Using a probability associative matrix algorithm to modify web pages

• US 6,928,474 B2
• Filed: 12/14/2000
• Issued: 08/09/2005
• Est. Priority Date: 12/14/2000
• Status: Active Grant

• Current Assignee: Ademco Inc. (Resideo Technologies, Inc.)
• Original Assignee: Honeywell International Inc.
• Inventors: Venkatesan, Thyagarajan
• Primary Examiner(s): Cardone, Jason R.
• Assistant Examiner(s): Pollack, Melvin H.
• Application Number:
  US09/737,113
• Publication Number:
  US 20020124075A1
• Time in Patent Office: 1,699 Days
• Field of Search: 709/203, 709/219, 709/224, 709/226, 709/231, 709/235, 709/236, 705/10, 707/6, 707/10
• US Class Current: 709/224
• CPC Class Codes: G06F 16/9574 of access to content, e.g. by caching
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]
• CPC Class Codes: H04L 9/40 Network security protocols
• CPC Class Codes: Y10S 707/99936 Pattern matching access

Abstract

A web based technique for predicting future web navigation sequences of users visiting a web site includes, in one example embodiment, a web server having browsable web pages including products and services offered by a business. A web-monitoring tool monitors each web navigation sequence of web navigation sequences performed by each user while browsing the web pages of the web site. A PAM analyzer analyzes each of the monitored web navigation sequences to predict the web navigation sequences of future users visiting the web site. A web site administrator implements changes to the web site based on the analysis of the monitored web navigation sequences by the PAM analyzer to enhance user friendliness of the web site.

Claims

1. A method of predicting future web navigation sequences of users visiting a web site, comprising:

monitoring web navigation sequences performed by each user while browsing the web site;

storing the monitored web navigating sequences in a probability associative matrix that includes page shift sequences separated from the web navigating sequences;

analyzing the stored web navigation sequences to predict future user patterns; and

modifying the web site based on the analyzed information to enhance the effectiveness of the web site usage by the users.

2. The method of claim 1, further comprising:

analyzing the stored web navigation sequences further to collect user information.

3. The method of claim 2, further comprising:

storing the predicted user patterns and the collected user navigating information within a database structure of the web site.

4. The method of claim 1, wherein monitoring the web navigation sequences of each user comprises:

monitoring the web navigation sequences of each user.

5. The method of claim 1, wherein navigating sequences comprises:

page shift sequences associated with each of the navigating sequences.

6. The method of claim 5, wherein monitoring each of the navigating sequences comprises:

monitoring page shift sequences associated with web navigation sequences.

7. The method of claim 6, wherein monitoring page shift sequences comprises:

monitoring each of the users navigating from a present page shift sequence to a next page shift sequence.

8. The method of claim 7, wherein the present page shift sequence comprises:

the user navigating from a previous web page to a present web page.

9. The method of claim 8, wherein the next page shift sequence comprises:

the user navigating from the present web page to a next web page.

10. The method of claim 9, wherein storing the monitored web navigation sequences comprises:

storing the page shift sequences associated with the web navigation sequences within the database structure of the web site.

11. The method of claim 10, wherein analyzing the stored web navigation sequences further comprises:

separating the page shift sequences from the stored page shifts associated with web navigation sequences;

counting the number of occurrences of each page shift sequence from the separated page shift sequences; and

analyzing the counted number of occurrences to predict the future web site user patterns.

12. The method of claim 11, wherein analyzing the counted number of occurrences comprises:

computing a probability of navigating from the present page shift sequence to the next page shift sequence based on using the counted number of occurrences.

13. The method claim 12, wherein analyzing the web navigation sequences to predict user patterns comprises:

using the probability associative matrix algorithm based on a two dimensional matrix including N rows for each stored web navigation sequence and M columns including separated page shift sequences, number of counted occurrences of each of the page shift sequences, and the probability associated with each of the number of counted occurrences to predict future user patterns.

14. A computer-implemented on-line web site for predicting navigation sequences of users visiting a web site, comprising:

a web server including browsable web pages of the web site, wherein the web pages include products and services offered by the business;

a web-monitoring tool to monitor web navigation sequences performed by each user while browsing the web pages of the web site; and

a PAM analyzer to analyze each of the monitored web navigation sequences to predict the web navigation sequences of future users visiting the web site.

15. The system of claim 14, further comprising:

a web site administrator to implement changes to the web site based on the analysis of the PAM analyzer.

16. The system of claim 15, further comprising:

a database structure to store web navigation sequences performed by each user visiting the web site.

17. The system of claim 16, wherein the PAM analyzer further analyzes the web navigation sequences to collect user navigation information.

18. The system of claim 17, wherein the database structure further stores collected user navigating information.

19. The system of claim 14, wherein the web-monitoring tool monitors the web navigation sequences of each user visiting the web site.

20. The system of claim 14, wherein each of the navigating sequences comprises:

page shift sequences.

21. The system of claim 20, wherein the PAM analyzer separates page shift sequences from the stored page shift sequences associated with each of the web navigation sequences,

wherein the PAM analyzer further counts the number of occurrences of each page shift sequence from the separated page shift sequences, and

then the PAM analyzer analyzes the counted number of occurrences to predict the future user web site navigation patterns.

22. The system of claim 21, wherein the PAM analyzer monitors each user'"'"'s navigating from a present page shift sequence to a next page shift sequence.

23. The system of claim 22, wherein present page shift sequence comprises:

navigation from a previous web page to a present web page.

24. The system of claim 23, wherein next page shift sequence comprises:

navigation from a present web page to a next web page.

25. The system of claim 24, wherein the PAM analyzer computes a probability of navigating from the present page shift sequence to the next page shift sequence based on using the counted number of occurrences.

26. The system of claim 25, wherein the PAM analyzer includes:

a probability associative matrix algorithm based on a two dimensional matrix including N rows for each stored web navigation sequence and M columns including separated page shift sequences, number of counted occurrences of each of the page shift sequence, and probability associated with each of the number of counted occurrences to predict user patterns.

27. A method of predicting future web navigation sequences of users visiting a web site, comprising:

monitoring web navigation sequences performed by each user while browsing the web site;

storing the monitored web navigating sequences in a probability associative matrix that includes page shift sequences separated from the web navigating sequences, and also includes a probability associated with each page shift sequence;

analyzing the stored web navigation sequences to predict future user patterns; and

modifying the web site to remove links to pages from a page based on the probabilities associated with the page shift sequences to enhance the effectiveness of the web site usage by the users.