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 integrate and analyze complex multi-modal data sets from disparate sources, leading to difficulties in recognizing patterns, correlations, and similarities due to the need to navigate through multiple storage locations and formats, and are often restricted to summary information.
Innovation Solution
A 3D data visualization platform that utilizes three-dimensional computer graphics to represent relationships and transformations of multi-modal data sets, allowing real-time manipulation and access to underlying data, enabling simultaneous multi-scale visualization and recognition of patterns across different data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in different storage locations and formats from multiple sources, then data completeness and variety are improved, but navigation complexity and analysis difficulty increase
Solution Approach 1:
The patent merges multiple data sources, storage locations, and formats into a unified 3D visualization environment. Different data types (tabular, time-series, spatial, unstructured) are combined and rendered together in a single cohesive 3D space, eliminating the need to navigate through separate tables, spreadsheets, and graphs. This integration allows users to access and analyze all data sources from one centralized interface.
Solution Approach 2:
The 3D visualization platform serves as a universal interface that can handle and display multiple data formats and types simultaneously. The system provides multi-functional capabilities to render tabular data, time-series data, spatial data, and unstructured data all within the same 3D environment, making it adaptable to various data sources without requiring separate tools or navigation methods.
2Loss of information
If traditional 2D visualization methods are used, then simplicity and ease of use are maintained, but information completeness and pattern recognition capability are limited
Solution Approach 1:
The patent transitions from traditional 2D visualization to 3D visualization to accommodate and display more data dimensions simultaneously. By adding the third dimension, the system can represent complex multi-dimensional data relationships that cannot be effectively shown in 2D, thereby reducing information loss while maintaining interpretability through spatial relationships and visual encodings.
Solution Approach 2:
The system uses color encoding and visual transformations to represent different data dimensions and relationships in the 3D space. Color changes and visual properties help encode additional information layers, making complex data patterns recognizable and interpretable without overwhelming the user, thus maintaining ease of operation while increasing information completeness.
3Ease of operation
If summary information is presented, then ease of understanding is improved, but access to underlying detailed data and analytical flexibility are reduced
Solution Approach 1:
The 3D visualization implements a nested structure where summary visualizations contain and provide access to underlying detailed data. Users can drill down from high-level summary representations to granular detailed data within the same 3D space, maintaining both ease of understanding through summaries and access to underlying data when needed, without requiring separate views or losing contextual information.
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.