Google Inc.

International Business Machines Corporation

Nuance Communications, Inc.

Lenovo (singapore) Pte. Ltd.

Goolge Inc.

Shumin Zhai

Yu Ouyang

Xiaojun Bi

Ciprian Ioan Chelba

Satoshi Kataoka

Philip Quinn

Ken Wakasa

Kurt Edward Partridge

Johan Schalkwyk

Francoise Beaufays

Barton Allen Smith

Ouais Alsharif

Per-Ola Kristensson

Thomas Breuel

Eser Kandogan

Lawrence Diao

Min Yin

Thad Eugene Starner

Kishore Sundara-Rajan

Nirmal Jitendra Patel

Debanjan Mukherjee

Yang Li

James Brooks Miller

Tyler Reed Kugler

Jingtao Wang

Alexey Polyudov

Seungyon Lee

Per Ola Kristensson

Myron Dale Flickner

Thomas Deselaers

Michael Andrew Cleron

Arnon Amir

Daniel M Keysers

Abraham Murray

Xu Liu

Keisuke Kuroyanagi

Isaac William Reynolds

Yun-Ling Lee

Billy Dou

Shiri Azenkot

Adrian Pittman

Roy Want

Edwin Joseph Selker

William Noah Schilit

Bay-Wei W Chang

John David Swansey

David James Beymer

Hui Su

Jeffrey William Nichols

Jeffrey Scott Pierce

David Bruce Koons

Elin R Pedersen

John Joseph Barton

Kun Li

Stephen Paul Farrell

Carlos Hitoshi Morimoto

Ed Huai-Hsin Chi

Madeleine Denise Mellor

Sherman Robert Alpert

Steven Carlyle Ihde

Stuart Murray Scott

Xuelin Huang

Christopher Jesse Salem

Seungyon (Claire) Lee

Ying Yin

Michael Clarence Wirth

Phillip Edward Partin

Gary Robert Mcclurg

Joel Iii Collins

Philip Edward Partin

An example method includes identifying, by a mobile computing device that includes a housing and a presence-sensitive display, and based on a first group of sensor signals provided at least by an inertial measurement unit included in one or more sensors, at least one first gesture that is performed at portions of the housing, wherein the one or more portions are separate from the display, initiating an interaction mode, outputting at least one visual or audio indicator for the interaction mode that is associated with a particular function of the mobile computing device, identifying, based on a third group of sensor signals provided by the one or more sensors, at least one second gesture that is performed at the one or more portions of the housing to confirm a user selection of the particular function, and, responsive to identifying the at least one second gesture, performing the particular function.

Initiating a computing device interaction mode using off-screen gesture detection

A computing device outputs for display at a presence-sensitive display, a graphical keyboard comprising a plurality of keys, receives an indication of at least one gesture to select a group of keys of the plurality of keys, and determines at least one characteristic associated with the at least one gesture to select the group of keys of the plurality of keys. The computing device modifies a spatial model based at least in part on the at least one characteristic and determines a candidate word based at least in part on data provided by the spatial model and a language model, wherein the spatial model provides data based at least in part on the indication of the at least one gesture and wherein the language model provides data based at least in part on a lexicon. The computing device outputs for display at the presence-sensitive display, the candidate word.

Touch keyboard using a trained model

A method includes detecting a touch input by a user at a position on a virtual keyboard. The method further includes applying a spatial model to map the touch input to one or more first candidate keys of the virtual keyboard, wherein the spatial model maps the touch input to the one or more first candidate keys based on a heatmap of prior touch inputs by the user. The method further includes applying a language model to map the touch input to one or more second candidate keys of the virtual keyboard, wherein the language model maps the touch input to the one or more second candidate keys based on comparing the position of the touch input to one or more predicted next keys. The method further includes selecting a particular key of the one or more first and second candidate keys as indicated by the touch input.

Virtual keyboard error correction based on a dynamic spatial model

In some examples, a computing device includes at least one processor; and at least one module, operable by the at least one processor to: output, for display at an output device, a graphical keyboard; receive an indication of a gesture detected at a location of a presence-sensitive input device, wherein the location of the presence-sensitive input device corresponds to a location of the output device that outputs the graphical keyboard; determine, based on at least one spatial feature of the gesture that is processed by the computing device using a neural network, at least one character string, wherein the at least one spatial feature indicates at least one physical property of the gesture; and output, for display at the output device, based at least in part on the processing of the at least one spatial feature of the gesture using the neural network, the at least one character string.

Neural network for keyboard input decoding

A computing device outputs a keyboard for display, receives an indication of a first gesture to select a first sequence of one or more keys, determines a set of candidate strings based in part on the first sequence of keys, and outputs for display at least one of the set of candidate strings. The computing device receives an indication of a second gesture to select a second sequence of one or more keys, and determines that characters associated with the second sequence of keys are included in a first candidate word based at least in part on the set of candidate strings, or are included in a second candidate word not based on the first sequence of keys. The computing device modifies the set of candidate strings based at least in part on the determination and outputs for display at least one of the modified candidate strings.

Multi-gesture text input prediction

A method includes a processor of an electronic device receiving first input signals from a first sensor in response to user contact at a first edge of the device and second input signals from a second sensor in response to user contact at a second edge of the electronic device. The first and second sensors are covered by a housing of the device. The processor determines an external context of the device based on analysis of the first input signals and the second input signals. The determined external context indicates at least a position of the device relative to a user or an orientation of the device relative to a user. Responsive to determining the external context, the electronic device executes a particular user input action.

Electronic device response to force-sensitive interface

A computing device is described that receives first input, at an initial time, of a first textual character and a second input, at a subsequent time, of a second textual character. The computing device determines, based on the first and second textual characters, a first character sequence that does not include a space character between the first and second textual characters and a second character sequence that includes the space character between the first and second textual characters. The computing device determines a first score associated with the first character sequence and a second score associated with the second character sequence. The computing device adjusts, based on a duration of time between the initial and subsequent times, the second score to determine a third score, and responsive to determining that the third score exceeds the first score, the computing device outputs the second character sequence.

Temporal based word segmentation

Touch keyboard using language and spatial models

A method includes one or more processors of an electronic device receiving signals from multiple sensors located along an edge of the device. The signals are received in response to external contact being provided to the edge of the device. At least one processor determines a distribution of forces applied to the sensors based on the input signals. Based on the determined distribution of forces, the processor determines: i) a location of the external contact that is offset from a location of each of the multiple sensors, and ii) a magnitude of the force of the external contact. The processor detects whether sensing criteria has been satisfied based on an analysis of: i) the location of the external contact and ii) the magnitude of the force of the external contact. Responsive to detecting that sensing criteria has been satisfied, the processor executes a user input action.

Force-sensitive user input interface for an electronic device

Growing community of inventors

Los Altos, CA, United States of America

Shumin Zhai