Microsoft Technology Licensing, LLC

Geoffrey John Hulten

Robert L Rounthwaite

Joshua T Goodman

Michael John Conrad

Manav Mishra

Kyle James Krum

Umaimah A Mendhro

Eliot Carleton Gillum

Darren B Remington

Anthony Paul Penta

David Maxwell Chickering

Krishna C Vitaldevara

Jason D Walter

Ivan Osipkov

Paul Stephen Rehfuss

David Earl Heckerman

Christopher A Meek

Elissa E S Murphy

John Scarrow

Robert L McCann

Gopalakrishnan Seshadrinathan

Darko Kirovski

Patrice Y Simard

Kristofer Noel Iverson

John D Mehr

Wei-Quiang Michael Guo

Aaron H Averbuch

Jesper B Lind

Malcolm Hollis Davis

Shawn Domenic Loveland

Kenneth Grey Richards

Elissa E Murphy

Ryan Charles Colvin

Mehrdad Bidgoli

Roderic C Deyo

Elliott Jeb Haber

Milenko Drinic

Derek M Hazeur

Kevin H Lau

Jay T Buckingham

Aravind K Ramachandran

Wen-Tau Yih

Fang Yu

Anatoliy Panasyuk

Rui Chen

Yinglian Xie

Bernhard Kohlmeier

Robert C J Pengelly

Madeline Schuster Kleiner

Tomasz Lukasz Religa

Wen-tau Yih

Chun-Te Chu

Wei-qiang Guo

Lin Huang

Vikas Rajvanshy

Harry Simon Katz

Bhaven P Dedhia

Jignesh Shah

Dijia Wu

Vaishali De

Kumar S Srivastava

John Deurbrouck

David Aaron Ward Snelling

Yaron Eshet

Daniel Fairweather

Vincent Etter

Roderict C Deyo

Pankaj Gaur

A method and system for providing a personalized user interface and personalized search suggestions when using an application is disclosed. The method includes receiving a request for providing a personalized user interface such as a personalized menu for an application and examining a history of the user's usage of the application, a history of other user's usage of the application, and/or the content of the current document in the application, before identifying one or more suggested options offered by the application based on one or more of these factors. A list may then be generated and provided for being displayed as part of the personalized user interface. Upon receiving an indication of a user's intent to perform a search within the application, the user's usage history, usage history of users with similar activities, and/or content of the current document may be examined to provide the user with a list of search suggestions.

Personalized user experience and search-based recommendations

This document describes techniques and apparatuses for determining an audience's state or interest using passive sensor data. The techniques receive sensor data that measures an audience during presentation of a media program. The techniques, based on this sensor data, then determine a state or interest of the audience members during the media program.

Determining audience state or interest using passive sensor data

This document describes techniques and apparatuses for determining a future portion of a currently presented media program. The techniques and apparatuses can receive current media reactions of one or many people to a currently presented media program and determine later portions to present in the media program based on the media reactions. In some embodiments, for example, a program can be presented live, reactions can be received during the live presentation, and the program altered on-the-fly and in real time based on those reactions. Further, the alterations can be general or tailored to a group or a particular person.

Determining a future portion of a currently presented media program

The description relates to estimating object orientation. One example includes determining a first estimate of object orientation using a first technique and image data. In this example, a second estimate of the object orientation can be determined using a second technique and the image data. The first estimate can be corrected with the second estimate to generate a corrected object orientation estimate which can be output.

Object orientation estimation

Examples are disclosed herein that relate to eye tracking based on two-dimensional image data. One example provides, on a computing device, a method of tracking an eye. The method includes receiving image data from an image sensor, detecting a face of the user in the image data, locating the eye in a region of the face in the image data to obtain an eye image, normalizing one or more of a scale and an illumination of the eye image, fitting an ellipse to an iris of the eye in the eye image, and outputting a determination of an eye gaze direction based upon the ellipse fitted.

Eye tracking

Access to some aspect of a service may be limited until a user has invested in performing some amount of computation. Legitimate users typically have excess cycles on their machines, which can be used to perform computation at little or no cost to the user. By contrast, computation is expensive for for-profit internet abusers (e.g., spammers). These abusers typically use all of their computing resources to run 'bots' that carry out their schemes, so computation increases the abuser's cost by forcing him or her to acquire new computing resources or to rent computer time. Thus, the providers of free services (e.g., web mail services, blogging sites, etc.), can allow newly registered users to use some limited form of the service upon registration. However, in order to make more extensive use of the service, the user can be asked to prove his legitimacy by investing in some amount of computation.

Computation to gain access to service

Geo-data spam filters are described. In one or more implementations, origin data and language data of a message are evaluated to establish a score for the message indicating a likelihood that the message is spam. The evaluation includes comparing the origin data and the language data to ranked lists indicating message origins and languages with which a respective message recipient interacts positively and ranked lists indicating message origins and languages with which the respective recipient interacts negatively. Interactions of the respective recipient with previously sent messages may be tracked to form these lists. Based on the score established by evaluating the origin data and the language data of the message, the message is filtered for delivery.

Geo-data spam filter

This document describes techniques and apparatuses enabling a user interface for presenting a media reaction. The techniques receive media reactions of a person to a media program, such as the person laughing at one point of a comedy show, then smiling at another point, and then departing at a third point. The techniques may present these and other media reactions in a user interface through which a user may interact.

User interface presenting an animated avatar performing a media reaction

The filtering of activities generated by nodes of a network while interacting with a device may be performed by evaluating the desirability of the activities (e.g., a spam or not-spam determination of email messages sent by the node) and assigning a trust rating to the node. However, nodes are often identified by network address, and an operator of a node sending undesirable activities may reassign the network address of the node in order to avoid heavy filtering. Instead, nodes may be identified as being controlled by a network entity (e.g., an autonomous system identified in a border gateway protocol routing table.) The network entity is assigned a network entity trust rating based on the trust ratings of the nodes controlled thereby, and an appropriate level of activity filtering based on the network entity trust rating may be selected for subsequent activities received from all nodes controlled by the network entity.

Activity filtering based on trust ratings of network

Human Interaction Proofs ('HIPs', sometimes referred to as 'captchas'), may be generated automatically. An captcha specification language may be defined, which allows a captcha scheme to be defined in terms of how symbols are to be chosen and drawn, and how those symbols are obscured. The language may provide mechanisms to specify the various ways in which to obscure symbols. New captcha schemes may be generated from existing specifications, by using genetic algorithms that combine features from existing captcha schemes that have been successful. Moreover, the likelihood that a captcha scheme has been broken by attackers may be estimated by collecting data on the time that it takes existing captcha schemes to be broken, and using regression to estimate the time to breakage as a function of either the captcha's features or its measured quality.

Automatic construction of human interaction proof engines

This document describes techniques and apparatuses for controlling a media program based on a media reaction. In some embodiments, the techniques pause, mute, or stop a media program when a user leaves the room, when a user in the room is talking or is otherwise not paying attention to the program, or when a child walks into the room.

Controlling a media program based on a media reaction

Described is a technology by which phishing-related data sources are processed into aggregated data and a given site evaluated the aggregated data using a predictive model to automatically determine whether the given site is likely to be a phishing site. The predictive model may be built using machine learning based on training data, e.g., including known phishing sites and/or known non-phishing sites. To determine whether an object corresponding to a site is likely a phishing-related object are described, various criteria are evaluated, including one or more features of the object when evaluated. The determination is output in some way, e.g., made available to a reputation service, used to block access to a site or warn a user before allowing access, and/or used to assist a hand grader in being more efficient in evaluating sites.

Finding phishing sites

Growing community of inventors

Lynnwood, WA, United States of America

Geoffrey John Hulten