Microsoft Technology Licensing, LLC

Michael M Santos

Brian Russell Glaeske

Anastasia Paushkina

Kevin M Honeyman

Adrian L Orth

Prasant Sivadasan

Julie B Hagen

Ray J Ridl

Michael J McCormack

Ted Cyrek

Ray Ridl

Ransom Lloyd Richardson

Anne Gerssen

Rui Hu

Brian L Welcker

Kyle Stapley Young

Wayne C Higgins

Lisa Renee Mueller

Julianne Prekaski

Crystal L Gilson

Siqing Chen

Yong Zhang

Yimeng Li

Christopher R Garty

John P Burrows

Sunil Garg

Sneha Jayaprakash

William Nain Saez

Tejprakash Singh Gill

Jichen Yang

Yash Ravi Punjabi

Tracy A Childers

Jinnie Park

Elijah John Scherrer

Raymond C Li

Zeguang Wu

Juan Antonio Karmy Tacla

Priya Tushar Nakhre

Anshul Basia

David Milićević

Jeremy S Ellsworth

Chris R Garty

Claudia Betz-Haubold

Rupa Mantravadi

Alyssa Nelson

Pramod Kumar Gupta

Sidhika Varshney

Joe A Herzog

Methods, systems, and computer storage media for providing action-recommendations (e.g., save action, collaboration action, and upload action) using cloud system operations in a cloud system. For example, action-recommendations are predicted using usage data of applications and communicated to a user (e.g., via a prompt) such that the user elects to perform or not perform the action-recommendation. In operation, usage data for an application and an action-recommendation profile are accessed. Determining that an action-recommendation should be generated is based on the usage data and the action-recommendation profile. The determination is further based on action scores and thresholds (e.g., save action, collaboration action, upload action). When the action scores meet certain thresholds, the action-recommendation is generated as a save-action-recommendation, a collaboration-action-recommendation, or an upload-action-recommendation. Based on generating the action-recommendation, an action recommendation interface element (e.g., an action prompt or action dialog) is generated to perform a corresponding action.

Prediction-based action-recommendations in a cloud system

A system for selectively rendering highlights in a working document. The system includes a user interface, a memory for storing executable program code, and one or more electronic processors. The electronic processors are functionally coupled to the memory and the user interface. The electronic processors are configured to receive a first highlight operation at a highlight manager associated with a first highlight via the user interface. The electronic processors are further configured to transmit the received first highlight operation to a highlight actor, update a document object model associated with the working document based on the received first highlight operation, and render the first highlight on the working document using a first color based on the received first highlight operation.

Differentiating in-canvas markups of document-anchored content

An entity hub display includes a plurality of different sections, each section including a plurality of different components. The sections each correspond to a different aspect of data that makes up an entity. Each component is a user interface display element that is related to data within the corresponding section. The individual components can be selected and placed on the entity hub display based on a user's role and activities or tasks performed by a user in that role.

Displaying different views of an entity

A workspace display includes a plurality of different groups, each group including a plurality of different components. Each group corresponds to a task, set of tasks or topic of information related to a user's role. The particular components included in each group are user interface display elements that are each related to an item of content within the corresponding group. The workspace display has filter mechanisms that are actuated to filter the content displayed in the components on the workspace display.

Filtering content on a role tailored workspace

Display screen with graphical user interface

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Michael M Santos