AI Visualization Code Scaffolds for Persuasive Data Infographics

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Solution Overview

Problem

Existing systems struggle to generate informative and persuasive visualizations of data, particularly for users lacking the necessary skills or resources, leading to inefficient and time-consuming manual processes.

Innovation Solution

A system utilizing a generative machine learning model to automatically generate visualization scenarios, code scaffolds, and infographics by processing natural language inputs, incorporating user feedback, and employing diffusion models for artistic rendering.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual processes are used to create data visualizations, then users can have full control over design details, but the process becomes time-consuming and requires significant user effort and skill

Engineering Contradiction:

Improveuser effortVSAvoidvisualization creation efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system enables self-service by allowing users to input raw data and receive automatically generated visualizations without requiring manual design skills. The generative model autonomously performs the complex tasks of data analysis, visualization selection, and graphic generation, making the system serve itself rather than requiring expert user intervention.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of visualization creation with an automated computational system. The generative model substitutes human designers and manual tools, transforming the mechanical act of creating visualizations into an automated AI-driven process that generates graphics from raw data inputs.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If users lack the necessary skills or resources for manual visualization creation, then accessibility is reduced, but automated systems can bridge this gap

Engineering Contradiction:

Improvedata accessibilityVSAvoiduser skill requirement

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The generative model acts as an intermediary between raw data and final visualizations. It mediates the transformation process by automatically analyzing data characteristics, selecting appropriate visualization types, and generating graphical representations, thereby bridging the gap between users with limited skills and meaningful data visualizations.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs self-analysis of the input data to automatically determine the most suitable visualization approach. The generative model independently evaluates data properties, selects appropriate chart types and styles, and generates visualizations without requiring users to possess domain-specific knowledge or design skills.

Inventive Principle:

Principle #25Self-service

3Extent of automation

If existing systems are used to generate visualizations, then some automation is achieved, but they struggle to produce informative and persuasive results without significant user input

Engineering Contradiction:

Improvevisualization generation automationVSAvoidvisualization quality

Core Design Contradiction:

Extent of automationVSReliability

Solution Approach 1:

The patent employs a generative model that replaces traditional rule-based automation systems. This AI-driven approach substitutes mechanical algorithms with learning-based models that can understand data semantics, select appropriate visualization types, and generate aesthetically pleasing and informative graphics, thereby improving reliability while maintaining high automation levels.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system dynamically adjusts multiple parameters including visualization type, color scheme, layout, and stylistic elements based on the characteristics of the input data. The generative model modifies these parameters automatically to optimize both the informativeness and persuiveness of the generated visualizations, achieving high quality results through adaptive parameter optimization.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12518447B2Automated generation of data visualizations and infographics using large language models and diffusion models

Publication Date: 2026.01.06 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Systems and methods are provided for generating visualization data associated with raw data using a machine learning model. For example, the machine learning model may automatically generate a set of candidate analytics and/or a scenario for visualizing the raw data based on summary data. Given the summary data and answers to prompts for visualizing data, the generated candidate analytics may reflect a context of the raw data as intended by the user. A visualization code scaffold according to a visualization specification may be used to generate programmatic output that corresponds to the candidate analytics, which may thus be used to generate a visualization accordingly. In some examples, an infographic may further be generated based on the visualization and a prompt using a diffusion model.