Microsoft Technology Licensing, LLC

Aleksandar Uzelac

David Alan Stevens

Weidong Zhao

Takahiro Shigemitsu

Vishwac Sena Kannan

Daniel J Hwang

John Graham Pierce

Briggs A Willoughby

Craig Steven Ranta

Pravin Kumar Santiago

Andrey Borissov Batchvarov

Timothy Allen Wright

Robert T Perry

Stanimir Naskov Kirilov

Jeffrey C Maier

David Abzarian

Bodin Dresevic

Silvano Bonacina

Bogdan Radakovic

Robert L Chambers

Adina Magdalena Trufinescu

Fei Su

Khuram Shahid

Miles Cohen

Anthony John Rolls Hodsdon

Sasa Galic

Austin Bradley Hodges

Alan Stephenson

Oren Trutner

Changsin Lee

Thomas Soemo

Ali Emami

Andrew David Wilson

Hrvoje Benko

Robert J Jarrett

Bret P Anderson

Lisa Joy Stifelman

Nigel Stuart Keam

Alice Jane Bernheim Brush

Thomas M Soemo

Christopher Doan

Sergio Paolantonio

Robert D Young

Jonathan Campbell

Shahin Shayandeh

John Lawrence Miller

Michael Nelte

Uros Batricevic

Ali Reza Emami

Yaser Khan

Steven James Frederickson

John G A Weiss

Jenny Kam

Dennis Mooney, Ii

Mouni Reddy

Molly Rose Suver

Adarsh Sridhar

Vivian Dsouza

Michael Seibert

Steven Welch

Zachary Thomas John Siddall

David Pinch

Dejan Lukacevic

Sasa Tomasevic

Alan W Stephenson

Michael A Nelte

David A Stevens

Dennis James Ii Mooney

Systems and methods are disclosed for transitioning communication sessions across services. In one implementation, a first communication is received from a user via a first service. The first communication is associated with a communication session. The first communication is processed to identify a request with respect to a second service. The second service is initiated in response to the request.

Transitioning communication sessions across services

In an embodiment described herein, a method for a mechanism to turn a post processing feature in the Device Media Foundation Transform is described. The method comprises managing, at a device integration service executing on a computing device, a current setting of a feature and updating the current setting as stored in a registry in response to a changed setting of the feature and asserting a setting changed event. The method also includes applying the updated current setting to captured media samples via the Device Media Foundation Transform in response to the asserted setting changed event.

Mechanism to turn on/off post-processing features in the device media foundation transform

An electronic device can receive user input via voice or text that includes tasks to be performed. A digital personal assistant infrastructure service can control to which registered action provider the task is assigned. Per-task action provider preferences can be stored. If a preferred action provider is not able to complete the task, the task can still be performed by a registered action provider that has appropriate capabilities. Machine learning can determine a user's preferences. Resource conservation and effective user interaction can result.

Extending digital personal assistant action providers

Touchscreen testing techniques are described. In one or more implementations, a piece of conductor (e.g., metal) is positioned as proximal to a touchscreen device and the touchscreen device is tested by simulating a touch of a user. This technique may be utilized to perform a variety of different testing of a touchscreen device, such as to test latency and probabilistic latency. Additional techniques are also described including contact geometry testing techniques.

Contact geometry tests

Techniques are described for headlessly completing a task of an application in the background of a digital personal assistant. For example, a method can include receiving a voice input via a microphone. Natural language processing can be performed using the voice input to determine a user voice command. The user voice command can include a request to perform a task of the application. The application can be caused to execute the task as a background process without a user interface of the application appearing. A user interface of the digital personal assistant can provide a response to the user, based on a received state associated with the task, so that the response comes from within a context of the user interface of the digital personal assistant without surfacing the user interface of the application.

Headless task completion within digital personal assistants

Techniques are described for discovering capabilities of voice-enabled resources. A voice-controlled digital personal assistant can respond to user requests to list available voice-enabled resources that are capable of performing a specific task using voice input. The voice-controlled digital personal assistant can also respond to user requests to list the tasks that a particular voice-enabled resource can perform using voice input. The voice-controlled digital personal assistant can also support a practice mode in which users practice voice commands for performing tasks supported by voice-enabled resources.

Discovering capabilities of third-party voice-enabled resources

An electronic device in a topology of interconnected electronic devices can listen for a wake phrase and voice commands. The device can control when and how it responds so that a single device responds to voice commands. Per-task device preferences can be stored for a user. If a preferred device is not available, the task can still be performed on a device that has appropriate capabilities. Machine learning can determine a user's preferences. Power conservation and effective user interaction can result.

Device arbitration for listening devices

Acoustic touch sensitive testing techniques are described. In one or more implementations, a touch-sensitive surface of a touch-sensitive device is tested by detecting contact made with the touch sensitive surface using an acoustic sensor and comparing data describing the contact that is received from the acoustic sensor with data describing the contact that is received from the touch-sensitive device.

Acoustic touch sensitive testing

Touchscreen testing techniques are described. In one or more implementations, a conductor is placed proximal to a touchscreen device and the touchscreen device is tested by simulating a touch of a user by placing the conductor in a grounded state and lack of a touch by the user by placing the conductor in an ungrounded state.

Touchscreen testing

Systems, methods, and computer-readable storage media are provided for efficient real-time ink stroke smoothing, trajectory prediction, and GPU-leveraged rendering of ink stroke input. First and second ink points are received and an active B&#xe9;zier approximation is computed based thereupon. Sequentially later in time that the first and second ink points, a third ink point is received. It is determined whether the third ink point adequately fits the active B&#xe9;zier approximation. Where it is determined that the third ink point adequately fits, an updated active B&#xe9;zier approximation is computed that includes the first, second and third ink points. Where it is determined that the third ink point fails to adequately fit, a different new B&#xe9;zier approximation is computed that includes the third ink point but not the first and second ink points. Leveraging a GPU, a smoothed ink stroke based upon the B&#xe9;zier approximation(s) is rendered.

Smoothing and GPU-enabled rendering of digital ink

A method for providing digital personal assistant responses may include receiving, by a digital personal assistant associated with a plurality of reactive agents, a user input initiating a dialog with the digital personal assistant within the computing device. In response to receiving the input, an operation mode of the computing device may be detected from a plurality of available operation modes. One of the plurality of reactive agents can be selected based on the received input. A plurality of response strings associated with the selected reactive agent can be accessed. At least one of the plurality of response strings is selected based at least on the operation mode and at least one hardware characteristic of the computing device. The selected at least one of the plurality of response strings is providing during the dialog, as a response to the user input.

Scaling digital personal assistant agents across devices

The techniques described herein are directed to receiving parameters directed to correcting spatial error and/or jitter associated with an interaction device connected to a computing device. In some instances, the parameters are encrypted parameters that may be decrypted and consumed to correct the spatial error and/or the jitter associated with the interaction device. For instance, the parameters may provide an adjustment to one or more reported positions of input received from a detection area of the interaction device, so that a display position more accurately reflects, based on the adjustment, an actual position of input on the detection area of the interaction device.

Interaction device corrective parameters

Systems and methods of blocking, ignoring, suspending, or otherwise altering edge-related UI gestures on touch-sensitive computing devices or on non-touch sensitive computing devices having active edge I/O commands in certain situations are disclosed. In one embodiment, a second UI gesture coming from an outside edge may be altered after a first UI gesture from a user using an running application under certain conditions&#x2014;e.g., if the second UI gesture is made within a certain time period after the first UI gesture, the second UI gesture is made within a certain proximity of the first UI gesture, etc. In another embodiment, a computing device is disclosed that comprises a controller, a display screen and an operating system that alters certain edge-related UI gestures that might be made by an operating system if, e.g., certain conditions are present.

Dynamic management of edge inputs by users on a touch device

In embodiments of prediction-based touch contact tracking, touch input sensor data is recognized as a series of components of a contact on a touch-screen display. A first component of the contact can be identified, and a second component can be determined to correlate to the contact. The first component and the second component can then be associated to represent a tracking of the contact. Subsequent components of the contact can be determined and associated with the previous components of the contact to further represent the tracking of the contact.

Prediction-based touch contact tracking

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Seattle, WA, United States of America

Aleksandar Uzelac