Microsoft Technology Licensing, LLC

Wendy Guo

Leanna Holly Robb

Shruti Poddar

Julian Alexander Zbogar-Smith

Shamsi Tamara Iqbal

Lisa Sarah Thomas

Danielle Amanda Scott

Nagendra Kalyan Nanduru

Vivian Caroline Hua

Maja Vladan Milosavljevic

Bichong Li

Sandeep Seri

Preeti Goel

Cathleen Kong

Kayla Mailinh Tran

Ananthatejas Raghavan

Feng Zhang

Vivek Chauhan

Siqi Yang

Jinkai Zhang

Noelle Renee Beaujon

Tor Andrew Imsland

Qi Zhang

Darren Glenn Austin

Andrew James Wald

Vineet Batta

Charu Kwatra

Chi Huang

Justin Joseph Fagnan

Danny Lee Fraser

Michelle Casanova

Helen Harmetz

Chelly Ashley Jin

The disclosure herein describes a system for calculating meeting inclusion metrics including insights and recommendations. Meeting data associated with one or more meetings attended by at least one participant remotely is converted into anonymized meeting data for inclusivity metric analysis. An inclusivity insights manager generates inclusivity metrics associated with inclusive behavior and language occurring during meetings to measure the level of inclusivity. The inclusivity metrics include attendee participation metrics measuring an amount of participation by each meeting attendee, participation in-person versus participation remotely, concurrent speech indicating attendees may be talking over one another or other interruptions occurring during meetings. Inclusivity metric data includes insights and actionable recommendations to improve inclusivity at future meetings provided at an individual level, group level or organizational level. The inclusivity insights can also include percentage metric values, graphs, feedback, and other metric-related information for improving participation by meeting attendees.

Meeting inclusion and hybrid workplace insights

A computer implemented method includes receiving a start date and an end date specifying a future absence or away-from-work time period for a user, generating an away from office communication specifying the away-from-work time period, retrieving via the computer a list of collaborators based on frequency and duration of recent interactions with the user, receiving via the computer a selection of collaborators from the list, identifying meetings involving the user during the away-from-work time period, generating via the computer a list of user viewable items for the user to complete prior to the away-from-work time period, and generating via the computer a list of items for the user to attend to upon return from the away-from-work time period for presentation to the user upon return.

Integrated time away management mechanism

The disclosure herein describes a virtual commute experience system. In some examples, the virtual commute experience includes receiving a plurality of information from one or more different programs; in response to a trigger event, launching a virtual commute experience; in response to the launch of the virtual commute experience, displaying a first portion of the plurality of information from the one or more different programs; receiving a first user interaction; in response to receiving the first user interaction, displaying a second portion of the plurality of information; receiving a second user interaction; outputting a prompt to continue or end the virtual commute experience; and in response to receiving a response to the prompt to end the virtual commute experience, pausing notifications generated by one or more different programs via the user interface until another trigger event.

Virtual commute

The disclosure herein describes an emotional insight management system that provides user-configured prompts and insights to the user. In some examples, an insights application provides personalized, recurring prompts to encourage users to provide current emotional state updates to a well-being journal. The user's responses over time are aggregated within a historical trends report, which provides emotional response data over time. The insights application generates insights into potential reasons for the user's emotional states on a given day. The system analyzes the user-provided emotion response data with integrated work-related data to identify reasons for the user's mood and make recommendations for actions to be taken by users to reduce stress and improve the user's emotional well-being.

Emotional management system

In non-limiting examples of the present disclosure, systems, methods and devices for surfacing email suggestions are presented. A first user account associated with a composer of an email may be identified. Core working hour and a time zone associated with that first user account may be determined. One or more recipient user accounts included in the email may be identified. Core working hours and a time zone associated with the recipient user accounts may be identified. If more than a threshold number, percentage, and/or ratio of the recipient users is not currently in core working hours when the email is being drafted/sent, a suggestion may be surfaced in association with the email to send the email at a later time when more recipient users are in their core working hours.

Suggested delay delivery of electronic messages

A system and method for computing a metric indicating a user operation of an application is described. The system accesses online activity data of a user operation of the application. The system filters the online activity data based on a preset time range. An after-hours activity score is calculated based on a type of activity or a duration of an activity from the filtered online activity data, and a weight assigned to the type of activity or the duration of the activity. The system computes an after-hours metric based on the after-hours activity score. A configuration setting for the application based on the after-hours metric is applied to the application.

Computation of after-hours activities metrics

Systems and methods for providing productivity insights regarding user networks are provided. Productivity insights are determinable based on event data, such as email messaging events and/or calendaring events, and enable a user to see at a glance how and with whom the user has spent his or her time. Additionally, productivity insights highlight any changes that occur over time within a user network. Analytics based on event data allow a user to easily identify top collaborators, which may or may not be the most important collaborators, as well as specific metrics for each collaborator, such as response time, email read rate, total collaboration time, etc. Thus, productivity insights serve to qualify and quantify collaborative relationships for individual employees so that they can leverage their time more effectively by improving collaboration within their networks, thereby increasing workplace productivity.

Productivity insight dashboard

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Edmonds, WA, United States of America

Wendy Guo