Multi-task learning framework for multi-context machine learning
US11604990B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 16, 2020 |
| Grant date | Mar 14, 2023 |
| Priority date | — |
| Expiry date | Jun 29, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.