Filtering of data

Details Filtering of data Filtering of data

2003-05-05

2009-12-29

Etienne, Ario (Department: 2457)

Electrical computers and digital processing systems: multicomput

Computer conferencing

Demand based messaging

C709S204000, C707S793000

Reexamination Certificate

active

07640305

ABSTRACT:
A method, apparatus, and signal-bearing medium that filter data based on a criteria. In an embodiment, the criteria may be related to filtering out unwanted or junk input data. In another embodiment, the criteria may be related to filtering based on desired data. In various embodiments, the data may be email, email attachments, faxes, popup windows, telephone messages, downloaded data or programs, image data, or other data. In a embodiment, a training mode and an automatic mode are provided. During the training mode, a user may be presented with data that may be junk, and feedback may be provided that is used to train a junk filter. During an automatic mode, junk data may be removed from view, transferred to a junk box, or highlighted.

REFERENCES:
patent: 5375244 (1994-12-01), McNair
patent: 5884033 (1999-03-01), Duvall et al.
patent: 5999932 (1999-12-01), Paul
patent: 6023723 (2000-02-01), McCormick et al.
patent: 6101515 (2000-08-01), Wical et al.
patent: 6161130 (2000-12-01), Horvitz et al.
patent: 6421709 (2002-07-01), McCormick et al.
patent: 6578025 (2003-06-01), Pollack et al.
patent: 6654787 (2003-11-01), Aronson et al.
patent: 6732149 (2004-05-01), Kephart
patent: 6807566 (2004-10-01), Bates et al.
patent: 6816885 (2004-11-01), Raghunandan
patent: 6901398 (2005-05-01), Horvitz et al.
patent: 6941466 (2005-09-01), Mastrianni
patent: 7058684 (2006-06-01), Ueda
patent: 7149778 (2006-12-01), Patel et al.
patent: 7158986 (2007-01-01), Oliver et al.
patent: 7249162 (2007-07-01), Rounthwaite et al.
patent: 7284033 (2007-10-01), Jhanji
patent: 7299261 (2007-11-01), Oliver et al.
patent: 7487132 (2009-02-01), Auvenshine
patent: 2002/0107925 (2002-08-01), Goldschneider et al.
patent: 2003/0046421 (2003-03-01), Horvitz et al.
patent: 2003/0187937 (2003-10-01), Yao et al.
patent: 2004/0025037 (2004-02-01), Hair
patent: 2004/0039786 (2004-02-01), Horvitz et al.
patent: 2004/0044907 (2004-03-01), Sun
patent: 2007/0208856 (2007-09-01), Rounthwaite et al.
Robert Losee, “Minimizing Information Overload: The Ranking of Electronic Messages”, Journal of Information Science, 1989, Bol. 15, pp. 179-189.
Terveen, L.G. et al., “Helping Users Program Their Personal Agents,” In Proceedings of the 1996 Conf. on Human Factors in Computing System (CHI '96), pp. 355-361, Vancouver, Apr. 1996, pp. 355-361.
Allan, J., Incremental Relevance Feedback for Information Filtering,Proc. Of SIGIR, Aug. 1996, pp. 1-9.
Androutsopoulos, I. et al., Learning to Filter Spam E-Mail A Comparison of a Naïve Bayesian and a Memory-Based Approach,In Workshop on Machine Learning and Textual Information Access, 4thEuropean Conference on Principles and Practice of Knowledge Discovery in Databases(PKDD 2000), 2000, p. 1-13.
Boone, G., Concept Features in Re: Agent, an Intelligent Email Agent, Proc. Of theSecond International Conference on Autonomous Agents, May 1988, pp. 141-148.
Cohen, W. W., Learning Rules that Classify E-Mail, Proc. Of the AAAI Spring Symposium on Machine Learning in Information Access, 1996, pp. 18-25.
Cohen, W. W. et al., Transferring and Retraining Learned Information Filters,American Association for Artificial Intelligence, 1997, 8 pages.
Crawford, E. et al., Automatic Induction of Rules for E-Mail Classification, Proc. Of the Sixth Australasian Document Computer Symposium, Dec. 2001, 8 pages.
Crestani, F. et al., “Is This Document Relevant? . . . Probably”: A Survey of Probabilistic Models in Information Retrieval,ACM Computing Surveys, vol. 30, No. 4, Dec. 1998, pp. 528-552.
Cunningham, P. et al., A Case-Based Approach to Spam Filtering that Can Track Concept Drift, in The ICCBR'03 Workshop on Long-Lived CBR Systems, Trondheim, Norway, Jun. 2003, 9 pages.
Gee, K. R., Using Latent Semantic Indexing to Filter Spam,Proceedings of the 2003 ACM Symposium on Applied Computing, Feb. 2003, pp. 460-464.
Graham, P., Better Bayesian Filtering,Spam Conference, Jan. 2003, [retrieved from the internet] (Dec. 2, 2003), <URL: http://www.paulgraham.com/better.html> pp. 1-11.
Harman, D., Overview of the Fourth Text Retrieval Conference, In D. K. Harman, editor, Proceedings of the Fourth Text Retrieval Conference (TREC-9). NIST Special Publication 500-236, Oct. 1996.
Helfman, J. I. et al., Ishmail: Immediate Identification of Important Information, Technical Report, AT&T Labs, 1995, (Submitted to CHI-96) 8 pages.
Madigan, D., Statistics and The War on Spam, (publication date unknown) [retrieved from the internet], Apr. 19, 2004, URL:<http://www.stat.rutgers.edu/˜madigan/PAPERS/sagtu.pdf>, pp. 1-3.
Pantel, P. et al., SpamCop: A Spam Classification & Organization Program,In Proc. Of AAAI-98 Workshop on Learning for Text Categorization, Mar. 11, 1998, pp. 1-8.
Provost, J., Naive-Bayes vs. Rule-Learning in Classification of Email, Technical Report AI-TR-99-284, Univ. of Texas at Austin, 4 pages.
Rennie, J. D. M., ifile: An Application of Machine Learning to E-Mail Filtering,In KDD-2000 Text Mining Workshop, Boston, 2000, 6 pages.
Robertson, S.E. et al., Microsoft Cambridge at TREC-9: Filtering Track, Ninth Text REtrieval Conference (TREC-9 ) held in Gaithersburg, Maryland, Nov. 13-16, 2000, 8 pages.
Robinson, G. Issue 107: A Statistical Approach to the Spam Problem,LINUX Journal, Mar. 1, 2003 [retrieved from the internet Dec. 3, 2003], <URL:http://www.linuxiournal.com/article.php?sid=6467> pp. 1-10.
Sahami, M. et al., A Bayesian Approach to Filtering Junk E-Mail,Learning for Text Categorization—Papers from the AAAI Workshop, AAAI Technical Report, 1998, pp. 55-62.
Shah, C. and Bhattacharyya, P., Improving Document Vectors Representation Using Semantic Links and Attributes,ICON-2003: International Conference On Natural Language Processing, 2003, pp. 1-10.
Soboroff, I. M. et al., Combining Content and Collaboration in Text Filtering,Proceedings of the IJCAI'99 Workshop on Machine Learning in Information Filtering, Aug. 1999, pp. 86-91.

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