Dashboard Text Generation Using Layout-Aware AI Guidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional dashboard tools lack features to aid authors in composing text, despite the importance of text in enhancing user comprehension and interaction, and existing support for visualizations is robust.
Innovation Solution
Systems and methods using generative artificial intelligence, such as large language models, to automatically generate and refine text for dashboards, considering layout, data, and visualization specifications, while ensuring semantic coherence and readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional dashboard tools are used, then visualization creation is well-supported with robust features, but text composition lacks adequate support and assistance
Solution Approach 1:
The system enables text to automatically generate and update itself based on the dashboard content and layout. The text composition process is automated through AI models that analyze the visualization elements and generate appropriate text content, eliminating the need for manual text creation and updating by authors.
Solution Approach 2:
The patent replaces manual text composition (mechanical human effort) with generative AI models and natural language processing systems. These automated systems analyze dashboard content, understand context, and generate text content, substituting the manual writing process with intelligent automated generation.
2Productivity
If manual text composition is used, then authors have full control over text content, but the process is tedious and time-consuming
Solution Approach 1:
The system performs preliminary text generation based on the dashboard layout and content before the author needs it. Text is automatically composed based on the visual elements present, providing draft content that can be reviewed and refined, thus preparing the text composition work in advance rather than requiring manual creation from scratch.
Solution Approach 2:
The system continuously monitors changes in dashboard content and automatically updates text accordingly. When visualizations are modified, the text composition system detects these changes and regens the appropriate text, creating a feedback loop that maintains text-relevance without requiring manual intervention for each change.
3Ease of operation
If text is strategically placed to complement visual elements, then user comprehension is enhanced, but determining meaningful placement and scope is complex
Solution Approach 1:
The system introduces an intermediary AI layer that analyzes the relationship between visual elements and determines appropriate text placement and scope. This intermediary system evaluates the dashboard layout, identifies where text would be most beneficial, and automatically positions text elements, mediating between the visual content and the text composition process.
Solution Approach 2:
The system dynamically adjusts text placement parameters based on the dashboard content and layout. Text position, size, and scope are automatically modified according to the visual elements present, allowing the text to adapt its placement parameters to complement the visualization without requiring manual positioning decisions.
4Productivity
If automated text generation is used, then text composition time is reduced, but ensuring semantic coherence and contextual appropriateness becomes challenging
Solution Approach 1:
The system employs feedback mechanisms where generated text is evaluated for semantic coherence and contextual appropriateness. The AI models analyze the relationship between generated text and dashboard content, detecting inconsistencies and refining the text to ensure it accurately reflects the visual elements and maintains semantic coherence.
Solution Approach 2:
The text generation system uses nested analysis where multiple levels of AI processing are applied - from understanding individual visual elements to comprehending overall dashboard context. This nested approach ensures that generated text is coherent at multiple levels, from local accuracy to global consistency, by embedding context analysis within the generation process.
Data Source
AI summary
A system can be used to generate interactive data visualizations and textual content. The system receives a layout of a content item. The content item can be a data visualization dashboard or an article. The system can generate a content structure tree based on the layout. The content structure tree represents hierarchical and semantic relationships between sections of the content item. The system can receive input adding content elements, such as data visualizations or textual paragraphs, to sections within the content item. The system can identify text roles for different sections based on the content structure tree and the content elements. The system can generate text suggestions for the content item based on the text roles. The system can also generate text content for a text suggestion using a large language model, display the generated text content within the content item, and iteratively refine the content item.