Microsoft Technology Licensing, LLC

Abhishek Arun

Kevin Timothy Moynihan

Selvaraj Nalliah

Vipindeep Vangala

Cindy Kwan

Sangita N Pitre

Rajeev Gupta

Gaurav Sunil Anand

Giacomo Servadei

Ioannis Gabrielides

Xiaohu Liu

Sarunas Marciuska

Andrea Moro

Jagruti Dushyant Pandya

Grzegorz S Kukla

Prakash Kumar Pandey

Chu Liu

Aspects of the present disclosure relate to identifying relationships between entities in a user graph relative to a specific user. The identified relationships may provide user specific insights on related entities from across the enterprise environment. The insights may be provided to the user in a graphical user interface which may organize the insights into categories of information with selectable features to permit user interaction with the insights. Thus, the insights provided from the user graph or user sub-graph according to aspects described herein, may improve productivity and the potential for collaboration across the enterprise environment while reducing user workload.

Recommendations over meeting life cycle with user centric graphs and artificial intelligence

Various embodiments discussed herein are directed to improving existing technologies by causing presentation, to one or more user devices associated with one or more meeting attendees, of one or more indications of one or more content items during or before a meeting based at least in part on one or more natural language utterances associated with the meeting, a context of the meeting, and/or a context associated with one or more meeting attendees. In other words, particular embodiments automatically recommend relevant content items responsive to the real-time natural language utterances in the meeting, and/or other context.

Near real-time in-meeting content item suggestions

In non-limiting examples of the present disclosure, systems, methods and devices for surfacing and interacting with electronic meeting insights are presented. A meeting request that includes a plurality of invitees may be received. The meeting request may be associated with a plurality of related documents based on one or more attributes and a meeting insight related to the related documents may be surfaced. The meeting request may have a low acceptance in relation to a threshold and a corresponding insight may be surfaced to reschedule the meeting. A user may send a time modification suggestion to the meeting organizer and a rescheduling insight may be surfaced. Time and location information corresponding to invitees and the meeting time may be analyzed and reminders may be surfaced for each of the invitees in relation to a meeting location. Attendees may interact with location-based insights to have directions to a meeting surfaced.

Intelligent meeting insights

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London, United Kingdom

Abhishek Arun