Invention Grant
- Patent Title: Machine learning model for estimating confidential information response
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Application No.: US15371874Application Date: 2016-12-07
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Publication No.: US10558923B1Publication Date: 2020-02-11
- Inventor: Krishnaram Kenthapadi , Stuart MacDonald Ambler , Edoardo M. Airoldi
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Schwegman Lundberg & Woessner, P.A.
- Main IPC: G06N5/04
- IPC: G06N5/04 ; G06N7/00 ; G06N20/00

Abstract:
In an example, one or more member profiles and corresponding Boolean attributes indicating, for each of the one or more member profiles, whether the corresponding member of a social networking service interacted with a request for confidential data, are obtained. A first set of one or more features are extracted from the one or more member profiles. The first set of one or more features and corresponding Boolean attributes are fed into a machine learning algorithm to train a confidential data response propensity prediction model to output a predicted propensity to interact with a request for confidential data for a candidate member profile. A second set of one or more features are extracted from the candidate member profile. The extracted second set of one or more features are fed to the confidential data response propensity prediction model, outputting the predicted propensity to interact with a request for confidential data.
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