Microsoft Technology Licensing, LLC

Susan Michele Hendrich

David E Gardner

William B Dolan

Christopher Brian Quirk

Christopher John Brockett

Shikha Devesh Desai

Domenic J Cipollone

Kristina Nikolova Toutanova

Kaushik Ramaiah Narayanan

Pallavi Choudhury

Zhang Li

Margaret Ann Mitchell

Michael Wilson Daniels

Mouni Reddy

Yu Been Lee

Maria Isabel Carpenter

Anav Silverman

Alyssa Rachel Mayo

Alice Yingming Lai

Xuetao Yin

Gwenyth Alanna Vabalis Hardiman

Juhi Amitkumar Naik

Patricia Hendricks Balik

Penelope Ann Collisson

Bill Dolan

Jay Banerjee

Rebecca Mason

Ron Owens

Yaxiao Song

Liang Xu

Mary P Czerwinski

Ran Gilad-Bachrach

Lee Dicks Clark

Kevin Roland Powell

Sogol Malekzadeh

Melissa Nicole Lim

Alfredo R Arnaiz

Kyle Matthew Von Haden

Olivier Gauthier

Deborah Harrison

Kevin Field

Catherine Lynn Maritan

David Manci Gardner

Catherine L Maritan

Deborah B Harrison

Status-based reading and authoring assistance is provided. A status-based reading and authoring assistance feature can automatically determine a status of a user and automatically adapt reading and authoring tools provided for display based on the determined status. The status-based reading and authoring assistance feature provides the right reading and authoring tools at the right time, according to the current use of the application. During a user session with respect to a productivity application that includes status-based reading and authoring assistance, signals (e.g., user actions and context information) associated with a file can be received. A status regarding the use of the file can be determined using at least the signals. The status can comprise a reading status or an authoring status (or even a sub status). Reading and authoring tools provided for a display of the productivity application can be adapted based on the status during the user session.

Status-based reading and authoring assistance

A method and system for providing replacement text segments for a given text segment may include receiving a request to provide the replacement text segment for the text segment in the document, examining a content characteristic of the document, and examining at least one of user-specific information, organization-specific information, or non-linguistic features of the document, before identifying at least one replacement text segment for the text segment, via a machine translation system, based on the content characteristic of the document and at least one of the user-specific information, the organization-specific information, or the non-linguistic features of the document. The method and system may include providing the identified replacement text segment for display to a user, receiving an input indicating a user's selection of the identified replacement text segment, and upon receiving the input, replacing the text segment in the document with the identified replacement text segment.

Method and system for intelligently suggesting paraphrases

The present disclosure relates to processing operations configured to tailor notifications of productivity feature suggestions based on predictive relevance to a context associate with user access to an electronic document. Machine learning modeling executes a contextual evaluation of user access to predictively determine relevance of a suggestion that relates to: 1) a confidence in the quality of the suggestion; and 2) a timing prediction as to the urgency for surfacing the suggestion to the user so that the suggestion is most applicable. Example notifications are proactive interruptions that aim to aid processing efficiency in task execution as well as an improve user interface experience when users work with an application/service and/or an application platform that comprises a suite of applications/services. A manner in which the notification is presented may vary based on the confidence in the relevance of the suggestion and timing relevance for interrupting a user's workflow.

Proactive notification of relevant feature suggestions based on contextual analysis

A method for providing targeted rewrites can include receiving a selection of text in a file; generating a set of target rewrites of the selection of text, the set of target rewrites comprising: at least one phrase or sentence having semantic similarity to a phrase or sentence of the selection of text; and a style that corresponds to a particular target style, wherein a target style is a representative style for a genre, profession, or environment; and providing for selection one or more of the target rewrites of the set of target rewrites.

Targeted rewrites

The present disclosure relates to processing operations configured to identify and present productivity features that are contextually relevant for user access to an electronic document. In doing so, signal data is evaluated to determine a context associated with user access to an electronic document and insights, from the determined context, are utilized to rank productivity features for relevance to a user workflow. As an example, an intelligent learning model is trained and implemented to identify what productivity features are most relevant to a current task of a user. Productivity features are identified and ranked for contextual relevance. A notification comprising one or more ranked productivity features is presented to a user. In one example, the notification is presented through a user interface of an application/service. For instance, a user interface pane is surfaced to present suggestions. However, in alternative examples, notification of ranked productivity features is presented through different modalities.

Relevance ranking of productivity features for determined context

A 'Facet Recommender' creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks. Further, responses or suggestions provided as utterances may be personalized to individual users.

Sentiment-based recommendations as a function of grounding factors associated with a user

Facet recommendations from sentiment-bearing content

Systems, methods, apparatuses, and computer program products are described for implementing a digital personal assistant. The digital personal assistant is capable of determining that a user has asked a question or made a statement that is intended to engage with a persona of the digital personal assistant. In response to determining that the user has asked such a question or made such a statement, the digital personal assistant provides a response thereto by displaying or playing back a multimedia object associated with a popular culture reference within or by a user interface of the digital personal assistant. Additionally or alternatively, in response to determining that the user has asked such a question or made such a statement, the digital personal assistant provides the response thereto by generating or playing back speech that comprises an impersonation of a voice of a person associated with the popular culture reference.

Leveraging user signals for improved interactions with digital personal assistant

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Redmond, WA, United States of America

Susan Michele Hendrich