Microsoft Technology Licensing, LLC

Webtv Networks, Inc.

Compaq Computer Corporation, Inc.

Stuart Ozer

Eric J Horvitz

Carl Myers Kadie

Curtis Glenn Wong

Wei Wei Ada Cho

Carolyn Khanh Chau

Michael Patrick Hart

Daniel John Zigmond

Timothy F Park

Michael A Killianey

Majd Bakar

Mark Wagner

Roger S Collins

Tony L Robinson

Paul J Roy

Warren Neal Thornthwaite

Sharon F Glusker

Steven Paul Breyer

Chris Briggs

Jane Devon

Maurice Voce

Bryan Freedman

Karla DiGrazia

Jeffrey W Huth

Yolanda Jenkins

Data clustering is described for determining related components of the data. In one example, information on biomolecules may be clustered into groups in which the biomolecular data in a clustered group may indicate data that shares a relationship. For example, monomers of a protein molecule or a nucleic acid molecule may be mapped to an evolutionary or phylogenetic tree. Candidate groupings of the information may be obtained based on evolutionary relationships among sequences corresponding to the molecules.

Identification of related residues in biomolecular sequences by multiple sequence alignment and phylogenetic analysis

A media planner displays descriptors that represent programs that are scheduled for broadcast on tiles according to a layout. The layout arranges the tiles according to a day and a day part based on a scheduled broadcast date and time associated with a program.

Media planner

A system, including a planning module, a control module and a receiver module, configured to schedule display of one or more advertising impressions of available advertising inventory. The planning module enables scheduling a requested quantity of advertising impressions in accordance with target criteria. Further, the planning module enables selecting an advertising impression goal for advertisement, assigning an advertising type and defining a weight for the advertisements. The control module receives the schedule, the advertising type and the defined weights and generates one or more metadata files that contain target criteria, advertising type and weights for the advertisements. The one or more metadata files, with the advertisements, are delivered to the receiver module that is configured to define a display frequency for the advertisements based upon one or more of the metadata files. The receiver module selectively displays advertisement content associated with the advertisements to achieve the advertising impression goal.

Methods and systems for selectively displaying advertisements

The present invention is related to a system and method of considering time segments or intervals in a collaborative filtering model. The present invention extends collaborative filtering approaches by integrating considerations of temporality into the training and/or vote input associated with the usage of collaborative filtering models. The present invention also applies filtering to the output with temporal models, so as to view a most appropriate subset of recommended content, centering on content that may be available at a target time. The present invention applies time to a collaborative filtering model by allowing weight to be associated with selections within a current time segment, selections historically watched within the current time segment by the user and selections historically watched within the current time segment by a large group of users.

Time-centric training, inference and user interface for personalized media program guides

A system and method of caching data employing probabilistic predictive techniques that provides local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user's viewing habits, the user's viewing habit over particular time segment and/or viewing habits of a group of user's during a particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies, where the value can be time-dependent functions that decay based on the class of the item, as determined by inference about the class or via a label associated with the item.

Training, inference and user interface for guiding the caching of media content on local stores

Time-centric training, interference and user interface for personalized media program guides

Described are methods and apparatus that allow viewers to access timely data showing what other viewers are watching at (or near) a given moment. Thus, for example, a viewer could tune in, find out which ten programs currently being broadcast are most popular, and then select from among these programs. In one embodiment, the data is displayed with links to those popular programs so viewers can easily tune to listed programs. In another embodiment, the viewer can identify a group of friends and find out what those friends are watching. The viewer could then tune into one of the programs his or her friends are watching, and so be prepared to discuss that program later.

Method and apparatus for alerting a television viewers to the programs other viewers are watching

The present invention is related to a system and method of caching data employing probabilistic predictive techniques. The system and method provides local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user's viewing habits, the user's viewing habit over particular time segment and/or viewing habits of a group of user's during a particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies, where the value can be time-dependent functions that decay based on the class of the item, as determined by inference about the class or via a label associated with the item.

A system and method of caching multimedia data employing probabilistic predictive techniques is provided for facilitating local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user (or group of users) will review an item at some time in the future, current viewing habits as well as habits over a particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies, where the value can be time-dependent functions that decay based on the class of the item, for example. A selections value may also change based on changes in viewing habits, time segments or a user's modification of the cache retention policies.

The present invention is related to caching data by employing probabilistic predictive techniques. The invention has particular application to multimedia systems for providing local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user's viewing habits, the user's or a group of user's viewing habit over particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies. A selection's value may be reduced based upon age, changes on a user's viewing habits, changes in time segments or a user's modification of the cache retention policies.

Methods, systems, and computer program products for generating a data stream for broadcast to one or more client systems. A source identifier identifies where the data for the data stream may be obtained. Scheduling information associated with the identifier determines when the data should be added to the data stream for broadcast. The data is requested and received from the data source using the identifier and added to the data stream to arrive at the one or more client systems in accordance with the scheduling information. The generated data stream may comprise one or more sub-streams. The scheduling information may be checked or validated to assure consistency and accuracy. Client systems may simultaneously receive data, including real-time data, from multiple sources.

Scheduling and multiplexing data for broadcast transmission over multiple streams

The present invention is related to a system and method of caching data employing probabilistic predictive techniques. The system and method has particular application to multimedia systems for providing local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-independent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user's viewing habits, the user's viewing habit over particular time segment (e.g., early morning, late morning, early afternoon, late afternoon, primetime, late night) and/or viewing habits of a group of user's during a particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies, where the value can be time-dependent functions that decay based on the class of the item, as determined by inference about the class or via a label associated with the item. A selections value may be reduced as the selection ages because a user is less likely to view the selection over time. Additionally, a value of a selection may change based on changes on a user's viewing habits, changes in time segments or a user's modification of the cache retention policies.

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San Francisco, CA, United States of America

Stuart Ozer