3D Multi-Modal Data Visualization for Real-Time Pattern Analysis
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Solution Overview
Problem
Current methods of data visualization, particularly in three dimensions, are limited in their ability to handle complex multi-modal data sets, often requiring users to navigate through disparate data sources and rely on summary information, hindering the recognition of patterns and correlations.
Innovation Solution
A 3D data visualization platform that utilizes three-dimensional computer graphics to dynamically display and manipulate multi-modal data sets, allowing users to view and manipulate underlying data in real-time, and generate 3D data objects with hub, axis, and track objects to facilitate easy comparison and recognition of patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If three-dimensional visualization is used to gain understanding of relationships among data sets, then the ability to recognize patterns and correlations is improved, but the complexity of navigating and manipulating the data increases
Solution Approach 1:
The data visualization is segmented into multiple hierarchical levels: summary level (high-level metrics and key insights), detailed level (individual data points and attributes), and intermediate levels. Users can navigate between these levels without being overwhelmed by the entire data set at once, reducing cognitive load while maintaining pattern recognition capabilities.
Solution Approach 2:
The patent transitions from traditional two-dimensional visualizations to three-dimensional spatial representations, adding a vertical dimension for hierarchy and depth. This allows data points to be positioned in 3D space based on multiple attributes simultaneously, enabling pattern recognition across different dimensions while maintaining intuitive spatial relationships.
2Ease of operation
If summary information is presented to simplify data analysis, then the ease of operation is improved, but the access to relevant underlying data is limited
Solution Approach 1:
The patent merges summary information and detailed underlying data into a single integrated visualization framework. Summary metrics are displayed prominently while maintaining direct links to the underlying data points, allowing users to drill down from high-level summaries to detailed attributes without switching views or losing context.
Solution Approach 2:
The visualization employs a nested structure where summary information contains references to detailed data, which in turn contains references to even more granular attributes. This nested doll approach allows users to access underlying data at multiple levels of detail while maintaining a coherent hierarchical organization, preserving information access while managing complexity.
Data Source
AI summary
Methods and systems are provided for viewing multi-modal data in three dimensions. In one example, a method for visualizing and manipulating multi-modal features of a plurality of data objects includes accessing a plurality of datasets associated with a subject of interest, where data in the plurality of datasets changes in real-time; displaying a hub object representing the subject of interest; dynamically displaying a plurality of axis objects associated with the subject of interest, each comprising a representation of a summary metric of a respective dataset of the plurality of datasets and each displayed proximate the hub object, including adjusting a length and/or intensity of one or more of the plurality of axis objects as the data change; and in response to selection of a first axis object, displaying a first track object including a plurality of representations of data points within a first dataset of the plurality of datasets.