Machine Learning Visualization Generation for Rapid Audience Customization
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Solution Overview
Problem
Manually creating infographics is time-consuming and requires substantial domain knowledge and software skills, especially when summarizing complex information from abundant data sources for different audiences.
Innovation Solution
A machine learning-based method that divides textual data into chunks, generates summaries and keywords, selects visualization templates and icons, and automatically generates infographics using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual creation of infographics is used, then customization and domain knowledge integration are improved, but time consumption and effort increase substantially
Solution Approach 1:
The system performs automatic text summarization, keyword extraction, template selection, and icon matching without requiring manual intervention. The machine learning models process the input text and autonomously generate the infographic, eliminating the need for manual customization while maintaining adaptability through algorithmic decision-making.
Solution Approach 2:
The patent replaces manual mechanical processes (hand-drawing, manual text placement, manual template selection) with automated machine learning-based systems. The ML models automatically perform tasks that previously required human creativity and domain knowledge, significantly reducing time consumption while maintaining quality.
2Adaptability or versatility
If multiple infographic representations are created for different audiences, then adaptability is improved, but manual effort increases proportionately
Solution Approach 1:
The system dynamically adapts to different audiences by adjusting the summarization depth, keyword selection, template complexity, and icon styling based on audience characteristics. The machine learning models can generate multiple versions of infographics from the same input text, each optimized for different target audiences without requiring separate manual creation processes.
Solution Approach 2:
The patent creates a universal system that handles multiple audiences and purposes through a single automated pipeline. The same machine learning framework can generate infographics for technical audiences, general audiences, executives, or students by adjusting parameters and selecting appropriate templates, eliminating the need for separate manual workflows for each audience type.
3Measurement precision
If comprehensive data analysis is performed, then information accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant information from the input text through automatic keyword extraction and selective summarization. The machine learning models identify and focus on key concepts, entities, and relationships while filtering out redundant information, achieving accurate representations without processing every detail of the source text.
Solution Approach 2:
The patent applies partial action by generating summaries at different levels of detail and selecting only the essential elements for visualization. The system performs sufficient analysis to capture the core meaning while avoiding excessive processing of minor details, balancing accuracy with processing efficiency through intelligent truncation and selection.
Data Source
AI summary
The present disclosure provides techniques for visualization generation using machine learning. A set of textual data is divided into a plurality of text chunks. A plurality of text summaries is generated based on processing the plurality of text chunks using one or more machine learning models. A plurality of keywords is generated based on processing at least one of the plurality of text chunks or the plurality of text summaries using the one or more machine learning models. A visualization template, from a library of visualization templates, is selected based on at least one of the plurality of keywords. A set of icons, from a library of icons, is selected based on at least one of the plurality of keywords. A visualization is generated using the visualization template and the set of icons and using at least one of the plurality of text summaries.