Microsoft Technology Licensing, LLC

Parthasarathy Sundararajan

Itai Almog

Timothy S Paek

Eric Norman Badger

Dmytro Rudchenko

Drew Elliott Linerud

Tim Paek

Shawna Julie Davis

Asela J Gunawardana

Jason Anthony Grieves

Tirthankar Sengupta

Kenneth R Walters

Andrew Douglas Peterson

John C Whytock

Roland Radtke

Jerome Joseph Turner

Kozo Miyano

Masahiro Ami

Randall Richards Cook

Prasanna Chromepet Padmanabhan

Sandeep Patnaik

Nathan P Pollock

Jinhua Fei

Songming Ho

Drew Elliot Linerud

Elizabeth Todaro

Songming He

Haley Mccolgan

Christiaan Brinkhoff

Matthew Shadbolt

Gary Liao

Alexander Ryan Fromm

Gleb Krivosheev

Gleb G Krivosheev

Pen input received by a computing device may first be evaluated to determine an associated intent. For example, it may be determined whether it is intended to be handwriting input, in which case the pen input may be intercepted (e.g., rather than provided to an underlying application for further processing). Subsequently, a target determination is made, where the pen input is processed to determine which input field should receive the input. When the pen input is processed to generate handwriting input, context associated with a determined input field may be used to improve recognition accuracy. Finally, various techniques may be used to ultimately provide the handwriting input to the determined input field, for example according to the set of capabilities that is supported by an application associated with the input field. Similarly, aspects of the disclosed system may be disabled or adapted when the application does not support such functionality.

Intent and target determination for digital handwriting input

A desktop switcher provides equally convenient user interface navigational access to remote virtual desktops as to local virtual desktops for reliable and convenient human-computer interaction. Underlying mechanisms transparently confirm remote computer availability and compatibility, transfer thumbnails between computers, and support remoting sessions. Virtual desktops may share a clipboard, a geographic location setting, and application thumbnails, despite being based on computers that are remote from one another, e.g., when only one of a session's computers resides in a cloud. Virtual desktop context menus may be shown and utilized remotely. A visual separator in the desktop switcher may distinguish local virtual desktops from remote virtual desktops.

Virtual desktop switching and communications

Apparatus and methods are disclosed for providing feedback and guidance to touch screen device users to improve the text entry user experience and performance. According to one embodiment, a method comprises receiving a text entry, receiving input on a touch screen in the form of a first single touch input located over a word of previously entered text, and presenting the user with one or more suggestion candidates indicated possible replacement words related to the selected word. The user can then select one of the suggestion candidates using a second single touch input to replace the selected word with a word associated with the selected suggestion candidate.

Typing assistance for editing

An apparatus and method are disclosed for providing feedback and guidance to touch screen device users to improve text entry user experience and performance by generating input history data including character probabilities, word probabilities, and touch models. According to one embodiment, a method comprises receiving first input data, automatically learning user tendencies based on the first input data to generate input history data, receiving second input data, and generating auto-corrections or suggestion candidates for one or more words of the second input data based on the input history data. The user can then select one of the suggestion candidates to replace a selected word with the selected suggestion candidate.

User-centric soft keyboard predictive technologies

In a mobile device, the text entered by users is analyzed to determine a set of responses commonly entered by users into text applications such as SMS applications in response to received messages. This set of responses is used to provide suggested responses to a user for a currently received message in a soft input panel based on the text of the currently received message. The suggested responses are provided before any characters are provided by the user. After the user provides one or more characters, the suggested responses in the soft input panel are updated. The number of suggested responses displayed to the user in the soft input panel is limited to a total confidence value to reduce user distraction and to allow for easier selection. An undo feature for inadvertent selections of suggested responses is also provided.

String prediction

In a mobile device, a context is determined for the mobile device. The context is determined based on a variety of characteristics of the mobile device environment including, for example, the current application being used, any contacts that a user of the mobile device is interacting with or having a conversation with, the current date and/or time, a current topic of the conversation, a current style of the conversation, etc. Based on a set of strings associated with the determined context and user generated text, one or more string predictions are generated for the user generated text. The string predictions may be presented to the user as suggested completions of the user generated text.

Generating string predictions using contexts

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Bellevue, WA, United States of America

Parthasarathy Sundararajan