3D Data Visualization Using Machine Learning Parameter Mapping
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Solution Overview
Problem
Existing data visualization systems struggle to efficiently render and provide insights from large-scale, high-dimensional data due to increased computational demands and user difficulty in interpreting complex datasets, particularly in VR and AR environments.
Innovation Solution
A data visualization system utilizing a processor, graphics processing module, and memory to generate 3D mesh objects from high-dimensional data, applying machine learning for smart mapping and correlation analysis to assign visualization parameters, and rendering these objects in parallel to reduce computational load and enhance user understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data visualization methods are used to render large-scale high-dimensional data, then the system can display data in traditional formats, but the computational demands increase and rendering efficiency decreases
Solution Approach 1:
The patent transitions data visualization from traditional 2D displays to immersive 3D virtual reality environments. By mapping high-dimensional data to three spatial dimensions plus additional visual channels (color, size, transparency), the system achieves more intuitive data representation while distributing computational load across the VR rendering pipeline, thereby improving rendering efficiency for large-scale datasets.
Solution Approach 2:
The patent segments the visualization of large-scale high-dimensional data into multiple manageable 3D point objects that can be rendered independently in parallel. This segmentation allows the system to process and display vast numbers of data points by dividing them into discrete visual elements, reducing the computational burden on any single processing unit while maintaining overall rendering efficiency.
2Loss of information
If more data dimensions are visualized to provide comprehensive insights, then the information completeness improves, but the user difficulty in interpreting complex datasets increases
Solution Approach 1:
The patent encodes multiple data dimensions simultaneously within the 3D spatial framework by mapping different data variables to spatial coordinates, color attributes, object sizes, and transparency levels. This multi-channel encoding allows comprehensive information display while maintaining interpretability through the natural spatial reasoning capabilities of human users in VR environments.
Solution Approach 2:
The patent applies different visual encoding strategies to different data dimensions based on their characteristics and importance. Critical dimensions are mapped to prominent visual channels (e.g., position, size), while secondary dimensions use supplementary channels (e.g., color, transparency). This differentiated encoding reduces interpretation difficulty by organizing information hierarchically according to user needs.
3Speed
If traditional rendering methods are used for 3D point objects, then the implementation is simple, but the rendering speed decreases for large datasets
Solution Approach 1:
The patent merges the rendering of multiple 3D point objects into a single optimized rendering pass by grouping points with similar visual properties and using instanced rendering techniques. This consolidation reduces the number of draw calls and shader invocations required, dramatically improving rendering speed for large datasets while managing graphics processing complexity through efficient batch processing.
Solution Approach 2:
The patent performs preliminary processing of 3D point object data before rendering, including pre-computation of visual properties, spatial indexing, and culling of invisible points. By preparing data in advance and organizing it for optimal rendering, the system achieves high rendering speeds without requiring overly complex real-time processing, thereby balancing speed and computational complexity.
Data Source
AI summary
Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes. As such, data visualization processes can allow for rapid, effective use of voluminous, high dimensional data sets.