Dynamic message filtering
AssigneeTumbleweed Communications Corp.
InventorsScott Loughmiller, Mike Olson, Jeff Ready, Ehren Maedge, Phil White, Jason Collier
Life of this patent
Estimating the term…
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.
Claims8
A method for blocking delivery of unwanted spam messages, comprising the steps of:
- recognizing patterns including words and groups of words in a messages;
- applying a plurality of machine learning techniques including a two-level neural network responsive to the recognized patterns in order to classify the message, the two levels of neural networks include:
- a primary neural network level that determines if the message is likely a non-spam message or likely a spam message;
- and
- a secondary neural network level that includes a pair of neural networks including:
- a first secondary level neural network that determines if a likely non-spam message from the first neural network level is actually a non-spam message or a bulk message, and
- a second secondary level neural network, different from said first secondary level neural network, that determines if a likely spam message from the first neural network level is a spam message or a bulk message;
- for messages classified as bulk, providing user access to at least a listing of message subject field data corresponding to said bulk messages;
- and
- for messages classified as spam, blocking delivery of the messages to at least one intended recipient.
Dependencies are inferred from the claim text.