AI-Guided Visual Data Analysis With Critical Region Highlighting

Resolve Bottlenecks,
Find Innovative Solutions
Generate Solutions

Solution Overview

Problem

In sites facing a shortage of human resources with specialized knowledge, analyzing visualized or numerical data is challenging for inexperienced personnel, leading to potential misinterpretation and inefficiency.

Innovation Solution

An image processing apparatus equipped with an image input unit, classification units, and an image generation unit to highlight important regions in visualized data, facilitating analysis by less experienced individuals.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If inexperienced personnel are assigned to data analysis due to human resource shortage, then labor cost is reduced, but analysis accuracy deteriorates

Engineering Contradiction:
Improvedata analysis efficiencyVSAvoidanalysis accuracy
Core Design Contradiction:
ProductivityVSReliability

Solution Approach 1:

An AI model acts as an intermediary between the visualized data and inexperienced analysts. The model automatically generates explanations highlighting critical regions and providing interpretation guidance, enabling non-specialists to perform accurate analysis without requiring domain expertise. This intermediary bridges the gap between data complexity and user capability.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical process of human expert analysis with an automated AI-based explanation system. Instead of relying on human specialists to interpret visualized data, the system uses machine learning models to automatically generate analytical explanations, substituting human cognitive processing with automated computational analysis.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If inexperienced personnel perform data analysis, then human resource requirements are reduced, but analysis time increases due to lack of expertise

Engineering Contradiction:
Improvehuman resource requirementsVSAvoidanalysis time
Core Design Contradiction:
Device complexityVSLoss of time

Solution Approach 1:

The AI model performs preliminary analysis and generates explanations before the analyst reviews the data. By pre-identifying critical regions and providing interpretation guidance in advance, the system prepares the analysis groundwork, allowing inexperienced users to quickly understand key findings without needing to manually explore the entire dataset or apply complex analytical methods.

Inventive Principle:
Principle #10Preliminary action

3Ease of operation

If visualized data is presented without interpretation support, then information presentation is simple, but understanding difficulty increases for non-specialists

Engineering Contradiction:
Improvedata presentation simplicityVSAvoiddata interpretation difficulty
Core Design Contradiction:
Ease of operationVSDifficulty of detecting and measuring

Solution Approach 1:

The patent segments the complex visualized data into manageable explanation components. The AI model divides the analysis into distinct elements such as critical regions, key findings, and interpretation guidance, presenting them in a structured manner that breaks down complex information into comprehensible parts for non-specialist users.

Inventive Principle:
Principle #1Segmentation

Data Source

PatentUS20260105654A1Image processing device for supporting analysis of visualized data or numerical data
Publication Date: 2026.04.16 PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO LTD
  • US20260105654A1 patent drawing
  • US20260105654A1 patent drawing
  • US20260105654A1 patent drawing

AI summary

An image input unit obtains an input image including visualized data. A first determination unit classifies the input image into one of a plurality of classes to obtain a classification result. A second determination unit determines a partial region of the input image, the partial region accounting for determining the classification result. An image generation unit highlights the partial region in the input image to generate an output image. A display device outputs the output image.