Abstract: Systems and methods for processing a message are provided. A message may be processed to generate a message summary by removing or replacing certain words, phrases, sentences, punctuation, and the like. Message signatures based upon the message summary may be generated and stored in a signature database, which may be used to identify and/or classify spam messages. Subsequently received messages may be classified by signature and processed based on classification.

Abstract: A system and method are disclosed for maintaining a whitelist, including: obtaining message data based on an email message sent by a user; extracting recipient information from message data; updating the whitelist using the recipient information.

Abstract: Systems and methods for processing a message are provided. A message may be processed to generate a message summary by removing or replacing certain words, phrases, sentences, punctuation, and the like. Message signatures based upon the message summary may be generated and stored in a signature database, which may be used to identify and/or classify spam messages. Subsequently received messages may be classified by signature and processed based on classification.

Abstract: Handling a message comprises: classifying an incoming message for a recipient, storing the classified message and providing a notification to the recipient, wherein the notification includes summary information about the classified message and an interface that allows the recipient to operate on the classified message.

Abstract: A system and method are disclosed for classifying a message. The method includes receiving the message, identifying in the message a distinguishing property; generating a signature using the distinguishing property; and comparing the signature to a database of signatures generated by previously classified messages.

Abstract: A system and method are disclosed for improving a statistical message classifier. A message may be tested with a machine classifier, wherein the machine classifier is capable of making a classification on the message. In the event the message is classifiable by the machine classifier, the statistical message classifier is updated according to the reliable classification made by the machine classifier. The message may also be tested with a first classifier. In the event that the message is not classifiable by the first classifier, it is tested with a second classifier, wherein the second classifier is capable of making a second classification. In the event that the message is classifiable by the second classifier, the statistical message classifier is updated according to the second classification.

Abstract: A technique for determining a boundary IP address is disclosed. The technique includes processing a header to extract candidate IP address, locating a gateway IP address, and selecting the boundary IP address based on the location of the gateway IP address.

Abstract: A system and method are disclosed for maintaining a whitelist, including: obtaining message data based on an email message sent by a user; extracting recipient information from message data; updating the whitelist using the recipient information.

Abstract: Systems and methods for processing a message are provided. A message may be processed to generate a message summary by removing or replacing certain words, phrases, sentences, punctuation, and the like. Message signatures based upon the message summary may be generated and stored in a signature database, which may be used to identify and/or classify spam messages. Subsequently received messages may be classified by signature and processed based on classification.

Abstract: A system and method are disclosed for maintaining a whitelist, including: obtaining message data based on an email message sent by a user; extracting recipient information from message data; updating the whitelist using the recipient information.

Abstract: A technique for determining a boundary IP address is disclosed. The technique includes processing a header to extract candidate IP address, locating a gateway IP address, and selecting the boundary IP address based on the location of the gateway IP address.

Abstract: A system and method are disclosed for routing a message through a plurality of test methods. The method includes: receiving a message; applying a first test method to the message; updating a state of the message based on the first test method; and determining a second test method to be applied to the message based on the state.

Abstract: A system and method are disclosed for routing a message through a plurality of test methods. The method includes: receiving a message; applying a first test method to the message; updating a state of the message based on the first test method; and determining a second test method to be applied to the message based on the state.

Abstract: A system and method are disclosed for classifying a message. The method includes receiving the message, identifying in the message a distinguishing property; generating a signature using the distinguishing property; and comparing the signature to a database of signatures generated by previously classified messages.

Abstract: A system and method are disclosed for classifying a message. The method includes receiving the message, identifying in the message a distinguishing property; generating a signature using the distinguishing property; and comparing the signature to a database of signatures generated by previously classified messages.

Abstract: Determining a video audience is disclosed, including: identifying a set of videos based at least in part on a received criterion; querying a video database to retrieve engagements associated with each of at least a subset of the set of videos; identifying a set of audience members associated with the engagements associated with each of the at least subset of the set of videos; and querying a user database to gather events associated with each of at least a subset of the set of audience members.

Abstract: A system and method are disclosed for improving a statistical message classifier. A message may be tested with a machine classifier, wherein the machine classifier is capable of making a classification on the message. In the event the message is classifiable by the machine classifier, the statistical message classifier is updated according to the reliable classification made by the machine classifier. The message may also be tested with a first classifier. In the event that the message is not classifiable by the first classifier, it is tested with a second classifier, wherein the second classifier is capable of making a second classification. In the event that the message is classifiable by the second classifier, the statistical message classifier is updated according to the second classification.

Abstract: A system and method are disclosed for registering a email sender for the purpose of sending an email message to an email receiver including receiving a challenge message wherein the challenge message includes a machine answerable question; processing the challenge message to determine that it is a challenge message sent for the purpose of authorizing delivery of the email message; analyzing the question to determine a valid response to the challenge message; and sending the valid response that includes the answer wherein a nontrivial amount of resources are required to send the valid response.

Abstract: Systems and methods for processing a message are provided. A message may be processed to generate a message summary by removing or replacing certain words, phrases, sentences, punctuation, and the like. Message signatures based upon the message summary may be generated and stored in a signature database, which may be used to identify and/or classify spam messages. Subsequently received messages may be classified by signature and processed based on classification.

Abstract: A system and method are disclosed for improving a statistical message classifier. A message may be tested with a machine classifier, wherein the machine classifier is capable of making a classification on the message. In the event the message is classifiable by the machine classifier, the statistical message classifier is updated according to the reliable classification made by the machine classifier. The message may also be tested with a first classifier. In the event that the message is not classifiable by the first classifier, it is tested with a second classifier, wherein the second classifier is capable of making a second classification. In the event that the message is classifiable by the second classifier, the statistical message classifier is updated according to the second classification.