Janet J Longhurst

US Patent:

20200374185, Nov 26, 2020

Filed:

Aug 6, 2020

Appl. No.:

16/987328

Inventors:

- Redmond WA, US
Janet Longhurst - Redmond WA, US
Tejas Pravin Mehta - Sammamish WA, US
Wenvi Hidayat - Seattle WA, US
John L. DeMaris - Seattle WA, US
Dieter P. Jansen - Renton WA, US
Mary Ellen Arndt - North Bend WA, US

International Classification:

H04L 12/24
G06Q 10/10
G06Q 50/00
H04L 29/08

Abstract:

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.

US Patent:

20190089581, Mar 21, 2019

Filed:

Sep 20, 2017

Appl. No.:

15/709634

Inventors:

- Redmond WA, US
Janet Longhurst - Redmond WA, US
Tejas Pravin Mehta - Sammamish WA, US
Wenvi Hidayat - Seattle WA, US
John L. DeMaris - Seattle WA, US
Dieter P. Jansen - Renton WA, US
Mary Ellen Arndt - North Bend WA, US

International Classification:

H04L 12/24
H04L 29/08

Abstract:

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.

US Patent:

20190079909, Mar 14, 2019

Filed:

Sep 12, 2017

Appl. No.:

15/702657

Inventors:

- Redmond WA, US
Janet Longhurst - Redmond WA, US
Tejas Pravin Mehta - Sammamish WA, US
Wenvi Hidayat - Seattle WA, US
John L. DeMaris - Seattle WA, US
Dieter P. Jansen - Renton WA, US
Mary Ellen Arndt - North Bend WA, US

International Classification:

G06F 17/22
G06N 99/00
G06F 3/0482
G06F 17/24

Abstract:

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.

US Patent:

20180121849, May 3, 2018

Filed:

May 25, 2017

Appl. No.:

15/605926

Inventors:

- Redmond WA, US
John DeMaris - Seattle WA, US
Janet Longhurst - Bellevue WA, US
Yimin Wu - Redmond WA, US
Jeremy Mazner - Redmond WA, US
Dmitriy Meyerzon - Bellevue WA, US
Nikita Voronkov - Bothell WA, US
Adam Ford - Redmond WA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06Q 10/06
H04L 29/08

Abstract:

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.