Microsoft Technology Licensing, LLC

Jeremy Lwanga

Songtao Guo

Daniel Francis

Daniel Sairom Krishnan Hewlett

Sara Smoot Gerrard

Runfang Zhou

Alexandre Patry

William Tang

Wenjun Wu

Wenxiang Chen

John Wu

Michael Mario Jennings

Tanvi Sudarshan Motwani

Chenhui Zhai

Sai Krishna Bollam

Jeffrey Wong

Bo Yang

Robert Perrin Reeves

Xuan Lu

Patrick Ryan Driscoll

Wan Qi Gao

Chaofan Huang

Kristina Caroline Ryan

Bijal Parikh

Manzarul Azad Kazi

Sarah Yu

In an example embodiment, a model is trained to specifically identify reversal points in data and then to rank these reversal points in order of importance. A reversal point shall be defined as a point in which a particular metric, specifically a first order derivative, crosses over from positive to negative or vice-versa. Users are more likely to be interested in abnormal and significant changes in data, and thus the machine-learned model is trained to evaluate a reversal point based on two dimensions: abnormality and significance.

Reversal-point-based detection and ranking

Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.

Context-aware query suggestions

Techniques are provided for dynamically generating content. A request for content about a first entity is received from a client device. In response to receiving the request, an identity of a second entity of the client device is determined. Based on the identity, attributes of the second entity are identified. It is determined whether the attributes satisfy targeting criteria of a campaign that was initiated by the first entity. In response to determining that the attributes satisfy the targeting criteria, a first number of entities that (i) share one or more attributes in common with the second entity and (ii) satisfy the targeting criteria is determined. Also, a second number of entities that share the one or more attributes in common with the second entity is determined. A ratio is calculated based on the two numbers. First content that specifies the ratio is sent to the client device for display.

Customizing requested electronic content based on requesting entity information and contextual information

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Jeremy Lwanga