Microsoft Technology Licensing, LLC

Janet Longhurst

John Lincoln DeMaris

Tejas Pravin Mehta

Dieter P Jansen

Mary Ellen Arndt

Sooraj Ashutosh Purandare

Wenvi Hidayat

Dmitriy Meyerzon

Nikita Voronkov

Jeremy Mazner

Nicholas Anthony Buelich, Ii

Melissa Torres

Adam Ford

Yimin Wu

John L Demaris

Techniques are described herein that are capable of intelligently updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Intelligently updating a collaboration site or template

Techniques are described herein that are capable of iteratively updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Iteratively updating a collaboration site or template

In one example, an activity feed server may describe events in a project by collecting events from across multiple services into an activity feed personalized to the user. The activity feed server may store an event set describing activities related to the project. The activity feed server may rank a mature event set from the event set of events older than a period matching a processing delay based on a relevance weighting for a user to generate a curated event list. The activity feed server may queue a recent event set of events younger than the processing delay in chronological order to generate a recent event list. The activity feed server may generate an event list having the curated event list and the recent event list. The activity feed server may send the activity feed having the event list to a client device for presentation to the user.

Personalized aggregated project team activity feed

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Redmond, WA, United States of America

Janet Longhurst