Portable Insight Objects for Cross-Device Data Analysis
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Solution Overview
Problem
Users face challenges in analyzing and leveraging large volumes of data across various devices and platforms due to the overwhelming amount of information and difficulty in sharing data analysis insights within organizations, especially when switching between small and large form-factor devices.
Innovation Solution
A data visualization framework that identifies target datasets to generate insight objects with processing lineage metadata, allowing for enhanced data analysis and visualization across devices, leveraging user and organizational knowledge to create dynamic and portable insight objects that adapt to user preferences and device capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users analyze large volumes of data across various devices, then data analysis capability is improved, but user overload increases due to the overwhelming amount of information
Solution Approach 1:
The patent extracts and presents only the most relevant data insights and conclusions from large datasets, rather than presenting all raw data. The system identifies and delivers actionable insights that directly address user needs, filtering out unnecessary information to prevent user overload while maintaining analytical capability.
Solution Approach 2:
The patent segments data analysis into multiple levels: raw data, processed data, insights, and conclusions. This hierarchical segmentation allows users to access detailed information only when needed, while receiving summarized insights by default, thereby reducing cognitive load while preserving analytical depth.
2Adaptability or versatility
If users leverage data analysis across different devices, then versatility is improved, but difficulty in sharing and collaborating increases
Solution Approach 1:
The patent creates a universal data analysis framework that operates consistently across multiple device types (mobile, tablet, desktop). The same analysis tools, data models, and collaboration features are available on all devices, enabling seamless switching and shared access without requiring users to relearn different interfaces or workflows.
3Adaptability or versatility
If users switch between small and large form-factor devices, then mobility is improved, but difficulty in maintaining data analysis context increases
Solution Approach 1:
The patent implements a nested architecture where detailed data analysis context is stored in a centralized cloud repository, while local devices maintain only essential interface elements and recent work state. This allows full contextual restoration upon device switching without requiring large amounts of local storage or creating complex synchronization logic.
Data Source
AI summary
Systems, methods, and software for data visualization frameworks are provided herein. An exemplary method includes identifying a target dataset from which to determine data insights for presentation in an insight interface to the user application. The method includes determining data insight candidates for the target dataset based at least on usage modalities associated with processing one or more past datasets, and establishing content of the data insight candidates according to at least the target dataset and the usage modalities, where the content of each of the data insight candidates includes at least one insight object described by object metadata that indicates at least a processing lineage used to produce the at least one insight object.