Adaptive task assignment
US11120373B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 31, 2014 |
| Grant date | Sep 14, 2021 |
| Priority date | — |
| Expiry date | Mar 30, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/063112
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
Crowdsourcing using active learning is described, for example, to select pairs of tasks and groups of workers so that information gained about answers to the tasks in the pool is optimized. In various examples a machine learning system learns variables describing characteristics of communities of workers, characteristics of workers, task variables and uncertainty of these variables. In various examples, the machine learning system predicts task variables and uncertainty of the predicted task variables for possible combinations of communities of workers and tasks. In examples the predicted variables and uncertainty are used to calculate expected information gain of the possible combinations and to rank the possible combinations. In examples, the crowdsourcing system uses the expected information gain to allocate tasks to worker communities and observe the results; the results may then be used to update the machine learning system.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.