Deep reinforcement learning framework for sequence level prediction of high dimensional data
US11829878B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 29, 2022 |
| Grant date | Nov 28, 2023 |
| Priority date | — |
| Expiry date | Jun 29, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/174
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In sequence level prediction of a sequence of frames of high dimensional data one or more affective labels are provided at the end of the sequence. Each label pertains to the entire sequence of frames. An action is taken with an agent controlled by a machine learning algorithm for a current frame of the sequence at a current time step. An output of the action represents affective label prediction for the frame at the current time step. A pool of actions taken up until the current time step including the action taken with the agent is transformed into a predicted affective history for a subsequent time step. A reward is generated on predicted actions up to the current time step by comparing the predicted actions against corresponding annotated affective labels.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.