Microsoft Technology Licensing, LLC

Pratik Daga

Guanchao Wang

Sowmitra Thallapragada

Richard Ramirez

Paul M Tyma

David Siuwai Lau

Tobias M Hauth

Shaunak Chatterjee

Ajith Muralidharan

Shipeng Yu

Kinjal Basu

Yiping Yuan

Yan Gao

Sandor Nyako

Matthew Walker

Nirav Nalinbhai Shingala

Matthew Hsing Hung Walker

Manoj Sivakumar

Varun Bharill

Technologies for generating dynamic notification content for notification messages using a machine learned model are provided. The disclosed techniques include identifying an event related to a particular user, where the event has a particular notification type that represents a subject type of the event. Based on the particular notification type of the event, a set of candidate headline and call-to-action combinations corresponding to the particular notification type are identified. Using the machine learned model, scores are calculated for each headline and call-to-action combination in the set of candidate headline and call-to-action combinations. One or more particular headline and call-to-action combinations from the set of candidate headline and call-to-action combinations are selected based upon the scores calculated for each combination of the set of candidate headline and call-to-action combinations. A notification message is generated for the event that includes the one or more particular headline and call-to-action combinations selected.

Machine learning driven dynamic notification content

A unified notification platform for offline creation and distribution of notification content from a variety of data sources is described. The notification platform provides data adaptors that are reusable for generating notifications of different types, specifically, for generating notifications of different types that have features that have meaning across different notification types such that these features can be used to generate comparable relevance scores with respect to candidate profiles. The relevance score calculated for a notification with respect to a member profile is used to determine whether the notification is to be presented to the member represented by the member profile.

Content generation and targeting

A system, apparatus, and method are provided for implementing secondary profiles for members of an online application or service. Each member has a corresponding primary profile populated by the member, and a secondary profile populated with information from data sources other than the member. Each fact or entry in the secondary (or inferred) profile is accompanied by a confidence score reflecting confidence in the source of the fact, confidence that the fact is correctly associated with this member, and/or other factors. A given fact may be obtained or extracted from multiple sources, with each copy or version assigned a separate confidence score. In response to a request to identify members having a particular attribute, in addition to identifying members that have the attribute in their primary profiles, members having the attribute in their secondary profiles may be identified if the corresponding confidence scores are greater than a threshold.

Secondary profiles with confidence scores

Secondary profiles with credibility scores

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Sunnyvale, CA, United States of America

Pratik Daga