Structured machine learning for improved whole-structure relevance of informational displays
US11475290B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 30, 2016 |
| Grant date | Oct 18, 2022 |
| Priority date | — |
| Expiry date | Jul 18, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure provides systems and methods that use machine learning to improve whole-structure relevance of hierarchical informational displays. In particular, the present disclosure provides systems and methods that employ a supervised, discriminative machine learning approach to jointly optimize the ranking of items and their display attributes. One example system includes a machine-learned display selection model that has been trained to jointly select a plurality of items and one or more attributes for each item for inclusion in an informational display. For example, the machine-learned display selection model can optimize a nested submodular objective function to jointly select the items and attributes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.