Microsoft Technology Licensing, LLC

Microsoft Corportion

Microsoft Corporation

John Carlton Platt

Joshua T Goodman

Christopher J C Burges

Bodin Dresevic

Erin L Renshaw

Gary William Flake

Gregory Carl Hitchcock

Jonathan David Goldstein

Oliver Hurst-Hiller

Ramez Naam

J Turner Whitted

David Earl Heckerman

Anoop Gupta

Hong-Jiang Zhang

Christopher A Meek

Christopher Bruce Weare

Arungunram Chandrasekaran Surendran

Jie Yan

Trenholme J Griffin

Jack W Stokes

Christopher J Burges

Alice Zheng

Xiangrong Chen

Eric J Horvitz

William Henry Gates, Iii

Surajit J Chaudhuri

Henricus Johannes Maria Meijer

Lev Novik

William Hill

Badrish Chandramouli

Claude Betrisey

Steven Mark Drucker

John Charles Krumm

Leroy B Keely

Robert L Rounthwaite

Patrice Y Simard

Susan Theresa Dumais

Ivan Jelev Tashev

Cormac E Herley

Michael J Duggan

Danyel Aharon Fisher

Jack Wilson Stokes, Iii

Beat Stamm

Mark R P Thomas

David C Brown

Robert Anthony DeLine

Kenneth Alan Moss

Mikhail Lyapunov

Steven E Swenson

Alejandro Acero

Li Deng

Raymond Edward Ozzie

Yu Chen

Hugues Herve Hoppe

Daniel F Plastina

Christopher Andrew Evans

Carl Myers Kadie

Eric David Brill

Jiahe Helen Wang

Efim Hudis

Kunal Mukerjee

Christopher Alan Hays

Eric I Horvitz

Hagai Thomas Attias

Alexander Vaschillo

Paul Stephen Hellyar

Chad E Verbowski

Scott E Dart

Steve Glenner

James Frederick Blinn

Nathaniel Harper Ballou

Bernhard Schoelkopf

Alexander G Gounares

Xuedong D Huang

Henrique Sarmento Malvar

Jianfeng Gao

Josh D Benaloh

Adrian Mihail Marinescu

Leroy Bertrand Keely, Jr

Bo Thiesson

Kumar Hemachandra Chellapilla

Milind V Mahajan

David A Maltz

Xiaodong He

Gina Danielle Venolia

Sumit Basu

Emre Mehmet Kiciman

Geoffrey G Zweig

Anil Francis Thomas

Asela J Gunawardana

Kyle G Peltonen

Michael Shilman

Jonathan C Kagle

Michael Barnett

David Alan Stevens

Dengyong Zhou

Sylvain Lefebvre

David W Steinkraus

D Maxwell Chickering

Christopher D Payne

A system enables metadata to be gathered about a data store beginning from the creation and generation of the data store, through subsequent use of the data store. This metadata can include keywords related to the data store and data appearing within the data store. Thus, keywords and other metadata can be generated without owner/creator intervention, with enough semantic meaning to make a discovery process associated with the data store much easier and efficient. Usage of or communication regarding a data store are monitored and keywords are extracted from the usage or communication. The keywords are then written to otherwise associated with metadata of the data store. During searching, keywords in the metadata are made available to be used to attempt to match query terms entered by a searcher.

Developing implicit metadata for data stores

The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.

Progressive query computation using streaming architectures

The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

HRTF personalization based on anthropometric features

In one embodiment, datasets are stored in a catalog. The datasets are enriched by establishing relationships among the domains in different datasets. A user searches for relevant datasets by providing examples of the domains of interest. The system identifies datasets corresponding to the user-provided examples. The system them identifies connected subsets of the datasets that are directly linked or indirectly linked through other domains. The user provides known relationship examples to filter the connected subsets and to identify the connected subsets that are most relevant to the user's query. The selected connected subsets may be further analyzed by business intelligence/analytics to create pivot tables or to process the data.

Finding data in connected corpuses using examples

Disclosed herein are technologies directed to discovering semantic similarities between images and text, which can include performing image search using a textual query, performing text search using an image as a query, and/or generating captions for images using a caption generator. A semantic similarity framework can include a caption generator and can be based on a deep multimodal similar model. The deep multimodal similarity model can receive sentences and determine the relevancy of the sentences based on similarity of text vectors generated for one or more sentences to an image vector generated for an image. The text vectors and the image vector can be mapped in a semantic space, and their relevance can be determined based at least in part on the mapping. The sentence associated with the text vector determined to be the most relevant can be output as a caption for the image.

Discovery of semantic similarities between images and text

Some examples include high-performance query processing of real-time and offline temporal-relational data. Further, some implementations include processing streaming data events by annotating individual events with a first timestamp (e.g., a 'sync-time') and second timestamp that may identify additional event information. The stream of incoming data events may be organized into a sequence of data batches that each include multiple data events. The individual data batches in the sequence may be processed in a non-decreasing 'sync-time' order.

Analytical data processing engine

Architecture that monitors interaction data (e.g., search queries, query results and click-through rates), and provides users with links to other users that fall into similar categories with respect to the foregoing monitored activities (e.g., providing links to individuals and groups that share common interests and/or profiles). A search engine can be interactively coupled with one or more social networks, and that maps individuals and/or groups within respective social networks to subsets of categories associated with searches. A database stores mapped information which can be continuously updated and reorganized as links within the system mapping become stronger or weaker. The architecture can comprise a social network system that includes a database for mapping search-related information to an entity of a social network, and a search component for processing a search query for search results and returning a link to an entity of a social network based on the search query.

Search engine that identifies and uses social networks in communications, retrieval, and electronic commerce

Data management in social networks

A system and method infer true labels for multiple items. The inferred labels are generated from judgments. Multiple judges select the judgments from a specified choice of labels for each item. The method includes determining a characterization of judge expertise and item difficulties based on the judgments. The method also includes determining, using maximum entropy, a probability distribution over the specified choice of labels for each judge and item, based on the judgments. The method further includes selecting improved labels for the items from the specified choice such that the entropy over the probability distribution is reduced. The improved labels represent an improvement from the judgments toward the true labels. Additionally, the method includes performing iterative procedure to determine the true labels, the characterizations of judge expertise and the labeling difficulties.

Learning with noisy labels from multiple judges

Growing community of inventors

Bellevue, WA, United States of America

John Carlton Platt