Saliency prediction for informational documents
US11263470B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 15, 2017 |
| Grant date | Mar 1, 2022 |
| Priority date | — |
| Expiry date | Oct 18, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/412
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A content saliency network is a machine-learned neural network that predicts the saliency of elements of a content item. The content saliency network may be used in a method that includes determining a set of elements in draft content and computing a first pixel-level vector for the content. The method may also include, for each element in the set of elements, computing a vector of simple features for the element, the simple features being computed from attributes of the element, computing a second pixel-level vector for the element, computing a third pixel-level vector for an intermediate context of the element, and providing the vectors to the content saliency network. The content saliency network provides a saliency score for the element. The method further includes generating an element-level saliency map of the content using the respective saliency scores for the set of elements and providing the saliency map to a requestor.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.