Microsoft Technology Licensing, LLC

Weizhen Wang

Xuan Li

Yi Feng

Min Lu

Dong Li

Yu Zheng

Mudit Goel

Chen Wang

Luwei Zhang

In various example embodiments, a system and method for generating and routing notifications of content extracted from emails are presented. A member's interest in a specific email among a set of emails are determined based on a relevance score. Content from the specific email are extracted, the content being identified as primary information and secondary information. A notification is generated comprising of the vital content extracted from the specific email. Presentation of the primary information is caused, the primary information being associated with the notification according to a ranked order, the ranked order being based on the relevance score. In response to a member action, presentation of the secondary information is caused, the secondary information being associated with the notification.

Generating and routing notifications of extracted email content

Disclosed in some examples are methods, systems, devices, and machine readable mediums for calculating an entity potential score for an entity that meets a defined entity selection criteria. The entity potential score quantifies an estimated likelihood that the entity will be successful in a predetermined timeframe by meeting predefined entity success criteria. Information on the entity may be determined (e.g., employee information, industry, financial information), various component subscores may be calculated and an entity potential score may be calculated for the entity. This entity potential score may be presented to one or more members of the network accessible computer-based service. For example, the entity potential score may be displayed along with a web-page describing the entity. The entity potential score may be searchable such that a user may specify a maximum, minimum, or range of entity potential scores as a search criteria for a search for entities.

Graphical user interfaces with criteria based entity ranking

Techniques for presenting a recommendation to a member of a social network in a specific field of study are described. A predictor can access, from a database in the social network, educational data of a plurality of students and post-graduate data of a plurality of graduates in the social network. Additionally, the predictor can determine a subset of students associated with a specific field of study, and can calculate a demand for the specific field of study based on the accessed data. A recommendation generator can calculate a competition value for the specific field of study based on the determined subset of students and the calculated demand for the specific field of study. Subsequently, the recommendation generator can cause a presentation, on a display of a device, of a recommendation associated with the specific field of study, the recommendation being based on the calculated competition value.

Calculating supply and demand for a field of study

Techniques for ranking members based on an influence score are described. For example, an influence score process can access, from a database in an online social network, member data of a first member in response to receiving a ranking request of the first member. Additionally, a contribution factor for each first-degree connection of the first member can be determined. A centrality score for the first member can be determined based on the contribution factor for each first-degree connection of the first member. Moreover, an expertise score for the first member can be determined based on the member data of the first member. Furthermore, an influence score of the first member can be calculated based on the centrality score and the expertise score. Subsequently, a user interface is configured to cause a presentation, on a display of a device, of the ranking of the first member based on the influence score.

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San Jose, CA, United States of America

Weizhen Wang