Machine learning model for estimating confidential information response
US10558923B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 7, 2016 |
| Grant date | Feb 11, 2020 |
| Priority date | — |
| Expiry date | Jun 8, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.