Machine learning based identification of visually complementary item collections
US10776626B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 14, 2018 |
| Grant date | Sep 15, 2020 |
| Priority date | — |
| Expiry date | Jan 12, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/30
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Aspects of the present disclosure relate to machine learning techniques for identifying collections of items that are visually complementary. These techniques can relying on computer vision and item imagery. For example, a first portion of a machine learning system can be trained to extract aesthetic item qualities from pixel values of images of the items. A second portion of the machine learning system can learn correlations between these extracted aesthetic qualities and the level of visual coordination between items. Thus, the disclosed techniques use computer vision machine learning to programmatically determine whether items visually coordinate with one another based on pixel values of images of those items.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.