Widget-of-interest identification via data change threshold

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Solution Overview

Problem

The challenge lies in efficiently presenting and managing large volumes of unstructured data for visualization, as existing methods struggle to identify relationships within this data, leading to cumbersome data display and increased computing resource consumption, particularly in dashboards where users may need to recreate similar widgets based on different variables.

Innovation Solution

A machine learning-based widget creator that scans unstructured data to suggest relevant widgets to users by analyzing changes in data values and user behavior, identifying relationships, and recommending widgets based on pre-defined thresholds, thereby reducing the need for users to create similar widgets from scratch.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If users manually create widgets based on different variables, then customization and adaptability are improved, but time consumption and operational complexity increase

Engineering Contradiction:

Improvewidget customizationVSAvoidwidget creation time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by automatically scanning unstructured data, identifying relationships between variables, and pre-configuring widget templates before users need them. This allows widgets to be ready for immediate use without manual creation, resolving the contradiction between customization and time consumption.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of widget templates that can be rapidly instantiated with different variables. Instead of manually creating each widget from scratch, users can select from pre-defined templates that are automatically adapted to different data sets, maintaining adaptability while dramatically reducing creation time.

Inventive Principle:

Principle #26Copying

2Ease of operation

If users manually create and manage widgets, then control over data presentation is improved, but computing resource consumption increases

Engineering Contradiction:

Improvedata presentation controlVSAvoidcomputing resource usage

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The system performs self-service by automatically scanning unstructured data, identifying relationships, and generating widget recommendations without requiring extensive manual user intervention. This automation reduces the computing resources needed for manual widget management while maintaining ease of operation through intelligent self-configuration.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual widget creation and management with an automated machine learning system that scans data and generates widget recommendations. This substitution reduces computing resource consumption by eliminating redundant manual operations while preserving user control through recommendation acceptance or modification.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If dashboards display vast amounts of unstructured data, then information completeness is improved, but data relationship identification becomes difficult

Engineering Contradiction:

Improvedata volumeVSAvoidrelationship identification

Core Design Contradiction:

Quantity of substanceVSDifficulty of detecting and measuring

Solution Approach 1:

The system introduces an intermediary machine learning component that sits between the vast unstructured data and the dashboard display. This intermediary automatically scans the data, identifies relationships between variables, and structures them into meaningful widget formats, making relationship identification straightforward even with large data volumes.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system extracts relationships and patterns from vast unstructured data using automated scanning and analysis. By taking out the relationship identification task from manual analysis and embedding it in an automated process, the system can handle large data volumes while maintaining ease of relationship detection through algorithmic pattern recognition.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS10824292B2Widget-of-interest identification

Publication Date: 2020.11.03 MICRO FOCUS LLC

AI summary

According to examples, an apparatus may include a processor and a non-transitory computer readable medium storing machine readable instructions. The instructions may cause the processor to access a plurality of widgets, in which each of the plurality of widgets includes a data value. The instructions may also cause the processor to identify a widget of the plurality of widgets including a data value that has changed over time by an amount that exceeds a pre-defined threshold as a widget-of-interest and output a notification regarding a suggestion for the widget-of-interest to be displayed on a user dashboard.