Hierarchical category classification scheme using multiple sets of fully-connected networks with a CNN based integrated circuit as feature extractor
US10366302B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 21, 2017 |
| Grant date | Jul 30, 2019 |
| Priority date | — |
| Expiry date | Dec 28, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L25/18
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
CNN based integrated circuit is configured with a set of pre-trained filter coefficients or weights as a feature extractor of an input data. Multiple fully-connected networks (FCNs) are trained for use in a hierarchical category classification scheme. Each FCN is capable of classifying the input data via the extracted features in a specific level of the hierarchical category classification scheme. First, a root level FCN is used for classifying the input data among a set of top level categories. Then, a relevant next level FCN is used in conjunction with the same extracted features for further classifying the input data among a set of subcategories to the most probable category identified using the previous level FCN. Hierarchical category classification scheme continues for further detailed subcategories if desired.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.