System and method to teach and evaluate image grading performance using prior learned expert knowledge base
US10657838B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 15, 2017 |
| Grant date | May 19, 2020 |
| Priority date | — |
| Expiry date | Mar 8, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/26
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A learning sub-system models search patterns of multiple experts in analyzing an image using a recurrent neural network (RNN) architecture, creates a knowledge base that models expert knowledge. A teaching sub-system teaches the search pattern captured by the RNN model and presents to a learning user the information for analyzing an image. The teaching sub-system determines the teaching image sequence based on a difficulty level identified using image features, audio cues, expert confidence and time taken by experts. An evaluation sub-system measures the learning user's performance in terms of search strategy that is evaluated against the RNN model and provides feedback on overall sequence followed by the learning user and time spent by the learning user on each region in the image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.