Microsoft Technology Licensing, LLC

Other

Microsoft Corporation

Zhigu Holdings Limited

Micosoft Corporation

Robert L Chambers

David Mowatt

Ralph A Lipe

Steve Dodge

Philipp Heinz Schmid

Todd A Torset

Edward W Connell

Michael Bodell

Alexander G Gounares

Adrian James Garside

Arin J Goldberg

Oliver Scholz

Bodin Dresevic

Kyril Feldman

Timothy H Kannapel

Rudolph Balaz

Manoj Kumar Biswas

Julian James Odell

Subha Bhattacharyay

Brigette Krantz

Shiraz M Somji

Tobiasz Alexander Zielinski

Takanobu Murayama

Jerome J Turner

Karen M Cross

Abhijit Sarkar

Edward Mark Tecot

Thomas R Wick

Stephen Alan Fisher

Nick Gedge

John Bain

Daniel Frederick Penn

Kunal Patel

Jeremy C Waltmunson

Oscar E Murillo

Gregory Nicholas Hullender

Andrew L Silverman

Felix G T I Andrew

Michael H Kim

Daphne Huetu Luong

Annie Wong

Faustinus K Gozali

Andrew Ho

Rod Philander

Corby Anderson

Tobias Zielinski

Qiang Wu

Ciprian Ioan Chelba

Marieke Watson Iwema

Aleksandar Uzelac

Felix Gti Andrew

Vishwac Sena Kannan

Patrick M Haluptzok

Yutaka Suzue

Ryan T Bickel

Daniel J Hwang

Joshua Clow

Angshuman Guha

Mukundan Bhoovaraghavan

Travis Wilson

Erik Castedo Ellerman

Ranjit Kumar Gupta

Vamshidhar G Reddy

Robert Brown

Anuradha N Padte

F Avery Bishop

Charlton E Lui

Daniel B Cook

Sashi Raghupathy

Erez Kikin-Gil

Thomas M Soemo

Shuangyu Chang

Felix Gerard Torquil Ifor Andrew

Matthew P Rhoten

Brian E King

Zoltan C Szilagyi

Adina Magdalena Trufinescu

Bo-June Hsu

Khuram Shahid

Emily Tran

Ali Reza Emami

Karen Kaushansky

Richard Kane Sailor

Jim Chou

Robert Edward Dewar

David Jeremy Guy Wood

Andrew K Krumel

Larry Coryell

David Pinch

Zachary Thomas John Siddall

Oscar H Newkerk, Iii

Eugene A Tsimberg

Michael Kim

Matthew Paul Rhoten

Tobiasz Zielinski

Brian King

Edward Conell

Ralph Lipe

Input context for a statistical dialog manager may be provided. Upon receiving a spoken query from a user, the query may be categorized according to at least one context clue. The spoken query may then be converted to text according to a statistical dialog manager associated with the category of the query and a response to the spoken query may be provided to the user.

Using multiple modality input to feedback context for natural language understanding

Techniques performed by a data processing system for processing voice content received from a user herein include receiving a first audio input from the user comprising a mixed-mode dictation, analyzing, using one or more machine learning (ML) models, the first audio input to obtain a first interpretation of the mixed-mode dictation, presenting the first interpretation to the user in an application on the data processing system, receiving a second audio input from the user comprising a corrective command, analyzing the second audio input to obtain a second interpretation of the restatement of the mixed-mode dictation presenting the second interpretation to the user, receiving an indication from the user that the second interpretation is a correct interpretation of the mixed-mode dictation, and modifying the operating parameters of the one or more machine learning models to interpret the subsequent instances of the mixed-mode dictation based on the second interpretation.

Compounding corrective actions and learning in mixed mode dictation

A computing system includes at least one processor and memory storing instructions that, when executed, cause the computing system to receive an indication of a set of grammar commands for an application associated with the computing system, each grammar command corresponding to an executable command that is executable by the application to perform a corresponding application action, receive an indication of a first speech input, from a user, captured by a speech capture component, detect an ambiguity based on a correlation between the first speech input and the set of grammar commands, provide, to the user, an output that represents the detected ambiguity, receive, in response to the output, an indication of a second speech input that is captured by the speech capture component and resolves the ambiguity, and, based on the second speech input execute the executable command that corresponds to one of the grammar commands.

Centralized method and system for determining voice commands

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

Techniques and systems to provide speech recognition services over a network using a standard interface are described. In an embodiment, a technique includes accepting a speech recognition request that includes at least audio input, via an application program interface (API). The speech recognition request may also include additional parameters. The technique further includes performing speech recognition on the audio according to the request and any specified parameters; and returning a speech recognition result as a hypertext protocol (HTTP) response. Other embodiments are described and claimed.

Techniques to provide a standard interface to a speech recognition platform

A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.

Centralized method and system for clarifying voice commands

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

A voice command definition file (VCDF) declaratively defines voice commands for an application. For example, the VCDF may include definitions for: voice commands; one or more phrases/utterances that may be said to execute each of the commands; a navigation location to navigate to within the application (e.g. a page); phrase lists containing items that may be used as a parameter in a voice command; examples; feedback; and the like. A user may say a single utterance to launch the application, navigate to the associated location of the command and execute the command. The VCDF may define multiple ways to listen for a particular command. The VCDF may be edited/defined by a user and may include a user friendly name for an application. A speech engine loads the VCDF for use such that it may recognize the commands associated with an application. The definitions may be updated during runtime.

Voice command definitions used in launching application with a command

An object is associated with one or more controls in a software application. An object associated with a control determines the operation of the data entry user interface when the data entry user interface is being employed to enter data into the control. More particularly, the object may communicate interface e characteristics to a component that is responsible for providing the user interface to the user. Such a component may be, for example, a shared software module that renders the user interface on a display, receives input data from the user through the user interface, and routes the entered data to a designated destination. Alternately, the object itself may create a user interface having the specified characteristics.

Utility object for specialized data entry

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Issaquah, WA, United States of America

Robert L Chambers