Context-Aware Image Selection Using Text Category Scoring

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Solution Overview

Problem

Existing technologies fail to automatically extract important images from content that can attract user attention, as they rely on predetermined rules and do not account for context-specific variations.

Innovation Solution

An information-processing device that includes modules for image classification, keyword extraction, classification, assessment, score determination, and image selection, utilizing machine learning models to analyze images and text context to identify and select important images based on category scores.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If predetermined rules are used for image selection, then the selection process is simple and fast, but the extracted images do not align with context-specific variations and fail to attract user attention

Engineering Contradiction:

Improveimage selection efficiencyVSAvoidcontext adaptation capability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system changes the parameter of image selection from fixed predetermined rules to dynamic category-based scoring. Each image is assigned a category (e.g., product, landscape, portrait) and a score is calculated based on text analysis, allowing the selection criteria to adapt to different contexts while maintaining automated efficiency

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system performs preliminary categorization of images and extraction of categories from text before the final selection process. This preliminary action organizes the data in advance, enabling both efficient processing and context-aware selection without requiring complex real-time analysis

Inventive Principle:

Principle #10Preliminary action

2Speed

If automated image extraction is implemented without context analysis, then processing speed is high, but the extracted images lack relevance to the content context

Engineering Contradiction:

Improveprocessing speedVSAvoidimage relevance accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The system segments the text into individual categories and matches them with corresponding image categories. This segmentation allows for targeted analysis of specific text-image relationships rather than processing the entire content as a single unit, maintaining speed while improving relevance accuracy

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces category matching as an intermediary step between raw image extraction and final selection. By using categories as a mediator, the system can quickly filter and rank images based on their relevance to the text context without requiring deep semantic analysis of every image-text pair

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12579187B2Information-processing device, information-processing method and information-processing program

Publication Date: 2026.03.17 RAKUTEN GROUP INC

AI summary

Provided is an information-processing device for extracting an important image from target information including images and a text, the information-processing device including: a CPU; and a memory storing instructions for causing the information-processing device, when executed by the CPU, to: determine a category indicating an attribute of each image; extract a category for each division of the text; determine a score of the category based on the text; and extract, as a most important image, one of the images corresponding to the category selected based on the score.