Comprehensive anti-spam system, method, and computer program product for filtering unwanted e-mail messages
First Claim
Patent Images
1. A method for filtering unwanted electronic mail messages, comprising:
- receiving electronic mail messages;
filtering the electronic mail messages that are unwanted utilizing;
compound filters, paragraph hashing by hashing a plurality of paragraphs and utilizing a database of hashes of paragraphs, wherein the paragraph hashing excludes at least one of a first paragraph and a last paragraph of content of the electronic mail messages wherein a plurality of hashes each has a level associated therewith, and the hashes having a higher level associated therewith are applied to the electronic mail messages prior to the hashes having a lower level associated therewith, and Bayes rules; and
categorizing the electronic mail messages that are filtered as being unwanted;
wherein the utilization of the Bayes rules includes identifying words of the electronic mail messages;
wherein the utilization of the Bayes rules further includes identifying a probability associated with each of the words;
wherein the probability associated with each of the words is identified using a Bayes rules database;
wherein the electronic mail messages are filtered as being unwanted based on a comparison involving the probability and a Bayes rules threshold;
wherein the threshold is user-defined.
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Abstract
A system, method and computer program product are provided for filtering unwanted electronic mail messages. After receiving electronic mail messages, the electronic mail messages that are unwanted are filtered utilizing a combination of techniques including: compound filters, paragraph hashing, and Bayes rules. The electronic mail messages that are filtered as being unwanted are then categorized.
388 Citations
17 Claims
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1. A method for filtering unwanted electronic mail messages, comprising:
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receiving electronic mail messages;
filtering the electronic mail messages that are unwanted utilizing;
compound filters, paragraph hashing by hashing a plurality of paragraphs and utilizing a database of hashes of paragraphs, wherein the paragraph hashing excludes at least one of a first paragraph and a last paragraph of content of the electronic mail messages wherein a plurality of hashes each has a level associated therewith, and the hashes having a higher level associated therewith are applied to the electronic mail messages prior to the hashes having a lower level associated therewith, and Bayes rules; and
categorizing the electronic mail messages that are filtered as being unwanted;
wherein the utilization of the Bayes rules includes identifying words of the electronic mail messages;
wherein the utilization of the Bayes rules further includes identifying a probability associated with each of the words;
wherein the probability associated with each of the words is identified using a Bayes rules database;
wherein the electronic mail messages are filtered as being unwanted based on a comparison involving the probability and a Bayes rules threshold;
wherein the threshold is user-defined. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A computer program product for filtering unwanted electronic mail messages, comprising:
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computer code for receiving electronic mail messages;
computer code for filtering the electronic mail messages that are unwanted utilizing;
compound filters, paragraph hashing by hashing a plurality of paragraphs and utilizing a database of hashes of paragraphs, wherein the paragraph hashing excludes at least one of a first paragraph and a last paragraph of content of the electronic mail messages, wherein a plurality of hashes each has a level associated therewith, and the hashes having a higher level associated therewith are applied to the electronic mail messages prior to the hashes having a lower level associated therewith, and Bayes rules; and
computer code for categorizing the electronic mail messages that are filtered as being unwanted;
wherein the utilization of the Bayes rules includes identifying words of the electronic mail messages;
wherein the utilization of the Bayes rules further includes identifying a probability associated with each of the words;
wherein the probability associated with each of the words is identified using a Bayes rules database;
wherein the electronic mail messages are filtered as being unwanted based on a comparison involving the probability and a Bayes rules threshold;
wherein the threshold is user-defined.
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15. A system for filtering unwanted electronic mail messages, comprising:
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means for;
receiving electronic mail messages;
filtering the electronic mail messages that are unwanted utilizing;
compound filters, paragraph hashing by hashing a plurality of paragraphs and utilizing a database of hashes of paragraphs, wherein the paragraph hashing excludes at least one of a first paragraph and a last paragraph of content of the electronic mail messages, wherein a plurality of hashes each has a level associated therewith, and the hashes having a higher level associated therewith are applied to the electronic mail messages prior to the hashes having a lower level associated therewith, and Bayes rules; and
categorizing the electronic mail messages that are filtered as being unwanted;
wherein the utilization of the Bayes rules includes identifying words of the electronic mail messages;
wherein the utilization of the Bayes rules further includes identifying a probability associated with each of the words;
wherein the probability associated with each of the words is identified using a Bayes rules database;
wherein the electronic mail messages are filtered as being unwanted based on a comparison involving the probability and a Bayes rules threshold;
wherein the threshold is user-defined.
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16. A system for filtering unwanted electronic mail messages, comprising:
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an anti-spain application for receiving electronic mail messages and filtering the electronic mail messages that are unwanted utilizing;
compound filters, paragraph hashing by hashing a plurality of paragraphs and utilizing a database of hashes of paragraphs, wherein the paragraph hashing excludes at least one of a first paragraph and a last paragraph of content of the electronic mail messages wherein a plurality of hashes each has a level associated therewith, and the hashes having a higher level associated therewith are applied to the electronic mail messages prior to the hashes having a lower level associated therewith, and Bayes rules; and
wherein the electronic mail messages that are filtered as being unwanted are categorized;
wherein the utilization of the Bayes rules includes identifying words of the electronic mail messages;
wherein the utilization of the Bayes rules further includes identifying a probability associated with each of the words;
wherein the probability associated with each of the words is identified using a Bayes rules database;
wherein the electronic mail messages are filtered as being unwanted based on a comparison involving the probability and a Bayes rules threshold;
wherein the threshold is user-defined.
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17. A method for filtering unwanted electronic mail messages, comprising:
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(a) applying a first level of compound filters and paragraph hashing to an electronic mail message to filter the electronic mail message if it is unwanted, the paragraph hashing including hashing a plurality of paragraph and utilizing a database of hashes of paragraphs, wherein the paragraph hashing excludes at least one of a first paragraph and a last paragraph of content of the electronic mail messages, wherein a plurality of hashes each has a level associated therewith, and the hashes having a higher level associated therewith are applied to the electronic mail messages prior to the hashes having a lower level associated therewith;
(b) if the electronic mail message has not been filtered as being unwanted and all applicable levels of the compound filters and the paragraph hashing have not been applied, applying a lower level of compound filters and paragraph hashing to the electronic mail message;
(c) repeating (b);
(d) if the electronic mail message is not filtered as being unwanted based on (a)-(c), identifying a user-defined Bayes rule threshold;
(e) utilizing Bayes rules to filter the electronic mail message as being unwanted based on the user-defined Bayes rule threshold; and
(f) if the electronic mail message is filtered as being unwanted, categorizing the filtered electronic mail message.
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Specification