Microsoft Technology Licensing, LLC

Shikha Devesh Desai

Ruth Kikin-Gil

Rolf Arnold Ebeling

Theresa Ann Estrada

Michelle Elena Keslin

Allison Anne Whilden

Taili Feng

Maria Del Mar Gines Marin

Paul Anthony Scudieri

YuBeen Lee

Alan Andrew Urdan

Kylan Matthew Nieh

Yu Been Lee

Kathryn V J Sullivan

Annice Jumani

Yu Mao

Stephanie Cooper

Alicia Drummond

Harold S Gomez

Han-Yi Shaw

Peter Loren Engrav

Michael Koenig

Ali Taleghani

Malia Morioka Douglas

Bernhard S J Kohlmeier

Kevin Roland Powell

Pradeep Chilakamarri

Domenic J Cipollone

Gregory Akselrod

Derik Bjorn Stenerson

James Squires Masson

Rachel Sirkin

Susan Michele Hendrich

Benjamin J Bunker

Stephen White

Trevor J Harris

Aaron E Erlandson

Alfredo R Arnaiz

Patrick Davis

Donald Gordon Hardy

Evan W Lavender

Anav Silverman

Alyssa Rachel Mayo

Kenichiro Chiba

Nicole Michel

Tev'n J Powers

Patricia Hendricks Balik

Jie Dong

Christian Harper-Cyr

Gwenyth Alanna Vabalis Hardiman

Penelope Ann Collisson

Dermot McLoughlin

Amy Tremper

Maria Isabel R Carpenter

Jesse Clay Satterfield

Gary Caldwell

Suzan Marashi

Caitlin E Ashley-Rollman

Ian Mikutel

Sung Joon Won

Robin Emily Wakefield

Michael Wilson Daniels

Eugene Y Suh

Erin M O'Connell

Yoon Jin Lee

Wesley Hodgson

Hany Grees Gerges

Maria Isabel Carpenter

Hani Farouq Abu-Huwaij

Navneet Singh Kambo

Zachary Burstain

Enrique J Dominguez

Rebecca Poulson

Maria Isabel-Rodriguez Carpenter

Seyit Yilmaz

Non-limiting examples of the present disclosure describe creation and management of a contact associated with a document. A contact for a document in a first application may be created. The contact may be used to add content, from a second application, to the document. The contact may be stored. Contact data for the contact may be transmitted to one or more processing devices. An exemplary created contact may be used to transfer content from one or more applications to a document of another application. Other examples are also described.

Contact creation and utilization

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

In non-limiting examples, the present disclosure is directed to systems and methods for performing an analysis of an electronic document comprising a review for a plurality of writing issues related to the electronic document's conformance to a specified target audience; causing, in an application associated with the electronic document, one or more flagged writing issues identified by the analysis of the electronic document to be displayed; receiving a selection of a flagged writing issue; and based on the selection of the flagged writing issue, causing, in a contextual menu, one or more alternative suggestions to replace text associated with the flagged writing issue to be displayed, the one or more alternative suggestions based, at least in part, on a fluency metric and a resemblance of the one or more alternative suggestions to the text associated with the flagged writing issue.

Contextual menu with additional information to help user choice

A method that enables time as data is described herein. The method includes constructing a tree structure, wherein data of the tree structure is a plurality of time intervals, and layers of the tree below a root node comprise identical sibling nodes for layers above a threshold interval length and parent nodes share identical sibling nodes. The method also includes assigning a node identification to each node of the tree structure, wherein each node of the tree structure corresponds to a time interval, and traversing the tree structure to convert a timestamp to a human readable time.

Time systems as data

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 system for a map calendar is described herein. The map calendar includes a calendar content database that is to store at least one item of calendar content and a navigation system that determines a zoom level and a panning position of the map calendar. The map calendar also includes a processor that is to determine a summarization level based on the zoom level and the panning position of the map calendar and render the calendar content according to the summarization level.

Digital map calendar user interface

A system and method includes receiving content of an electronic document during a time the content is being entered into an electronic document by a user by via an electronic document editor application. A content characteristic is extracted from the content and transmitted to an online data source. A content item related to the content characteristic is received from the online data source. A user interface presents the content item concurrently with the electronic document editor application.

Electronic document supplementation with online social networking information

In non-limiting examples of the present disclosure, systems and methods for providing customizable electronic document review options within electronic document processing applications are provided. An analysis of an electronic document may be performed comprising a review of an electronic document for a plurality of style issues, a plurality of spelling issues and a plurality of grammar issues. A proofing task pane having a plurality of selectable options may be displayed. A total number of identified issues associated with each of the plurality of style issues, the plurality of spelling issues and the plurality of grammar issues may be displayed in the task pane based on the analysis of the electronic document. A value associated with the electronic document may be calculated based on a fluency metric associated with the electronic document and an indication of the fluency value for the electronic document may be displayed in the task pane.

Proofing task pane

Non-limiting examples of the present disclosure describe transformation of content of a digital document on behalf of a user of an application/service. Examples describes herein extend to instances where an existing digital document (e.g. content portion) is transformed as well as examples where templatized transformations are used to create a digital document. As an example, content of a digital document may be converted into preset document format representations which are generated and suggested automatically to the user through a user interface of a productivity service or through an alternative modality (e.g. message, email, notification, etc.). This improves processing efficiency of computing devices associated with productivity services, for example, by enabling efficient digital document management through proactive contextual analysis, reducing the number of processing operations for users in real-time and reducing latency at run-time, among other examples.

Content based transformation for digital documents

Access and management of a user's content may be facilitated by, in response to receiving a request for content related to a specified entity from the repository associated with at least the user's account, identifying, in a content of a file in the repository, an entity container of at least one entity container associated with the specified entity; and communicating the entity container that is associated with the specific entity to a source of the request.

Access and management of entity-augmented content

A method of automatically augmenting content through augmentation services can include invoking a service to receive an entity determination based on the content and an entity container comprising an object of the content and one or more attributes of the object. The entity determination and corresponding entity container can be assigned as a marker to content being clipped. The marker can be used to enable access and actions that can be taken with respect to the clipping or the structured information augmenting the clipping.

Automatic augmentation of content through augmentation services

A system and method includes receiving an electronic document having content divided into components and, for each of the components, comparing content of the component with component content characteristics of a classification schema associated with a document type and storing, in an electronic data storage, in a record associated with the component, indications of the component content characteristics of the classification schema that are included in the component. Document content characteristics of the classification schema are compared with the content of the electronic document and storing, in the electronic data storage, in a record for the electronic document, indications of the document content characteristics of the classification schema that are included in the electronic document. The electronic document is identified as the document type based on the content characteristics of the records meeting a document type criteria.

Electronic document content classification and document type determination

A system and method includes obtaining content extracted from an electronic document, the electronic document having a document type. At least some of the content is transmitted to an online social networking system based on the document type. A recommended content change is received from the online social networking system. A user interface dynamically displays the recommended content change concurrently with display of the electronic document as the electronic document is updated.

Electronic document content augmentation

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Bellevue, WA, United States of America

Shikha Devesh Desai