Art Medlar

RE41940, Nov 16, 2010

Jan 31, 2010

12/697288

Daniel Alex Aronson - Berkeley CA, US
Sunil Paul - Sunnyvale CA, US
Kirpal Singh Khalsa - San Francisco CA, US
Timothy Milan Pozar - Mill Valley CA, US
Art Medlar - Berkeley CA, US

Symantec Corporation - Mountain View CA

G06F 15/16

709206, 709207

A server is disclosed for filtering e-mail messages. The server receives requests to retrieve e-mail messages on behalf of a client and then retrieves e-mail messages from a mail server on behalf of the client. The server then filters the e-mail messages based on one or more rules and transfers the filtered e-mail messages to the client. In addition, the server continues to filter the e-mail messages after the client has disconnected from the server. In one embodiment of the invention the e-mail message recipient is sent a notification by the server indicating that messages have been filtered. The recipient is then able to scan the filtered messages and insure that the messages have been filtered correctly. In another embodiment, a third party scans the e-mail messages on behalf of the e-mail user to make this determination. Also disclosed is an e-mail filter comprising an application programming interface and a plurality of dynamically loaded rule modules adapted to interface with the API.

7882193, Feb 1, 2011

Apr 4, 2002

10/117065

Daniel Alex Aronson - Berkeley CA, US
Sunil Paul - San Francisco CA, US
Kirpal Singh Khalsa - Sunnyvale CA, US
Timothy Milan Pozar - San Francisco CA, US
Art Medlar - Berkeley CA, US

Symantec Corporation - Mountain View CA

G06F 15/16

709207, 709206, 709202

A server is disclosed for filtering e-mail messages. The server receives requests to retrieve e-mail messages on behalf of a client and then retrieves e-mail messages from a mail server on behalf of the client. The server then filters the e-mail messages based on one or more rules and transfers the filtered e-mail messages to the client. In addition, the server continues to filter the e-mail messages after the client has disconnected from the server. In one embodiment of the invention the e-mail message recipient is sent a notification by the server indicating that messages have been filtered. The recipient is then able to scan the filtered messages and insure that the messages have been filtered correctly. In another embodiment, a third party scans the e-mail messages on behalf of the e-mail user to make this determination. Also disclosed is an e-mail filter comprising an application programming interface and a plurality of dynamically loaded rule modules adapted to interface with the API.

8145710, Mar 27, 2012

Jun 17, 2004

10/871583

David Cowings - El Cerrito CA, US
David Hoogstrate - San Francisco CA, US
Sandy Jensen - Berkeley CA, US
Art Medlar - Berkeley CA, US
Ken Schneider - San Francisco CA, US

Symantec Corporation - Mountain View CA

G06F 15/16

709205, 709206

Systems and methods for filtering spam messages utilizing a URL filtering module are described. In one embodiment, the method includes detecting, in an incoming message, data indicative of a URL and comparing the URL from the incoming message with URLs characterizing spam. The method further includes determining whether the incoming message is spam based on the comparison of the URL from the incoming message with the URLs characterizing spam.

8402102, Mar 19, 2013

Nov 8, 2010

12/941939

Matt Gleeson - San Francisco CA, US
David Hoogstrate - San Francisco CA, US
Sandy Jensen - Berkeley CA, US
Eli Mantel - Palo Alto CA, US
Art Medlar - Berkeley CA, US
Ken Schneider - San Francisco CA, US

Symantec Corporation - Mountain View CA

G06F 15/16

709206, 709224

A method and system for filtering email spam using email noise reduction are described. In one embodiment, the method includes detecting, in an email message, data indicative of noise added to the email message to avoid spam filtering. The method further includes modifying the content of the email message to reduce the noise, and comparing the modified content of the email message with the content of a spam message.

20050108339, May 19, 2005

May 13, 2004

10/845819

Matt Gleeson - San Francisco CA, US
David Hoogstrate - San Francisco CA, US
Sandy Jensen - Berkeley CA, US
Eli Mantel - Palo Alto CA, US
Art Medlar - Berkeley CA, US
Ken Schneider - San Francisco CA, US

G06F015/16

709206000, 713200000

A method and system for filtering email spam using email noise reduction are described. In one embodiment, the method includes detecting, in an email message, data indicative of noise added to the email message to avoid spam filtering. The method further includes modifying the content of the email message to reduce the noise, and comparing the modified content of the email message with the content of a spam message.

20050108340, May 19, 2005

May 13, 2004

10/846723

Matt Gleeson - San Francisco CA, US
David Hoogstrate - San Francisco CA, US
Sandy Jensen - Berkyley CA, US
Eli Mantel - Palo Alto CA, US
Art Medlar - Berkeley CA, US
Ken Schneider - San Francisco CA, US

G06F015/16

709206000

A method and system for filtering email spam based on similarity measures are described. In one embodiment, the method includes receiving an incoming email message, generating data characterizing the incoming email message based on the content of the incoming email message, and comparing the generated data with a set of data characterizing spam messages. The method further includes determining whether a resemblance between the data characterizing the incoming email message and any data item from the set of data characterizing spam messages exceeds a threshold.

20050132197, Jun 16, 2005

May 13, 2004

10/845648

Art Medlar - Berkeley CA, US

H04L009/00

713176000

A method and system for a character-based document comparison are described. In one embodiment, the method includes dividing a first document into tokens. Each token includes a predefined number of sequential characters from the first document. The method further includes calculating hash values for the tokens and creating, for the first document, a signature including a subset of hash values from the calculated hash values and additional information pertaining to the tokens of the first document. The signature of the first document is subsequently compared with a signature of a second document to determine resemblance between the first document and the second document.