Automatic Image Relevance Scoring and Transfer
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Solution Overview
Problem
Users of content management systems face growing content overload and increasing organizational challenges in managing large collections of images, as manual identification and categorization become increasingly time-consuming and effort-intensive with the proliferation of image data.
Innovation Solution
A method is implemented on a server with a processor and memory to dynamically analyze image and text data, building relevance rules and categories to automatically identify and transfer relevant images from a smartphone gallery to content collections, using pre-defined conditions and rules to assess and prioritize image relevance, with options for immediate transfer or periodic evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and categorization of images is performed, then image organization accuracy is improved, but user time consumption and effort increase significantly
Solution Approach 1:
The system performs automatic image analysis, categorization, and collection assignment without requiring user intervention. The server independently evaluates captured images against defined rules and criteria, automatically organizing them into appropriate content collections based on image content, metadata, and user preferences.
Solution Approach 2:
The patent replaces manual mechanical categorization operations with automated computational image analysis and machine learning algorithms. The system uses computer vision techniques, natural language processing, and pattern recognition to substitute human effort in image organization tasks.
2Measurement precision
If comprehensive image analysis and categorization rules are implemented, then image relevance determination accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides the image analysis process into distinct modular components: image preprocessing, feature extraction, rule evaluation, relevance scoring, and collection assignment. Each module handles a specific aspect of the analysis, making the overall system more manageable and maintainable while achieving high accuracy.
Solution Approach 2:
The system performs preliminary image analysis and pre-defines categorization rules and relevance criteria before actual image organization occurs. By establishing evaluation frameworks and thresholds in advance, the system reduces real-time processing complexity while maintaining accurate relevance determination.
3Productivity
If automatic image transfer is performed for all captured images, then productivity is improved, but irrelevant images may be incorrectly organized
Solution Approach 1:
The system applies automatic transfer selectively rather than universally. It processes images through multiple evaluation stages and only transfers images that meet predefined relevance thresholds, applying partial automation to high-confidence cases while allowing manual review for borderline cases.
Solution Approach 2:
The system incorporates feedback mechanisms where user corrections and manual adjustments are used to refine and update categorization rules and relevance criteria over time. This continuous learning process improves both the speed and accuracy of automatic image organization.
Data Source
AI summary
This application is directed to a method for automatically identifying and transferring relevant image data. The method includes obtaining a plurality of content items from a personal content collection and determining attributes based on the plurality of content items. The method includes generating a plurality of relevance rules. The method further includes obtaining unclassified content items and determining for a first unclassified content item a plurality of aggregate relevance scores using the plurality of relevance rules. The method include determining whether a first aggregate relevance score and/or a second aggregate relevance score satisfy threshold score. The method includes, in accordance with a determination that the first aggregate relevance score and the second aggregate relevance score do not satisfy the threshold score, forgoing determining the attributes corresponding to the first unclassified content item, and storing the first unclassified content item in a candidate list of the personal content collection.