Microsoft Technology Licensing, LLC

Harvey Rook

Bryan T Starbuck

Robert Novitskey

Daniel Paul Costenaro

Ashish Consul

Erik R Ashby

Rajasi Saha

Shengquan Yan

John Carlton Platt

Selvaraj Nalliah

Yogesh Bansal

Edward Thomas Banti

Naresh Sundaram

Karim Batthish

Julian Alexander Zbogar-Smith

Kumarswamy P Valegerepura

Greg Thiel

Galen Michael Elias

Andrew John Sullivan

Robin Thomas

Michael Patrick Loughry

Elroy Dyksen

Robert Congdon

Ilya Smirnov

As described herein, a client system is automatically configured to access message objects in a plurality of mailboxes stored at a server system. The client system sends an autodiscover request to the server system. In response, the server system sends to the client system an autodiscover response that contains an autodiscover response document that comprises a primary mailbox element and at least one alternate mailbox element. The client system uses a first set of mailbox identification data specified by the primary mailbox element to access message objects in a primary mailbox stored at the server system and uses sets of mailbox identification data specified by the alternate mailbox elements to access message objects in alternate mailboxes stored at the server system. Email messages are one example type of message objects. The alternate mailbox may be an archive mailbox used to store message objects for occasional reference.

Automatic discovery of alternate mailboxes

Methods, systems, and computer-readable mediums configure electronic messaging applications to communicate with a server storing messages to and/or from an electronic messaging account of a user. A method involves receiving an electronic message address and a password of the user as inputs, deriving server connection settings based on the electronic message address, and applying the server connection settings to the electronic messaging account. Another method involves receiving an electronic message address of the user and discovering the server connection settings for the electronic messaging account on a server having a designated protocol name as a server prefix name to assist in locating the server and having the server connection settings. The method also involves retrieving the server connection settings from a file provided by the server in response to locating the server having the designated name and applying the server connection settings to the electronic messaging account.

Methods, systems, and computer-readable mediums for configuring electronic messaging applications

Techniques to provide proxies for web services are described. A technique may include providing proxies for web services that translate one format to a format native used, or understood, by the web service without having to rewrite existing APIs. In one embodiment, for example, an apparatus may comprise a processing unit and a network interface coupled to the processing unit. The apparatus may further include a web service executing web service requests on the processing unit using a native format. The apparatus may further include a proxy to receive a web service request in a first format via the network interface and to translate the web service request from the first format to the native format. Other embodiments are described and claimed.

Techniques to provide proxies for web services

Systems, methods, and software are provided for facilitating a message service. A multi-role system is identified based on where an active message database associated with a user identity resides. Service communications generated by a messaging client associated with the user identity are directed from an entry system to the multi-role system for handling in the multi-role system by a messaging server and a content server to provide the message service.

Facilitating message services using multi-role systems

Architecture that employs machine-based learning to automatically categorize data on a per-user basis. Auto-tagging reduces the burden on infoworkers by creating a machine learning model to learn from user tagging behavior or preferences. Once this information is obtained, a trained model for this specific user is used to assign tags to incoming data, such as emails. The architecture finds particular applicability to compliance and message retention policies that otherwise would mandate extra work for the infoworker. The architecture learns the tagging behavior of a user and uses this learned behavior to automatically tag data based on the user's prior tagging habits. A regression algorithm is employed to process the training data according to an n-dimensional framework for prediction and application of the tag(s) to the incoming messages.

Machine-based learning for automatically categorizing data on per-user basis

Techniques to manage retention policy tags are described. A system may comprise a tagging component operative to tag a message with an expiration tag to form an expiration tagged message, the expiration tag having expiration information used to expire the expiration tagged message in accordance with a retention policy. The system may further comprise a presentation component communicatively coupled to the tagging component, the presentation component operative to present a view of the expiration tagged message with an expiration field having the expiration information for the expiration tagged message.

Techniques to manage retention policy tags

Determining indicative features may be provided. First, a first set of features may be determined using a document frequency process. Then a second set of features may be determined using a boosting process. Using the boosting process may comprise using an approximation for a one-dimensional optimization. The approximation may include an upper bound. Next, the first set of features and the second set of features may be combined into a combined set of features. The combined set of features may comprise a union of the first set of features and the second set of features. At least one document may then be classified based on the combined set of features.

Boosting to determine indicative features from a training set

Growing community of inventors

Newcastle, WA, United States of America

Harvey Rook