Dynamic message filtering

Data processing: artificial intelligence – Neural network – Learning task

Reexamination Certificate

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Details

C706S010000, C706S014000, C709S202000, C709S206000, C714S001000, C714S004110, C714S100000

Reexamination Certificate

active

10678602

ABSTRACT:
Dynamically filtering and classifying messages, as good messages, bulk periodicals, or spam. A regular expression recognizer, and pre-trained neural networks. The neural networks distinguish “likely good” from “likely spam,” and also operate at a more discriminating level to distinguish among the three categories above. A dynamic whitelist and blacklist; sending addresses are collected when the number of their messages indicates the sender is good or a spammer. A dynamically selected set of regular expressions input to the neural networks.

REFERENCES:
patent: 6161130 (2000-12-01), Horvitz et al.
patent: 6321267 (2001-11-01), Donaldson
patent: 6393465 (2002-05-01), Leeds
patent: 6769016 (2004-07-01), Rothwell et al.
patent: 2001/0032245 (2001-10-01), Fodor
patent: 2002/0035605 (2002-03-01), McDowell et al.
patent: 2002/0038296 (2002-03-01), Margolus et al.
patent: 2002/0105545 (2002-08-01), Carter et al.
patent: 2003/0009698 (2003-01-01), Lindeman et al.
Graham, Paul, A Plan for Spam, Aug. 2002.
Graham, Paul, Better Bayesian Filtering, Jan. 2003.
Zhou, Feng; Zhuang, Li; Zhao Ben Y; Hunag, Ling, Joseph, Anthony D. and Kubiatowiz, John, Approximate Object Location and Spam Filtering on Peer-to-peer System, Proc. Of ACM/IFIP/USENIX Intl. Middleware Conf (Middleware 2003) ( printed May 16, 2003).
Carreras, Xavier and Marquez, Lluis, Boosting Trees for Anti-Spam Email Filtering, TALP Research Center, LSI Department, Universitat Politecnica de Catalunya, Barcelona, Spain (undated, printed May 16, 2003).
Yerazunis, William S., Spare Binary Polynomial Hashing and the CRM114 Discriminator, Mitsubishi Electric Research Laboratories, Version Jan. 20, 2003, Cambridge, MA.
How the Spam Filter Works, Edward Technologies, Inc., Ft. Lauderdale, Florida, 2002 (printed May 16, 2003).
Buchmann, Jeremy, Using a Gentic Algorythm to Select Features for Span Filtering, Dec. 13, 2002.
GFI launches freeware version of Exchange Server anti-span product, Mar. 5, 2003, London, United Kingdom.
Sahami, Mehran; Dumais, Susan; Heckerman, David; Horvitz, Eric, A Bayesian Approach to Filtering Junk E-Mail, Computer Science Department, Stanford University, Stanford, CA (printed May 16, 2003).
Androutsopolous, Ion; Koutsias, John; Chandrinos, Konstantionos V.; Paliouras George and Spyropolous, Constantine D., An Evaluation of Naive Bayesian Anti-Span Filtering, Software and Knowledge Engineering Laboratory, National Centre for Scientific Research “Demokritos,” Athens, Greece (printed May 16, 2003).
Androutsopoulous, Ion; Paliouras, Georgios; Karkaletsis, Vangelis; Sakkis, Georgios; Spyropoulos, Constantine D; Stamatopoulos, Panagiotis, Learning to Filter Spam E-mail: A comparison of a Naive Bayesian and a Memory-Based Approach, Department of Informatics, National Centre for Scientific Research “Demokritos,” University of Athens, Greece (printed May 16, 2003).
Sakkis, Georgios; Androutsopoulous, Ion; Paliouras, Georgios; Karkaletsis, Vangelis; Spyropoulos, Constantine D; Stamatopoulos, Panagiotis, A Memory-Based Approach to Anti-Spam Filtering, National Centre for Scientific Research “Demokritos,” Technical Report DEMO 2001, Athens, Greece (printed May 16, 2003).
Urbas, Eytan, White Paper Analysis: Mailshell SpamCatcher Accuracy vs. Error, Aug. 2002.
Using Natural Language Processing to Filter Spam, printed May 16, 2003.
Kolathur, Satheesh and Subramanian, Subha, Collaborative Spam Filter, Sep. 27, 2000.
378 Semester Project Spam Filtering Using Neural Networks, printed May 16, 2003.
Spamassassin—Mail Filter to Identify Spam Using Text Analysis, printed May 16, 2003.
Boyer, Bob and Kerney, William, Spamfilter v1.0; printed May 16, 2003.
Drewes, Rich, An Artificial Neural Network Spam Classifier, May 8, 2002.
Build a Cool Spam Filter Using Outlook Express, printed May 16, 2003.
Joachims, Thorsten, Transductive Inference for Text Classification using Support Vector Machines, Universität Dortmund, Germany, printed May 16, 2003.
Burton, Brian, SpamProbe—Bayesian Spam Filtering Tweaks, printed May 16, 2003.
Statistical Spam Filter Works for Me, Now with updating “whitelist,” printed May 16, 2003.
Metzger, Jörg; Schillo, Michael and Fischer, Klaus, A Multiagent-based Peer-to-Peer Network in Java for Distributed Spam Filtering, German Research Center for Artificial Inteligence, Saarbrücken, Germany, printed May 16, 2003.

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