US20240185490
2024-06-06
Physics
G06T11/206
Systems and methods are designed to automate the generation of visualizations from raw data using advanced machine learning models. By leveraging summary data, the system can create candidate analytics and visualization scenarios tailored to user needs. This process enhances the accessibility and understanding of complex datasets, allowing users to visualize information effectively based on their specific contexts.
As data usage continues to rise, creating meaningful visualizations has become increasingly complex. Traditional methods often require significant skill and effort, which can lead to less informative results. Users may struggle to derive insights from raw data without proper tools or guidance, making it essential to develop systems that simplify this process and enhance user engagement.
The technology facilitates the automatic generation of visualization scenarios based on user input and summary data. Users can specify relevant data fields and contexts through prompts, allowing the system to create tailored visualization scenarios. This interaction ensures that the visualizations produced reflect the user's perspective and highlight the most pertinent aspects of the data.
Once a visualization scenario is established, the system generates code scaffolds for producing programmatic output. Utilizing a generative machine learning model, these scaffolds define how data should be visually represented, including choices about graphical elements like types and colors. The system also incorporates validation processes to ensure the accuracy of the generated visualizations.
In addition to standard visualizations, the technology can produce infographics using a diffusion model for text-to-image generation. By combining visualization outputs with contextual prompts, the system crafts visually appealing infographics that encapsulate data insights in an artistic manner. This capability further enhances communication of complex information in an easily digestible format.