Microsoft Technology Licensing, LLC

Workman Nydegger

Microsoft Corporation

Robert L Rounthwaite

Joshua T Goodman

David Earl Heckerman

Geoffrey John Hulten

David Maxwell Chickering

Manav Mishra

Ryen William White

Eric David Brill

Christopher A Meek

Bryan T Starbuck

Christopher J C Burges

John D Mehr

Nathan D Howell

Paul Stephen Rehfuss

Scott Kirk Imig

Anthony Paul Penta

Gopalakrishnan Seshadrinathan

Eliot Carleton Gillum

Alexei V Bocharov

Zhaohui Tang

Scott Conrad Oveson

Micah C Rupersburg

John Carlton Platt

Robert George Atkinson

James M Lyon

Silviu-Petru Cucerzan

Roy Williams

Khaja Ehteshamuddin Ahmed

Matthew R Richardson

Elissa E Murphy

Kenneth Grey Richards

Ryan Charles Colvin

D Maxwell Chickering

Harry Simon Katz

Derek M Hazeur

Allison P Heath

Jay T Buckingham

Carl Myers Kadie

Fileno A Alleva

Cynthia Dwork

Raman Chandrasekar

Yoram Yaacovi

John L Manferdelli

Mei-Yuh Hwang

Elissa E S Murphy

Andrew V Goldberg

Aaron H Averbuch

Daniel Gwozdz

Tomasz Lukasz Religa

Jeffrey Bernhardt

Roderic C Deyo

Daniel Rosen

Dean Alan Slawson

Galen Andrew

Kenneth R Aldinger

Yang Song

Amir Netz

Michael Dean Stokes

Josh D Benaloh

Susan Theresa Dumais

Johnson T Apacible

Ye-Yi Wang

Bo Thiesson

Li-wei He

Kenneth Ward Church

Bernhard Kohlmeier

Emre Mehmet Kiciman

Hagai Thomas Attias

Peter Bailey

Robert J Ragno

Aravind Bala

Dengyong Zhou

Michelle Elena Keslin

Robert C J Pengelly

Madeline Schuster Kleiner

Hung-Chih Yang

Jamie Paul Buckley

Xiaoxin Yin

Nina Kang

Curtis Dean Anderson

Elliott Jeb Haber

Jignesh Shah

Andrew Shuman

Ian M Williams

Thierry D'Hers

Xiaohan Shi

John Deurbrouck

Mark H Schmidt

David Aaron Ward Snelling

Michael Thomas Gahrns

Vincent Etter

Shantanu Sharma

Gayathri Ravichandran Geetha

Priyanka Subhash Kulkarni

Igor Dvorkin

Wen-Tau Yih

Erik Thomas Oveson

Emily Blythe Pitler

Daniel Fairweather

Taft Sandbakken

Techniques performed by a data processing system for analyzing the lexical difficulty of words of textual content include analyzing a plurality of textual content sources to determine a first frequency at which each of a plurality of first words appears, analyzing search data to determine a second frequency at which each of the plurality of first words appear in searches for a definition, generating a lexical difficulty model based on the first frequency and the second frequency, the model is configured to receive a word as an input and to output a prediction for how difficult the word is likely to be for a user, receiving a request to analyze first textual content from a client device, analyzing the first textual content using the lexical difficulty model to generate lexical difficulty information, and sending a response to the client device that includes requested information.

Determining lexical difficulty in textual content

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

Non-limiting examples of the present disclosure describe surfacing a representation of digital artifacts for a user. An operation context associated with a user is identified. The operational context may correspond with an electronic document that is accessed through a productivity application. One or more of a plurality of digital artifacts may be identified based on the identified operational context. An exemplary representation of the one or more digital artifacts is surfaced through a user interface. An exemplary user interface may be a user interface of a productivity application, for example, where the electronic document is being accessed through the productivity application. In alternative examples, an exemplary user interface may be associated with a home service that is executed in association with an operating system (OS). In some instances, an operational context may change resulting in an update to an exemplary representation of digital artifacts. Other examples are also described.

Recall service for productivity applications

A computer-implemented technique is described herein for removing sensitive content from documents in a manner that preserves the usefulness of the documents for subsequent analysis. For instance, the technique obscures sensitive content in the documents, while retaining meaningful information in the documents for subsequent processing by a machine-learning engine or other machine-implemented analysis mechanisms. According to one illustrative aspect, the technique removes sensitive content from documents using a modification strategy that is chosen based on one or more selection factors. One selection factor pertains to the nature of the processing that is to be performed on the documents after they have been anonymized.

Removing sensitive content from documents while preserving their usefulness for subsequent processing

In many computing scenarios, an individual may choose to interact with a service in a variety of roles, and may therefore create a set of accounts respectively representing the service. However, the use of multiple accounts by the same individual may introduce considerable administrative complications (e.g., failing to update all accounts with new information results in stale and/or conflicting account information), and may reduce the efficiency and/or scalability of the service. Presented herein are techniques for enabling individuals to interact with services through various roles. Such techniques involve evaluating the individual's role determinants to identify and automatically select the individual's current role; selecting a current role profile, as a subset of the details of the individual profile that are associated with the current role, and excluding details that are not associated with the current role; and performing the service according to the current role profile of the individual.

Role-specific service customization

Distribution displays for categories are provided which illuminate the distribution of continuous attributes over all cases in a category, and which provide a histogram of the population of the different states of categorical attributes. An array of such displays by attribute (in one dimension) and category (in another dimension) may be provided. Category diagram displays are also provided for visualizing the different categories, and their distributions, populations, and similarities. These are displayed through different shading of nodes and edges representing categories and the relationship between two categories, and through proximity of nodes.

System and method for visualization of categories

One or more techniques and/or systems are disclosed for improving reading ease of text displayed by a device. Reading-related feedback, such as feedback that indicates user reading speed and/or user reading comprehension, can be received for a user of the device. One or more display-related characteristics, such as characteristics that indicate how text is displayed by the device, can be received for the device. Using the reading-related feedback and the one or more display-related characteristics, a reading model can be created that can indicate desired display parameters for displaying the text on the device. The desired display parameters indicated by the reading model may be used to improve reading ease of the text, displayed by the device, for the user.

Reading ease of text on a device

The subject invention provides for an advanced and robust system and method that facilitates detecting spam. The system and method include components as well as other operations which enhance or promote finding characteristics that are difficult for the spammer to avoid and finding characteristics in non-spam that are difficult for spammers to duplicate. Exemplary characteristics include analyzing character and/or number sequences, strings, and sub-strings, detecting various entropy levels of one or more character sequences, strings and/or sub-strings and analyzing message headers.

Advanced spam detection techniques

Functionality is described for identifying a discussion topic based on user interest information. The user interest information may originate from one or more sources, such as the user's search-related behavior. The functionality then provides an opportunity for the user to engage in a communication exchange associated with the identified discussion topic.

Identifying a discussion topic based on user interest information

Topical search engines can add contextual keywords to an input query to bias results toward a particular topic or domain. In one instance, query context models can be constructed to facilitate topical search. Upon receipt of one or more topic-relevant sites, a plurality of topical queries can identified automatically. Contextual keywords can be identified with respect to the plurality of topical queries as a function of lexical generality, among other things. Subsequently, a query context model, comprising the identified topical queries and related contextual keywords, can be employed to restrict query results to a particular topic.

Topical search engines and query context models

The automatic search engine recommendation technique described herein automatically recommends topic-specific search engines for user queries. In one embodiment, it automatically matches each query submitted to a non-topic specific or general search engine with one or more vertical search engines using a recommendation model and a set of features. For a given query, one embodiment of the technique suggests vertical search engines and topic-specific search results along with the search results from the general search engine.

Automatic recommendation of vertical search engines

Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein.

Technologies for encouraging search engine switching based on behavior patterns

Described is the presenting of suggested queries for web pages that are not search engine results pages, based upon the URL and/or content of a currently displayed page. The suggested query set may be dynamically extracted (locally or remotely) based upon the content of the web page, and/or obtained from a data store of per-URL suggested query sets, e.g., generated from historical logs. Also described are various techniques for generating suggested queries, and user interface mechanisms that display and allow interaction with suggested queries.

Suggesting related search queries during web browsing

Architecture that automatically detects historical search contexts as well as behaviors related to a search query. Machine learning and hand-authored rules are employed to automatically identify search contexts. Historical information likely to be useful in the current context is surfaced. When a user enters a search query or executes another search behavior, past behaviors are exposed which are contextually related to the current behavior. The architecture also provides automatic discovery of historical contexts, features related to the contexts, and training or authoring of a system for classifying behavior into contexts, using some combination of the machine learning and/or hand-authored rules. A runtime system classifies the current user behavior into a context and surfaces contextual information to the user.

Auto-detection of historical search context

This patent application pertains to dynamic search interaction. One example includes an organizational component configured to obtain a search query from a user. The organizational component can also be configured to obtain related search queries. The organizational component can further be configured to organize the related search queries by topic and to estimate a relative likelihood that an intent of the user matches an individual topic. This example also includes an image generation component configured to cause the organized related search queries to be presented on a graphical user interface (GUI) in a manner that reflects the relative likelihood.

Dynamic search interaction

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Fall City, WA, United States of America

Robert L Rounthwaite