Smart Tag Generation for Digital Content Search
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Solution Overview
Problem
Conventional mechanisms for managing and searching through large collections of digital content, such as images and videos, are inefficient, requiring users to spend significant time finding specific content due to the manual process of entering descriptive data and the lack of effective automated tagging systems.
Innovation Solution
A smart detect algorithm that analyzes digital content to automatically create tags based on detected characteristics, such as camera movements, face detection, and image quality, allowing users to quickly find specific events or occurrences within the content without manual previewing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually enter descriptive data and preview digital content to search for specific items, then the search accuracy is improved, but the time required increases significantly
Solution Approach 1:
The system automatically generates tags by analyzing visual characteristics of images (such as detecting objects, scenes, and visual patterns) without requiring manual user input. The tagging system serves itself by autonomously processing and organizing content, eliminating the need for users to manually describe their own content while maintaining accurate search capabilities through automated visual analysis
Solution Approach 2:
The patent replaces the manual mechanical process of users typing descriptions and previewing images with an automated computational system that uses image processing algorithms, machine learning models, and visual pattern recognition to generate tags. This substitution transforms manual labor into automated digital processing, dramatically reducing time while maintaining search accuracy
2Manufacturing precision
If users manually tag and organize digital content, then the organization quality is improved, but the productivity decreases due to repetitive manual work
Solution Approach 1:
The tagging system automatically performs the organization function by analyzing visual content and generating appropriate tags without human intervention. The system organizes itself by processing images, videos, and other media files, creating structured metadata that enables efficient retrieval and organization while eliminating repetitive manual tagging work
Solution Approach 2:
The system changes the fundamental parameter of content organization from manual text-based tagging to automated visual-based tagging. By transforming the organizing process from human cognitive processing to automated computational analysis, the system maintains high organization quality through sophisticated visual recognition while achieving high productivity through automated processing speeds
3Ease of operation
If the system provides detailed visual analysis and automatic tagging, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between the user and the complex content management system. This intermediary is the automated tagging system that translates complex visual content into simple, organized tags without requiring users to understand or manually create the complexity. The intermediary handles the complex analysis while presenting simple results to users, maintaining ease of operation while managing system complexity internally
Data Source
AI summary
Generating smart tags that allow a user to locate any portion of image content without viewing the image content is disclosed. Image-based processing is performed on image content to find an event of interest that is an occurrence captured by the image content. Thus, metadata is derived from analyzing the image content. The metadata is then analyzed. Different types of characteristics associated with portions of the image content as indicated by the metadata are detected. Responsive to this, tags are created, and different types of tags are applied to the portions of image content to categorize the portions into classes. Thus, a tag is associated with each portion of the image content including the event of interest. The tag describes a characteristic of that portion of the image content. Display of the different types of tags is initiated for selective viewing of the portions of the image content.