Patent 7162522 Issued on January 9, 2007. Estimated Expiration Date: November 2, 2021. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.
709/224, Computer network monitoring707/6, Pattern matching access250/363.04, Emission tomography382/215, Using dynamic programming or elastic templates (e.g., warping)705/10, Market analysis, demand forecasting or surveying725/116, Control process705/14, Distribution or redemption of coupon, or incentive or promotion program707/7, Sorting706/22, Signal processing (e.g., filter)704/1, LINGUISTICS706/52, Reasoning under uncertainty (e.g., fuzzy logic)382/305, Image storage or retrieval709/217, REMOTE DATA ACCESSING707/104.1, Application of database or data structure (e.g., distributed, multimedia, image)709/245, COMPUTER-TO-COMPUTER DATA ADDRESSING702/181Probability determination
Demographic information of an Internet user is predicted based on an analysis of accessed web pages. Web pages accessed by the Internet user are detected and mapped to a user path vector which is converted to a normalized weighted user path vector. A centroid vector identifies web page access patterns of users with a shared user profile attribute. The user profile attribute is assigned to the Internet user based on a comparison of the vectors. Bias values are also assigned to a set of web pages and a user profile attribute can be predicted for an Internet user based on the bias values of web pages accessed by the user. User attributes can also be predicted based on the results of an expectation maximization process. Demographic information can be predicted based on the combined results of a vector comparison, bias determination, or expectation maximization process.
Other References
“Maximum Likelihood from Incomplete Data via the EM Algorithm” by A. P. Dempster et al., pub. in Journal of Royal Statistical Society, B39, pp. 1-38, 1977.
“The Binomial Distribution,” http://www.stat.yale.edu/Courses/1997-98/binom.htm, May 18, 2001.
“Web Usage Analysis and User Profiling,” http://www.acm.org/sigkdd/proceedings/webkdd99/, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
“Web Usage Analysis and User Profiling,” http://www.acm.org/sigkdd/proceedings/webkdd99/toconline.htm, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Bilmes, J.A., “A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models,” International Computer Science Institute, TR-97-021, Apr. 1998.
Borges, J., et al., “Data Mining of User Navigation Patterns,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper7-borges.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Büchner, A.G., et al., “Navigation Pattern Discovery from Internet Data,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper13-buchner.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Chan, P.K., “A non-invasive learning approach to building Web user profiles,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper22-chan.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Cooley, R., et al., “WebSIFT: The Web Site Information Filter System,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper11-cooley.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Dempster, et al.., “Maximum Likelihood from Incomplete Data via the EM Algorithm,” J. Royal Statist. Soc. B39, pp. 1-38, 1977 (Read before the Royal Statistical Society, Dec. 8, 1976).
Etzioni, O., “The World-Wide Web: Quagmire or gold mine?” CACM, 39(11):65-68, Nov. 1996.
Fu, Y., et al., “Clustering of Web Users Based on Access Patterns,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper10-yfu.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Getoor, L., et al., “Using Probabilistic Relational Models for Collaborative Filtering,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper21-getoor.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Gomory, S., et al., “Analysis and Visualization of Metrics,for Online Merchandising,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper8-jylee.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Green, H., “The Information Gold Mine,” Business Week, Jul. 30, 1999.
Hofmann, T., “Probabilistic Latent Semantic Indexing,” Proc. SIGIR '99, pp. 50-57, Aug. 1999.
James, M., “Classification Algorithms,” John Wiley & Sons, Inc., 1985.
Lan, B., et al., “Making Web Servers Pushier,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper12-blan.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Leon-Garcia, A., “Probability and Random Processes for Electrical Engineering,” 2nd Ed., Addison-Wesley Publishing Company, Reading, MA, pp. 53-54, 61-64, 126-136, Sep. 1993.
Martin, D. C., “Abstract for the Invited Talk: The IBM SurfAid Project: Transactive Analysis and Prediction,” http://www.acm.org/sigkdd/proceedings/webkdd99/invited.htm, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Masand, B., et al., “Foreword,” http://www.acm.org/sigkdd/proceedings/webkdd99/forewordonline.htm, Web Usage Analysis and User Profiling. KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Meng, et al., “Maximum likelihood estimation via the ECM Algorithm: A general framework,” Biometrika, 80, 2, pp. 267-278, 1993.
Murray, D., et al., “Inferring Demographic Attributes of Anonymous Internet Users,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper2-murray.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Russell, S., “The EM Algorithm,” Machine Learning, CS 281, Spring 1998.
Spiliopoulou, M., et al., “Improving the Effectiveness of a Web Site with Web Usage Mining,” http://www.acm.org/sigkdd/proceedings/webkdd99/papers/paper18-myra.ps, KDD-99 Workshop Program, San Diego, CA, Aug. 15, 1999.
Toutanova, et al., “Text Classification in a Hierarchical Mixture Model for Small Training Sets,” http://www.standford.edu/˜krist/papers/cikm2001.pdf, see entire document and Sections 2.2 and 3.2.2 in particular, Aug. 29, 2001.
Vasconcelos, et al., “A Bayesian framework for semantic content characterization,” In Proc. Of IEEE Conf. On Computer Vision and Pattern Recognition, CVPR '98, pp. 566-571, Santa Barbara, Jun. 1998.
Weisstein, E. W., “Bayes' Formula,” http://br.crashed.net/˜akrowne/crc/math/b/b076.htm, Sep. 1996, May 26, 1999.
November 2, 2021. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.
