Training machine-learned models for perceptual tasks using biometric data
US11823439B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 16, 2020 |
| Grant date | Nov 21, 2023 |
| Priority date | — |
| Expiry date | Jul 30, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Generally, the present disclosure is directed to systems and methods that train machine-learned models (e.g., artificial neural networks) to perform perceptual or cognitive task(s) based on biometric data (e.g., brain wave recordings) collected from living organism(s) while the living organism(s) are performing the perceptual or cognitive task(s). In particular, aspects of the present disclosure are directed to a new supervision paradigm, by which machine-learned feature extraction models are trained using example stimuli paired with companion biometric data such as neural activity recordings (e g electroencephalogram data, electrocorticography data, functional near-infrared spectroscopy, and/or magnetoencephalography data) collected from a living organism (e.g., human being) while the organism perceived those examples (e.g., viewing the image, listening to the speech, etc.).
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.