公开(公告)号：US20240257971A1

公开(公告)日：2024-08-01

申请号：US18430445

申请日：2024-02-01

Applicant: Arizona Board of Regents on behalf of the University of Arizona

Inventor： William Killgore ,  Janet Roveda ,  Jerzy Rozenblit ,  Ao Li ,  Huayu Li

IPC: G16H50/20

CPC classification number: G16H50/20

Abstract: A virtual reality neuropsychological assessment (VRNA) system uses a deep learning network and a VR headset to administer multi-domain assessments of human cognitive performance. The deep learning network is trained to identify features in sensor data indicative of neuropsychological performance and classify users based on the features identified in the sensor data. The VR headset provides a user with a virtual simulation of an activity involving decision-making scenarios. During the virtual simulation, sensor data via a plurality of sensors of the VR headset is captured. The sensor data is applied to the deep learning network to identify features of the user and classify the user based on the features into a neuropsychological domains, such as attention, memory, processing speed, and executive function. Sensor data includes eye-tracking, hand-eye motor coordination, reaction time, working memory, learning and delayed memory, and inhibitory control.

公开(公告)号：US11868479B2

公开(公告)日：2024-01-09

申请号：US17290627

申请日：2019-11-01

Applicant: Arizona Board of Regents on Behalf of the University of Arizona

Inventor： Roman Lysecky ,  Jerzy Rozenblit ,  Johannes Sametinger ,  Aakarsh Rao ,  Nadir Carreon

IPC: G06F21/57 ,  G16H20/17 ,  A61B5/00 ,  A61N1/362 ,  A61N1/372 ,  G06F21/56 ,  A61M5/142

CPC classification number: G06F21/577 ,  A61B5/0022 ,  A61B5/0031 ,  A61N1/362 ,  A61N1/37223 ,  G06F21/566 ,  G16H20/17 ,  A61M5/14276 ,  G06F2221/034

Abstract: A security framework for life-critical and safety-critical devices, specifically medical devices, using: a) runtime, adaptive methods that dynamically assess the risk of newly discovered vulnerabilities and threats, and b) automatic mitigation methods that reduce system risk by seamlessly reconfiguring the device to operate within different execution modes. This technology automatically isolates threats by disabling affected system components. A multi-modal software design uses adaptive software in which operational modes have monotonically decreasing cumulative risk. Formal risk models are used to model the individual risk of accessing or controlling system components and to automatically calculate the cumulative risk of software modes. The automated detection of potential threats by the system or reporting of known vulnerabilities will dynamically change the system risk. To support an accurate and fine grained adaptive risk model, novel statistical methods non-intrusively detect potential threats, isolate the threat to a specific component, and estimate the threat probability.