ML Pathway Recommendations for 3D VR Geological Interpretation
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Solution Overview
Problem
Interpreting and navigating complex three-dimensional virtual reality environments, such as subterranean regions for geological analysis, is cognitively intensive and time-consuming due to the vast amount of data and unknown geological features, requiring efficient tools to identify key features and provide actionable insights.
Innovation Solution
A computer-implemented method using machine learning algorithms to generate personalized route recommendations within a 3D VR environment, trained on historical data and user feedback, to assist users in navigating and interpreting the environment by predicting features of interest and providing explanatory annotations, thereby enhancing user task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually navigates and interprets a complex 3D VR environment for geological analysis, then the user can explore all features in detail, but the cognitive load and time required increase significantly
Solution Approach 1:
The machine learning algorithm performs preliminary analysis of the 3D VR environment before the user arrives, pre-identifying features of interest and preparing annotations. This advance processing allows the user to immediately focus on relevant geological features without manual exploration, significantly reducing analysis time while maintaining identification accuracy
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a bridge between the raw 3D VR environment and the user. This intermediary automatically detects and annotates geological features, translating complex environmental data into user-friendly insights, thereby reducing both cognitive load and analysis time
2Loss of information
If a user manually interprets complex 3D VR environments, then comprehensive analysis is possible, but cognitive load becomes excessive
Solution Approach 1:
The system enables self-service by allowing the machine learning algorithm to automatically detect, classify, and annotate geological features without user intervention. The environment essentially analyzes itself through the ML model, providing comprehensive feature detection while completely eliminating the cognitive burden of manual interpretation from the user
Solution Approach 2:
The patent replaces the mechanical cognitive process of manual feature identification with an automated machine learning system. The ML algorithm substitutes human cognitive functions, performing detection and interpretation tasks that would otherwise require significant mental effort, thereby reducing cognitive load while maintaining detection completeness
3Productivity
If machine learning algorithms are used to generate pathway recommendations, then navigation efficiency improves, but system complexity increases
Solution Approach 1:
The machine learning algorithm serves as an intermediary component that bridges the simple 3D VR environment display and the user's navigation needs. Rather than requiring complex user-side processing, the ML intermediary handles the complexity of path optimization and feature detection on the server side, improving navigation efficiency while keeping the user interface relatively simple
Data Source
AI summary
Embodiments of the invention are directed to a computer-implemented method of generating a pathway recommendation. The computer-implemented method includes using a processor system to generate an intermediate three-dimensional (3D) virtual reality (VR) environment of a target environment. A machine learning algorithm is used to perform a machine learning task on the intermediate 3D VR environment to generate machine learning task results including predicted features of interest (FOI) and FOI annotations for the intermediate 3D VR environment. The processor system is used to generate, based at least in part on the machine learning task results, the pathway recommendation configured to assist a user with navigating and interpreting a 3D VR environment including the intermediate 3D VR environment having the pathway recommendation.