Microsoft Technology Licensing, LLC

Steven Mark Drucker

Curtis Glenn Wong

Danyel Aharon Fisher

Asta Glatzer

Donald James Lindsay

Brett David Brewer

Stephen L Lawler

Karim Taji Farouki

Gary William Flake

Blaise Aguera Y Arcas

Adam P Sheppard

Michael F Cohen

Steve Glenner

Kentaro Toyama

Richard S Szeliski

George G Robertson

Daniel Chaim Robbins

Gonzalo Alberto Ramos

Roland Lucas Fernandez

Eyal Ofek

Michel Pahud

Jeffrey Jon Weir

Asta Jane Roseway

Nathalie Henry Riche

James E Allard

Joel P Dehlin

Christophe Hurter

Manny Vellon

Robert Anthony DeLine

Marc A Smith

Harry R Chesley

David Hendler Sloo

Andrew David Wilson

Lili Cheng

John Carlton Platt

Desney S Tan

Patrice Y Simard

Kim Cameron

Jonathan David Goldstein

Badrish Chandramouli

Todd E Holmdahl

Don P Mitchell

James Charles Finger, Ii

Nicholas Robert Baker

Zhengyou Zhang

Craig J Mundie

Gang Hua

Dawson Yee

Patrick Markus Baudisch

Paul A Viola

Bongshin Lee

Brian R Meyers

Ashish Kapoor

Li-wei He

Sumit Basu

Maneesh Agrawala

Rajesh Hegde

Ray Bittner

Julio Estrada

Johannes P Kopf

Arnd Christian König

Saleema Amin Amershi

Michael David Revow

Li-Wei He

Ariel J Lazier

Kenneth Paul Hinckley

Tovi Samuel Grossman

Anoop Gupta

Sing Bing Kang

David Maxwell Chickering

Ryen William White

James Morris Alkove

Mary P Czerwinski

John Michael Snyder

Hugues Herve Hoppe

Lubomira A Dontcheva

Matthew Uyttendaele

Eric David Brill

Brian Kevin Guenter

Aamer Hydrie

Ying-Qing Xu

Neel Suresh Joshi

Christopher Bruce Weare

Gina Danielle Venolia

Tim Paek

Hao Lu

Denis X Charles

Xiaoji Chen

Sasa Junuzovic

James Thomas Kajiya

Henry A Sowizral

Jian Zhao

Anthony T Chor

Carlos Garcia Jurado Suarez

Christopher J Burges

Wolf Kienzle

Greg A Kimberly

Yu-ting Kuo

Aparna Lakshmiratan

Vikram Padmakar Bapat

Methods and systems for rendering augmented reality display data to locations of a physical presentation environment based on a presentation configuration are provided. A physical presentation environment configuration may be accessed that includes locations of a physical presentation environment for mapping augmented reality display data. The augmented reality display data may include a plurality of augmented reality objects that are rendered for display. Presentation attributes of the augmented reality display data may be used in conjunction with the presentation configuration for mapping and rendering the augmented reality display data. The rendered augmented reality display data may be dynamically interactive, and may be generated based on previous presentation configurations, mapping preferences, mapping limitations, and/or other factors.

Presenting augmented reality display data in physical presentation environments

Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

Interactive semantic data exploration for error discovery

In various embodiments, methods and systems for implementing a multi-device mixed interactivity system are provided. The interactivity system includes paired mixed-input devices for interacting and controlling virtual objects. In operation, a selection profile associated with a virtual object is accessed. The selection profile is generated based on a selection input determined using real input associated with a selection device and virtual input associated with a mixed-reality device. The selection device has a first display and the mixed-reality device has a second display that both display the virtual object. An annotation input for the virtual object based on a selected portion corresponding to the selection profile is received. An annotation profile based on the annotation input is generated. The annotation profile includes annotation profile attributes for annotating a portion of the virtual object. An annotation of the selected portion of the virtual reality object is caused to be displayed.

Annotation using a multi-device mixed interactivity system

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

Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations may of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

In various embodiments, methods and systems for implementing a multi-device mixed interactivity system are provided. The interactivity system includes paired mixed-input devices for interacting and controlling virtual objects. In operation, a selection input associated with a virtual object is accessed. The selection input is based on real input associated with a selection device and virtual input associated with a mixed-reality device. The selection device has a first display that displays the virtual object and the mixed-reality device has a second display that displays the virtual object. A selection profile is generated based on the selection input. The selection profile comprises one or more selection profile attributes for isolating a portion of the virtual object. A selected portion of the virtual object is determined based on the selection profile. The selected portion of the virtual reality object is caused to be displayed on the first display of the selection device.

Selection using a multi-device mixed interactivity system

Functionality is described herein for directly interacting with parts of a data visualization. For instance, the functionality allows a user to directly interact with data items to filter them out from the data visualization, and later restore them to the data visualization. The functionality also allows a user to directly interact with an axis to sort the data items in the data visualization. The functionality also allows a user to directly interact with a label of the data visualization to choose a new label, and to reorganize the information represented by the data visualization in response thereto. Further, before finalizing any update to the data visualization, the functionality may provide a preview of the updated data visualization.

Gestures involving direct interaction with a data visualization

A three dimensional (3D) model of an object or environment may be created from images and other information of the object or environment. The 3D model may be created by aligning the images. The 3D model may include surfaces that are based on surfaces of the object or environment. The 3D model may be displayed, exported, modified and so on.

Procedural authoring

Disclosed herein are technologies directed to a feature ideator. The feature ideator can initiate a classifier that analyzes a training set of data in a classification process. The feature ideator can generate one or more suggested features relating to errors generated during the classification process. The feature ideator can generate an output to cause the errors to be rendered in a format that provides for an interaction with a user. A user can review the summary of the errors or the individual errors and select one or more features to increase the accuracy of the classifier.

Error-driven feature ideation in machine learning

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for visualizing a performance of a machine-learned model. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by the machine-learned model. The plurality of item representations are arranged according to scores assigned to the plurality of items by the machine-learned model. Further, each of the plurality of item representations is visually configured to represent a label assigned to a corresponding item.

Interactive visualization of machine-learning performance

Described is a technology by which an image such as a stitched panorama is automatically cropped based upon predicted quality data with respect to filling missing pixels. The image may be completed, including by completing only those missing pixels that remain after cropping. Predicting quality data may be based on using restricted search spaces corresponding to the missing pixels. The crop is computed based upon the quality data, in which the crop is biased towards including original pixels and excluding predicted low quality pixels. Missing pixels are completed by using restricted search spaces to find replacement values for the missing pixels, and may use histogram matching for texture synthesis.

Image completion including automatic cropping

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Bellevue, WA, United States of America

Steven Mark Drucker