Classifying objects using recurrent neural network and classifier neural network subsystems
US11093819B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 16, 2016 |
| Grant date | Aug 17, 2021 |
| Priority date | — |
| Expiry date | Jan 10, 2040 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB60W2420/408
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed herein are neural networks for generating target classifications for an object from a set of input sequences. Each input sequence includes a respective input at each of multiple time steps, and each input sequence corresponds to a different sensing subsystem of multiple sensing subsystems. For each time step in the multiple time steps and for each input sequence in the set of input sequences, a respective feature representation is generated for the input sequence by processing the respective input from the input sequence at the time step using a respective encoder recurrent neural network (RNN) subsystem for the sensing subsystem that corresponds to the input sequence. For each time step in at least a subset of the multiple time steps, the respective feature representations are processed using a classification neural network subsystem to select a respective target classification for the object at the time step.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.