System and method for error detection and correction in virtual reality and augmented reality environments
Abstract
Embodiments of the present disclosure are related to training one or more of machine learning algorithms in a virtual reality environment for error detection and correction and/or for employing one or more trained machine learning models in an augmented reality environment to detect and/or correct user errors associated the performance of one or more tasks.
- Inventors:
- Issue Date:
- Research Org.:
- FactualVR, Inc., Jersey City, NJ (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1924872
- Patent Number(s):
- 11379287
- Application Number:
- 16/932,328
- Assignee:
- FactualVR, Inc. (Jersey City, NJ)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- DOE Contract Number:
- SC0018732
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 07/17/2020
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Neeter, Eduardo. System and method for error detection and correction in virtual reality and augmented reality environments. United States: N. p., 2022.
Web.
Neeter, Eduardo. System and method for error detection and correction in virtual reality and augmented reality environments. United States.
Neeter, Eduardo. Tue .
"System and method for error detection and correction in virtual reality and augmented reality environments". United States. https://www.osti.gov/servlets/purl/1924872.
@article{osti_1924872,
title = {System and method for error detection and correction in virtual reality and augmented reality environments},
author = {Neeter, Eduardo},
abstractNote = {Embodiments of the present disclosure are related to training one or more of machine learning algorithms in a virtual reality environment for error detection and correction and/or for employing one or more trained machine learning models in an augmented reality environment to detect and/or correct user errors associated the performance of one or more tasks.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {7}
}
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