LLM Visualization Scaffolds for Faster Data Infographic Creation

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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 knowledge, 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 adaptation.

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 inaccessible to users lacking design skills

Engineering Contradiction:

Improveease of creating visualizationsVSAvoidtime required for manual visualization creation

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables self-service by allowing users to input raw data and receive automatically generated visualizations without requiring manual design intervention. The generative model autonomously processes the data, selects appropriate visualization types, and produces polished graphical outputs, making the service accessible to users regardless of their design expertise.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual design process with an automated computational system. Instead of users manually selecting chart types, configuring parameters, and adjusting aesthetics, a generative machine learning model performs these tasks algorithmically, substituting human manual operations with automated intelligent processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated systems are used to generate visualizations, then the process becomes efficient and accessible, but the quality and persuiveness of visualizations may be compromised

Engineering Contradiction:

Improvespeed of visualization generationVSAvoidquality and persuiveness of visualizations

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system incorporates feedback mechanisms where the generative model analyzes the input data characteristics, user requirements, and context to iteratively refine visualization selections. The model learns from data patterns and adjusts visualization parameters to optimize both aesthetic quality and informational effectiveness, ensuring automated outputs meet professional standards.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent dynamically adjusts multiple visualization parameters including chart type selection, color schemes, layout configurations, and data aggregation levels based on the specific characteristics of the input data and user context. This adaptive parameter optimization enables the automated system to produce high-quality, context-appropriate visualizations rather than generic outputs.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If users provide detailed feedback and control inputs, then the accuracy and relevance of generated visualizations improve, but the complexity of interaction increases

Engineering Contradiction:

Improveaccuracy of visualization to user requirementsVSAvoidcomplexity of user interaction interface

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system provides a universal interface that handles multiple types of user inputs and requirements through a single cohesive interaction model. Users can specify various constraints and preferences using consistent input formats, and the generative model adapts to different data types, visualization goals, and user skill levels without requiring separate interaction protocols for each scenario.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260094325A1Automated generation of data visualizations and infographics using large language models and diffusion models

Publication Date: 2026.04.02 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.