Adaptive Virtual Training Environment Using Predictive Machine Learning

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Solution Overview

Problem

Conventional multi-user virtual training systems lack realism and effectiveness in preparing users for real-world scenarios, particularly in planning and responding to threats and adversary actions, and fail to engage users with immersive and collaborative training experiences.

Innovation Solution

The system employs web-based virtual environments with advanced simulations, virtual entities, and predictive machine learning models to provide realistic training scenarios, self-monitored user progress, and collaborative tools, utilizing VR or AR technologies to create immersive 3D environments and adaptive training experiences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional virtual training simulations are used, then training can be conducted in a virtual environment, but the simulations lack realism and fail to engage users

Engineering Contradiction:

Improverealism of training simulationVSAvoidcomplexity of simulation environment

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The training simulation employs dynamic adaptive scenarios that change in real-time based on user responses and performance. The virtual environment adapts its complexity and content dynamically to match user needs, transitioning from simple to complex scenarios as users progress, thereby maintaining engagement while managing computational complexity efficiently

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes multiple parameters of the virtual environment including visual fidelity, scenario complexity, and interaction depth based on user performance metrics. By adjusting these parameters adaptively, the system achieves high realism when needed while reducing complexity during introductory phases

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If conventional virtual training is used, then training can be delivered remotely, but it fails to prepare users for real-world scenarios requiring quick decision-making

Engineering Contradiction:

Improvepreparedness for real-world scenariosVSAvoidtime for decision-making practice

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by presenting users with simplified versions of real-world scenarios before progressing to more complex situations. Users practice decision-making patterns in a controlled virtual environment first, then apply these patterns to increasingly realistic scenarios, building readiness incrementally rather than jumping directly to complex real-world simulations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The training system incorporates real-time feedback mechanisms that analyze user decisions and provide immediate correction. This feedback loop allows users to learn from mistakes in real-time, accelerating their decision-making capability development without requiring extended practice time in low-stakes environments

Inventive Principle:

Principle #23Feedback

3Extent of automation

If multi-user virtual training is used, then collaborative learning can occur, but the system lacks automated monitoring and individualized adaptive training

Engineering Contradiction:

Improveautomated training monitoringVSAvoiduser performance data collection

Core Design Contradiction:

Extent of automationVSLoss of information

Solution Approach 1:

The training system performs self-service by automatically collecting, analyzing, and utilizing user performance data without requiring manual intervention. The system self-monitors user interactions, self-evaluates performance metrics, and self-adjusts training scenarios based on collected data, eliminating the need for manual monitoring while comprehensive data collection

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual monitoring mechanisms with automated digital sensing and analysis systems. Instead of instructors manually observing and recording user performance, the system uses automated software agents that track user actions, measure performance metrics, and generate individualized training recommendations through computational analysis

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11868519B2Systems and methods for virtual training within three-dimensional adaptive learning environments

Publication Date: 2024.01.09 ARCHITECTURE TECH CORP

AI summary

Disclosed herein are embodiments for managing a task including one or more skills. A server stores a virtual environment, software agents configured to collect data generated when a user interacts with the virtual environment to perform the task, and a predictive machine learning model. The server generates virtual entities during the performance of the task, and executes the predictive machine learning model to configure the virtual entities based upon data generated when the user interacts with the virtual environment. The server generates the virtual environment and the virtual entities configured for interaction with the user during display by the client device, and receives the data collected by the software agents. The system displays a user interface at the client device to indicate a measurement of each of the skills during performance of the task. The server trains the predictive machine learning model using this measurement of skills during task performance.