Custom labeling workflows in an active learning-based data labeling service
US11481906B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 29, 2019 |
| Grant date | Oct 25, 2022 |
| Priority date | — |
| Expiry date | May 5, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L67/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The process may continue iteratively until the model converges. This enables a dataset to be labeled without requiring each item in the data set to be individually and manually labeled by human labelers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.