Invention Application
- Patent Title: Training Machine-Learned Models for Perceptual Tasks Using Biometric Data
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Application No.: US17428659Application Date: 2020-01-16
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Publication No.: US20220130134A1Publication Date: 2022-04-28
- Inventor: Aren Jansen , Malcolm Slaney
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- International Application: PCT/US2020/013772 WO 20200116
- Main IPC: G06V10/774
- IPC: G06V10/774 ; G06V10/80 ; G06V40/10

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.).
Public/Granted literature
- US11823439B2 Training machine-learned models for perceptual tasks using biometric data Public/Granted day:2023-11-21
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