Systems and methods for objective-based scoring using machine learning techniques
US10419440B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 11, 2019 |
| Grant date | Sep 17, 2019 |
| Priority date | — |
| Expiry date | Feb 11, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/102
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.