Narrative Analytics for Interactive Data Visualization Focus
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Solution Overview
Problem
Conventional data visualization systems require users to have prior knowledge of specific data elements to focus on interesting aspects, limiting their ability to discover and highlight hidden insights within the data.
Innovation Solution
Integration of new data structures and artificial intelligence logic that allows for notional specifications of focus criteria, using narrative analytics to generate derived features and categories that can be used to interactively focus visualizations on salient elements without prior knowledge of specific entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional selection mechanisms are used to focus visualizations, then users can select specific data elements to highlight, but users must have prior knowledge of specific data elements which limits their ability to discover hidden insights
Solution Approach 1:
The system performs self-service by automatically generating narrative analytics and identifying salient data elements without requiring user knowledge of specific data elements. The narrative generation engine autonomously analyzes the data, creates natural language descriptions, and highlights interesting aspects, allowing the system to serve itself in the focus operation.
Solution Approach 2:
The patent replaces the mechanical selection system (manual clicking, menu selection, filtering) with an intelligent narrative analytics system. Instead of mechanically selecting data elements through UI interactions, the system uses natural language generation and automated data analysis to identify and present interesting aspects, substituting manual mechanical operations with intelligent automated processes.
2Adaptability or versatility
If manual selection of data elements is required, then users can control which aspects are visualized, but the complexity of the interface increases with multiple selection mechanisms
Solution Approach 1:
The narrative analytics engine serves multiple functions: it generates natural language descriptions of the data, identifies salient elements automatically, provides contextual insights, and enables focus operations all through a single unified system. This multi-functional approach replaces multiple separate interface mechanisms (selection tools, filters, navigation aids) with one universal narrative generation system.
Solution Approach 2:
The natural language narrative acts as an intermediary between the raw data and the user. Instead of requiring users to directly interact with complex data structures and selection mechanisms, the narrative serves as a mediating layer that translates data into understandable stories, automatically identifying and presenting interesting aspects without requiring users to navigate complex interface elements.
3Measurement precision
If users select specific entities to focus on, then the visualization becomes more focused and easier to understand, but users cannot discover interesting aspects they do not know about in advance
Solution Approach 1:
The system performs preliminary analysis by generating narrative analytics and identifying salient data elements before the user makes any selections. The narrative generation engine proactively analyzes the complete dataset, creates descriptions, and identifies interesting aspects in advance, so when users do interact with the system, the interesting elements have already been detected and are ready for focused visualization.
Solution Approach 2:
The patent replaces the mechanical process of user discovery (browsing, experimenting with selections, trial and error) with an intelligent detection system. The narrative analytics engine uses automated data analysis and natural language generation to detect interesting aspects, substituting the mechanical exploration process with intelligent automated detection that can identify insights the user would not know to look for.
Data Source
AI summary
To provide users with more flexibility for focusing and controlling visualizations of data, the inventors disclose new data structures and artificial intelligence logic that can be utilized in conjunction with notional specifications of focus criteria for visualizations. In an example embodiment, the inventors disclose technology that can be used to generate data structures that represent notional characteristics of the visualization data which in turn can be tied to specific elements of the visualization data to support interactive focusing of visualizations in notional terms that correspond to interesting aspects of the data. This allows a user to specify using notional criteria how a visualization should be focused on specific elements without needing to know in advance what those specific elements are.