14 wherein said scoring comprises:

utilizing a machine learning model to update a score of at least one network address when said network address is selected in said context of the activity pattern.

16. The method of claim 14 wherein said scoring comprises:

utilizing a machine learning model to update a score of at least one network address based on historical click distribution, said distribution comprising;

generating a historical click distribution;

generating a probability that said user will select at least one network address on said list of network addresses during the context of the activity pattern; and

calculating the frequency of distribution in which the user clicks on the more frequently clicked network addresses and adjusting the network address'"'"'s score accordingly.

17. The method of claim 12 further comprising:

dynamically changing said adaptive bookmark based on time of day, user, day of week, time of year, repeatable actions of said user, and characteristics of said user.

18. The method of claim 12 wherein providing said adaptive bookmark to said user includes providing said list of network addresses in an order based on previous pattern behavior of said user.

19. A computer-usable medium having computer-readable program code embodied therein for causing a computer system to perform a method for utilizing user information to provide a network address in a targeted data delivery system, said method comprising:

collecting user information about a user from at least one electronic device associated with said user, said user information including previous user action information of web sites visited, software applications on said electronic device, web searches performed, and email usage;

generating a user profile based on said user information, wherein said user profile is used to achieve delivery of data targeted to said user based on said user profile;

identifying a network address activity pattern based on present network browsing activity of said user and said previous user action information, said activity pattern comprising an established routine of network browsing of said user;

generating an adaptive bookmark for said user based on said activity pattern, said adaptive bookmark including a list of network addresses routinely and previously accessed by said user in context of said activity pattern; and

providing said adaptive bookmark to said user,

wherein said activity pattern includes a network address recently accessed, applications recently used, applications in use, time of day, presence in bookmark list, predicted user on a computer that has multiple users, time of week, time of month and time of year.

20. The computer-usable medium of claim 19 wherein said collecting of user information further comprises:

monitoring the present network browsing activity of said user.

21. The computer-usable medium of claim 19 further comprising:

comparing said present network browsing activity of said user and said previous user action information; and

scoring at least one network address on said list of network addresses based on said comparison.

22. The computer-usable medium of claim 21 wherein said scoring comprises:

utilizing a machine learning model to update a score of at least one network address when said network address is selected in said context of the activity pattern.

23. The computer-usable medium of claim 21 wherein said scoring comprises:

utilizing a machine learning model to update a score of at least one network address based on historical click distribution, said distribution comprising;

generating a historical click distribution;

generating a probability that said user will select at least one network address on said list of network addresses during the context of the activity pattern; and

calculating the frequency of distribution in which the user clicks on the more frequently clicked network addresses and adjusting the network address'"'"'s score accordingly.

24. The computer-usable medium of claim 19 further comprising:

dynamically sorting and ranking said list of network addresses based on previous user actions.

25. The computer-usable medium of claim 24 wherein dynamically sorting and ranking said list of network addresses includes providing said list of network addresses in an order based on said established routine of said user.

26. The computer-usable medium of claim 19 further comprising:

dynamically changing said adaptive bookmark based on time of day, user, day of week, time of year, repeatable actions of said user, and characteristics of said user.