Abstract: Disclosed is a system and method for email management that leverages information derived from automatically generated templates in order to identify types of message and message content. The systems and methods discussed herein involve identifying messages matching specific template types and structures, and automatically extracting important data from email messages matching those templates. The extracted data enables improvements for a user's experience and increased monetization. That is, templates can be analyzed to determine a type of email message, which in turn can be presented to a receiving user within an automatic folder or tag designation. Additionally, email snippets or previews can be generated from the extracted data for display within a user's inbox. Also, the extracted data can be used for monetization purposes, by serving targeted advertisements based upon the data extracted from such messages.

Abstract: A computer system, computer program product, and computer-implemented method for communicating electronic messages over a communication network coupled thereto are provided. The computer system comprises a network interface for receiving messages sent over the network and addressed to a user of the computer system; and computer executable electronic message processing software. The software comprises instructions for directing the computer system to receive a message over the network, and to identify whether a sender of the received electronic message is a human or a machine. The identifying includes first and second phases of operation. The first phase includes an offline phase employing information and activities resident on the computer system. The second phase includes an online phase employing resources remotely accessible over the network.

Abstract: As is disclosed herein, user behavior in connection with a number of electronic messages, such as electronic mail (email) messages, can be used to automatically learn from, and predict, whether a message is wanted or unwanted by the user, where an unwanted message is referred to herein as gray spam. A gray spam predictor is personalized for a given user in vertical learning that uses the user's electronic message behavior and horizontal learning that uses other users' message behavior. The gray spam predictor can be used to predict whether a new message for the user is, or is not, gray spam. A confidence in a prediction may be used in determining the disposition of the message, such as and without limitation placing the message in a spam folder, a gray spam folder and/or requesting input from the user regarding the disposition of the message, for example.

Abstract: A search query for searching electronic messages, such as email, may be used to search for different types of items, such as and without limitation electronic messages, contacts, photos, documents, such as and without limitation papers, presentations, etc., business entities, personal information extracted from messages, such as and without limitation purchase orders, shipments, reservations, travel itineraries, etc. Several sources of data, which may be indexed for searching, such as and without limitation a personal mail search index, contacts, or business entity, index, attachments index, extracted data index, etc. may be searched using the search query. A number of top search result items, which may include different types of items, may be presented apart from other search result items.

Abstract: As is disclosed herein, user behavior in connection with a number of electronic messages, such as electronic mail (email) messages, can be used to automatically learn from, and predict, whether a message is wanted or unwanted by the user, where an unwanted message is referred to herein as gray spam. A gray spam predictor is personalized for a given user in vertical learning that uses the user's electronic message behavior and horizontal learning that uses other users' message behavior. The gray spam predictor can be used to predict whether a new message for the user is, or is not, gray spam. A confidence in a prediction may be used in determining the disposition of the message, such as and without limitation placing the message in a spam folder, a gray spam folder and/or requesting input from the user regarding the disposition of the message, for example.

Abstract: A search query for searching electronic messages, such as email, may be used to search for different types of items, such as and without limitation electronic messages, contacts, photos, documents, such as and without limitation papers, presentations, etc., business entities, personal information extracted from messages, such as and without limitation purchase orders, shipments, reservations, travel itineraries, etc. Several sources of data, which may be indexed for searching, such as and without limitation a personal mail search index, contacts, or business entity, index, attachments index, extracted data index, etc. may be searched using the search query. A number of top search result items, which may include different types of items, may be presented apart from other search result items.

Abstract: A computer system, computer program product, and computer-implemented method for communicating electronic messages over a communication network coupled thereto are provided. The computer system comprises a network interface for receiving messages sent over the network and addressed to a user of the computer system; and computer executable electronic message processing software. The software comprises instructions for directing the computer system to receive a message over the network, and to identify whether a sender of the received electronic message is a human or a machine. The identifying includes first and second phases of operation. The first phase includes an offline phase employing information and activities resident on the computer system. The second phase includes an online phase employing resources remotely accessible over the network.

Abstract: Disclosed is a system and method for email management that leverages information derived from automatically generated templates in order to identify types of message and message content. The systems and methods discussed herein involve identifying messages matching specific template types and structures, and automatically extracting important data from email messages matching those templates. The extracted data enables improvements for a user's experience and increased monetization. That is, templates can be analyzed to determine a type of email message, which in turn can be presented to a receiving user within an automatic folder or tag designation. Additionally, email snippets or previews can be generated from the extracted data for display within a user's inbox. Also, the extracted data can be used for monetization purposes, by serving targeted advertisements based upon the data extracted from such messages.